The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status
response.raise_for_status()
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/52/4d/524d2f44bf448156b368481ff6588322fdf58acf5431ea8b8d453e9ef80ea54b/738ad0c53a2ac95ab760c762688783f35a0c71617451195effdb8a190f7e6aaa?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQLC2QXPN7%2F20241116%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20241116T140125Z&X-Amz-Expires=259200&X-Amz-Signature=7b7e85e227a24bb422b7711a21e629a207ecea29b240bcee223dbeb4811b5aff&X-Amz-SignedHeaders=host&response-content-disposition=inline%3B%20filename%2A%3DUTF-8%27%27000_00000.parquet%3B%20filename%3D%22000_00000.parquet%22%3B&x-id=GetObject
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 298, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 58, in _split_generators
self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 2325, in read_schema
file = ParquetFile(
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 318, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1470, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 88, in pyarrow.lib.check_status
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 826, in read_with_retries
out = read(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 757, in read
return super().read(length)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1846, in read
out = self.cache._fetch(self.loc, self.loc + length)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/caching.py", line 189, in _fetch
self.cache = self.fetcher(start, end) # new block replaces old
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 720, in _fetch_range
hf_raise_for_status(r)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 404 Client Error: Not Found for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/52/4d/524d2f44bf448156b368481ff6588322fdf58acf5431ea8b8d453e9ef80ea54b/738ad0c53a2ac95ab760c762688783f35a0c71617451195effdb8a190f7e6aaa?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQLC2QXPN7%2F20241116%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20241116T140125Z&X-Amz-Expires=259200&X-Amz-Signature=7b7e85e227a24bb422b7711a21e629a207ecea29b240bcee223dbeb4811b5aff&X-Amz-SignedHeaders=host&response-content-disposition=inline%3B%20filename%2A%3DUTF-8%27%27000_00000.parquet%3B%20filename%3D%22000_00000.parquet%22%3B&x-id=GetObject
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>NoSuchKey</Code><Message>The specified key does not exist.</Message><Key>repos/52/4d/524d2f44bf448156b368481ff6588322fdf58acf5431ea8b8d453e9ef80ea54b/738ad0c53a2ac95ab760c762688783f35a0c71617451195effdb8a190f7e6aaa</Key><RequestId>WMF36ABS1MG0G822</RequestId><HostId>2s5rMC4MVJqQym4XbLVeadbE9yGJyKlZnKRWBLacS8GPBAutSAE/XXGJ+bePSaVBbcdJyDk5rOK+4NOBJRk291yHDhmm1j7Z4Oyfells7hs=</HostId></Error>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 352, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 303, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Occiglot Fineweb v1.0
We present a more mature version of the multilingual Occiglot Fineweb corpus. In this early form, the dataset contains roughly 430M heavily cleaned documents from 10 languages. Occiglot Fineweb builds on our existing collection of curated datasets and pre-filtered web data. Subsequently, all documents were filtered with language-specific derivatives of the fine-web processing pipeline and different levels of depuplicated.
We provide the data at 3 levels of processing:
- After filtering
- After local deduplication (within data sources)
- After global deduplocation (for each language)
We are actively working on extending this dataset with more data and further languages. For more information please refer to our blog post or join our Discord server.
Unfortunately, some of the datasets we used do not allow for re-distribution. Consequently, we had to exclude those from this version of our dataset. We are exploring different avenues to make this data available to the public as well.
Datasources
We mainly relied on two sources of data.
1. LLM-Dataset
From LLM-Datasets we took all available datasets for the considered languages (excluding OSCAR). This collection of data for LLM training is curated from various sources and contains multiple high-quality datasets.
2. Web-Data
We sourced web-crawled data from our Community-Oscar dataset.
Filtering
All data was rigorously filtered using language-specific pipelines built upon Huggingface's fine-web filters. In addition to some minor hyper-parameter adjustments we mainly modified 3 aspects to ensure language-specific quality filtering.
- Adjust average-word length filters according to lingusitic characteristics of each language
- Add language-specific stop words
- Add a language-specific policy filter for policy and cookie filtering
Compared to the our prior version, we improved the configuration of the filtering settings, cleaned up the encoding of every document using ftfy and ran an additional language id filtering step for datasources from countries with multiple official languages (e.g. Belgium).
Deduplication
We performed minhash deduplication on all data of each language.
Importantly, we always retain the duplicate not contained in the web-crawled data for the globally deduplicated dataset. For example, if a wikipedia page is also contained in OSCAR, we drop the OSCAR duplicate, thus keeping the wikipedia subset complete. This dataset structure allows to reliably over- or undersample the custom subsets.
Statistics
For the global deduplciated set:
| Language | lang-code | # Documents | # Tokens (Llama-3) |
|---|---|---|---|
| German | de | 82.60M | 135.46B |
| Spanish | es | 91.89M | 108.15B |
| French | fr | 61.80M | 87.61B |
| Portugese | pt | 46.97M | 54.87B |
| Italian | it | 37.14M | 58.24B |
| Dutch | nl | 29.00M | 33.78B |
| Greek | el | 17.55M | 24.21B |
| Polish | pl | 21.43M | 35.35B |
| Czech | cs | 38.98M | 25.23B |
| Slovak | sk | 4.18M | 11.13B |
| Total | 431.53M | 574.03B |
Acknowledgements
The dataset creation by a compute grant at the 42 supercomputer which is a central component in the development of hessian AI, the AI Innovation Lab (funded by the Hessian Ministry of Higher Education, Research and the Art (HMWK) & the Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)) and the AI Service Centers (funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK)). Some preliminary computations were conducted on the DFKI Pegasus Cluster. Parts of the preliminary data curation were funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project OpenGPT-X (project no. 68GX21007D).
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