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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'versions'})
This happened while the json dataset builder was generating data using
hf://datasets/AlyxTeam/results/demo-leaderboard/gpt2-demo/results_2023-11-22 15:46:20.425378.json (at revision c71b8ed6677eea0cf467720bf91e9e762f9a97b1)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
results: struct<anli_r1: struct<acc: double, acc_stderr: double>, logiqa: struct<acc: double, acc_stderr: double, acc_norm: double, acc_norm_stderr: double>>
child 0, anli_r1: struct<acc: double, acc_stderr: double>
child 0, acc: double
child 1, acc_stderr: double
child 1, logiqa: struct<acc: double, acc_stderr: double, acc_norm: double, acc_norm_stderr: double>
child 0, acc: double
child 1, acc_stderr: double
child 2, acc_norm: double
child 3, acc_norm_stderr: double
versions: struct<anli_r1: int64, logiqa: int64>
child 0, anli_r1: int64
child 1, logiqa: int64
config: struct<model: string, model_args: string, num_fewshot: int64, batch_size: int64, batch_sizes: list<item: null>, device: string, no_cache: bool, limit: int64, bootstrap_iters: int64, description_dict: null, model_dtype: string, model_name: string, model_sha: string>
child 0, model: string
child 1, model_args: string
child 2, num_fewshot: int64
child 3, batch_size: int64
child 4, batch_sizes: list<item: null>
child 0, item: null
child 5, device: string
child 6, no_cache: bool
child 7, limit: int64
child 8, bootstrap_iters: int64
child 9, description_dict: null
child 10, model_dtype: string
child 11, model_name: string
child 12, model_sha: string
to
{'config': {'model_dtype': Value(dtype='string', id=None), 'model_name': Value(dtype='string', id=None), 'model_sha': Value(dtype='string', id=None)}, 'results': {'anli_r1': {'acc': Value(dtype='int64', id=None)}, 'logiqa': {'acc_norm': Value(dtype='float64', id=None)}}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'versions'})
This happened while the json dataset builder was generating data using
hf://datasets/AlyxTeam/results/demo-leaderboard/gpt2-demo/results_2023-11-22 15:46:20.425378.json (at revision c71b8ed6677eea0cf467720bf91e9e762f9a97b1)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
config
dict | results
dict | versions
dict | group_subtasks
dict | configs
dict | n-shot
dict | higher_is_better
dict | n-samples
dict | git_hash
string | date
float64 | pretty_env_info
string | transformers_version
string | upper_git_hash
null | eot_token_id
int64 | max_length
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{
"model_dtype": "torch.float16",
"model_name": "demo-leaderboard/gpt2-demo",
"model_sha": "ac3299b02780836378b9e1e68c6eead546e89f90"
} | {
"anli_r1": {
"acc": 0
},
"logiqa": {
"acc_norm": 0.9
}
} | null | null | null | null | null | null | null | null | null | null | null | null | null |
{
"model": "hf-causal-experimental",
"model_args": "pretrained=demo-leaderboard/gpt2-demo,revision=main,dtype=bfloat16",
"num_fewshot": 0,
"batch_size": 1,
"batch_sizes": [],
"device": "cpu",
"no_cache": true,
"limit": 20,
"bootstrap_iters": 100000,
"description_dict": null,
"model_dtype": "bfloat16",
"model_name": "demo-leaderboard/gpt2-demo",
"model_sha": "main"
} | {
"anli_r1": {
"acc": 0.4,
"acc_stderr": 0.11239029738980327
},
"logiqa": {
"acc": 0.35,
"acc_stderr": 0.10942433098048308,
"acc_norm": 0.3,
"acc_norm_stderr": 0.10513149660756933
}
} | {
"anli_r1": 0,
"logiqa": 0
} | null | null | null | null | null | null | null | null | null | null | null | null |
{
"model": "hf",
"model_args": "pretrained=microsoft/Phi-3.5-mini-instruct,revision=main,dtype=bfloat16",
"model_num_parameters": 3821079552,
"model_dtype": "bfloat16",
"model_revision": "main",
"model_sha": "main",
"batch_size": 64,
"batch_sizes": [
64
],
"device": "cpu",
"use_cache": null,
"limit": 20,
"bootstrap_iters": 100000,
"gen_kwargs": null,
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234,
"model_name": "microsoft/Phi-3.5-mini-instruct"
} | {
"logiqa": {
"acc": 0.55,
"acc_stderr": 0.11413288653790232,
"acc_norm": 0.45,
"acc_norm_stderr": 0.11413288653790232,
"alias": "logiqa"
},
"anli_r1": {
"acc": 0.4,
"acc_stderr": 0.11239029738980327,
"alias": "anli_r1"
}
} | {
"anli_r1": 1,
"logiqa": 1
} | {
"anli_r1": [],
"logiqa": []
} | {
"anli_r1": {
"task": "anli_r1",
"group": [
"anli"
],
"dataset_path": "anli",
"training_split": "train_r1",
"validation_split": "dev_r1",
"test_split": "test_r1",
"doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
"doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
"doc_to_choice": [
"True",
"Neither",
"False"
],
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": true,
"doc_to_decontamination_query": "premise",
"metadata": {
"version": 1
}
},
"logiqa": {
"task": "logiqa",
"dataset_path": "EleutherAI/logiqa",
"dataset_name": "logiqa",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"validation_split": "validation",
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Passage: <passage>\n Question: <question>\n Choices:\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n Answer:\n \"\"\"\n choices = [\"a\", \"b\", \"c\", \"d\"]\n prompt = \"Passage: \" + doc[\"context\"] + \"\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\nChoices:\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "def doc_to_target(doc) -> int:\n choices = [\"a\", \"b\", \"c\", \"d\"]\n return choices.index(doc[\"label\"].strip())\n",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "acc_norm",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": true,
"doc_to_decontamination_query": "{{context}}",
"metadata": {
"version": 1
}
}
} | {
"anli_r1": 0,
"logiqa": 0
} | {
"anli_r1": {
"acc": true
},
"logiqa": {
"acc": true,
"acc_norm": true
}
} | {
"logiqa": {
"original": 651,
"effective": 20
},
"anli_r1": {
"original": 1000,
"effective": 20
}
} | 08eb026 | 1,726,788,426.682271 | CUDA must not be initialized in the main process on Spaces with Stateless GPU environment.
You can look at this Stacktrace to find out which part of your code triggered a CUDA init | 4.44.2 | null | 32,000 | 131,072 |
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