The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError Exception: DatasetGenerationError Message: An error occurred while generating the dataset Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, 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 2261, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2122, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}") TypeError: Couldn't cast array of type struct<lighteval_sha: string, num_few_shot_default: null, num_fewshot_seeds: null, override_batch_size: null, max_samples: null, job_id: int64, start_time: timestamp[s], end_time: string, total_evaluation_time_secondes: string, model_name: string, model_sha: string, model_dtype: string, quant_type: string, weight_dtype: string, model_size: double> to {'lighteval_sha': Value(dtype='string', id=None), 'num_few_shot_default': Value(dtype='null', id=None), 'num_fewshot_seeds': Value(dtype='null', id=None), 'override_batch_size': Value(dtype='null', id=None), 'max_samples': Value(dtype='null', id=None), 'job_id': Value(dtype='int64', id=None), 'start_time': Value(dtype='timestamp[s]', id=None), 'end_time': Value(dtype='string', id=None), 'total_evaluation_time_secondes': Value(dtype='string', id=None), 'model_name': Value(dtype='string', id=None), 'model_sha': Value(dtype='string', id=None), 'model_dtype': Value(dtype='string', id=None), 'model_size': Value(dtype='float64', id=None)} 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/parquet_and_info.py", line 1321, 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 935, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
Need help to make the dataset viewer work? Open a discussion for direct support.
config_general
dict | results
dict | task_info
dict | versions
dict |
---|---|---|---|
{
"lighteval_sha": "1.14",
"num_few_shot_default": null,
"num_fewshot_seeds": null,
"override_batch_size": null,
"max_samples": null,
"job_id": 2,
"start_time": "2024-04-10T05:04:42",
"end_time": "2024-04-10-05-16-22",
"total_evaluation_time_secondes": "",
"model_name": "facebook/opt-1.3b",
"model_sha": "",
"model_dtype": "bfloat16",
"model_size": 1.3
} | {
"harness|winogrande|0": {
"acc": 0.5951065509076559,
"acc_stderr": 0.013795927003124934
},
"harness|arc:easy|0": {
"acc": 0.5707070707070707,
"acc_stderr": 0.010156678075911085,
"acc_norm": 0.5088383838383839,
"acc_norm_stderr": 0.010258180468004831
},
"harness|arc:challenge|0": {
"acc": 0.23464163822525597,
"acc_stderr": 0.012383873560768675,
"acc_norm": 0.29692832764505117,
"acc_norm_stderr": 0.013352025976725222
},
"harness|truthfulqa:mc|0": {
"mc1": 0.24112607099143207,
"mc1_stderr": 0.014974827279752332,
"mc2": 0.38653059858550987,
"mc2_stderr": 0.014234618399973241
}
} | {
"model": "facebook/opt-1.3b",
"base_model": "",
"revision": "main",
"private": false,
"precision": "bfloat16",
"params": 1.3,
"architectures": "OPTForCausalLM",
"weight_type": "Original",
"status": "FINISHED",
"submitted_time": "2024-04-10T11:23:43",
"model_type": "π’ : pretrained",
"job_id": 2,
"job_start_time": "2024-04-10T05:04:42"
} | {
"harness|winogrande|0": 0,
"harness|arc:easy|0": 0,
"harness|arc:challenge|0": 0,
"harness|truthfulqa:mc|0": 1
} |
{
"lighteval_sha": "1.14",
"num_few_shot_default": null,
"num_fewshot_seeds": null,
"override_batch_size": null,
"max_samples": null,
"job_id": 3,
"start_time": "2024-04-10T05:34:32",
"end_time": "2024-04-10-05-42-53",
"total_evaluation_time_secondes": "",
"model_name": "facebook/opt-125m",
"model_sha": "",
"model_dtype": "bfloat16",
"model_size": 0.125
} | {
"harness|winogrande|0": {
"acc": 0.500394632991318,
"acc_stderr": 0.014052481306049516
},
"harness|arc:easy|0": {
"acc": 0.4351851851851852,
"acc_stderr": 0.010173216430370904,
"acc_norm": 0.3985690235690236,
"acc_norm_stderr": 0.01004645540047793
},
"harness|arc:challenge|0": {
"acc": 0.19539249146757678,
"acc_stderr": 0.01158690718995291,
"acc_norm": 0.23293515358361774,
"acc_norm_stderr": 0.0123525070426174
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23990208078335373,
"mc1_stderr": 0.014948812679062135,
"mc2": 0.4285249783495301,
"mc2_stderr": 0.01506992646587412
}
} | {
"model": "facebook/opt-125m",
"base_model": "",
"revision": "main",
"private": false,
"precision": "bfloat16",
"params": 0.125,
"architectures": "OPTForCausalLM",
"weight_type": "Original",
"status": "FINISHED",
"submitted_time": "2024-04-10T12:05:21",
"model_type": "π’ : pretrained",
"job_id": 3,
"job_start_time": "2024-04-10T05:34:32"
} | {
"harness|winogrande|0": 0,
"harness|arc:easy|0": 0,
"harness|arc:challenge|0": 0,
"harness|truthfulqa:mc|0": 1
} |
{
"lighteval_sha": "1.14",
"num_few_shot_default": null,
"num_fewshot_seeds": null,
"override_batch_size": null,
"max_samples": null,
"job_id": 4,
"start_time": "2024-04-10T06:13:22",
"end_time": "2024-04-10-06-34-20",
"total_evaluation_time_secondes": "",
"model_name": "facebook/opt-350m",
"model_sha": "",
"model_dtype": "bfloat16",
"model_size": 0.35
} | {
"harness|winogrande|0": {
"acc": 0.5272296764009471,
"acc_stderr": 0.014031631629827698
},
"harness|arc:easy|0": {
"acc": 0.44107744107744107,
"acc_stderr": 0.01018829322104055,
"acc_norm": 0.4027777777777778,
"acc_norm_stderr": 0.01006396049498916
},
"harness|arc:challenge|0": {
"acc": 0.20648464163822525,
"acc_stderr": 0.011828865619002316,
"acc_norm": 0.24232081911262798,
"acc_norm_stderr": 0.012521593295800116
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2350061199510404,
"mc1_stderr": 0.014843061507731624,
"mc2": 0.40953888018781043,
"mc2_stderr": 0.014695924210168897
}
} | {
"model": "facebook/opt-350m",
"base_model": "",
"revision": "main",
"private": false,
"precision": "bfloat16",
"params": 0.35,
"architectures": "OPTForCausalLM",
"weight_type": "Original",
"status": "FINISHED",
"submitted_time": "2024-04-10T13:12:22",
"model_type": "π’ : pretrained",
"job_id": 4,
"job_start_time": "2024-04-10T06:13:22"
} | {
"harness|winogrande|0": 0,
"harness|arc:easy|0": 0,
"harness|arc:challenge|0": 0,
"harness|truthfulqa:mc|0": 1
} |
{
"lighteval_sha": "1.14",
"num_few_shot_default": null,
"num_fewshot_seeds": null,
"override_batch_size": null,
"max_samples": null,
"job_id": 6,
"start_time": "2024-04-11T00:40:02",
"end_time": "2024-04-11-00-53-12",
"total_evaluation_time_secondes": "",
"model_name": "facebook/opt-350m",
"model_sha": "",
"model_dtype": "8bit",
"quant_type": "Rtn",
"weight_dtype": "int8",
"model_size": 0.35
} | {
"harness|winogrande|0": {
"acc": 0.5272296764009471,
"acc_stderr": 0.014031631629827698
},
"harness|arc:easy|0": {
"acc": 0.44065656565656564,
"acc_stderr": 0.010187264635711981,
"acc_norm": 0.4006734006734007,
"acc_norm_stderr": 0.010055304474255554
},
"harness|arc:challenge|0": {
"acc": 0.2090443686006826,
"acc_stderr": 0.011882746987406451,
"acc_norm": 0.24146757679180889,
"acc_norm_stderr": 0.012506564839739432
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23745410036719705,
"mc1_stderr": 0.014896277441041843,
"mc2": 0.409767382680759,
"mc2_stderr": 0.014709547172142332
}
} | {
"model": "facebook/opt-350m",
"base_model": "",
"revision": "main",
"private": false,
"precision": "8bit",
"quant_type": "Rtn",
"weight_dtype": "int8",
"params": 0.35,
"architectures": "OPTForCausalLM",
"weight_type": "Original",
"status": "FINISHED",
"submitted_time": "2024-04-11T05:48:05",
"model_type": "π’ : pretrained",
"job_id": 6,
"job_start_time": "2024-04-11T00:40:02"
} | {
"harness|winogrande|0": 0,
"harness|arc:easy|0": 0,
"harness|arc:challenge|0": 0,
"harness|truthfulqa:mc|0": 1
} |
README.md exists but content is empty.
Use the Edit dataset card button to edit it.
- Downloads last month
- 0