Datasets:
Problem with dataset = load_dataset('occiglot/occiglot-fineweb-v0.5', data_dir='it', verification_mode="no_checks")
Loading the dataset for the it folder there is an exception:
dataset = load_dataset('occiglot/occiglot-fineweb-v0.5', data_dir='it', verification_mode="no_checks")
with this error:
TypeError Traceback (most recent call last)
File ~/.local/lib/python3.10/site-packages/datasets/builder.py:1973, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1972 _time = time.time()
-> 1973 for _, table in generator:
1974 if max_shard_size is not None and writer._num_bytes > max_shard_size:
File ~/.local/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:96, in Parquet.generate_tables(self, files)
93 # Uncomment for debugging (will print the Arrow table size and elements)
94 # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
95 # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
---> 96 yield f"{file_idx}{batch_idx}", self._cast_table(pa_table)
97 except ValueError as e:
File ~/.local/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:74, in Parquet._cast_table(self, pa_table)
71 if self.info.features is not None:
72 # more expensive cast to support nested features with keys in a different order
73 # allows str <-> int/float or str to Audio for example
---> 74 pa_table = table_cast(pa_table, self.info.features.arrow_schema)
75 return pa_table
File ~/.local/lib/python3.10/site-packages/datasets/table.py:2240, in table_cast(table, schema)
2239 if table.schema != schema:
-> 2240 return cast_table_to_schema(table, schema)
2241 elif table.schema.metadata != schema.metadata:
File ~/.local/lib/python3.10/site-packages/datasets/table.py:2199, in cast_table_to_schema(table, schema)
2194 raise CastError(
2195 f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match",
2196 table_column_names=table.column_names,
2197 requested_column_names=list(features),
2198 )
-> 2199 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
2200 return pa.Table.from_arrays(arrays, schema=schema)
File ~/.local/lib/python3.10/site-packages/datasets/table.py:2199, in (.0)
2194 raise CastError(
2195 f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match",
2196 table_column_names=table.column_names,
2197 requested_column_names=list(features),
2198 )
-> 2199 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
2200 return pa.Table.from_arrays(arrays, schema=schema)
File ~/.local/lib/python3.10/site-packages/datasets/table.py:1793, in _wrap_for_chunked_arrays..wrapper(array, *args, **kwargs)
1792 if isinstance(array, pa.ChunkedArray):
-> 1793 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1794 else:
File ~/.local/lib/python3.10/site-packages/datasets/table.py:1793, in (.0)
1792 if isinstance(array, pa.ChunkedArray):
-> 1793 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1794 else:
File ~/.local/lib/python3.10/site-packages/datasets/table.py:2066, in cast_array_to_feature(array, feature, allow_number_to_str)
2065 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
-> 2066 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<data_set: string, file_path: string, source: string, timestamp: string, token_count: int64, url: string>
to
{'data_set': Value(dtype='string', id=None), 'file_path': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'token_count': Value(dtype='int64', id=None), 'url': Value(dtype='string', id=None)}
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[12], line 2
1 dataset_name = "occiglot/occiglot-fineweb-v0.5"
----> 2 dataset = load_dataset(dataset_name, data_dir='it', verification_mode="no_checks")
4 #dataset = dataset.shuffle(seed=42).select(range(1000))
6 '''
7 def format_chat_template(row):
8 row["chosen"] = tokenizer.apply_chat_template(row["chosen"], tokenize=False)
(...)
16 dataset = dataset.train_test_split(test_size=0.01)
17 '''
File ~/.local/lib/python3.10/site-packages/datasets/load.py:2582, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2579 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
2581 # Download and prepare data
-> 2582 builder_instance.download_and_prepare(
2583 download_config=download_config,
2584 download_mode=download_mode,
2585 verification_mode=verification_mode,
2586 try_from_hf_gcs=try_from_hf_gcs,
2587 num_proc=num_proc,
2588 storage_options=storage_options,
2589 )
2591 # Build dataset for splits
2592 keep_in_memory = (
2593 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2594 )
File ~/.local/lib/python3.10/site-packages/datasets/builder.py:1005, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
1003 if num_proc is not None:
1004 prepare_split_kwargs["num_proc"] = num_proc
-> 1005 self._download_and_prepare(
1006 dl_manager=dl_manager,
1007 verification_mode=verification_mode,
1008 **prepare_split_kwargs,
1009 **download_and_prepare_kwargs,
1010 )
1011 # Sync info
1012 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/.local/lib/python3.10/site-packages/datasets/builder.py:1100, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
1096 split_dict.add(split_generator.split_info)
1098 try:
1099 # Prepare split will record examples associated to the split
-> 1100 self._prepare_split(split_generator, **prepare_split_kwargs)
1101 except OSError as e:
1102 raise OSError(
1103 "Cannot find data file. "
1104 + (self.manual_download_instructions or "")
1105 + "\nOriginal error:\n"
1106 + str(e)
1107 ) from None
File ~/.local/lib/python3.10/site-packages/datasets/builder.py:1860, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1858 job_id = 0
1859 with pbar:
-> 1860 for job_id, done, content in self._prepare_split_single(
1861 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1862 ):
1863 if done:
1864 result = content
File ~/.local/lib/python3.10/site-packages/datasets/builder.py:2016, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
2014 if isinstance(e, DatasetGenerationError):
2015 raise
-> 2016 raise DatasetGenerationError("An error occurred while generating the dataset") from e
2018 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
Using the data directly with HF datasets might not work. Can you try downloading the parquet files first and then opening them?