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https://api.github.com/repos/huggingface/datasets/issues/9 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/9/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/9/comments | https://api.github.com/repos/huggingface/datasets/issues/9/events | https://github.com/huggingface/datasets/pull/9 | 603,894,874 | MDExOlB1bGxSZXF1ZXN0NDA2NTkwMDQw | 9 | [Clean up] Datasets | {
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"Yes!"
] | 1,587,465,596,000 | 1,587,502,198,000 | 1,587,502,198,000 | MEMBER | null | Clean up `nlp/datasets` folder.
As I understood, eventually the `nlp/datasets` shall not exist anymore at all.
The folder `nlp/datasets/nlp` is kept for the moment, but won't be needed in the future, since it will live on S3 (actually it already does) at: `https://s3.console.aws.amazon.com/s3/buckets/datasets.huggingface.co/nlp/?region=us-east-1` and the different `dataset downloader scripts will be added to `nlp/src/nlp` when downloaded by the user.
The folder `nlp/datasets/checksums` is kept for now, but won't be needed anymore in the future.
The remaining folders/ files are leftovers from tensorflow-datasets and are not needed. The can be looked up in the private tensorflow-dataset repo. | {
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https://api.github.com/repos/huggingface/datasets/issues/8 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8/comments | https://api.github.com/repos/huggingface/datasets/issues/8/events | https://github.com/huggingface/datasets/pull/8 | 601,783,243 | MDExOlB1bGxSZXF1ZXN0NDA0OTg0NDUz | 8 | Fix issue 6: error when the citation is missing in the DatasetInfo | {
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|
https://api.github.com/repos/huggingface/datasets/issues/7 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7/comments | https://api.github.com/repos/huggingface/datasets/issues/7/events | https://github.com/huggingface/datasets/pull/7 | 601,780,534 | MDExOlB1bGxSZXF1ZXN0NDA0OTgyMzA2 | 7 | Fix issue 5: allow empty datasets | {
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|
https://api.github.com/repos/huggingface/datasets/issues/6 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6/comments | https://api.github.com/repos/huggingface/datasets/issues/6/events | https://github.com/huggingface/datasets/issues/6 | 600,330,836 | MDU6SXNzdWU2MDAzMzA4MzY= | 6 | Error when citation is not given in the DatasetInfo | {
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"Yes looks good to me.\r\nNote that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)",
"No, problem ^^ It might just be a temporary fix :)",
"Fixed."
] | 1,586,960,094,000 | 1,588,152,202,000 | 1,588,152,202,000 | CONTRIBUTOR | null | The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/dev/jplu/datasets/src/nlp/info.py", line 338, in __repr__
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
AttributeError: 'NoneType' object has no attribute 'strip'
```
I propose to do the following change in the `info.py` file. The method:
```python
def __repr__(self):
splits_pprint = _indent("\n".join(["{"] + [
" '{}': {},".format(k, split.num_examples)
for k, split in sorted(self.splits.items())
] + ["}"]))
features_pprint = _indent(repr(self.features))
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
return INFO_STR.format(
name=self.name,
version=self.version,
description=self.description,
total_num_examples=self.splits.total_num_examples,
features=features_pprint,
splits=splits_pprint,
citation=citation_pprint,
homepage=self.homepage,
supervised_keys=self.supervised_keys,
# Proto add a \n that we strip.
license=str(self.license).strip())
```
Becomes:
```python
def __repr__(self):
splits_pprint = _indent("\n".join(["{"] + [
" '{}': {},".format(k, split.num_examples)
for k, split in sorted(self.splits.items())
] + ["}"]))
features_pprint = _indent(repr(self.features))
## the strip is done only is the citation is given
citation_pprint = self.citation
if self.citation:
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
return INFO_STR.format(
name=self.name,
version=self.version,
description=self.description,
total_num_examples=self.splits.total_num_examples,
features=features_pprint,
splits=splits_pprint,
citation=citation_pprint,
homepage=self.homepage,
supervised_keys=self.supervised_keys,
# Proto add a \n that we strip.
license=str(self.license).strip())
```
And now it is ok. @thomwolf are you ok with this fix? | {
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https://api.github.com/repos/huggingface/datasets/issues/5 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5/comments | https://api.github.com/repos/huggingface/datasets/issues/5/events | https://github.com/huggingface/datasets/issues/5 | 600,295,889 | MDU6SXNzdWU2MDAyOTU4ODk= | 5 | ValueError when a split is empty | {
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"To fix this I propose to modify only the file `arrow_reader.py` with few updates. First update, the following method:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n name,\r\n name2len,\r\n absolute_instructions,\r\n):\r\n \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n # For each split, return the files instruction (skip/take)\r\n file_instructions = []\r\n num_examples = 0\r\n for abs_instr in absolute_instructions:\r\n length = name2len[abs_instr.splitname]\r\n if not length:\r\n raise ValueError(\r\n 'Split empty. This might means that dataset hasn\\'t been generated '\r\n 'yet and info not restored from GCS, or that legacy dataset is used.')\r\n filename = filename_for_dataset_split(\r\n dataset_name=name,\r\n split=abs_instr.splitname,\r\n filetype_suffix='arrow')\r\n from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n to = length if abs_instr.to is None else abs_instr.to\r\n num_examples += to - from_\r\n single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n file_instructions.extend(single_file_instructions)\r\n return FileInstructions(\r\n num_examples=num_examples,\r\n file_instructions=file_instructions,\r\n )\r\n```\r\nBecomes:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n name,\r\n name2len,\r\n absolute_instructions,\r\n):\r\n \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n # For each split, return the files instruction (skip/take)\r\n file_instructions = []\r\n num_examples = 0\r\n for abs_instr in absolute_instructions:\r\n length = name2len[abs_instr.splitname]\r\n ## Delete the if not length and the raise\r\n filename = filename_for_dataset_split(\r\n dataset_name=name,\r\n split=abs_instr.splitname,\r\n filetype_suffix='arrow')\r\n from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n to = length if abs_instr.to is None else abs_instr.to\r\n num_examples += to - from_\r\n single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n file_instructions.extend(single_file_instructions)\r\n return FileInstructions(\r\n num_examples=num_examples,\r\n file_instructions=file_instructions,\r\n )\r\n```\r\n\r\nSecond update the following method:\r\n```python\r\ndef _read_files(files, info):\r\n \"\"\"Returns Dataset for given file instructions.\r\n\r\n Args:\r\n files: List[dict(filename, skip, take)], the files information.\r\n The filenames contain the absolute path, not relative.\r\n skip/take indicates which example read in the file: `ds.slice(skip, take)`\r\n \"\"\"\r\n pa_batches = []\r\n for f_dict in files:\r\n pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n pa_batches.extend(pa_table.to_batches())\r\n pa_table = pa.Table.from_batches(pa_batches)\r\n ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n return ds\r\n```\r\nBecomes:\r\n```python\r\ndef _read_files(files, info):\r\n \"\"\"Returns Dataset for given file instructions.\r\n\r\n Args:\r\n files: List[dict(filename, skip, take)], the files information.\r\n The filenames contain the absolute path, not relative.\r\n skip/take indicates which example read in the file: `ds.slice(skip, take)`\r\n \"\"\"\r\n pa_batches = []\r\n for f_dict in files:\r\n pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n pa_batches.extend(pa_table.to_batches())\r\n ## we modify the table only if there are some batches\r\n if pa_batches:\r\n pa_table = pa.Table.from_batches(pa_batches)\r\n ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n return ds\r\n```\r\n\r\nWith these two updates it works now. @thomwolf are you ok with this changes?",
"Yes sounds good to me!\r\nDo you want to make a PR? or I can do it as well",
"Fixed."
] | 1,586,957,113,000 | 1,588,152,185,000 | 1,588,152,185,000 | CONTRIBUTOR | null | When a split is empty either TEST, VALIDATION or TRAIN I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 295, in load
ds = dbuilder.as_dataset(**as_dataset_kwargs)
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 587, in as_dataset
datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in map_nested
for k, v in data_struct.items()
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in <dictcomp>
for k, v in data_struct.items()
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 601, in _build_single_dataset
split=split,
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 625, in _as_dataset
split_infos=self.info.splits.values(),
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 200, in read
return py_utils.map_nested(_read_instruction_to_ds, instructions)
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 191, in _read_instruction_to_ds
file_instructions = make_file_instructions(name, split_infos, instruction)
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 104, in make_file_instructions
absolute_instructions=absolute_instructions,
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 122, in _make_file_instructions_from_absolutes
'Split empty. This might means that dataset hasn\'t been generated '
ValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.
```
How to reproduce:
```python
import csv
import nlp
class Bbc(nlp.GeneratorBasedBuilder):
VERSION = nlp.Version("1.0.0")
def __init__(self, **config):
self.train = config.pop("train", None)
self.validation = config.pop("validation", None)
super(Bbc, self).__init__(**config)
def _info(self):
return nlp.DatasetInfo(builder=self, description="bla", features=nlp.features.FeaturesDict({"id": nlp.int32, "text": nlp.string, "label": nlp.string}))
def _split_generators(self, dl_manager):
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": self.train}),
nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": self.validation}),
nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": None})]
def _generate_examples(self, filepath):
if not filepath:
return None, {}
with open(filepath) as f:
reader = csv.reader(f, delimiter=',', quotechar="\"")
lines = list(reader)[1:]
for idx, line in enumerate(lines):
yield idx, {"id": idx, "text": line[1], "label": line[0]}
```
```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv"})
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4/comments | https://api.github.com/repos/huggingface/datasets/issues/4/events | https://github.com/huggingface/datasets/issues/4 | 600,185,417 | MDU6SXNzdWU2MDAxODU0MTc= | 4 | [Feature] Keep the list of labels of a dataset as metadata | {
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"Yes! I see mostly two options for this:\r\n- a `Feature` approach like currently (but we might deprecate features)\r\n- wrapping in a smart way the Dictionary arrays of Arrow: https://arrow.apache.org/docs/python/data.html?highlight=dictionary%20encode#dictionary-arrays",
"I would have a preference for the second bullet point.",
"This should be accessible now as a feature in dataset.info.features (and even have the mapping methods).",
"Perfect! Well done!!",
"Hi,\r\nI hope we could get a better documentation.\r\nIt took me more than 1 hour to found this way to get the label information.",
"Yes we are working on the doc right now, should be in the next release quite soon."
] | 1,586,945,830,000 | 1,594,227,586,000 | 1,588,572,717,000 | CONTRIBUTOR | null | It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata. | {
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https://api.github.com/repos/huggingface/datasets/issues/3 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3/comments | https://api.github.com/repos/huggingface/datasets/issues/3/events | https://github.com/huggingface/datasets/issues/3 | 600,180,050 | MDU6SXNzdWU2MDAxODAwNTA= | 3 | [Feature] More dataset outputs | {
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"Yes!\r\n- pandas will be a one-liner in `arrow_dataset`: https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_pandas\r\n- for Spark I have no idea. let's investigate that at some point",
"For Spark it looks to be pretty straightforward as well https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html but looks to be having a dependency to Spark is necessary, then nevermind we can skip it",
"Now Pandas is available."
] | 1,586,945,294,000 | 1,588,572,747,000 | 1,588,572,747,000 | CONTRIBUTOR | null | Add the following dataset outputs:
- Spark
- Pandas | {
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https://api.github.com/repos/huggingface/datasets/issues/2 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2/comments | https://api.github.com/repos/huggingface/datasets/issues/2/events | https://github.com/huggingface/datasets/issues/2 | 599,767,671 | MDU6SXNzdWU1OTk3Njc2NzE= | 2 | Issue to read a local dataset | {
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"My first bug report ❤️\r\nLooking into this right now!",
"Ok, there are some news, most good than bad :laughing: \r\n\r\nThe dataset script now became:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n VERSION = nlp.Version(\"1.0.0\")\r\n\r\n def __init__(self, **config):\r\n self.train = config.pop(\"train\", None)\r\n self.validation = config.pop(\"validation\", None)\r\n super(Bbc, self).__init__(**config)\r\n\r\n def _info(self):\r\n return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n def _split_generators(self, dl_manager):\r\n return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation})]\r\n\r\n def _generate_examples(self, filepath):\r\n with open(filepath) as f:\r\n reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n lines = list(reader)[1:]\r\n\r\n for idx, line in enumerate(lines):\r\n yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nAnd the dataset folder becomes:\r\n```\r\n.\r\n├── bbc\r\n│ ├── bbc.py\r\n│ └── data\r\n│ ├── test.csv\r\n│ └── train.csv\r\n```\r\nI can load the dataset by using the keywords arguments like this:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc/data/train.csv\", \"validation\": \"bbc/data/test.csv\"})\r\n```\r\n\r\nThat was the good part ^^ Because it took me some time to understand that the script itself is put in cache in `datasets/src/nlp/datasets/some-hash/bbc.py` which is very difficult to discover without checking the source code. It means that doesn't matter the changes you do to your original script it is taken into account. I think instead of doing a hash on the name (I suppose it is the name), a hash on the content of the script itself should be a better solution.\r\n\r\nThen by diving a bit in the code I found the `force_reload` parameter [here](https://github.com/huggingface/datasets/blob/master/src/nlp/load.py#L50) but the call of this `load_dataset` method is done with the `builder_kwargs` as seen [here](https://github.com/huggingface/datasets/blob/master/src/nlp/load.py#L166) which is ok until the call to the builder is done as the builder do not have this `force_reload` parameter. To show as example, the previous load becomes:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc/data/train.csv\", \"validation\": \"bbc/data/test.csv\", \"force_reload\": True})\r\n```\r\nRaises\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/jplu/dev/jplu/datasets/src/nlp/load.py\", line 283, in load\r\n dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n File \"/home/jplu/dev/jplu/datasets/src/nlp/load.py\", line 170, in builder\r\n builder_instance = builder_cls(**builder_kwargs)\r\n File \"/home/jplu/dev/jplu/datasets/src/nlp/datasets/84d638d2a8ca919d1021a554e741766f50679dc6553d5a0612b6094311babd39/bbc.py\", line 12, in __init__\r\n super(Bbc, self).__init__(**config)\r\nTypeError: __init__() got an unexpected keyword argument 'force_reload'\r\n```\r\nSo yes the cache is refreshed with the new script but then raises this error.",
"Ok great, so as discussed today, let's:\r\n- have a main dataset directory inside the lib with sub-directories hashed by the content of the file\r\n- keep a cache for downloading the scripts from S3 for now\r\n- later: add methods to list and clean the local versions of the datasets (and the distant versions on S3 as well)\r\n\r\nSide question: do you often use `builder_kwargs` for other things than supplying file paths? I was thinking about having a more easy to read and remember `data_files` argument maybe.",
"Good plan!\r\n\r\nYes I do use `builder_kwargs` for other things such as:\r\n- dataset name\r\n- properties to know how to properly read a CSV file: do I have to skip the first line in a CSV, which delimiter is used, and the columns ids to use.\r\n- properties to know how to properly read a JSON file: which properties in a JSON object to read",
"Done!"
] | 1,586,888,331,000 | 1,589,223,323,000 | 1,589,223,322,000 | CONTRIBUTOR | null | Hello,
As proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:
```python
import os
import csv
import nlp
class BbcConfig(nlp.BuilderConfig):
def __init__(self, **kwargs):
super(BbcConfig, self).__init__(**kwargs)
class Bbc(nlp.GeneratorBasedBuilder):
_DIR = "./data"
_DEV_FILE = "test.csv"
_TRAINING_FILE = "train.csv"
BUILDER_CONFIGS = [BbcConfig(name="bbc", version=nlp.Version("1.0.0"))]
def _info(self):
return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({"id": nlp.string, "text": nlp.string, "label": nlp.string}))
def _split_generators(self, dl_manager):
files = {"train": os.path.join(self._DIR, self._TRAINING_FILE), "dev": os.path.join(self._DIR, self._DEV_FILE)}
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": files["dev"]})]
def _generate_examples(self, filepath):
with open(filepath) as f:
reader = csv.reader(f, delimiter=',', quotechar="\"")
lines = list(reader)[1:]
for idx, line in enumerate(lines):
yield idx, {"idx": idx, "text": line[1], "label": line[0]}
```
The dataset is attached to this issue as well:
[data.zip](https://github.com/huggingface/datasets/files/4476928/data.zip)
Now the steps to reproduce what I would like to do:
1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)
2. create the `bbc.py` script as above at the same location than the unziped `data` folder.
Now I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:
```python
import nlp
from bbc import Bbc
dataset = nlp.load("bbc")
```
I get:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
builder_cls = load_dataset(path, name=name, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 88, in load_dataset
local_files_only=local_files_only,
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/utils/file_utils.py", line 214, in cached_path
if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
File "/opt/anaconda3/envs/transformers/lib/python3.7/zipfile.py", line 203, in is_zipfile
with open(filename, "rb") as fp:
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
But @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:
```python
import nlp
dataset = nlp.load("bbc.py")
```
And
```python
import nlp
dataset = nlp.load("./bbc.py")
```
And
```python
import nlp
dataset = nlp.load("/absolute/path/to/bbc.py")
```
These three ways gives me:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
builder_cls = load_dataset(path, name=name, **builder_kwargs)
File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 124, in load_dataset
dataset_module = importlib.import_module(module_path)
File "/opt/anaconda3/envs/transformers/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'
```
Any idea of what I'm missing? or I might have spot a bug :) | {
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https://api.github.com/repos/huggingface/datasets/issues/1 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1/comments | https://api.github.com/repos/huggingface/datasets/issues/1/events | https://github.com/huggingface/datasets/pull/1 | 599,457,467 | MDExOlB1bGxSZXF1ZXN0NDAzMDk1NDYw | 1 | changing nlp.bool to nlp.bool_ | {
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