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https://github.com/huggingface/datasets/pull/316 | add AG News dataset | Thanks @jxmorris12 for adding this adding.
Can you please add a small description of the PR? | adds support for the AG-News topic classification dataset | 16 | text: add AG News dataset
adds support for the AG-News topic classification dataset
Thanks @jxmorris12 for adding this adding.
Can you please add a small description of the PR? | [
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https://github.com/huggingface/datasets/pull/309 | Add narrative qa | Does it make sense to download the full stories? I remember attempting to implement this dataset a while ago and ended up with something like:
```python
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
data_dir = os.path.join(dl_dir, "narrativeqa-master")
urls = {"test":{}, "train": {},"valid":{}}
with open(os.path.join(data_dir,"documents.csv")) as f_in:
csv_reader = csv.reader(f_in)
next(csv_reader) # discard header row
for i,row in enumerate(csv_reader):
if i > 1572:
break
if row != []:
urls[row[1]][row[0]] = row[3]
url_files = {}
for key in urls.keys():
url_files[key] = dl_manager.download_and_extract(urls[key])
return [
nlp.SplitGenerator(
name=nlp.Split.TRAIN,
gen_kwargs={
"data_dir":data_dir,
"split":"train",
"doc_id_to_path":url_files["train"]
}
),
....
```
It does end up cluttering your huggingface cache dir though. | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | 103 | text: Add narrative qa
Test cases for dummy data don't pass
Only contains data for summaries (not whole story)
Does it make sense to download the full stories? I remember attempting to implement this dataset a while ago and ended up with something like:
```python
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
data_dir = os.path.join(dl_dir, "narrativeqa-master")
urls = {"test":{}, "train": {},"valid":{}}
with open(os.path.join(data_dir,"documents.csv")) as f_in:
csv_reader = csv.reader(f_in)
next(csv_reader) # discard header row
for i,row in enumerate(csv_reader):
if i > 1572:
break
if row != []:
urls[row[1]][row[0]] = row[3]
url_files = {}
for key in urls.keys():
url_files[key] = dl_manager.download_and_extract(urls[key])
return [
nlp.SplitGenerator(
name=nlp.Split.TRAIN,
gen_kwargs={
"data_dir":data_dir,
"split":"train",
"doc_id_to_path":url_files["train"]
}
),
....
```
It does end up cluttering your huggingface cache dir though. | [
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https://github.com/huggingface/datasets/pull/309 | Add narrative qa | Also since there doesn't seem to be any meaning in the order of answer_1 and answer_2, it might make sense to combine them (see [squad.py](https://github.com/huggingface/nlp/blob/8b0ffc85e4e52ae1f18d31be99b6c70b82c991ca/datasets/squad/squad.py#L86-L88)):
```python
"answers": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokenized": nlp.features.Sequence(nlp.Value("string"))
})
```
(the tokenized features should also probably be lists of strings not just strings - see [natural_questions.py](https://github.com/huggingface/nlp/blob/4cd34287300a1135ce7b22f6dd209ca305c71b3a/datasets/natural_questions/natural_questions.py#L83))
Again, this is a personal preference thing, but it might be useful to combine the document-related features:
```python
{
"document": {
"id": nlp.Value("string"),
"kind": nlp.Value("string"),
"url": nlp.Value("string"),
"file_size": nlp.Value("int32"),
"word_count": nlp.Value("int32"),
"start": nlp.Value("string"),
"end": nlp.Value("string"),
"wiki_url": nlp.Value("string"),
"wiki_title": nlp.Value("string"),
"summary": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokens": nlp.features.Sequence(nlp.Value("string"))
}),
"text": nlp.Value("string"),
},
"question": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokens": nlp.features.Sequence(nlp.Value("string"))
}),
"answers": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokens": nlp.features.Sequence(nlp.Value("string"))
})
}
``` | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | 115 | text: Add narrative qa
Test cases for dummy data don't pass
Only contains data for summaries (not whole story)
Also since there doesn't seem to be any meaning in the order of answer_1 and answer_2, it might make sense to combine them (see [squad.py](https://github.com/huggingface/nlp/blob/8b0ffc85e4e52ae1f18d31be99b6c70b82c991ca/datasets/squad/squad.py#L86-L88)):
```python
"answers": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokenized": nlp.features.Sequence(nlp.Value("string"))
})
```
(the tokenized features should also probably be lists of strings not just strings - see [natural_questions.py](https://github.com/huggingface/nlp/blob/4cd34287300a1135ce7b22f6dd209ca305c71b3a/datasets/natural_questions/natural_questions.py#L83))
Again, this is a personal preference thing, but it might be useful to combine the document-related features:
```python
{
"document": {
"id": nlp.Value("string"),
"kind": nlp.Value("string"),
"url": nlp.Value("string"),
"file_size": nlp.Value("int32"),
"word_count": nlp.Value("int32"),
"start": nlp.Value("string"),
"end": nlp.Value("string"),
"wiki_url": nlp.Value("string"),
"wiki_title": nlp.Value("string"),
"summary": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokens": nlp.features.Sequence(nlp.Value("string"))
}),
"text": nlp.Value("string"),
},
"question": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokens": nlp.features.Sequence(nlp.Value("string"))
}),
"answers": nlp.features.Sequence({
"text": nlp.Value("string"),
"tokens": nlp.features.Sequence(nlp.Value("string"))
})
}
``` | [
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https://github.com/huggingface/datasets/pull/309 | Add narrative qa | @lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.
The real value (and original purpose) of this dataset is doing question answering on the full text. | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | 52 | text: Add narrative qa
Test cases for dummy data don't pass
Only contains data for summaries (not whole story)
@lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.
The real value (and original purpose) of this dataset is doing question answering on the full text. | [
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https://github.com/huggingface/datasets/pull/309 | Add narrative qa | > @lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.
>
> The real value (and original purpose) of this dataset is doing question answering on the full text.
What's the problem exactly with the cache ? | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | 63 | text: Add narrative qa
Test cases for dummy data don't pass
Only contains data for summaries (not whole story)
> @lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.
>
> The real value (and original purpose) of this dataset is doing question answering on the full text.
What's the problem exactly with the cache ? | [
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https://github.com/huggingface/datasets/pull/309 | Add narrative qa | Nothing, just that because each story is a separate download it gets a bit messy as all 1573 files are under `~/.cache/hugginface/datasets` rather than organized under a subdir.
Probably doesn't matter to the end user though. | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | 36 | text: Add narrative qa
Test cases for dummy data don't pass
Only contains data for summaries (not whole story)
Nothing, just that because each story is a separate download it gets a bit messy as all 1573 files are under `~/.cache/hugginface/datasets` rather than organized under a subdir.
Probably doesn't matter to the end user though. | [
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https://github.com/huggingface/datasets/pull/309 | Add narrative qa | I got the PR merged to have a cleaner the cache directory (everything is downloaded inside the 'downloads' sub-directory).
Feel free to download all the stories then @ghomasHudson @Varal7 x)
If you have the possibility of downloading a compressed file with most of the stories at once it would be better though. | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | 52 | text: Add narrative qa
Test cases for dummy data don't pass
Only contains data for summaries (not whole story)
I got the PR merged to have a cleaner the cache directory (everything is downloaded inside the 'downloads' sub-directory).
Feel free to download all the stories then @ghomasHudson @Varal7 x)
If you have the possibility of downloading a compressed file with most of the stories at once it would be better though. | [
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https://github.com/huggingface/datasets/pull/309 | Add narrative qa | Looks good @lhoestq . The problem I'm having at the moment is that stories from project Gutenberg occasionally fail. All books are out of copyright so we should be able to host them.
Here's a zip file of the full text if we have anywhere to put them: https://drive.google.com/file/d/17jOR7NqvzDwSlPXrlHaYV-PGI8JG-KY5/view?usp=sharing
| Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | 49 | text: Add narrative qa
Test cases for dummy data don't pass
Only contains data for summaries (not whole story)
Looks good @lhoestq . The problem I'm having at the moment is that stories from project Gutenberg occasionally fail. All books are out of copyright so we should be able to host them.
Here's a zip file of the full text if we have anywhere to put them: https://drive.google.com/file/d/17jOR7NqvzDwSlPXrlHaYV-PGI8JG-KY5/view?usp=sharing
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https://github.com/huggingface/datasets/pull/306 | add pg19 dataset | @lucidrains - Thanks a lot for making the PR - PG19 is a super important dataset! Thanks for making it. Many people are asking for PG-19, so it would be great to have that in the library as soon as possible @thomwolf . | https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style` | 43 | text: add pg19 dataset
https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style`
@lucidrains - Thanks a lot for making the PR - PG19 is a super important dataset! Thanks for making it. Many people are asking for PG-19, so it would be great to have that in the library as soon as possible @thomwolf . | [
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https://github.com/huggingface/datasets/pull/306 | add pg19 dataset | Oh I just noticed but as we changed the urls to download the files, we have to update `dataset_infos.json`.
Could you re-rurn `nlp-cli test ./datasets/pg19 --save_infos` ? | https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style` | 27 | text: add pg19 dataset
https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style`
Oh I just noticed but as we changed the urls to download the files, we have to update `dataset_infos.json`.
Could you re-rurn `nlp-cli test ./datasets/pg19 --save_infos` ? | [
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https://github.com/huggingface/datasets/pull/306 | add pg19 dataset | `dataset_infos.json` is still not up to date with the new urls (we can see that there are urls like `gs://deepmind-gutenberg/train/*` instead of `https://storage.googleapis.com/deepmind-gutenberg/train/*` in the json file)
Can you check that you re-ran the command to update the json file, and that you pushed the changes @lucidrains ? | https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style` | 48 | text: add pg19 dataset
https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style`
`dataset_infos.json` is still not up to date with the new urls (we can see that there are urls like `gs://deepmind-gutenberg/train/*` instead of `https://storage.googleapis.com/deepmind-gutenberg/train/*` in the json file)
Can you check that you re-ran the command to update the json file, and that you pushed the changes @lucidrains ? | [
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https://github.com/huggingface/datasets/pull/306 | add pg19 dataset | @lhoestq ohhh, I made the change in this commit https://github.com/lucidrains/nlp/commit/f3e23d823ad9942031be80b7c4e4212c592cd90c , that's interesting that the pull request didn't pick it up. maybe it's because I did it on another machine, let me check and get back to you! | https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style` | 38 | text: add pg19 dataset
https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style`
@lhoestq ohhh, I made the change in this commit https://github.com/lucidrains/nlp/commit/f3e23d823ad9942031be80b7c4e4212c592cd90c , that's interesting that the pull request didn't pick it up. maybe it's because I did it on another machine, let me check and get back to you! | [
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https://github.com/huggingface/datasets/pull/298 | Add searchable datasets | Alright I think I've checked all your comments, thanks :)
Moreover I just added a way to serialize faiss indexes.
This is important because for big datasets the index construction can take some time.
Examples:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))
ds_with_embeddings.add_faiss_index(column='embeddings')
# query
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)
# save index
ds_with_embeddings.get_index('embeddings').save('my_index.faiss')
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
# load index
faiss_index = nlp.search.FaissIndex.load('my_index.faiss')
ds.add_faiss_index('embeddings', faiss_index=faiss_index)
# query
scores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)
```
Let me know what you think | # Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :) | 92 | text: Add searchable datasets
# Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :)
Alright I think I've checked all your comments, thanks :)
Moreover I just added a way to serialize faiss indexes.
This is important because for big datasets the index construction can take some time.
Examples:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))
ds_with_embeddings.add_faiss_index(column='embeddings')
# query
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)
# save index
ds_with_embeddings.get_index('embeddings').save('my_index.faiss')
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
# load index
faiss_index = nlp.search.FaissIndex.load('my_index.faiss')
ds.add_faiss_index('embeddings', faiss_index=faiss_index)
# query
scores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)
```
Let me know what you think | [
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https://github.com/huggingface/datasets/pull/298 | Add searchable datasets | Nice!
Here are a few comments:
I think it would be good to separate (1) the name of the column we use for indexing and (2) the name of the index itself, at least in our head. As I understand it, once the index is created, the column we used to create it is irrelevant so the column name will only be relevant in the `add_faiss_index` and we should be able to supply a different index name, e.g. `my_faiss_index`. When we reload an index, we don't really care about the column that was used to create it, right? so it's maybe better to have an `index_name` (which default to the column name for a simple user experience but it can also be something else and this should be clear in our head when we define the API).
I'm wondering if we should not have a triple of methods for each retrieval engine: `add_xxx_index`, `save_xxx_index` and `load_xxx_index` when `xxx` can be `faiss` or `elasticsearch`. I'm not a fan of exposing `nlp.search.FaissIndex` unless you think there is a strong reason to have the user learn this abstraction.
Last but not least, I think we should already think about hosting index on our S3. I would maybe go for something like this: host the index serialized with the cached dataset on user-provided namespaces:
```python
wiki_indexed = load_dataset('thom/wiki_indexed_with_dpr_faiss')
``` | # Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :) | 225 | text: Add searchable datasets
# Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :)
Nice!
Here are a few comments:
I think it would be good to separate (1) the name of the column we use for indexing and (2) the name of the index itself, at least in our head. As I understand it, once the index is created, the column we used to create it is irrelevant so the column name will only be relevant in the `add_faiss_index` and we should be able to supply a different index name, e.g. `my_faiss_index`. When we reload an index, we don't really care about the column that was used to create it, right? so it's maybe better to have an `index_name` (which default to the column name for a simple user experience but it can also be something else and this should be clear in our head when we define the API).
I'm wondering if we should not have a triple of methods for each retrieval engine: `add_xxx_index`, `save_xxx_index` and `load_xxx_index` when `xxx` can be `faiss` or `elasticsearch`. I'm not a fan of exposing `nlp.search.FaissIndex` unless you think there is a strong reason to have the user learn this abstraction.
Last but not least, I think we should already think about hosting index on our S3. I would maybe go for something like this: host the index serialized with the cached dataset on user-provided namespaces:
```python
wiki_indexed = load_dataset('thom/wiki_indexed_with_dpr_faiss')
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] |
https://github.com/huggingface/datasets/pull/298 | Add searchable datasets | To summarize:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))
ds_with_embeddings.add_faiss_index(column='embeddings')
# query
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)
# save index
ds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
# load index
ds.load_faiss_index('embeddings', 'my_index.faiss')
# query
scores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)
``` | # Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :) | 51 | text: Add searchable datasets
# Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :)
To summarize:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))
ds_with_embeddings.add_faiss_index(column='embeddings')
# query
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)
# save index
ds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
# load index
ds.load_faiss_index('embeddings', 'my_index.faiss')
# query
scores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)
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https://github.com/huggingface/datasets/pull/298 | Add searchable datasets | Good to me. I understand that for now there is no check that the index matches the dataset on loading.
Maybe just add a basic test on the number of examples? | # Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :) | 31 | text: Add searchable datasets
# Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :)
Good to me. I understand that for now there is no check that the index matches the dataset on loading.
Maybe just add a basic test on the number of examples? | [
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https://github.com/huggingface/datasets/pull/292 | Update metadata for x_stance dataset | We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ? | Thank you for featuring the x_stance dataset in your library. This PR updates some metadata:
- Citation: Replace preprint with proceedings
- URL: Use a URL with long-term availability
| 20 | text: Update metadata for x_stance dataset
Thank you for featuring the x_stance dataset in your library. This PR updates some metadata:
- Citation: Replace preprint with proceedings
- URL: Use a URL with long-term availability
We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ? | [
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https://github.com/huggingface/datasets/pull/291 | break statement not required | We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ? | 20 | text: break statement not required
We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ? | [
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|
https://github.com/huggingface/datasets/pull/291 | break statement not required | If I'm not wrong this function returns None if no main class was found.
I think it makes things less clear not to have a return at the end of the function.
I guess we can have one return in the for loop instead of the break statement, AND one return at the end to explicitly return None.
What do you think ? | 63 | text: break statement not required
If I'm not wrong this function returns None if no main class was found.
I think it makes things less clear not to have a return at the end of the function.
I guess we can have one return in the for loop instead of the break statement, AND one return at the end to explicitly return None.
What do you think ? | [
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|
https://github.com/huggingface/datasets/pull/289 | update xsum | Looks cool!
@mariamabarham can you add a detailed description here what exactly is changed and how the user can load xsum now? | This PR makes the following update to the xsum dataset:
- Manual download is not required anymore
- dataset can be loaded as follow: `nlp.load_dataset('xsum')`
**Important**
Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore.
There might be slight breaking changes here for xsum. | 22 | text: update xsum
This PR makes the following update to the xsum dataset:
- Manual download is not required anymore
- dataset can be loaded as follow: `nlp.load_dataset('xsum')`
**Important**
Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore.
There might be slight breaking changes here for xsum.
Looks cool!
@mariamabarham can you add a detailed description here what exactly is changed and how the user can load xsum now? | [
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https://github.com/huggingface/datasets/pull/289 | update xsum | This is a super useful PR :-) @sshleifer - maybe you can take a look at the updated version of xsum if you can use it for your use case. Now, one should be able to just load it with:
```python
nlp.load_datasets("xsum", ....) # no manual dir required anymore
```
| This PR makes the following update to the xsum dataset:
- Manual download is not required anymore
- dataset can be loaded as follow: `nlp.load_dataset('xsum')`
**Important**
Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore.
There might be slight breaking changes here for xsum. | 50 | text: update xsum
This PR makes the following update to the xsum dataset:
- Manual download is not required anymore
- dataset can be loaded as follow: `nlp.load_dataset('xsum')`
**Important**
Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore.
There might be slight breaking changes here for xsum.
This is a super useful PR :-) @sshleifer - maybe you can take a look at the updated version of xsum if you can use it for your use case. Now, one should be able to just load it with:
```python
nlp.load_datasets("xsum", ....) # no manual dir required anymore
```
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https://github.com/huggingface/datasets/pull/284 | Fix manual download instructions | But I get
```python
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py
```
When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).
Both machines can run
```bash
aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/
```
but it seems one must be in the nlp directory to run the command?
(I ran `pip install -e . ` on this branch in both situations.)
| This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`.
Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.
After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.
Also this PR should unblock solves a bug with `wmt16 - ro-en`
@sshleifer from this branch you should be able to succesfully run
```python
import nlp
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```
and once this PR is merged S3 should be synched so that
```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```
works as well.
**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility. | 63 | text: Fix manual download instructions
This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`.
Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.
After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.
Also this PR should unblock solves a bug with `wmt16 - ro-en`
@sshleifer from this branch you should be able to succesfully run
```python
import nlp
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```
and once this PR is merged S3 should be synched so that
```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```
works as well.
**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility.
But I get
```python
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py
```
When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).
Both machines can run
```bash
aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/
```
but it seems one must be in the nlp directory to run the command?
(I ran `pip install -e . ` on this branch in both situations.)
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https://github.com/huggingface/datasets/pull/284 | Fix manual download instructions | > But I get
>
> ```python
> ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py
> ```
>
> When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).
>
> Both machines can run
>
> ```shell
> aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/
> ```
>
> but it seems one must be in the nlp directory to run the command?
>
> (I ran `pip install -e . ` on this branch in both situations.)
As soon as it is on master, the dataset script wmt16.py will be synced on S3 and you'll be able to do `load_dataset("wmt16")` | This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`.
Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.
After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.
Also this PR should unblock solves a bug with `wmt16 - ro-en`
@sshleifer from this branch you should be able to succesfully run
```python
import nlp
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```
and once this PR is merged S3 should be synched so that
```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```
works as well.
**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility. | 103 | text: Fix manual download instructions
This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`.
Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.
After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.
Also this PR should unblock solves a bug with `wmt16 - ro-en`
@sshleifer from this branch you should be able to succesfully run
```python
import nlp
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```
and once this PR is merged S3 should be synched so that
```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```
works as well.
**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility.
> But I get
>
> ```python
> ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py
> ```
>
> When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).
>
> Both machines can run
>
> ```shell
> aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/
> ```
>
> but it seems one must be in the nlp directory to run the command?
>
> (I ran `pip install -e . ` on this branch in both situations.)
As soon as it is on master, the dataset script wmt16.py will be synced on S3 and you'll be able to do `load_dataset("wmt16")` | [
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https://github.com/huggingface/datasets/pull/276 | Fix metric compute (original_instructions missing) | Awesome! This is working now:
```python
import nlp
seqeval = nlp.load_metric("seqeval")
y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
results = seqeval.compute(y_true, y_pred)
```
I heavily need this fix for an upcoming `nlp` integration PR for Transformers (token classification example) 😅 | When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).
In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.
This should fix #269 | 57 | text: Fix metric compute (original_instructions missing)
When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).
In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.
This should fix #269
Awesome! This is working now:
```python
import nlp
seqeval = nlp.load_metric("seqeval")
y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
results = seqeval.compute(y_true, y_pred)
```
I heavily need this fix for an upcoming `nlp` integration PR for Transformers (token classification example) 😅 | [
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https://github.com/huggingface/datasets/pull/276 | Fix metric compute (original_instructions missing) | Haha nice ! We'll ship this fix with the next release that will probably come out on thursday :) | When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).
In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.
This should fix #269 | 19 | text: Fix metric compute (original_instructions missing)
When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).
In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.
This should fix #269
Haha nice ! We'll ship this fix with the next release that will probably come out on thursday :) | [
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https://github.com/huggingface/datasets/pull/271 | Fix allociné dataset configuration | Actually when there is only one configuration, then you don't need to specify the configuration in `load_dataset`. You can run:
```python
dataset = load_dataset('allocine')
```
and it works.
Maybe we should take that into account in the nlp viewer @srush ? | This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use. | 41 | text: Fix allociné dataset configuration
This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use.
Actually when there is only one configuration, then you don't need to specify the configuration in `load_dataset`. You can run:
```python
dataset = load_dataset('allocine')
```
and it works.
Maybe we should take that into account in the nlp viewer @srush ? | [
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https://github.com/huggingface/datasets/pull/271 | Fix allociné dataset configuration | @lhoestq Just to understand the exact semantics. Are you suggesting that if there is exactly 1 configuration I should not show the configuration menu and just treat it as if there were 0 configurations? | This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use. | 34 | text: Fix allociné dataset configuration
This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use.
@lhoestq Just to understand the exact semantics. Are you suggesting that if there is exactly 1 configuration I should not show the configuration menu and just treat it as if there were 0 configurations? | [
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https://github.com/huggingface/datasets/pull/271 | Fix allociné dataset configuration | The configuration menu is fine imo.
It was more about the code snippet presented in the viewer.
For example for Allociné it currently shows this snippet to load the dataset:
```python
!pip install nlp
from nlp import load_dataset
dataset = load_dataset('allocine', 'allocine')
```
However for datasets with one or zero configurations, the second argument in `load_dataset` is optional. For Allociné, that has one configuration, we can expect to show instead:
```python
!pip install nlp
from nlp import load_dataset
dataset = load_dataset('allocine')
``` | This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use. | 82 | text: Fix allociné dataset configuration
This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use.
The configuration menu is fine imo.
It was more about the code snippet presented in the viewer.
For example for Allociné it currently shows this snippet to load the dataset:
```python
!pip install nlp
from nlp import load_dataset
dataset = load_dataset('allocine', 'allocine')
```
However for datasets with one or zero configurations, the second argument in `load_dataset` is optional. For Allociné, that has one configuration, we can expect to show instead:
```python
!pip install nlp
from nlp import load_dataset
dataset = load_dataset('allocine')
``` | [
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https://github.com/huggingface/datasets/pull/271 | Fix allociné dataset configuration | > Actually when there is only one configuration, then you don't need to specify the configuration in `load_dataset`. You can run:
>
> ```python
> dataset = load_dataset('allocine')
> ```
>
> and it works.
>
> Maybe we should take that into account in the nlp viewer @srush ?
Oh ok, I didn't expect it would work!
Anyway, I think it's intrinsically better to simply remove the optional parameter.
The dummy data folder architecture seems also more logical this way.
| This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use. | 81 | text: Fix allociné dataset configuration
This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use.
> Actually when there is only one configuration, then you don't need to specify the configuration in `load_dataset`. You can run:
>
> ```python
> dataset = load_dataset('allocine')
> ```
>
> and it works.
>
> Maybe we should take that into account in the nlp viewer @srush ?
Oh ok, I didn't expect it would work!
Anyway, I think it's intrinsically better to simply remove the optional parameter.
The dummy data folder architecture seems also more logical this way.
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https://github.com/huggingface/datasets/pull/266 | Add sort, shuffle, test_train_split and select methods | Nice !
Also it looks like we can have a train_test_split method for free:
```python
train_indices, test_indices = train_test_split(range(len(dataset)))
train = dataset.sort(indices=train_indices)
test = dataset.sort(indices=test_indices)
```
and a shuffling method for free:
```python
shuffled_indices = shuffle(range(len(dataset)))
shuffled_dataset = dataset.sort(indices=shuffled_indices)
```
Maybe we can have a specific API for train_test_split and shuffle. They are two features asked quite often (see #147, #166) | Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259 | 61 | text: Add sort, shuffle, test_train_split and select methods
Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259
Nice !
Also it looks like we can have a train_test_split method for free:
```python
train_indices, test_indices = train_test_split(range(len(dataset)))
train = dataset.sort(indices=train_indices)
test = dataset.sort(indices=test_indices)
```
and a shuffling method for free:
```python
shuffled_indices = shuffle(range(len(dataset)))
shuffled_dataset = dataset.sort(indices=shuffled_indices)
```
Maybe we can have a specific API for train_test_split and shuffle. They are two features asked quite often (see #147, #166) | [
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https://github.com/huggingface/datasets/pull/266 | Add sort, shuffle, test_train_split and select methods | Ok, I think this one is ready to merge.
@patrickvonplaten @jplu @mariamabarham @joeddav @n1t0 @julien-c you may want to give it a look, it adds a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``, which is the default behavior in the library. | Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259 | 149 | text: Add sort, shuffle, test_train_split and select methods
Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259
Ok, I think this one is ready to merge.
@patrickvonplaten @jplu @mariamabarham @joeddav @n1t0 @julien-c you may want to give it a look, it adds a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``, which is the default behavior in the library. | [
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https://github.com/huggingface/datasets/pull/266 | Add sort, shuffle, test_train_split and select methods | > Might be a solution to put 0.25 and 0.75 as default values for respectively `test_size` and `train_size`. WDYT?
According to sklearn documentation, it is indeed set to 0.25 and 0.75 if both `test_size` and `train_size` are None.
Let me add it. | Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259 | 42 | text: Add sort, shuffle, test_train_split and select methods
Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259
> Might be a solution to put 0.25 and 0.75 as default values for respectively `test_size` and `train_size`. WDYT?
According to sklearn documentation, it is indeed set to 0.25 and 0.75 if both `test_size` and `train_size` are None.
Let me add it. | [
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https://github.com/huggingface/datasets/pull/262 | Add new dataset ANLI Round 1 | Hello ! Thanks for adding this one :)
This looks great, you just have to do the last steps to make the CI pass.
I can see that two things are missing:
1. the dummy data that is used to test that the script is working as expected
2. the json file with all the infos about the dataset
You can see the steps to help you create the dummy data and generate the dataset_infos.json file right [here](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset) | Adding new dataset [ANLI](https://github.com/facebookresearch/anli/).
I'm not familiar with how to add new dataset. Let me know if there is any issue. I only include round 1 data here. There will be round 2, round 3 and more in the future with potentially different format. I think it will be better to separate them. | 78 | text: Add new dataset ANLI Round 1
Adding new dataset [ANLI](https://github.com/facebookresearch/anli/).
I'm not familiar with how to add new dataset. Let me know if there is any issue. I only include round 1 data here. There will be round 2, round 3 and more in the future with potentially different format. I think it will be better to separate them.
Hello ! Thanks for adding this one :)
This looks great, you just have to do the last steps to make the CI pass.
I can see that two things are missing:
1. the dummy data that is used to test that the script is working as expected
2. the json file with all the infos about the dataset
You can see the steps to help you create the dummy data and generate the dataset_infos.json file right [here](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset) | [
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | The dummy data file was wrong. I only fixed it for the book config. Even though the tests are all green here, this should also be fixed for all other configs. Could you take a look there @mariamabarham ? | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 39 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
The dummy data file was wrong. I only fixed it for the book config. Even though the tests are all green here, this should also be fixed for all other configs. Could you take a look there @mariamabarham ? | [
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | Hi @mariamabarham
FLUE can indeed become a very interesting benchmark for french NLP !
Unfortunately, it seems that we've both been working on adding it to the repo...
I was going to open a pull request before I came across yours.
I didn't want to open a duplicate, that's why I'm commenting here (I hope it's not rude).
When I look at your code there is one issue that jump out at me: for both `vsd` and `nsd`, the labels are missing. I believe this is more a data issue, as they were not kept in the cleaned dataframes of #223. I think the *word sense disambiguation* task was a bit misunderstood.
Maybe you should directly use the data provided by FLUE for these ? | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 125 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
Hi @mariamabarham
FLUE can indeed become a very interesting benchmark for french NLP !
Unfortunately, it seems that we've both been working on adding it to the repo...
I was going to open a pull request before I came across yours.
I didn't want to open a duplicate, that's why I'm commenting here (I hope it's not rude).
When I look at your code there is one issue that jump out at me: for both `vsd` and `nsd`, the labels are missing. I believe this is more a data issue, as they were not kept in the cleaned dataframes of #223. I think the *word sense disambiguation* task was a bit misunderstood.
Maybe you should directly use the data provided by FLUE for these ? | [
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | Hi @TheophileBlard thanks for pointing this out. I will give a look at it or maybe if you already done it you can update this PR. Also I haven't added yet the parsing datasets, I submited a request to get access to them. If you already have them, you can also add them. | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 53 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
Hi @TheophileBlard thanks for pointing this out. I will give a look at it or maybe if you already done it you can update this PR. Also I haven't added yet the parsing datasets, I submited a request to get access to them. If you already have them, you can also add them. | [
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | Hi,
As @TheophileBlard pointed out, the labels for the vsd and nsd stains are missing.
For the wsd, it is my mistake, I added the files containing the labels on the drive.
There is still the join to do between the files that I didn't have time to do. It can be done after importing the two files, however if you wish to have a single dataframe already containing all the information, I could do it but only when I have free time because I have a lot of work at the moment at INSERM with the covid.
For the nsd, I've downloaded the files at https://zenodo.org/record/3549806, and if you do the same you'll see that they don't contain any labels.
In the files, you can see that some words have a WN code. I don't know what it corresponds to. On the FLUE github, they say to use the disambiguate tool (https://github.com/getalp/disambiguate) but I don't understand what he's doing.
@mariamabarham for the parsing datasets, I have them in my possession. What it does that I haven't shared them is that they are licensed and you have to make a request to their creators. They give them away very easily for research purposes. For another use, you have to ask a commercial licence. All this means that if the data is freely available on your librairy, their licence and their application form are no longer of interest, which is why I did not add them.
Afterwards, maybe the authors will change their policies and decide to make the data freely available through your librairy | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 264 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
Hi,
As @TheophileBlard pointed out, the labels for the vsd and nsd stains are missing.
For the wsd, it is my mistake, I added the files containing the labels on the drive.
There is still the join to do between the files that I didn't have time to do. It can be done after importing the two files, however if you wish to have a single dataframe already containing all the information, I could do it but only when I have free time because I have a lot of work at the moment at INSERM with the covid.
For the nsd, I've downloaded the files at https://zenodo.org/record/3549806, and if you do the same you'll see that they don't contain any labels.
In the files, you can see that some words have a WN code. I don't know what it corresponds to. On the FLUE github, they say to use the disambiguate tool (https://github.com/getalp/disambiguate) but I don't understand what he's doing.
@mariamabarham for the parsing datasets, I have them in my possession. What it does that I haven't shared them is that they are licensed and you have to make a request to their creators. They give them away very easily for research purposes. For another use, you have to ask a commercial licence. All this means that if the data is freely available on your librairy, their licence and their application form are no longer of interest, which is why I did not add them.
Afterwards, maybe the authors will change their policies and decide to make the data freely available through your librairy | [
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | @mariamabarham @lbourdois, Yea I don't think we can had the parsing datasets without asking the authors permission first. I also hope they'll change their policy.
Regarding `vsd` and `nsd`, if I understand well the task, the labels are "word senses" and the goal is to find the correct word sense for each ambiguous word. For `vsd` there is one ambiguous verb per sentence, and the labels we manually annotated with "wiktionary senses". For `nsd`, there are multiple ambiguous word per sentence, and the labels are WordNet Princeton Identifiers (hence the WN tag). This dataset was translated in french & automatically aligned.
Imo, for these 2 datasets, each example should be made of:
- a list of string tokens (the words of the sentence)
- a list of string labels (the word senses or 'O' when the word is not ambiguous.
In fact, for `vsd` it could be even simpler, with a single string label (as there is only one ambiguous verb), + some "idx" feature to indicate the location of the ambiguous verb.
Unfortunately, I cannot update your PR as I'm not a maintainer of the project. Maybe we could work together on a fork ? Here's [mine](https://github.com/TheophileBlard/nlp/commits/flue-benchmark).
| This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 198 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
@mariamabarham @lbourdois, Yea I don't think we can had the parsing datasets without asking the authors permission first. I also hope they'll change their policy.
Regarding `vsd` and `nsd`, if I understand well the task, the labels are "word senses" and the goal is to find the correct word sense for each ambiguous word. For `vsd` there is one ambiguous verb per sentence, and the labels we manually annotated with "wiktionary senses". For `nsd`, there are multiple ambiguous word per sentence, and the labels are WordNet Princeton Identifiers (hence the WN tag). This dataset was translated in french & automatically aligned.
Imo, for these 2 datasets, each example should be made of:
- a list of string tokens (the words of the sentence)
- a list of string labels (the word senses or 'O' when the word is not ambiguous.
In fact, for `vsd` it could be even simpler, with a single string label (as there is only one ambiguous verb), + some "idx" feature to indicate the location of the ambiguous verb.
Unfortunately, I cannot update your PR as I'm not a maintainer of the project. Maybe we could work together on a fork ? Here's [mine](https://github.com/TheophileBlard/nlp/commits/flue-benchmark).
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | Hi
Any news about this PR ?
Because thinking back FLUE basically offers only two new datasets : those for the Word Sense Disambiguation task (vsd and nsd).
Wouldn't it be more clever to make separate PRs to add the datasets of the other tasks which are multi-lingual (and therefore can be used for other languages) ?
XNLI being already present on your library, there would only be PAWS-X (datasets and bibtex available here : https://github.com/google-research-datasets/paws/tree/master/pawsx) and the Webis-CLS-10 dataset (dataset : https://zenodo.org/record/3251672#.XvCXN-d8taQ and bibtex : https://zenodo.org/record/3251672/export/hx#.XvCXZ-d8taQ) to do.
And next for the FLUE benchmark, all you would have to do would be to use your own library by making an nlp.load_dataset() (for example nlp.load_dataset('xnli') which is already present in your library) for each of the datasets of the benchmark tasks and to keep only the 'fr' data.
Also @mariamabarham , did you get any feedback for the parsing task dataset request?
In case of refusal from the authors, there are other datasets in French to perform this task and in this case, I would open a new topic
| This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 179 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
Hi
Any news about this PR ?
Because thinking back FLUE basically offers only two new datasets : those for the Word Sense Disambiguation task (vsd and nsd).
Wouldn't it be more clever to make separate PRs to add the datasets of the other tasks which are multi-lingual (and therefore can be used for other languages) ?
XNLI being already present on your library, there would only be PAWS-X (datasets and bibtex available here : https://github.com/google-research-datasets/paws/tree/master/pawsx) and the Webis-CLS-10 dataset (dataset : https://zenodo.org/record/3251672#.XvCXN-d8taQ and bibtex : https://zenodo.org/record/3251672/export/hx#.XvCXZ-d8taQ) to do.
And next for the FLUE benchmark, all you would have to do would be to use your own library by making an nlp.load_dataset() (for example nlp.load_dataset('xnli') which is already present in your library) for each of the datasets of the benchmark tasks and to keep only the 'fr' data.
Also @mariamabarham , did you get any feedback for the parsing task dataset request?
In case of refusal from the authors, there are other datasets in French to perform this task and in this case, I would open a new topic
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | Hi @lbourdois ,
PAWS-X is also present in the lib, it's part of `xtreme` dataset, so it can be loaded by `nlp.load_dataset('xtreme', 'PAWS-X.fr')` for the french version.
I think the parsing and the Word Sense Disambiguation task datasets are the only missing in the lib now.
I did not get a feedback yet for the parsing dataset.
| This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 57 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
Hi @lbourdois ,
PAWS-X is also present in the lib, it's part of `xtreme` dataset, so it can be loaded by `nlp.load_dataset('xtreme', 'PAWS-X.fr')` for the french version.
I think the parsing and the Word Sense Disambiguation task datasets are the only missing in the lib now.
I did not get a feedback yet for the parsing dataset.
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | By the way, @TheophileBlard I commented some days ago in your fork. It would be great if you can maybe open a new PR with your code or if you have a better way to make it available to others for review. | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 42 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
By the way, @TheophileBlard I commented some days ago in your fork. It would be great if you can maybe open a new PR with your code or if you have a better way to make it available to others for review. | [
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https://github.com/huggingface/datasets/pull/253 | add flue dataset | > By the way, @TheophileBlard I commented some days ago in your fork. It would be great if you can maybe open a new PR with your code or if you have a better way to make it available to others for review.
Yea sorry, missed that! I think @lbourdois has a point, it helps no one to have the same dataset in multiple places. I will try to find some time to adapt the code of my fork and open PRs for `Webis-CLS-10` and `nsd`/`vsd`. Maybe we should group `nsd`/`vsd` together ? | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
| 93 | text: add flue dataset
This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
> By the way, @TheophileBlard I commented some days ago in your fork. It would be great if you can maybe open a new PR with your code or if you have a better way to make it available to others for review.
Yea sorry, missed that! I think @lbourdois has a point, it helps no one to have the same dataset in multiple places. I will try to find some time to adapt the code of my fork and open PRs for `Webis-CLS-10` and `nsd`/`vsd`. Maybe we should group `nsd`/`vsd` together ? | [
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https://github.com/huggingface/datasets/pull/250 | Remove checksum download in c4 | Commenting again in case [previous thread](https://github.com/huggingface/nlp/pull/233) was inactive.
@lhoestq I am facing `IsADirectoryError` while downloading with this command.
Can you pls look into it & help me.
I'm using version 0.4.0 of `nlp`.
```
dataset = load_dataset("c4", 'en', data_dir='.', beam_runner='DirectRunner')
```
Here's the complete stack trace.
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/devops/.cache/huggingface/datasets/c4/en/2.3.0/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7...
---------------------------------------------------------------------------
IsADirectoryError Traceback (most recent call last)
<ipython-input-11-f622e6705e03> in <module>
----> 1 dataset = load_dataset("c4", 'en', data_dir='.', beam_runner='DirectRunner')
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
547 # Download and prepare data
548 builder_instance.download_and_prepare(
--> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
550 )
551
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
461 if not downloaded_from_gcs:
462 self._download_and_prepare(
--> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
464 )
465 # Sync info
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
964 pipeline = beam_utils.BeamPipeline(runner=beam_runner, options=beam_options,)
965 super(BeamBasedBuilder, self)._download_and_prepare(
--> 966 dl_manager, verify_infos=False, pipeline=pipeline,
967 ) # TODO handle verify_infos in beam datasets
968 # Run pipeline
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
516 split_dict = SplitDict(dataset_name=self.name)
517 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 518 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
519 # Checksums verification
520 if verify_infos:
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/datasets/c4/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7/c4.py in _split_generators(self, dl_manager, pipeline)
187 if self.config.realnewslike:
188 files_to_download["realnews_domains"] = _REALNEWS_DOMAINS_URL
--> 189 file_paths = dl_manager.download_and_extract(files_to_download)
190
191 if self.config.webtextlike:
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download_and_extract(self, url_or_urls)
218 extracted_path(s): `str`, extracted paths of given URL(s).
219 """
--> 220 return self.extract(self.download(url_or_urls))
221
222 def get_recorded_sizes_checksums(self):
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download(self, url_or_urls)
156 lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
157 )
--> 158 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
159 return downloaded_path_or_paths
160
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in _record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)
106 flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths)
107 for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths):
--> 108 self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)
109
110 def download_custom(self, url_or_urls, custom_download):
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/info_utils.py in get_size_checksum_dict(path)
77 """Compute the file size and the sha256 checksum of a file"""
78 m = sha256()
---> 79 with open(path, "rb") as f:
80 for chunk in iter(lambda: f.read(1 << 20), b""):
81 m.update(chunk)
IsADirectoryError: [Errno 21] Is a directory: '/'
```
Can anyone please try to see what I am doing wrong or is this a bug? | There was a line from the original tfds script that was still there and causing issues when loading the c4 script. This one should fix #233 and allow anyone to load the c4 script to generate the dataset | 349 | text: Remove checksum download in c4
There was a line from the original tfds script that was still there and causing issues when loading the c4 script. This one should fix #233 and allow anyone to load the c4 script to generate the dataset
Commenting again in case [previous thread](https://github.com/huggingface/nlp/pull/233) was inactive.
@lhoestq I am facing `IsADirectoryError` while downloading with this command.
Can you pls look into it & help me.
I'm using version 0.4.0 of `nlp`.
```
dataset = load_dataset("c4", 'en', data_dir='.', beam_runner='DirectRunner')
```
Here's the complete stack trace.
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/devops/.cache/huggingface/datasets/c4/en/2.3.0/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7...
---------------------------------------------------------------------------
IsADirectoryError Traceback (most recent call last)
<ipython-input-11-f622e6705e03> in <module>
----> 1 dataset = load_dataset("c4", 'en', data_dir='.', beam_runner='DirectRunner')
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
547 # Download and prepare data
548 builder_instance.download_and_prepare(
--> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
550 )
551
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
461 if not downloaded_from_gcs:
462 self._download_and_prepare(
--> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
464 )
465 # Sync info
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
964 pipeline = beam_utils.BeamPipeline(runner=beam_runner, options=beam_options,)
965 super(BeamBasedBuilder, self)._download_and_prepare(
--> 966 dl_manager, verify_infos=False, pipeline=pipeline,
967 ) # TODO handle verify_infos in beam datasets
968 # Run pipeline
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
516 split_dict = SplitDict(dataset_name=self.name)
517 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 518 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
519 # Checksums verification
520 if verify_infos:
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/datasets/c4/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7/c4.py in _split_generators(self, dl_manager, pipeline)
187 if self.config.realnewslike:
188 files_to_download["realnews_domains"] = _REALNEWS_DOMAINS_URL
--> 189 file_paths = dl_manager.download_and_extract(files_to_download)
190
191 if self.config.webtextlike:
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download_and_extract(self, url_or_urls)
218 extracted_path(s): `str`, extracted paths of given URL(s).
219 """
--> 220 return self.extract(self.download(url_or_urls))
221
222 def get_recorded_sizes_checksums(self):
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download(self, url_or_urls)
156 lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
157 )
--> 158 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
159 return downloaded_path_or_paths
160
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in _record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)
106 flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths)
107 for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths):
--> 108 self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)
109
110 def download_custom(self, url_or_urls, custom_download):
/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/info_utils.py in get_size_checksum_dict(path)
77 """Compute the file size and the sha256 checksum of a file"""
78 m = sha256()
---> 79 with open(path, "rb") as f:
80 for chunk in iter(lambda: f.read(1 << 20), b""):
81 m.update(chunk)
IsADirectoryError: [Errno 21] Is a directory: '/'
```
Can anyone please try to see what I am doing wrong or is this a bug? | [
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https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | Thanks for adding this one !
About the three points you mentioned:
1. I think the `toronto_books_corpus` branch can be removed @mariamabarham ?
2. You can use the download manager to download from google drive. For you case you can just do something like
```python
URL = "https://drive.google.com/uc?export=download&id=16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z"
...
arch_path = dl_manager.download_and_extract(URL)
```
Also this is is an unofficial host of the dataset, we should probably host it ourselves if we can.
3. Not sure about the copyright here, but I maybe @thomwolf has better insights about it. | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 88 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
Thanks for adding this one !
About the three points you mentioned:
1. I think the `toronto_books_corpus` branch can be removed @mariamabarham ?
2. You can use the download manager to download from google drive. For you case you can just do something like
```python
URL = "https://drive.google.com/uc?export=download&id=16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z"
...
arch_path = dl_manager.download_and_extract(URL)
```
Also this is is an unofficial host of the dataset, we should probably host it ourselves if we can.
3. Not sure about the copyright here, but I maybe @thomwolf has better insights about it. | [
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https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | I just downloaded the file and put it on gs. The public url is
https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2
Could you try to change the url to this one and heck that everything is ok ? | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 32 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
I just downloaded the file and put it on gs. The public url is
https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2
Could you try to change the url to this one and heck that everything is ok ? | [
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https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | In `books.py`
```
URL = "https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2"
```
```
Python 3.7.6 (default, Jan 8 2020, 19:59:22)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from nlp import load_dataset
>>> book = load_dataset("nlp/datasets/bookscorpus/books.py", cache_dir='~/tmp')
Downloading and preparing dataset bookscorpus/plain_text (download: 1.10 GiB, generated: 4.52 GiB, total: 5.62 GiB) to /home/yisiang/tmp/bookscorpus/plain_text/1.0.0...
Downloading: 100%|███████████████████████████████████████████████████████████| 1.18G/1.18G [00:39<00:00, 30.0MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/yisiang/nlp/src/nlp/load.py", line 520, in load_dataset
save_infos=save_infos,
File "/home/yisiang/nlp/src/nlp/builder.py", line 420, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/yisiang/nlp/src/nlp/builder.py", line 460, in _download_and_prepare
verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
File "/home/yisiang/nlp/src/nlp/utils/info_utils.py", line 31, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
nlp.utils.info_utils.ExpectedMoreDownloadedFiles: {'16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z'}
>>>
```
BTW, I notice the path `huggingface-nlp/datasets/toronto_books_corpus`, does it mean I have to change folder name "bookscorpus" to "toronto_books_corpus" | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 129 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
In `books.py`
```
URL = "https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2"
```
```
Python 3.7.6 (default, Jan 8 2020, 19:59:22)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from nlp import load_dataset
>>> book = load_dataset("nlp/datasets/bookscorpus/books.py", cache_dir='~/tmp')
Downloading and preparing dataset bookscorpus/plain_text (download: 1.10 GiB, generated: 4.52 GiB, total: 5.62 GiB) to /home/yisiang/tmp/bookscorpus/plain_text/1.0.0...
Downloading: 100%|███████████████████████████████████████████████████████████| 1.18G/1.18G [00:39<00:00, 30.0MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/yisiang/nlp/src/nlp/load.py", line 520, in load_dataset
save_infos=save_infos,
File "/home/yisiang/nlp/src/nlp/builder.py", line 420, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/yisiang/nlp/src/nlp/builder.py", line 460, in _download_and_prepare
verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
File "/home/yisiang/nlp/src/nlp/utils/info_utils.py", line 31, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
nlp.utils.info_utils.ExpectedMoreDownloadedFiles: {'16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z'}
>>>
```
BTW, I notice the path `huggingface-nlp/datasets/toronto_books_corpus`, does it mean I have to change folder name "bookscorpus" to "toronto_books_corpus" | [
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] |
https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | > In `books.py`
>
> ```
> URL = "https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2"
> ```
>
> ```
> Python 3.7.6 (default, Jan 8 2020, 19:59:22)
> [GCC 7.3.0] :: Anaconda, Inc. on linux
> Type "help", "copyright", "credits" or "license" for more information.
> >>> from nlp import load_dataset
> >>> book = load_dataset("nlp/datasets/bookscorpus/books.py", cache_dir='~/tmp')
> Downloading and preparing dataset bookscorpus/plain_text (download: 1.10 GiB, generated: 4.52 GiB, total: 5.62 GiB) to /home/yisiang/tmp/bookscorpus/plain_text/1.0.0...
> Downloading: 100%|███████████████████████████████████████████████████████████| 1.18G/1.18G [00:39<00:00, 30.0MB/s]
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "/home/yisiang/nlp/src/nlp/load.py", line 520, in load_dataset
> save_infos=save_infos,
> File "/home/yisiang/nlp/src/nlp/builder.py", line 420, in download_and_prepare
> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
> File "/home/yisiang/nlp/src/nlp/builder.py", line 460, in _download_and_prepare
> verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
> File "/home/yisiang/nlp/src/nlp/utils/info_utils.py", line 31, in verify_checksums
> raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
> nlp.utils.info_utils.ExpectedMoreDownloadedFiles: {'16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z'}
> >>>
> ```
>
> BTW, I notice the path `huggingface-nlp/datasets/toronto_books_corpus`, does it mean I have to change folder name "bookscorpus" to "toronto_books_corpus"
Let me change the url to match "bookscorpus", so that you don't have to change anything. Good catch.
About the error you're getting: you just have to remove the `dataset_infos.json` and regenerate it | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 191 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
> In `books.py`
>
> ```
> URL = "https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2"
> ```
>
> ```
> Python 3.7.6 (default, Jan 8 2020, 19:59:22)
> [GCC 7.3.0] :: Anaconda, Inc. on linux
> Type "help", "copyright", "credits" or "license" for more information.
> >>> from nlp import load_dataset
> >>> book = load_dataset("nlp/datasets/bookscorpus/books.py", cache_dir='~/tmp')
> Downloading and preparing dataset bookscorpus/plain_text (download: 1.10 GiB, generated: 4.52 GiB, total: 5.62 GiB) to /home/yisiang/tmp/bookscorpus/plain_text/1.0.0...
> Downloading: 100%|███████████████████████████████████████████████████████████| 1.18G/1.18G [00:39<00:00, 30.0MB/s]
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "/home/yisiang/nlp/src/nlp/load.py", line 520, in load_dataset
> save_infos=save_infos,
> File "/home/yisiang/nlp/src/nlp/builder.py", line 420, in download_and_prepare
> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
> File "/home/yisiang/nlp/src/nlp/builder.py", line 460, in _download_and_prepare
> verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
> File "/home/yisiang/nlp/src/nlp/utils/info_utils.py", line 31, in verify_checksums
> raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
> nlp.utils.info_utils.ExpectedMoreDownloadedFiles: {'16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z'}
> >>>
> ```
>
> BTW, I notice the path `huggingface-nlp/datasets/toronto_books_corpus`, does it mean I have to change folder name "bookscorpus" to "toronto_books_corpus"
Let me change the url to match "bookscorpus", so that you don't have to change anything. Good catch.
About the error you're getting: you just have to remove the `dataset_infos.json` and regenerate it | [
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https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | Hi, I found I made a mistake. I found the ELECTRA paper refer it as "BooksCorpus", but actually it is caleld "BookCorpus", according to the original paper. Sorry, I should have checked the original paper .
Can you do me a favor and change the url path to ` https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2` ? | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 51 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
Hi, I found I made a mistake. I found the ELECTRA paper refer it as "BooksCorpus", but actually it is caleld "BookCorpus", according to the original paper. Sorry, I should have checked the original paper .
Can you do me a favor and change the url path to ` https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2` ? | [
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https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | Yep I'm doing it right now. Could you please rename all the references to `bookscorpus` and `BooksCorpus` to `book_corpus` and `BookCorpus` (with the right casing) ? | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 26 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
Yep I'm doing it right now. Could you please rename all the references to `bookscorpus` and `BooksCorpus` to `book_corpus` and `BookCorpus` (with the right casing) ? | [
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https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | Thank you @lhoestq ,
Just to confirm it fits your naming convention
* make the file path `book_corpus/book_corpus.py` ?
* make `class Bookscorpus(nlp.GeneratorBasedBuilder)` -> `BookCorpus` (which make cache folder name `book_corpus` and user use `load_dataset('book_corpus')`) ?
(Cuz I found "HellaSwag" dataset is named "nlp/datasets/hellaswag" and `class Hellaswag` ) | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 48 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
Thank you @lhoestq ,
Just to confirm it fits your naming convention
* make the file path `book_corpus/book_corpus.py` ?
* make `class Bookscorpus(nlp.GeneratorBasedBuilder)` -> `BookCorpus` (which make cache folder name `book_corpus` and user use `load_dataset('book_corpus')`) ?
(Cuz I found "HellaSwag" dataset is named "nlp/datasets/hellaswag" and `class Hellaswag` ) | [
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https://github.com/huggingface/datasets/pull/248 | add Toronto BooksCorpus | Oh yea you're right about the Hellaswag example. We should keep the "_" symbol to replace spaces. As there are no space in BookCorpus, what we should do here is use:
- class name: 'Bookcorpus'
- script name: `bookcorpus/bookcorpus.py`
- use url https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2
And therefore the dataset name will be `bookcorpus`
Don't forget to regenerate the `dataset_infos.json` and we'll be good :D | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | 62 | text: add Toronto BooksCorpus
1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131
Oh yea you're right about the Hellaswag example. We should keep the "_" symbol to replace spaces. As there are no space in BookCorpus, what we should do here is use:
- class name: 'Bookcorpus'
- script name: `bookcorpus/bookcorpus.py`
- use url https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2
And therefore the dataset name will be `bookcorpus`
Don't forget to regenerate the `dataset_infos.json` and we'll be good :D | [
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https://github.com/huggingface/datasets/pull/247 | Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir | That's great!
I think it would be nice to test "deterministic-ness" of datasets in CI if we can do it (should be left for future PR of course)
Here is a possibility (maybe there are other ways to do it): given that we may soon have efficient and large-scale hashing (cf our discussion on versioning/tracability), we could incorporate a hash of the final Arrow Dataset to the `dataset.json` file and have a test on it as well as CI on a diversity of platform to test the hash (Win/Mac/Linux + various python/env).
What do you think @lhoestq @patrickvonplaten? | This PR makes all datasets loading deterministic by applying `sorted()` to all `glob.glob` and `os.listdir` statements.
Are there other "non-deterministic" functions apart from `glob.glob()` and `os.listdir()` that you can think of @thomwolf @lhoestq @mariamabarham @jplu ?
**Important**
It does break backward compatibility for these datasets because
1. When loading the complete dataset the order in which the examples are saved is different now
2. When loading only part of a split, the examples themselves might be different.
@patrickvonplaten - the nlp / longformer notebook has to be updated since the examples might now be different | 98 | text: Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir
This PR makes all datasets loading deterministic by applying `sorted()` to all `glob.glob` and `os.listdir` statements.
Are there other "non-deterministic" functions apart from `glob.glob()` and `os.listdir()` that you can think of @thomwolf @lhoestq @mariamabarham @jplu ?
**Important**
It does break backward compatibility for these datasets because
1. When loading the complete dataset the order in which the examples are saved is different now
2. When loading only part of a split, the examples themselves might be different.
@patrickvonplaten - the nlp / longformer notebook has to be updated since the examples might now be different
That's great!
I think it would be nice to test "deterministic-ness" of datasets in CI if we can do it (should be left for future PR of course)
Here is a possibility (maybe there are other ways to do it): given that we may soon have efficient and large-scale hashing (cf our discussion on versioning/tracability), we could incorporate a hash of the final Arrow Dataset to the `dataset.json` file and have a test on it as well as CI on a diversity of platform to test the hash (Win/Mac/Linux + various python/env).
What do you think @lhoestq @patrickvonplaten? | [
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] |
https://github.com/huggingface/datasets/pull/247 | Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir | > That's great!
>
> I think it would be nice to test "deterministic-ness" of datasets in CI if we can do it (should be left for future PR of course)
>
> Here is a possibility (maybe there are other ways to do it): given that we may soon have efficient and large-scale hashing (cf our discussion on versioning/tracability), we could incorporate a hash of the final Arrow Dataset to the `dataset.json` file and have a test on it as well as CI on a diversity of platform to test the hash (Win/Mac/Linux + various python/env).
> What do you think @lhoestq @patrickvonplaten?
I think that's a great idea! The test should be a `RUN_SLOW` test, since it takes a considerable amount of time to download the dataset and generate the examples, but I think we should add some kind of hash test for each dataset. | This PR makes all datasets loading deterministic by applying `sorted()` to all `glob.glob` and `os.listdir` statements.
Are there other "non-deterministic" functions apart from `glob.glob()` and `os.listdir()` that you can think of @thomwolf @lhoestq @mariamabarham @jplu ?
**Important**
It does break backward compatibility for these datasets because
1. When loading the complete dataset the order in which the examples are saved is different now
2. When loading only part of a split, the examples themselves might be different.
@patrickvonplaten - the nlp / longformer notebook has to be updated since the examples might now be different | 147 | text: Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir
This PR makes all datasets loading deterministic by applying `sorted()` to all `glob.glob` and `os.listdir` statements.
Are there other "non-deterministic" functions apart from `glob.glob()` and `os.listdir()` that you can think of @thomwolf @lhoestq @mariamabarham @jplu ?
**Important**
It does break backward compatibility for these datasets because
1. When loading the complete dataset the order in which the examples are saved is different now
2. When loading only part of a split, the examples themselves might be different.
@patrickvonplaten - the nlp / longformer notebook has to be updated since the examples might now be different
> That's great!
>
> I think it would be nice to test "deterministic-ness" of datasets in CI if we can do it (should be left for future PR of course)
>
> Here is a possibility (maybe there are other ways to do it): given that we may soon have efficient and large-scale hashing (cf our discussion on versioning/tracability), we could incorporate a hash of the final Arrow Dataset to the `dataset.json` file and have a test on it as well as CI on a diversity of platform to test the hash (Win/Mac/Linux + various python/env).
> What do you think @lhoestq @patrickvonplaten?
I think that's a great idea! The test should be a `RUN_SLOW` test, since it takes a considerable amount of time to download the dataset and generate the examples, but I think we should add some kind of hash test for each dataset. | [
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https://github.com/huggingface/datasets/pull/244 | Add Allociné Dataset | LGTM, thanks a lot for adding dummy data tests :-) Was it difficult to create the correct dummy data folder? | This is a french binary sentiment classification dataset, which was used to train this model: https://huggingface.co/tblard/tf-allocine.
Basically, it's a french "IMDB" dataset, with more reviews.
More info on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert). | 20 | text: Add Allociné Dataset
This is a french binary sentiment classification dataset, which was used to train this model: https://huggingface.co/tblard/tf-allocine.
Basically, it's a french "IMDB" dataset, with more reviews.
More info on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert).
LGTM, thanks a lot for adding dummy data tests :-) Was it difficult to create the correct dummy data folder? | [
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https://github.com/huggingface/datasets/pull/241 | Fix empty cache dir | Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think | If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
| 26 | text: Fix empty cache dir
If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think | [
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https://github.com/huggingface/datasets/pull/241 | Fix empty cache dir | > Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think
No it shouldn't force to redownload | If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
| 33 | text: Fix empty cache dir
If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
> Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think
No it shouldn't force to redownload | [
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https://github.com/huggingface/datasets/pull/236 | CompGuessWhat?! dataset | Hi @aleSuglia, thanks for this great PR. Indeed you can have both datasets in one file. You need to add a config class which will allows you to specify the different subdataset names and then you will be able to load them as follow.
nlp.load_dataset("compguesswhat", "compguesswhat-gameplay")
nlp.load_dataset("compguesswhat", "compguesswhat-zs-gameplay").
Maybe you can refer to this file https://github.com/huggingface/nlp/blob/master/datasets/discofuse/discofuse.py | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | 56 | text: CompGuessWhat?! dataset
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
Hi @aleSuglia, thanks for this great PR. Indeed you can have both datasets in one file. You need to add a config class which will allows you to specify the different subdataset names and then you will be able to load them as follow.
nlp.load_dataset("compguesswhat", "compguesswhat-gameplay")
nlp.load_dataset("compguesswhat", "compguesswhat-zs-gameplay").
Maybe you can refer to this file https://github.com/huggingface/nlp/blob/master/datasets/discofuse/discofuse.py | [
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https://github.com/huggingface/datasets/pull/236 | CompGuessWhat?! dataset | @mariamabarham Thanks for your suggestions. I've followed your advice and integrated the additional dataset using another `DatasetConfig` class. It looks like all tests passed. What do you think? | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | 28 | text: CompGuessWhat?! dataset
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
@mariamabarham Thanks for your suggestions. I've followed your advice and integrated the additional dataset using another `DatasetConfig` class. It looks like all tests passed. What do you think? | [
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https://github.com/huggingface/datasets/pull/236 | CompGuessWhat?! dataset | great @aleSuglia. I requested an additional review from @thomwolf @lhoestq and @patrickvonplaten @jplu . You can merge it after an approval from one of them | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | 25 | text: CompGuessWhat?! dataset
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
great @aleSuglia. I requested an additional review from @thomwolf @lhoestq and @patrickvonplaten @jplu . You can merge it after an approval from one of them | [
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https://github.com/huggingface/datasets/pull/236 | CompGuessWhat?! dataset | Not sure whether it's the most appropriate place but I'll ask another design question. For Vision+Language dataset, is very common to have visual features associated with each example. At the moment, for instance, I'm only integrating the image identifier so that people can later on lookup the image features during training. Do you recommend this approach or do you think it should be done in a different way?
Thank you for your answer! | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | 73 | text: CompGuessWhat?! dataset
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
Not sure whether it's the most appropriate place but I'll ask another design question. For Vision+Language dataset, is very common to have visual features associated with each example. At the moment, for instance, I'm only integrating the image identifier so that people can later on lookup the image features during training. Do you recommend this approach or do you think it should be done in a different way?
Thank you for your answer! | [
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https://github.com/huggingface/datasets/pull/236 | CompGuessWhat?! dataset | Hi @aleSuglia your remark on the visual features is a good point.
We haven't started to dive deeply into how CV datasets are usually structured (cc @sgugger)
Do you have a pointer to how visual features are currently loaded and accessed by people using GuessCompWhat? | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | 45 | text: CompGuessWhat?! dataset
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
Hi @aleSuglia your remark on the visual features is a good point.
We haven't started to dive deeply into how CV datasets are usually structured (cc @sgugger)
Do you have a pointer to how visual features are currently loaded and accessed by people using GuessCompWhat? | [
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https://github.com/huggingface/datasets/pull/236 | CompGuessWhat?! dataset | @thomwolf As far as I know, people using Language+Vision tasks they typically have their reference dataset (either in JSON or JSONL format) and for each example in it they have an identifier that specifies the reference image. Currently, images are represented by either pooling-based visual features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https://arxiv.org/abs/1611.08481), [Shekhar et.al, 2019](https://www.aclweb.org/anthology/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https://arxiv.org/abs/1502.03044)). A more common and recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https://arxiv.org/abs/1908.03557), is to use FastRCNN features.
For all these types of features, people use either HD5F or NumPy compressed representations. In my personal projects, I've ditched altogether HD5F because it doesn't have out-of-the-box support for multi-processing (unless you have an ad-hoc installation of it). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it (see [numpy.savez](https://numpy.org/doc/stable/reference/generated/numpy.savez.html)). However, I believe that Apache Arrow would be a really good fit for this type of features.
Looking forward to hearing your thoughts about it! | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | 201 | text: CompGuessWhat?! dataset
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
@thomwolf As far as I know, people using Language+Vision tasks they typically have their reference dataset (either in JSON or JSONL format) and for each example in it they have an identifier that specifies the reference image. Currently, images are represented by either pooling-based visual features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https://arxiv.org/abs/1611.08481), [Shekhar et.al, 2019](https://www.aclweb.org/anthology/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https://arxiv.org/abs/1502.03044)). A more common and recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https://arxiv.org/abs/1908.03557), is to use FastRCNN features.
For all these types of features, people use either HD5F or NumPy compressed representations. In my personal projects, I've ditched altogether HD5F because it doesn't have out-of-the-box support for multi-processing (unless you have an ad-hoc installation of it). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it (see [numpy.savez](https://numpy.org/doc/stable/reference/generated/numpy.savez.html)). However, I believe that Apache Arrow would be a really good fit for this type of features.
Looking forward to hearing your thoughts about it! | [
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https://github.com/huggingface/datasets/pull/236 | CompGuessWhat?! dataset | @thomwolf I was thinking that I should create an issue regarding the visual features so that we can keep track of it for future work. I think it would be great to have it in NLP and I'll be happy to contribute. Let me know what you think :) | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | 49 | text: CompGuessWhat?! dataset
Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences?
@thomwolf I was thinking that I should create an issue regarding the visual features so that we can keep track of it for future work. I think it would be great to have it in NLP and I'll be happy to contribute. Let me know what you think :) | [
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https://github.com/huggingface/datasets/pull/235 | Add experimental datasets | I think it would be nicer to not create a new folder `datasets_experimental` , but just put your datasets also into the folder `datasets` for the following reasons:
- From my point of view, the datasets are not very different from the other datasets (assuming that we soon have C4, and the beam datasets) so I don't see why we require a new dataset folder
- I'm not a big fan of adding a boolean flag to the `load_dataset()` function that basically switches between folder names on S3. The user has to know whether a dataset script is experimental or not. User installing nlp with pip won't see that there are folders called `datasets` and `datasets_experimental`
- If we do this just to bypass the test, I think a good solution could be: For all tests that are too complicated to be currently tested with the testing framework, we can add a class variable called `do_test = False` to the dataset builder class and a default `do_test = True` to the abstract dataset class and skip all tests that have that variable in the dataset test framework similar to what is done to beam datasets: https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/tests/test_dataset_common.py#L79
We can also print a warning for all dataset tests having `do_test = False`. This variable would only concern testing and we would not have a problem removing it at a later stage IMO.
- This way the datascripts are backward compatible and can be used with earlier versions of `nlp` (not that this matters too much atm)
What is your opinion on this @lhoestq @thomwolf ? | ## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
| 263 | text: Add experimental datasets
## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
I think it would be nicer to not create a new folder `datasets_experimental` , but just put your datasets also into the folder `datasets` for the following reasons:
- From my point of view, the datasets are not very different from the other datasets (assuming that we soon have C4, and the beam datasets) so I don't see why we require a new dataset folder
- I'm not a big fan of adding a boolean flag to the `load_dataset()` function that basically switches between folder names on S3. The user has to know whether a dataset script is experimental or not. User installing nlp with pip won't see that there are folders called `datasets` and `datasets_experimental`
- If we do this just to bypass the test, I think a good solution could be: For all tests that are too complicated to be currently tested with the testing framework, we can add a class variable called `do_test = False` to the dataset builder class and a default `do_test = True` to the abstract dataset class and skip all tests that have that variable in the dataset test framework similar to what is done to beam datasets: https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/tests/test_dataset_common.py#L79
We can also print a warning for all dataset tests having `do_test = False`. This variable would only concern testing and we would not have a problem removing it at a later stage IMO.
- This way the datascripts are backward compatible and can be used with earlier versions of `nlp` (not that this matters too much atm)
What is your opinion on this @lhoestq @thomwolf ? | [
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https://github.com/huggingface/datasets/pull/235 | Add experimental datasets | Very cool to have add those datasets :)
I understand that making the dummy data for this case is not fun. I'm sure we'll be able to add them soon. For now it's still interesting to have them in the library, even if we can't test all the code with dummy data.
I like the idea of the `do_tests=False` class variable.
However it would be cool to test at least that we can load the module and instantiate the builder (only ignore the dummy data test for now). In that case a better name could be `test_dummy_data=False` or something like that.
If we want to be picky we can also add a warning in `_download_and_prepare` to tell the user that datasets with `test_dummy_data=False` are still experimental. | ## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
| 126 | text: Add experimental datasets
## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
Very cool to have add those datasets :)
I understand that making the dummy data for this case is not fun. I'm sure we'll be able to add them soon. For now it's still interesting to have them in the library, even if we can't test all the code with dummy data.
I like the idea of the `do_tests=False` class variable.
However it would be cool to test at least that we can load the module and instantiate the builder (only ignore the dummy data test for now). In that case a better name could be `test_dummy_data=False` or something like that.
If we want to be picky we can also add a warning in `_download_and_prepare` to tell the user that datasets with `test_dummy_data=False` are still experimental. | [
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https://github.com/huggingface/datasets/pull/235 | Add experimental datasets | Yeah I really like the idea of a partial test.
My main concern with the class variable is visibility, but having a warning would help with that. Maybe even get the user to agree > "are you sure you want to go ahead?" | ## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
| 43 | text: Add experimental datasets
## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
Yeah I really like the idea of a partial test.
My main concern with the class variable is visibility, but having a warning would help with that. Maybe even get the user to agree > "are you sure you want to go ahead?" | [
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https://github.com/huggingface/datasets/pull/235 | Add experimental datasets | > Very cool to have add those datasets :)
> I understand that making the dummy data for this case is not fun. I'm sure we'll be able to add them soon. For now it's still interesting to have them in the library, even if we can't test all the code with dummy data.
>
> I like the idea of the `do_tests=False` class variable.
> However it would be cool to test at least that we can load the module and instantiate the builder (only ignore the dummy data test for now). In that case a better name could be `test_dummy_data=False` or something like that.
>
> If we want to be picky we can also add a warning in `_download_and_prepare` to tell the user that datasets with `test_dummy_data=False` are still experimental.
`test_dummy_data=False` sounds good to me! | ## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
| 138 | text: Add experimental datasets
## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
> Very cool to have add those datasets :)
> I understand that making the dummy data for this case is not fun. I'm sure we'll be able to add them soon. For now it's still interesting to have them in the library, even if we can't test all the code with dummy data.
>
> I like the idea of the `do_tests=False` class variable.
> However it would be cool to test at least that we can load the module and instantiate the builder (only ignore the dummy data test for now). In that case a better name could be `test_dummy_data=False` or something like that.
>
> If we want to be picky we can also add a warning in `_download_and_prepare` to tell the user that datasets with `test_dummy_data=False` are still experimental.
`test_dummy_data=False` sounds good to me! | [
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https://github.com/huggingface/datasets/pull/235 | Add experimental datasets | There we go: added a `test_dummy_data` class variable that is `False` by default for the `BeamBasedBuilder` and `True` for everyone else (except the new `explainlikeimfive` and `wiki_snippets`)
Note that `wiki_snippets` should become obsolete as soon as @lhoestq adds in the `IndexedDataset` class | ## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
| 42 | text: Add experimental datasets
## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
There we go: added a `test_dummy_data` class variable that is `False` by default for the `BeamBasedBuilder` and `True` for everyone else (except the new `explainlikeimfive` and `wiki_snippets`)
Note that `wiki_snippets` should become obsolete as soon as @lhoestq adds in the `IndexedDataset` class | [
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https://github.com/huggingface/datasets/pull/229 | Rename dataset_infos.json to dataset_info.json |
This was actually the right name. `dataset_infos.json` is used to have the infos of all the dataset configurations.
On the other hand `dataset_info.json` (without 's') is a cache file with the info of one specific configuration.
To fix #228, we probably just have to clear and reload the nlp-viewer cache. | As the file required for the viewing in the live nlp viewer is named as dataset_info.json | 50 | text: Rename dataset_infos.json to dataset_info.json
As the file required for the viewing in the live nlp viewer is named as dataset_info.json
This was actually the right name. `dataset_infos.json` is used to have the infos of all the dataset configurations.
On the other hand `dataset_info.json` (without 's') is a cache file with the info of one specific configuration.
To fix #228, we probably just have to clear and reload the nlp-viewer cache. | [
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https://github.com/huggingface/datasets/pull/221 | Fix tests/test_dataset_common.py | Thanks ! Good catch :)
To fix the CI you can do:
1 - rebase from master
2 - then run `make style` as specified in [CONTRIBUTING.md](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md) ? | When I run the command `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_arcd` while working on #220. I get the error ` unexpected keyword argument "'download_and_prepare_kwargs'"` at the level of `load_dataset`. Indeed, this [function](https://github.com/huggingface/nlp/blob/master/src/nlp/load.py#L441) no longer has the argument `download_and_prepare_kwargs` but rather `download_config`. So here I change the tests accordingly. | 28 | text: Fix tests/test_dataset_common.py
When I run the command `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_arcd` while working on #220. I get the error ` unexpected keyword argument "'download_and_prepare_kwargs'"` at the level of `load_dataset`. Indeed, this [function](https://github.com/huggingface/nlp/blob/master/src/nlp/load.py#L441) no longer has the argument `download_and_prepare_kwargs` but rather `download_config`. So here I change the tests accordingly.
Thanks ! Good catch :)
To fix the CI you can do:
1 - rebase from master
2 - then run `make style` as specified in [CONTRIBUTING.md](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md) ? | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | I agree that a `.filter` method would be VERY useful and appreciated. I'm not a big fan of using `flatten_nested` as it completely breaks down the structure of the example and it may create bugs. Right now I think it may not work for nested structures. Maybe there's a simpler way that we've not figured out yet. | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 57 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
I agree that a `.filter` method would be VERY useful and appreciated. I'm not a big fan of using `flatten_nested` as it completely breaks down the structure of the example and it may create bugs. Right now I think it may not work for nested structures. Maybe there's a simpler way that we've not figured out yet. | [
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] |
https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:
```python
for i in range(num_examples):
example = map_nested(lambda x: x[i], batch)
# ... then test to keep it or not
``` | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 39 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:
```python
for i in range(num_examples):
example = map_nested(lambda x: x[i], batch)
# ... then test to keep it or not
``` | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | > Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:
>
> ```python
> for i in range(num_examples):
> example = map_nested(lambda x: x[i], batch)
> # ... then test to keep it or not
> ```
Awesome I'll check it out :-) | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 52 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
> Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:
>
> ```python
> for i in range(num_examples):
> example = map_nested(lambda x: x[i], batch)
> # ... then test to keep it or not
> ```
Awesome I'll check it out :-) | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | > Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:
>
> ```python
> for i in range(num_examples):
> example = map_nested(lambda x: x[i], batch)
> # ... then test to keep it or not
> ```
Awesome this function is definitely much nicer! | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 53 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
> Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:
>
> ```python
> for i in range(num_examples):
> example = map_nested(lambda x: x[i], batch)
> # ... then test to keep it or not
> ```
Awesome this function is definitely much nicer! | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:
```python
one_example = {
"title": "blabla",
"paragraphs": [
"p1", "p2", ...
]
}
``` | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 52 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:
```python
one_example = {
"title": "blabla",
"paragraphs": [
"p1", "p2", ...
]
}
``` | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch. | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 16 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch. | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | > Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:
>
> ```python
> one_example = {
> "title": "blabla",
> "paragraphs": [
> "p1", "p2", ...
> ]
> }
> ```
They both work. I'm using it on trivia_qa which is pretty nested. If you use the option `dict_only=True` I think it's fine. | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 84 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
> Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:
>
> ```python
> one_example = {
> "title": "blabla",
> "paragraphs": [
> "p1", "p2", ...
> ]
> }
> ```
They both work. I'm using it on trivia_qa which is pretty nested. If you use the option `dict_only=True` I think it's fine. | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | > We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch.
Why? | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 18 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
> We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch.
Why? | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | Actually it's fine. I guess this is going to be yet another thing to be unit-tested just to make sure ^^ | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 21 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
Actually it's fine. I guess this is going to be yet another thing to be unit-tested just to make sure ^^ | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | Yes, I will need to add tests and documentation!
@thomwolf - would a function like this be ok? It abstracts `.map()` a bit which might be hard to understand. | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 29 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
Yes, I will need to add tests and documentation!
@thomwolf - would a function like this be ok? It abstracts `.map()` a bit which might be hard to understand. | [
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https://github.com/huggingface/datasets/pull/214 | [arrow_dataset.py] add new filter function | I tried on some datasets with nested structure and it works fine ! Great work :D
| The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | 16 | text: [arrow_dataset.py] add new filter function
The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
I tried on some datasets with nested structure and it works fine ! Great work :D
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https://github.com/huggingface/datasets/pull/209 | Add a Google Drive exception for small files | Can you run the style formatting tools to pass the code quality test?
You can find all the details in CONTRIBUTING.md: https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-contribute-to-nlp | I tried to use the ``nlp`` library to load personnal datasets. I mainly copy-paste the code for ``multi-news`` dataset because my files are stored on Google Drive.
One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly.
Currently the ``nlp`` raises a error: ``ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=1DGnbUY9zwiThTdgUvVTSAvSVHoloCgun`` while the url is working. So I just add a new exception as you have already done for ``firebasestorage.googleapis.com`` :
```
elif (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url)
```
I make an example of the error that you can run on [](https://colab.research.google.com/drive/1ae_JJ9uvUt-9GBh0uGZhjbF5aXkl-BPv?usp=sharing)
I avoid the error by adding an exception but there is maybe a proper way to do it.
Many thanks :hugs:
Best, | 22 | text: Add a Google Drive exception for small files
I tried to use the ``nlp`` library to load personnal datasets. I mainly copy-paste the code for ``multi-news`` dataset because my files are stored on Google Drive.
One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly.
Currently the ``nlp`` raises a error: ``ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=1DGnbUY9zwiThTdgUvVTSAvSVHoloCgun`` while the url is working. So I just add a new exception as you have already done for ``firebasestorage.googleapis.com`` :
```
elif (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url)
```
I make an example of the error that you can run on [](https://colab.research.google.com/drive/1ae_JJ9uvUt-9GBh0uGZhjbF5aXkl-BPv?usp=sharing)
I avoid the error by adding an exception but there is maybe a proper way to do it.
Many thanks :hugs:
Best,
Can you run the style formatting tools to pass the code quality test?
You can find all the details in CONTRIBUTING.md: https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-contribute-to-nlp | [
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https://github.com/huggingface/datasets/pull/200 | [ArrowWriter] Set schema at first write example | Good point!
I guess we could add this to `write_batch` as well (before using `self._schema` in the first line of this method)? | Right now if the schema was not specified when instantiating `ArrowWriter`, then it could be set with the first `write_table` for example (it calls `self._build_writer()` to do so).
I noticed that it was not done if the first example is added via `.write`, so I added it for coherence. | 22 | text: [ArrowWriter] Set schema at first write example
Right now if the schema was not specified when instantiating `ArrowWriter`, then it could be set with the first `write_table` for example (it calls `self._build_writer()` to do so).
I noticed that it was not done if the first example is added via `.write`, so I added it for coherence.
Good point!
I guess we could add this to `write_batch` as well (before using `self._schema` in the first line of this method)? | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | I think that's not related to the config name but the filenames in the dummy data. Mostly it occurs with files downloaded from drive. In that case the dummy file name is extracted from the google drive link and it corresponds to what comes after `https://drive.google.com/`
| As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 46 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
I think that's not related to the config name but the filenames in the dummy data. Mostly it occurs with files downloaded from drive. In that case the dummy file name is extracted from the google drive link and it corresponds to what comes after `https://drive.google.com/`
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | > I think that's not related to the config name but the filenames in the dummy data. Mostly it occurs with files downloaded from drive. In that case the dummy file name is extracted from the google drive link and it corresponds to what comes after `https://drive.google.com/`
The filenames of the dummy data are now encoded (see #173). So this is not a problem anymore.
The problem here is different and comes from the directory names where we save the arrow files (basically `dataset_name/config_name/version`). In this case we could have invalid directory names because of the config name
| As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 98 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
> I think that's not related to the config name but the filenames in the dummy data. Mostly it occurs with files downloaded from drive. In that case the dummy file name is extracted from the google drive link and it corresponds to what comes after `https://drive.google.com/`
The filenames of the dummy data are now encoded (see #173). So this is not a problem anymore.
The problem here is different and comes from the directory names where we save the arrow files (basically `dataset_name/config_name/version`). In this case we could have invalid directory names because of the config name
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | I like the method, but I'm wondering whether it should just be a test method instead of a `__post_init__` function. From a logical point of view the only reason this error would be thrown is because of an invalid config name introduced when creating the dataset script / adding a new dataset => so I think it might be better to write a simple test for this in `test_dataset_common.py`...what do you think @lhoestq ? | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 74 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
I like the method, but I'm wondering whether it should just be a test method instead of a `__post_init__` function. From a logical point of view the only reason this error would be thrown is because of an invalid config name introduced when creating the dataset script / adding a new dataset => so I think it might be better to write a simple test for this in `test_dataset_common.py`...what do you think @lhoestq ? | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | `test_dataset_common.py` only tests canonical datasets no ? What if users wants to create their own script ? | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 17 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
`test_dataset_common.py` only tests canonical datasets no ? What if users wants to create their own script ? | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | > `test_dataset_common.py` only tests canonical datasets no ? What if users wants to create their own script ?
It tests all dataset that can be loaded either locally or on AWS (which includes all non-canonical datasets as well)...by their own script you mean like a private dataset script that they don't want to be public? I guess even then they could locally run the test functions to check... | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 68 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
> `test_dataset_common.py` only tests canonical datasets no ? What if users wants to create their own script ?
It tests all dataset that can be loaded either locally or on AWS (which includes all non-canonical datasets as well)...by their own script you mean like a private dataset script that they don't want to be public? I guess even then they could locally run the test functions to check... | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | We could have a bunch of simple consistency tests that run before uploading with the CLI (without loading data if we don't want to force the user to have dummy data)? | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 31 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
We could have a bunch of simple consistency tests that run before uploading with the CLI (without loading data if we don't want to force the user to have dummy data)? | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | Let's say someone want to create his own private script. As the script is not meant to be shared, it's not going to be placed in `/datasets` right ? Maybe the script is going to be inside another project. If I'm not wrong in this case the `test_dataset_common.py` is not going to test his script.
Raising an error in the post init is a sanity check that would tell the user immediately what's wrong.
The error is raised if he tried to load the script or if he uses `nlp-cli test` | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 91 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
Let's say someone want to create his own private script. As the script is not meant to be shared, it's not going to be placed in `/datasets` right ? Maybe the script is going to be inside another project. If I'm not wrong in this case the `test_dataset_common.py` is not going to test his script.
Raising an error in the post init is a sanity check that would tell the user immediately what's wrong.
The error is raised if he tried to load the script or if he uses `nlp-cli test` | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | > Let's say someone want to create his own private script. As the script is not meant to be shared, it's not going to be placed in `/datasets` right ? Maybe the script is going to be inside another project. If I'm not wrong in this case the `test_dataset_common.py` is not going to test his script.
>
> Raising an error in the post init is a sanity check that would tell the user immediately what's wrong.
> The error is raised if he tried to load the script or if he uses `nlp-cli test`
OK, fair point! I'm good with this then :-) | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 104 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
> Let's say someone want to create his own private script. As the script is not meant to be shared, it's not going to be placed in `/datasets` right ? Maybe the script is going to be inside another project. If I'm not wrong in this case the `test_dataset_common.py` is not going to test his script.
>
> Raising an error in the post init is a sanity check that would tell the user immediately what's wrong.
> The error is raised if he tried to load the script or if he uses `nlp-cli test`
OK, fair point! I'm good with this then :-) | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | I'm fine with this as well (even though I understand what you meant @patrickvonplaten, we can still change it later if needed) | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 22 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
I'm fine with this as well (even though I understand what you meant @patrickvonplaten, we can still change it later if needed) | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | > We could have a bunch of simple consistency tests that run before uploading with the CLI (without loading data if we don't want to force the user to have dummy data)?
Yes! I guess that's a big question whether we should force the user to add dummy data. It's probably too tedious for the user...so when uploading to circle ci should we just check
- 1) All configs can be instantiated (if there are any)
- 2) The BuilderClass can be instantiated ...
- 3) ... maybe some more
and maybe suggest to the user to add dummy data using the dummy data command? | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 105 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
> We could have a bunch of simple consistency tests that run before uploading with the CLI (without loading data if we don't want to force the user to have dummy data)?
Yes! I guess that's a big question whether we should force the user to add dummy data. It's probably too tedious for the user...so when uploading to circle ci should we just check
- 1) All configs can be instantiated (if there are any)
- 2) The BuilderClass can be instantiated ...
- 3) ... maybe some more
and maybe suggest to the user to add dummy data using the dummy data command? | [
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https://github.com/huggingface/datasets/pull/196 | Check invalid config name | I really like that we have a test with dummy data for canonical datasets. This is insurance that they'll keep working in the long run.
On the other hand I understand that we will probably not force this practice for scripts uploaded on S3 by a user under his namespace (non-canonical), as it is tedious. As I understand right now the test is done for all the datasets on aws, even the non-canonical ? We should think about different tests for non-canonical datasets.
I also like the idea of a simple consistency test ! | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | 94 | text: Check invalid config name
As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows.
I really like that we have a test with dummy data for canonical datasets. This is insurance that they'll keep working in the long run.
On the other hand I understand that we will probably not force this practice for scripts uploaded on S3 by a user under his namespace (non-canonical), as it is tedious. As I understand right now the test is done for all the datasets on aws, even the non-canonical ? We should think about different tests for non-canonical datasets.
I also like the idea of a simple consistency test ! | [
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https://github.com/huggingface/datasets/pull/194 | Add Dataset: Qanta | @lhoestq - the config name is rather special here: *E.g.* `mode=first,char_skip=25`. It includes `=` and `,` - will that be a problem for windows folders, you think?
Apart from that good to merge for me. | Fixes dummy data for #169 @EntilZha | 35 | text: Add Dataset: Qanta
Fixes dummy data for #169 @EntilZha
@lhoestq - the config name is rather special here: *E.g.* `mode=first,char_skip=25`. It includes `=` and `,` - will that be a problem for windows folders, you think?
Apart from that good to merge for me. | [
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https://github.com/huggingface/datasets/pull/194 | Add Dataset: Qanta | It's ok to have `=` and `,`.
Windows doesn't like things like `?`, `:`, `/` etc.
I'll add some lines to raise an error if the config name is invalid. | Fixes dummy data for #169 @EntilZha | 30 | text: Add Dataset: Qanta
Fixes dummy data for #169 @EntilZha
It's ok to have `=` and `,`.
Windows doesn't like things like `?`, `:`, `/` etc.
I'll add some lines to raise an error if the config name is invalid. | [
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https://github.com/huggingface/datasets/pull/194 | Add Dataset: Qanta | Thanks for fixing things up! I'm curious to take a look at the zip files now to know the format for future reference. | Fixes dummy data for #169 @EntilZha | 23 | text: Add Dataset: Qanta
Fixes dummy data for #169 @EntilZha
Thanks for fixing things up! I'm curious to take a look at the zip files now to know the format for future reference. | [
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https://github.com/huggingface/datasets/pull/190 | add squad Spanish v1 and v2 | Nice ! :)
Can we group them into one dataset with two versions, instead of having two datasets ? | This PR add the Spanish Squad versions 1 and 2 datasets.
Fixes #164 | 19 | text: add squad Spanish v1 and v2
This PR add the Spanish Squad versions 1 and 2 datasets.
Fixes #164
Nice ! :)
Can we group them into one dataset with two versions, instead of having two datasets ? | [
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https://github.com/huggingface/datasets/pull/190 | add squad Spanish v1 and v2 | @mariamabarham - feel free to merge when you're ready. I only checked the dummy files. I did not run the SLOW tests. | This PR add the Spanish Squad versions 1 and 2 datasets.
Fixes #164 | 22 | text: add squad Spanish v1 and v2
This PR add the Spanish Squad versions 1 and 2 datasets.
Fixes #164
@mariamabarham - feel free to merge when you're ready. I only checked the dummy files. I did not run the SLOW tests. | [
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https://github.com/huggingface/datasets/pull/173 | Rm extracted test dirs | Thanks for cleaning up the extracted dummy data folders! Instead of changing the file_utils we could also just put these folders under `.gitignore` (or maybe already done?). | All the dummy data used for tests were duplicated. For each dataset, we had one zip file but also its extracted directory. I removed all these directories
Furthermore instead of extracting next to the dummy_data.zip file, we extract in the temp `cached_dir` used for tests, so that all the extracted directories get removed after testing.
Finally there was a bug in the `mock_download_manager` that would let it create directories with invalid names, as in #172. I fixed that by encoding url arguments. I had to rename the dummy data for `scientific_papers` and `cnn_dailymail` (the aws tests don't pass for those 2 in this PR, but they will once aws will be synced, as the local ones do)
Let me know if it sounds good to you @patrickvonplaten . I'm still not entirely familiar with the mock downloader | 27 | text: Rm extracted test dirs
All the dummy data used for tests were duplicated. For each dataset, we had one zip file but also its extracted directory. I removed all these directories
Furthermore instead of extracting next to the dummy_data.zip file, we extract in the temp `cached_dir` used for tests, so that all the extracted directories get removed after testing.
Finally there was a bug in the `mock_download_manager` that would let it create directories with invalid names, as in #172. I fixed that by encoding url arguments. I had to rename the dummy data for `scientific_papers` and `cnn_dailymail` (the aws tests don't pass for those 2 in this PR, but they will once aws will be synced, as the local ones do)
Let me know if it sounds good to you @patrickvonplaten . I'm still not entirely familiar with the mock downloader
Thanks for cleaning up the extracted dummy data folders! Instead of changing the file_utils we could also just put these folders under `.gitignore` (or maybe already done?). | [
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