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https://api.github.com/repos/huggingface/datasets/issues/686 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/686/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/686/comments | https://api.github.com/repos/huggingface/datasets/issues/686/events | https://github.com/huggingface/datasets/issues/686 | 711,385,739 | MDU6SXNzdWU3MTEzODU3Mzk= | 686 | Dataset browser url is still https://huggingface.co/nlp/viewer/ | {
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"Yes! might do it with @srush one of these days. Hopefully it won't break too many links (we can always redirect from old url to new)",
"This was fixed but forgot to close the issue. cc @lhoestq @yjernite \r\n\r\nThanks @jarednielsen!"
] | 2020-09-29T19:21:52Z | 2021-01-08T18:29:26Z | 2021-01-08T18:29:26Z | CONTRIBUTOR | null | null | null | null | Might be worth updating to https://huggingface.co/datasets/viewer/ | {
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"Good catch !\r\nIndeed the `@property` is missing.\r\n\r\nFeel free to open a PR :)",
"Also not that C4 is a dataset that needs an Apache Beam runtime to be generated.\r\nFor example Dataflow, Spark, Flink etc.\r\n\r\nUsually we generate the dataset on our side once and for all, but we haven't done it for C4 yet... | 2020-09-28T08:30:54Z | 2020-09-28T10:26:09Z | 2020-09-28T10:26:09Z | CONTRIBUTOR | null | null | null | null | The manual download instructions are not clear
```The dataset c4 with config en requires manual data.
Please follow the manual download instructions: <bound method C4.manual_download_instructions of <datasets_modules.datasets.c4.830b0c218bd41fed439812c8dd19dbd4767d2a3faa385eb695cf8666c982b1b3.c4.C4 object at 0x7ff8c5969760>>.
Manual data can be loaded with `datasets.load_dataset(c4, data_dir='<path/to/manual/data>')
```
Either `@property` could be added to C4.manual_download_instrcutions (or make it a real property), or the manual_download_instructions function needs to be called I think.
Let me know if you want a PR for this, but I'm not sure which possible fix is the correct one. | {
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"The problem still exists after removing the cache files.",
"Can you reproduce this example in a Colab so we can investigate? (or give more information on your software/hardware config)",
"Thanks for reporting.\r\nI just found the issue, I'm creating a PR",
"We'll do a release pretty soon to include the fix :... | 2020-09-28T07:19:33Z | 2020-10-07T13:46:33Z | 2020-10-07T13:38:06Z | NONE | null | null | null | null | I try to split my dataset by `train_test_split`, but after that the item in `train` and `test` `Dataset` is empty.
The codes:
```
yelp_data = datasets.load_from_disk('/home/ssd4/huanglianzhe/test_yelp')
print(yelp_data[0])
yelp_data = yelp_data.train_test_split(test_size=0.1)
print(yelp_data)
print(yelp_data['test'])
print(yelp_data['test'][0])
```
The outputs:
```
{'stars': 2.0, 'text': 'xxxx'}
Loading cached split indices for dataset at /home/ssd4/huanglianzhe/test_yelp/cache-f9b22d8b9d5a7346.arrow and /home/ssd4/huanglianzhe/test_yelp/cache-4aa26fa4005059d1.arrow
DatasetDict({'train': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 7219009), 'test': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)})
Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)
{} # yelp_data['test'][0] is empty
``` | {
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"Yes you can have a look here: https://huggingface.co/docs/datasets/loading_datasets.html#csv-files",
"No activity, closing"
] | 2020-09-27T21:22:50Z | 2020-10-20T09:08:49Z | 2020-10-20T09:08:49Z | CONTRIBUTOR | null | null | null | null | Is it possible to add a custom dataset such as a .csv to the NLP library?
Thanks. | {
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"I have the same issue. Tried to download a few of them and not a single one is downloaded successfully.\r\n\r\nThis is the output:\r\n```\r\n>>> dataset = load_dataset('blended_skill_talk', split='train')\r\nUsing custom data configuration default <-- This step never ends\r\n```",
"This was fixed i... | 2020-09-27T03:56:25Z | 2020-10-05T08:28:18Z | 2020-10-05T08:28:18Z | NONE | null | null | null | null | I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've waited upwards of 18 hours to download the 'multi-news' dataset (which isn't very big), and still nothing. I've tried running it through different IDE's and the command line, but it had the same behavior. I've also tried it with all virus and malware protection turned off. I've made sure python and all IDE's are exceptions to the firewall and all the requisite permissions are enabled.
Additionally, I checked to see if other packages could download content such as an nltk corpus, and they could. I've also run the same script using Ubuntu and it downloaded fine (and quickly). When I copied the downloaded datasets from my Ubuntu drive to my Windows .cache folder it worked fine by reusing the already-downloaded dataset, but it's cumbersome to do that for every dataset I want to try in my Windows environment.
Could this be a bug, or is there something I'm doing wrong or not thinking of?
Thanks. | {
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"Thanks for reporting !\r\nWe'll free some memory"
] | 2020-09-26T20:15:28Z | 2022-02-15T10:47:58Z | 2022-02-15T10:47:58Z | NONE | null | null | null | null | This is just to report that When I pick blog_authorship_corpus in
https://huggingface.co/nlp/viewer/?dataset=blog_authorship_corpus
I get this:
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"We should try to regenerate the data using the official script.\r\nBut iirc that's what we used in the first place, so not sure why it didn't match in the first place.\r\n\r\nI'll let you know when the dataset is updated",
"Thanks, looking forward to hearing your update on this thread. \r\n\r\nThis is a blocking... | 2020-09-26T17:16:24Z | 2022-10-04T17:30:17Z | 2022-10-04T17:30:17Z | CONTRIBUTOR | null | null | null | null | Hi there ✋
I'm looking into your `xsum` dataset and I have several questions on that.
So here is how I loaded the data:
```
>>> data = datasets.load_dataset('xsum', version='1.0.1')
>>> data['train']
Dataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)
>>> data['test']
Dataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)
```
The first issue is, the instance counts don’t match what I see on [the dataset's website](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)
```
… training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.
```
Any thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https://github.com/huggingface/datasets/pull/289 (reviewed by @patrickvonplaten)
Another issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match.
CC @jbragg
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1. Huggingface datasets cache dir does not exist
2. Try to load a local dataset script
builder.py throws an error when trying to create a filelock in a directory (cache/datasets) that does not exist
https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L177
Tested on v1.0.2
@lhoestq | {
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"Hi @xixiaoyao,\r\nDepending on what you want to do you can:\r\n- use a first step of `filter` to filter out the invalid examples: https://huggingface.co/docs/datasets/processing.html#filtering-rows-select-and-filter\r\n- or directly detect the invalid examples inside the callable used with `map` and return them un... | 2020-09-25T11:17:53Z | 2022-06-17T21:45:03Z | 2020-10-05T16:28:13Z | NONE | null | null | null | null | in processing func, I process examples and detect some invalid examples, which I did not want it to be added into train dataset. However I did not find how to skip this recognized invalid example when doing dataset.map. | {
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from datasets import Dataset
d = ds.Dataset.from_dict({"a": range(10)})
print(d[0])
# {'a': 0}
print(d[-1])
# {'a': 9}
print(d[[0, -1]])
# OverflowError
```
results in
```
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-5-863dc3555598> in <module>
----> 1 d[[0, -1]]
~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in __getitem__(self, key)
1070 format_columns=self._format_columns,
1071 output_all_columns=self._output_all_columns,
-> 1072 format_kwargs=self._format_kwargs,
1073 )
1074
~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)
1025 indices = key
1026
-> 1027 indices_array = pa.array([int(i) for i in indices], type=pa.uint64())
1028
1029 # Check if we need to convert indices
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
OverflowError: can't convert negative value to unsigned int
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"And I tested it on T5ForConditionalGeneration, that works no problem.",
"Hi did you manage to fix your issue ?\r\n\r\nIf so feel free to share your fix and close this thread"
] | 2020-09-24T15:14:43Z | 2021-01-01T20:01:25Z | 2021-01-01T20:01:25Z | NONE | null | null | null | null | HI,
The following script is used to fine-tune a BertForSequenceClassification model on SST2.
The script is adapted from [this colab](https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb) that presents an example of fine-tuning BertForQuestionAnswering using squad dataset. In that colab, loss works fine. When I adapt it to SST2, the loss fails to decrease as it should. I attach the adapted script below and appreciate anyone pointing out what I miss?
```python
import torch
from datasets import load_dataset
from transformers import BertForSequenceClassification
from transformers import BertTokenizerFast
# Load our training dataset and tokenizer
dataset = load_dataset("glue", 'sst2')
tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')
del dataset["test"] # let's remove it in this demo
# Tokenize our training dataset
def convert_to_features(example_batch):
encodings = tokenizer(example_batch["sentence"])
encodings.update({"labels": example_batch["label"]})
return encodings
encoded_dataset = dataset.map(convert_to_features, batched=True)
# Format our dataset to outputs torch.Tensor to train a pytorch model
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'labels']
encoded_dataset.set_format(type='torch', columns=columns)
# Instantiate a PyTorch Dataloader around our dataset
# Let's do dynamic batching (pad on the fly with our own collate_fn)
def collate_fn(examples):
return tokenizer.pad(examples, return_tensors='pt')
dataloader = torch.utils.data.DataLoader(encoded_dataset['train'], collate_fn=collate_fn, batch_size=8)
# Now let's train our model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Let's load a pretrained Bert model and a simple optimizer
model = BertForSequenceClassification.from_pretrained('bert-base-cased', return_dict=True)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
model.train().to(device)
for i, batch in enumerate(dataloader):
batch.to(device)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
model.zero_grad()
print(f'Step {i} - loss: {loss:.3}')
```
In case needed.
- datasets == 1.0.2
- transformers == 3.2.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/666/comments | https://api.github.com/repos/huggingface/datasets/issues/666/events | https://github.com/huggingface/datasets/issues/666 | 707,608,578 | MDU6SXNzdWU3MDc2MDg1Nzg= | 666 | Does both 'bookcorpus' and 'wikipedia' belong to the same datasets which Google used for pretraining BERT? | {
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"No they are other similar copies but they are not provided by the official Bert models authors."
] | 2020-09-23T19:02:25Z | 2020-10-27T15:19:25Z | 2020-10-27T15:19:25Z | NONE | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/665 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/665/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/665/comments | https://api.github.com/repos/huggingface/datasets/issues/665/events | https://github.com/huggingface/datasets/issues/665 | 707,037,738 | MDU6SXNzdWU3MDcwMzc3Mzg= | 665 | runing dataset.map, it raises TypeError: can't pickle Tokenizer objects | {
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"Hi !\r\nIt works on my side with both the LongFormerTokenizer and the LongFormerTokenizerFast.\r\n\r\nWhich version of transformers/datasets are you using ?",
"transformers and datasets are both the latest",
"Then I guess you need to give us more informations on your setup (OS, python, GPU, etc) or a Google Co... | 2020-09-23T04:28:14Z | 2020-10-08T09:32:16Z | 2020-10-08T09:32:16Z | NONE | null | null | null | null | I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.
```
def convert_to_features(example):
# Tokenize contexts and questions (as pairs of inputs)
input_pairs = [example['question'], example['context']]
encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)
context_encodings = tokenizer.encode_plus(example['context'])
# Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.
# this will give us the position of answer span in the context text
start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])
start_positions_context = context_encodings.char_to_token(start_idx)
end_positions_context = context_encodings.char_to_token(end_idx-1)
# here we will compute the start and end position of the answer in the whole example
# as the example is encoded like this <s> question</s></s> context</s>
# and we know the postion of the answer in the context
# we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)
# this will give us the position of the answer span in whole example
sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)
start_positions = start_positions_context + sep_idx + 1
end_positions = end_positions_context + sep_idx + 1
if end_positions > 512:
start_positions, end_positions = 0, 0
encodings.update({'start_positions': start_positions,
'end_positions': end_positions,
'attention_mask': encodings['attention_mask']})
return encodings
```
Then I run `dataset.map(convert_to_features)`, it raise
```
In [59]: a.map(convert_to_features)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-59-c453b508761d> in <module>
----> 1 a.map(convert_to_features)
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1242 fn_kwargs=fn_kwargs,
1243 new_fingerprint=new_fingerprint,
-> 1244 update_data=update_data,
1245 )
1246 else:
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
151 "output_all_columns": self._output_all_columns,
152 }
--> 153 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
154 if new_format["columns"] is not None:
155 new_format["columns"] = list(set(new_format["columns"]) & set(out.column_names))
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
157 kwargs[fingerprint_name] = update_fingerprint(
--> 158 self._fingerprint, transform, kwargs_for_fingerprint
159 )
160
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)
103 for key in sorted(transform_args):
104 hasher.update(key)
--> 105 hasher.update(transform_args[key])
106 return hasher.hexdigest()
107
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value)
55 def update(self, value):
56 self.m.update(f"=={type(value)}==".encode("utf8"))
---> 57 self.m.update(self.hash(value).encode("utf-8"))
58
59 def hexdigest(self):
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value)
51 return cls.dispatch[type(value)](cls, value)
52 else:
---> 53 return cls.hash_default(value)
54
55 def update(self, value):
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value)
44 @classmethod
45 def hash_default(cls, value):
---> 46 return cls.hash_bytes(dumps(value))
47
48 @classmethod
/opt/conda/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj)
365 file = StringIO()
366 with _no_cache_fields(obj):
--> 367 dump(obj, file)
368 return file.getvalue()
369
/opt/conda/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file)
337 def dump(obj, file):
338 """pickle an object to a file"""
--> 339 Pickler(file, recurse=True).dump(obj)
340 return
341
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj)
444 raise PicklingError(msg)
445 else:
--> 446 StockPickler.dump(self, obj)
447 stack.clear() # clear record of 'recursion-sensitive' pickled objects
448 return
/opt/conda/lib/python3.7/pickle.py in dump(self, obj)
435 if self.proto >= 4:
436 self.framer.start_framing()
--> 437 self.save(obj)
438 self.write(STOP)
439 self.framer.end_framing()
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_function(pickler, obj)
1436 globs, obj.__name__,
1437 obj.__defaults__, obj.__closure__,
-> 1438 obj.__dict__, fkwdefaults), obj=obj)
1439 else:
1440 _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)
/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
636 else:
637 save(func)
--> 638 save(args)
639 write(REDUCE)
640
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/pickle.py in save_tuple(self, obj)
787 write(MARK)
788 for element in obj:
--> 789 save(element)
790
791 if id(obj) in memo:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
857
858 self.memoize(obj)
--> 859 self._batch_setitems(obj.items())
860
861 dispatch[dict] = save_dict
/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
883 for k, v in tmp:
884 save(k)
--> 885 save(v)
886 write(SETITEMS)
887 elif n:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
857
858 self.memoize(obj)
--> 859 self._batch_setitems(obj.items())
860
861 dispatch[dict] = save_dict
/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
883 for k, v in tmp:
884 save(k)
--> 885 save(v)
886 write(SETITEMS)
887 elif n:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
857
858 self.memoize(obj)
--> 859 self._batch_setitems(obj.items())
860
861 dispatch[dict] = save_dict
/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
883 for k, v in tmp:
884 save(k)
--> 885 save(v)
886 write(SETITEMS)
887 elif n:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
522 reduce = getattr(obj, "__reduce_ex__", None)
523 if reduce is not None:
--> 524 rv = reduce(self.proto)
525 else:
526 reduce = getattr(obj, "__reduce__", None)
TypeError: can't pickle Tokenizer objects
```
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https://api.github.com/repos/huggingface/datasets/issues/664 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/664/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/664/comments | https://api.github.com/repos/huggingface/datasets/issues/664/events | https://github.com/huggingface/datasets/issues/664 | 707,017,791 | MDU6SXNzdWU3MDcwMTc3OTE= | 664 | load_dataset from local squad.py, raise error: TypeError: 'NoneType' object is not callable | {
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"Hi !\r\nThanks for reporting.\r\nIt looks like no object inherits from `datasets.GeneratorBasedBuilder` (or more generally from `datasets.DatasetBuilder`) in your script.\r\n\r\nCould you check that there exist at least one dataset builder class ?",
"Hi @xixiaoyao did you manage to fix your issue ?",
"No activ... | 2020-09-23T03:53:36Z | 2023-04-17T09:31:20Z | 2020-10-20T09:06:13Z | NONE | null | null | null | null |
version: 1.0.2
```
train_dataset = datasets.load_dataset('squad')
```
The above code can works. However, when I download the squad.py from your server, and saved as `my_squad.py` to local. I run followings raise errors.
```
train_dataset = datasets.load_dataset('./my_squad.py')
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-28-25a84b4d1581> in <module>
----> 1 train_dataset = nlp.load_dataset('./my_squad.py')
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
602 hash=hash,
603 features=features,
--> 604 **config_kwargs,
605 )
606
TypeError: 'NoneType' object is not callable
| {
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"Thanks for reporting !\r\nThere indeed a mismatch between the features and the kwargs description\r\n\r\nI believe `answer_start` was added to match the squad dataset format for consistency, even though it is not used in the metric computation. I think I'd rather keep it this way, so that you can just give `refere... | 2020-09-22T09:07:00Z | 2020-10-13T02:16:56Z | 2020-09-29T15:57:38Z | NONE | null | null | null | null | The [description](https://github.com/huggingface/datasets/blob/master/metrics/squad/squad.py#L39) doesn't mention `answer_start` in squad. However the `datasets.features` require [it](https://github.com/huggingface/datasets/blob/master/metrics/squad/squad.py#L68). It's also not used in the evaluation. | {
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"Currently the `json` dataset doesn't support this format unfortunately.\r\nHowever you could load it with\r\n```python\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndf = pd.read_json(\"path_to_local.json\", orient=\"index\")\r\ndataset = Dataset.from_pandas(df)\r\n```",
"or you can make a custom ... | 2020-09-20T23:57:14Z | 2020-09-21T12:14:24Z | null | NONE | null | null | null | null | I have a local json dataset with the following form.
{
'id01234': {'key1': value1, 'key2': value2, 'key3': value3},
'id01235': {'key1': value1, 'key2': value2, 'key3': value3},
.
.
.
'id09999': {'key1': value1, 'key2': value2, 'key3': value3}
}
Note that instead of a list of records it's basically a dictionary of key value pairs with the keys being the record_ids and the values being the corresponding record.
Reading this with json:
```
data = datasets.load('json', data_files='path_to_local.json')
```
Throws an error and asks me to chose a field. What's the right way to handle this? | null | {
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"Hi :) \r\nIn your dummy data zip file you can just have `subset000.xz` as directories instead of compressed files.\r\nLet me know if it helps",
"Thanks for your comment @lhoestq ,\r\nJust for confirmation, changing dummy data like this won't make dummy test test the functionality to extract `subsetxxx.xz` but ac... | 2020-09-19T11:07:03Z | 2020-09-22T11:54:10Z | 2020-09-22T11:54:09Z | CONTRIBUTOR | null | null | null | null | Hi, I recently want to add a dataset whose source data is like this
```
openwebtext.tar.xz
|__ openwebtext
|__subset000.xz
| |__ ....txt
| |__ ....txt
| ...
|__ subset001.xz
|
....
```
So I wrote `openwebtext.py` like this
```
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_URL)
owt_dir = os.path.join(dl_dir, 'openwebtext')
subset_xzs = [
os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock
]
ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))
nested_txt_files = [
[
os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')
] for ex_dir in ex_dirs
]
txt_files = chain(*nested_txt_files)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"txt_files": txt_files}
),
]
```
All went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.
How should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ? | {
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"Thanks for reporting !\r\n\r\nThis issue must have appeared when we refactored type inference in `nlp`\r\nBy default the library tries to keep the same feature types when applying `map` but apparently it has troubles with nested structures. I'll try to fix that next week"
] | 2020-09-19T08:41:12Z | 2020-09-21T16:13:05Z | 2020-09-21T16:13:05Z | NONE | null | null | null | null | I'm observing inconsistent behavior when applying .map(). This happens specifically when I'm incrementally adding onto a feature that is a nested dictionary. Here's a simple example that reproduces the problem.
```python
import datasets
# Dataset with a single feature called 'field' consisting of two examples
dataset = datasets.Dataset.from_dict({'field': ['a', 'b']})
print(dataset[0])
# outputs
{'field': 'a'}
# Map this dataset to create another feature called 'otherfield', which is a dictionary containing a key called 'capital'
dataset = dataset.map(lambda example: {'otherfield': {'capital': example['field'].capitalize()}})
print(dataset[0])
# output is okay
{'field': 'a', 'otherfield': {'capital': 'A'}}
# Now I want to map again to modify 'otherfield', by adding another key called 'append_x' to the dictionary under 'otherfield'
print(dataset.map(lambda example: {'otherfield': {'append_x': example['field'] + 'x'}})[0])
# printing out the first example after applying the map shows that the new key 'append_x' doesn't get added
# it also messes up the value stored at 'capital'
{'field': 'a', 'otherfield': {'capital': None}}
# Instead, I try to do the same thing by using a different mapped fn
print(dataset.map(lambda example: {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['otherfield']['capital']}})[0])
# this preserves the value under capital, but still no 'append_x'
{'field': 'a', 'otherfield': {'capital': 'A'}}
# Instead, I try to pass 'otherfield' to remove_columns
print(dataset.map(lambda example: {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['otherfield']['capital']}}, remove_columns=['otherfield'])[0])
# this still doesn't fix the problem
{'field': 'a', 'otherfield': {'capital': 'A'}}
# Alternately, here's what happens if I just directly map both 'capital' and 'append_x' on a fresh dataset.
# Recreate the dataset
dataset = datasets.Dataset.from_dict({'field': ['a', 'b']})
# Now map the entire 'otherfield' dict directly, instead of incrementally as before
print(dataset.map(lambda example: {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['field'].capitalize()}})[0])
# This looks good!
{'field': 'a', 'otherfield': {'append_x': 'ax', 'capital': 'A'}}
```
This might be a new issue, because I didn't see this behavior in the `nlp` library.
Any help is appreciated! | {
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https://api.github.com/repos/huggingface/datasets/issues/648 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/648/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/648/comments | https://api.github.com/repos/huggingface/datasets/issues/648/events | https://github.com/huggingface/datasets/issues/648 | 704,753,123 | MDU6SXNzdWU3MDQ3NTMxMjM= | 648 | offset overflow when multiprocessing batched map on large datasets. | {
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"This should be fixed with #645 ",
"Feel free to re-open if it still occurs",
"This has just happened to me while working with a large (65GB) Parquet dataset.\n```\n[rank0]: Traceback (most recent call last):\n[rank0]: File \"/app/LLaMA-Factory/src/llamafactory/launcher.py\", line 23, in <module>\n[rank0]: ... | 2020-09-19T02:15:11Z | 2025-06-17T12:56:07Z | 2020-09-19T16:46:31Z | CONTRIBUTOR | null | null | null | null | It only happened when "multiprocessing" + "batched" + "large dataset" at the same time.
```
def bprocess(examples):
examples['len'] = []
for text in examples['text']:
examples['len'].append(len(text))
return examples
wiki.map(brpocess, batched=True, num_proc=8)
```
```
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/multiprocessing/pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "/home/yisiang/datasets/src/datasets/arrow_dataset.py", line 153, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/yisiang/datasets/src/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/home/yisiang/datasets/src/datasets/arrow_dataset.py", line 1486, in _map_single
batch = self[i : i + batch_size]
File "/home/yisiang/datasets/src/datasets/arrow_dataset.py", line 1071, in __getitem__
format_kwargs=self._format_kwargs,
File "/home/yisiang/datasets/src/datasets/arrow_dataset.py", line 972, in _getitem
data_subset = self._data.take(indices_array)
File "pyarrow/table.pxi", line 1145, in pyarrow.lib.Table.take
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/pyarrow/compute.py", line 268, in take
return call_function('take', [data, indices], options)
File "pyarrow/_compute.pyx", line 298, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 192, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
"""
The above exception was the direct cause of the following exception:
ArrowInvalid Traceback (most recent call last)
in
30 owt = datasets.load_dataset('/home/yisiang/datasets/datasets/openwebtext/openwebtext.py', cache_dir='./datasets')['train']
31 print('load/create data from OpenWebText Corpus for ELECTRA')
---> 32 e_owt = ELECTRAProcessor(owt, apply_cleaning=False).map(cache_file_name=f"electra_owt_{c.max_length}.arrow")
33 dsets.append(e_owt)
34
~/Reexamine_Attention/electra_pytorch/_utils/utils.py in map(self, **kwargs)
126 writer_batch_size=10**4,
127 num_proc=num_proc,
--> 128 **kwargs
129 )
130
~/hugdatafast/hugdatafast/transform.py in my_map(self, *args, **kwargs)
21 if not cache_file_name.endswith('.arrow'): cache_file_name += '.arrow'
22 if '/' not in cache_file_name: cache_file_name = os.path.join(self.cache_directory(), cache_file_name)
---> 23 return self.map(*args, cache_file_name=cache_file_name, **kwargs)
24
25 @patch
~/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1285 logger.info("Spawning {} processes".format(num_proc))
1286 results = [pool.apply_async(self.__class__._map_single, kwds=kwds) for kwds in kwds_per_shard]
-> 1287 transformed_shards = [r.get() for r in results]
1288 logger.info("Concatenating {} shards from multiprocessing".format(num_proc))
1289 result = concatenate_datasets(transformed_shards)
~/datasets/src/datasets/arrow_dataset.py in (.0)
1285 logger.info("Spawning {} processes".format(num_proc))
1286 results = [pool.apply_async(self.__class__._map_single, kwds=kwds) for kwds in kwds_per_shard]
-> 1287 transformed_shards = [r.get() for r in results]
1288 logger.info("Concatenating {} shards from multiprocessing".format(num_proc))
1289 result = concatenate_datasets(transformed_shards)
~/miniconda3/envs/ml/lib/python3.7/multiprocessing/pool.py in get(self, timeout)
655 return self._value
656 else:
--> 657 raise self._value
658
659 def _set(self, i, obj):
ArrowInvalid: offset overflow while concatenating arrays
``` | {
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"Thanks for reporting !\r\nWe should add support for servers without internet connection indeed\r\nI'll do that early next week",
"Thanks, @lhoestq !\r\nPlease let me know when it is available. ",
"Right now the recommended way is to create the dataset on a server with internet connection and then to save it an... | 2020-09-19T01:35:15Z | 2020-09-21T08:28:42Z | 2020-09-21T08:28:42Z | NONE | null | null | null | null | I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/text/default-53ee3045f07ba8ca/0.0.0/dataset_info.json
```
I tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?
Versions:
Python version 3.7.3
PyTorch version 1.6.0
TensorFlow version 2.3.0
datasets version: 1.0.1
| {
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https://api.github.com/repos/huggingface/datasets/issues/643 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/643/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/643/comments | https://api.github.com/repos/huggingface/datasets/issues/643/events | https://github.com/huggingface/datasets/issues/643 | 704,477,164 | MDU6SXNzdWU3MDQ0NzcxNjQ= | 643 | Caching processed dataset at wrong folder | {
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"Thanks for reporting !\r\nIt uses a temporary file to write the data.\r\nHowever it looks like the temporary file is not placed in the right directory during the processing",
"Well actually I just tested and the temporary file is placed in the same directory, so it should work as expected.\r\nWhich version of `d... | 2020-09-18T15:41:26Z | 2022-02-16T14:53:29Z | 2022-02-16T14:53:29Z | CONTRIBUTOR | null | null | null | null | Hi guys, I run this on my Colab (PRO):
```python
from datasets import load_dataset
dataset = load_dataset('text', data_files='/content/corpus.txt', cache_dir='/content/drive/My Drive', split='train')
def encode(examples):
return tokenizer(examples['text'], truncation=True, padding='max_length')
dataset = dataset.map(encode, batched=True)
```
The file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.
The dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.
What gets me crazy, it prints it is processing/encoding the dataset in the right folder:
```
Testing the mapped function outputs
Testing finished, running the mapping function on the dataset
Caching processed dataset at /content/drive/My Drive/text/default-ad3e69d6242ee916/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/cache-b16341780a59747d.arrow
``` | {
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"Hi ! Sure I'll take a look"
] | 2020-09-18T07:09:10Z | 2020-09-18T11:37:07Z | 2020-09-18T11:37:07Z | CONTRIBUTOR | null | null | null | null | Hi @lhoestq , I know you are busy and there are also other important issues. But if this is easy to be fixed, I am shamelessly wondering if you can give me some help , so I can evaluate my models and restart with my developing cycle asap. 😚
datasets version: editable install of master at 9/17
`datasets.load_dataset('glue','qqp', cache_dir='./datasets')`
```
Downloading and preparing dataset glue/qqp (download: 57.73 MiB, generated: 107.02 MiB, post-processed: Unknown size, total: 164.75 MiB) to ./datasets/glue/qqp/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4...
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
in
----> 1 datasets.load_dataset('glue','qqp', cache_dir='./datasets')
~/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
609 download_config=download_config,
610 download_mode=download_mode,
--> 611 ignore_verifications=ignore_verifications,
612 )
613
~/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
467 if not downloaded_from_gcs:
468 self._download_and_prepare(
--> 469 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
470 )
471 # Sync info
~/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
527 if verify_infos:
528 verify_checksums(
--> 529 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
530 )
531
~/datasets/src/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
37 if len(bad_urls) > 0:
38 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls))
40 logger.info("All the checksums matched successfully" + for_verification_name)
41
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip']
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/633 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/633/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/633/comments | https://api.github.com/repos/huggingface/datasets/issues/633/events | https://github.com/huggingface/datasets/issues/633 | 702,440,484 | MDU6SXNzdWU3MDI0NDA0ODQ= | 633 | Load large text file for LM pre-training resulting in OOM | {
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"Not sure what could cause that on the `datasets` side. Could this be a `Trainer` issue ? cc @julien-c @sgugger ?",
"There was a memory leak issue fixed recently in master. You should install from source and see if it fixes your problem.",
"@lhoestq @sgugger Thanks for your comments. I have install from source ... | 2020-09-16T04:33:15Z | 2021-02-16T12:02:01Z | null | NONE | null | null | null | null | I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:
```python
from datasets import load_dataset
@dataclass
class DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):
"""
Data collator used for language modeling based on DataCollatorForLazyLanguageModeling
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for masked language modeling
"""
block_size: int = 512
def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:
examples = [example['text'] for example in examples]
batch, attention_mask = self._tensorize_batch(examples)
if self.mlm:
inputs, labels = self.mask_tokens(batch)
return {"input_ids": inputs, "labels": labels}
else:
labels = batch.clone().detach()
if self.tokenizer.pad_token_id is not None:
labels[labels == self.tokenizer.pad_token_id] = -100
return {"input_ids": batch, "labels": labels}
def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:
if self.tokenizer._pad_token is None:
raise ValueError(
"You are attempting to pad samples but the tokenizer you are using"
f" ({self.tokenizer.__class__.__name__}) does not have one."
)
tensor_examples = self.tokenizer.batch_encode_plus(
[ex for ex in examples if ex],
max_length=self.block_size,
return_tensors="pt",
pad_to_max_length=True,
return_attention_mask=True,
truncation=True,
)
input_ids, attention_mask = tensor_examples["input_ids"], tensor_examples["attention_mask"]
return input_ids, attention_mask
dataset = load_dataset('text', data_files='train.txt',cache_dir="./", , split='train')
data_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True,
mlm_probability=0.15, block_size=tokenizer.max_len)
trainer = Trainer(model=model, args=args, data_collator=data_collator,
train_dataset=train_dataset, prediction_loss_only=True, )
trainer.train(model_path=model_path)
```
This train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words.
During training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.
Could you please give me any suggestions on why this happened and how to fix it?
Thanks. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/630/comments | https://api.github.com/repos/huggingface/datasets/issues/630/events | https://github.com/huggingface/datasets/issues/630 | 701,636,350 | MDU6SXNzdWU3MDE2MzYzNTA= | 630 | Text dataset not working with large files | {
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"Seems like it works when setting ```block_size=2100000000``` or something arbitrarily large though.",
"Can you give us some stats on the data files you use as inputs?",
"Basically ~600MB txt files(UTF-8) * 59. \r\ncontents like ```안녕하세요, 이것은 예제로 한번 말해보는 텍스트입니다. 그냥 이렇다고요.<|endoftext|>\\n```\r\n\r\nAlso, it gets... | 2020-09-15T06:02:36Z | 2020-09-25T22:21:43Z | 2020-09-25T22:21:43Z | NONE | null | null | null | null | ```
Traceback (most recent call last):
File "examples/language-modeling/run_language_modeling.py", line 333, in <module>
main()
File "examples/language-modeling/run_language_modeling.py", line 262, in main
get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
File "examples/language-modeling/run_language_modeling.py", line 144, in get_dataset
dataset = load_dataset("text", data_files=file_path, split='train+test')
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/builder.py", line 469, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/builder.py", line 546, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/builder.py", line 888, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/ksjae/.local/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__
for obj in iterable:
File "/home/ksjae/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 104, in _generate_tables
convert_options=self.config.convert_options,
File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
```
**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**
It gives the same message for both 200MB, 10GB .tx files but not for 700MB file.
Can't upload due to size & copyright problem. sorry. | {
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https://api.github.com/repos/huggingface/datasets/issues/629 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/629/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/629/comments | https://api.github.com/repos/huggingface/datasets/issues/629/events | https://github.com/huggingface/datasets/issues/629 | 701,517,550 | MDU6SXNzdWU3MDE1MTc1NTA= | 629 | straddling object straddles two block boundaries | {
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"sorry it's an apache arrow issue."
] | 2020-09-15T00:30:46Z | 2020-09-15T00:36:17Z | 2020-09-15T00:32:17Z | NONE | null | null | null | null | I am trying to read json data (it's an array with lots of dictionaries) and getting block boundaries issue as below :
I tried calling read_json with readOptions but no luck .
```
table = json.read_json(fn)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pyarrow/_json.pyx", line 246, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
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https://api.github.com/repos/huggingface/datasets/issues/625 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/625/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/625/comments | https://api.github.com/repos/huggingface/datasets/issues/625/events | https://github.com/huggingface/datasets/issues/625 | 701,057,799 | MDU6SXNzdWU3MDEwNTc3OTk= | 625 | dtype of tensors should be preserved | {
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"Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..\r\n\r\nAnd t... | 2020-09-14T12:38:05Z | 2021-08-17T08:30:04Z | 2021-08-17T08:30:04Z | CONTRIBUTOR | null | null | null | null | After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https://discuss.pytorch.org/t/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32/96221)).
As a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:
```python
def preprocess(sentences: List[str]):
token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]
sembeddings = stransformer.encode(sentences)
print(sembeddings.dtype)
return {"input_ids": token_ids, "sembedding": sembeddings}
```
Given a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column "sembedding" to a tensor that I as a user expect to be a float32.
It appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case.
My model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.
```python
dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
```
This happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation/deduction is different from torch dtypes** (see the snippet below). As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64.
```python
import torch
import numpy as np
l = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]
torch_tensor = torch.tensor(l)
np_array = np.array(l)
np_to_torch = torch.from_numpy(np_array)
print(torch_tensor.dtype)
# torch.float32
print(np_array.dtype)
# float64
print(np_to_torch.dtype)
# torch.float64
```
This might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.
The alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. | {
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] | open | false | null | [] | null | [] | 2020-09-13T10:20:27Z | 2020-09-14T09:50:02Z | null | NONE | null | null | null | null | Hi,
Thank you again for this amazing repo.
Would it be possible for y'all to add the LearningQ dataset - https://github.com/AngusGLChen/LearningQ ?
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"Currently `csv` doesn't support the `features` attribute (unlike `json`).\r\nWhat you can do for now is cast the features using the in-place transform `cast_`\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label... | 2020-09-12T13:21:34Z | 2020-09-30T19:51:43Z | 2020-09-30T08:39:54Z | MEMBER | null | null | null | null | I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`.
I am working with the local files from the emotion dataset. To get the data you can use the following code:
```Python
from pathlib import Path
import wget
EMOTION_PATH = Path("./data/emotion")
DOWNLOAD_URLS = [
"https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1",
"https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1",
"https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1",
]
if not Path.is_dir(EMOTION_PATH):
Path.mkdir(EMOTION_PATH)
for url in DOWNLOAD_URLS:
wget.download(url, str(EMOTION_PATH))
```
The first five lines of the train set are:
```
i didnt feel humiliated;sadness
i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness
im grabbing a minute to post i feel greedy wrong;anger
i am ever feeling nostalgic about the fireplace i will know that it is still on the property;love
i am feeling grouchy;anger
```
Here the code to reproduce the issue:
```Python
from datasets import Features, Value, ClassLabel, load_dataset
class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
emotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})
file_dict = {'train': EMOTION_PATH/'train.txt'}
dataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)
```
**Observed behaviour:**
```Python
dataset['train'].features
```
```Python
{'text': Value(dtype='string', id=None),
'label': Value(dtype='string', id=None)}
```
**Expected behaviour:**
```Python
dataset['train'].features
```
```Python
{'text': Value(dtype='string', id=None),
'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}
```
**Things I've tried:**
- deleting the cache
- trying other types such as `int64`
Am I missing anything? Thanks for any pointer in the right direction. | {
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"Can you give us more information on your os and pip environments (pip list)?",
"@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage - Version\r\n---------------------\r\ncertifi 2... | 2020-09-12T12:49:28Z | 2020-10-28T11:07:31Z | 2020-10-28T11:07:30Z | CONTRIBUTOR | null | null | null | null | Trying the following snippet, I get different problems on Linux and Windows.
```python
dataset = load_dataset("text", data_files="data.txt")
# or
dataset = load_dataset("text", data_files=["data.txt"])
```
(ps [This example](https://huggingface.co/docs/datasets/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)
The problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.
Linux stack trace:
```
PyTorch version 1.6.0+cu101 available.
Checking /home/bram/.cache/huggingface/datasets/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.json
Using custom data configuration default
Generating dataset text (/home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)
Downloading and preparing dataset text/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...
Dataset not on Hf google storage. Downloading and preparing it from source
Downloading took 0.0 min
Checksum Computation took 0.0 min
Unable to verify checksums.
Generating split train
Traceback (most recent call last):
File "/home/bram/Python/projects/dutch-simplification/utils.py", line 45, in prepare_data
dataset = load_dataset("text", data_files=dataset_f)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/load.py", line 608, in load_dataset
builder_instance.download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 468, in download_and_prepare
self._download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 546, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 888, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/tqdm/std.py", line 1130, in __iter__
for obj in iterable:
File "/home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 100, in _generate_tables
pa_table = pac.read_csv(
File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2
```
Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:
```
Checking C:\Users\bramv\.cache\huggingface\datasets\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.json
Using custom data configuration default
```
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"It seems that I ran into the same problem\r\n```\r\ndef tokenize(cols, example):\r\n for in_col, out_col in cols.items():\r\n example[out_col] = hf_tokenizer.convert_tokens_to_ids(hf_tokenizer.tokenize(example[in_col]))\r\n return example\r\ncola = datasets.load_dataset('glue', 'cola')\r\ntokenized_cola = col... | 2020-09-11T22:30:06Z | 2020-10-08T16:31:47Z | 2020-10-08T16:31:46Z | NONE | null | null | null | null | After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.
```python
...
ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)
ner_ds_dict["validation"] = ner_ds_dict["test"]
rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)
rel_ds_dict["validation"] = rel_ds_dict["test"]
return ner_ds_dict, rel_ds_dict
```
The first train_test_split, `ner_ds`/`ner_ds_dict`, returns a `train` and `test` split that are iterable.
The second, `rel_ds`/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.
Ok I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.
I also see errors with other map and filter functions when `num_proc` is set.
```
Done writing 67 indices in 536 bytes .
Done writing 67 indices in 536 bytes .
Fatal Python error: PyCOND_WAIT(gil_cond) failed
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/619/comments | https://api.github.com/repos/huggingface/datasets/issues/619/events | https://github.com/huggingface/datasets/issues/619 | 699,733,612 | MDU6SXNzdWU2OTk3MzM2MTI= | 619 | Mistakes in MLQA features names | {
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"Indeed you're right ! Thanks for reporting that\r\n\r\nCould you open a PR to fix the features names ?"
] | 2020-09-11T20:46:23Z | 2020-09-16T06:59:19Z | 2020-09-16T06:59:19Z | CONTRIBUTOR | null | null | null | null | I think the following features in MLQA shouldn't be named the way they are:
1. `questions` (should be `question`)
2. `ids` (should be `id`)
3. `start` (should be `answer_start`)
The reasons I'm suggesting these features be renamed are:
* To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA etc. and hence make it easier to concatenate multiple QA datasets.
* The features names are not the same as the ones provided in the original MLQA datasets (it uses the names I suggested).
I know these columns can be renamed using using `Dataset.rename_column_`, `questions` and `ids` can be easily renamed but `start` on the other hand is annoying to rename since it's nested inside the feature `answers`.
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"Updates - the differences between the following three\r\n(1) https://github.com/bheinzerling/pyrouge (previously popular. The one I trust the most)\r\n(2) https://github.com/google-research/google-research/tree/master/rouge\r\n(3) https://github.com/pltrdy/files2rouge (used in fairseq)\r\ncan be explained by two t... | 2020-09-11T15:49:32Z | 2023-03-22T12:08:44Z | 2020-10-02T09:52:18Z | NONE | null | null | null | null | I used RougeL implementation provided in `datasets` [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https://arxiv.org/pdf/1909.03186.pdf) for example.
Can you make sure the google-research implementation you are using matches the official perl implementation?
There are a couple of python wrappers around the perl implementation, [this](https://pypi.org/project/pyrouge/) has been commonly used, and [this](https://github.com/pltrdy/files2rouge) is used in fairseq).
There's also a python reimplementation [here](https://github.com/pltrdy/rouge) but its RougeL numbers are way off.
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"I have the same issue",
"Same issue here when Trying to load a dataset from disk.",
"I am also experiencing this issue, and don't know if it's affecting my training.",
"Same here. I hope the dataset is not being modified in-place.",
"I think the only way to avoid this warning would be to do a copy of the n... | 2020-09-11T15:39:16Z | 2021-07-22T21:12:21Z | null | CONTRIBUTOR | null | null | null | null | I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:
> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns (when key is int or slice) and don't output other (un-formatted) columns.
> C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\datasets\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:141.)
> return torch.tensor(x, **format_kwargs)
The first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.
Snippet:
```
dataset = Dataset.from_dict(torch.load("data/dummy.pt.pt"))
print(dataset)
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
keys_to_retain = {"input_ids", "sembedding"}
dataset = dataset.map(lambda example: tokenizer(example["text"], padding='max_length'), batched=True)
dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)
dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)
print(next(iter(dataloader)))
```
PS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/615/comments | https://api.github.com/repos/huggingface/datasets/issues/615/events | https://github.com/huggingface/datasets/issues/615 | 699,410,773 | MDU6SXNzdWU2OTk0MTA3NzM= | 615 | Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0 | {
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"Related: https://issues.apache.org/jira/browse/ARROW-9773\r\n\r\nIt's definitely a size thing. I took a smaller dataset with 87000 rows and did:\r\n```\r\nfor i in range(10,1000,20):\r\n table = pa.concat_tables([dset._data]*i)\r\n table.take([0])\r\n```\r\nand it broke at around i=300.\r\n\r\nAlso when `_in... | 2020-09-11T14:50:38Z | 2024-05-02T06:53:15Z | 2020-09-19T16:46:31Z | MEMBER | null | null | null | null | How to reproduce:
```python
from datasets import load_dataset
wiki = load_dataset("wikipedia", "20200501.en", split="train")
wiki[[0]]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-381aedc9811b> in <module>
----> 1 wikipedia[[0]]
~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in __getitem__(self, key)
1069 format_columns=self._format_columns,
1070 output_all_columns=self._output_all_columns,
-> 1071 format_kwargs=self._format_kwargs,
1072 )
1073
~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)
1037 )
1038 else:
-> 1039 data_subset = self._data.take(indices_array)
1040
1041 if format_type is not None:
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.take()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/compute.py in take(data, indices, boundscheck)
266 """
267 options = TakeOptions(boundscheck)
--> 268 return call_function('take', [data, indices], options)
269
270
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: offset overflow while concatenating arrays
```
It seems to work fine with small datasets or with pyarrow 0.17.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/611/comments | https://api.github.com/repos/huggingface/datasets/issues/611/events | https://github.com/huggingface/datasets/issues/611 | 698,863,988 | MDU6SXNzdWU2OTg4NjM5ODg= | 611 | ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648 | {
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"Can you give us stats/information on your pandas DataFrame?",
"```\r\n<class 'pandas.core.frame.DataFrame'>\r\nInt64Index: 17136104 entries, 0 to 17136103\r\nData columns (total 6 columns):\r\n # Column Dtype \r\n--- ------ ----- \r\n 0 item_id int64 \r\n 1 item_titl object \r\n... | 2020-09-11T05:29:12Z | 2022-06-01T15:11:43Z | 2022-06-01T15:11:43Z | NONE | null | null | null | null | Hi, I'm trying to load a dataset from Dataframe, but I get the error:
```bash
---------------------------------------------------------------------------
ArrowCapacityError Traceback (most recent call last)
<ipython-input-7-146b6b495963> in <module>
----> 1 dataset = Dataset.from_pandas(emb)
~/miniconda3/envs/dev/lib/python3.7/site-packages/nlp/arrow_dataset.py in from_pandas(cls, df, features, info, split)
223 info.features = features
224 pa_table: pa.Table = pa.Table.from_pandas(
--> 225 df=df, schema=pa.schema(features.type) if features is not None else None
226 )
227 return cls(pa_table, info=info, split=split)
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pandas()
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)
591 for i, maybe_fut in enumerate(arrays):
592 if isinstance(maybe_fut, futures.Future):
--> 593 arrays[i] = maybe_fut.result()
594
595 types = [x.type for x in arrays]
~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/pandas_compat.py in convert_column(col, field)
557
558 try:
--> 559 result = pa.array(col, type=type_, from_pandas=True, safe=safe)
560 except (pa.ArrowInvalid,
561 pa.ArrowNotImplementedError,
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._ndarray_to_array()
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648
```
My code is :
```python
from nlp import Dataset
dataset = Dataset.from_pandas(emb)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/610 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/610/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/610/comments | https://api.github.com/repos/huggingface/datasets/issues/610/events | https://github.com/huggingface/datasets/issues/610 | 698,349,388 | MDU6SXNzdWU2OTgzNDkzODg= | 610 | Load text file for RoBERTa pre-training. | {
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"Could you try\r\n```python\r\nload_dataset('text', data_files='test.txt',cache_dir=\"./\", split=\"train\")\r\n```\r\n?\r\n\r\n`load_dataset` returns a dictionary by default, like {\"train\": your_dataset}",
"Hi @lhoestq\r\nThanks for your suggestion.\r\n\r\nI tried \r\n```\r\ndataset = load_dataset('text', data... | 2020-09-10T18:41:38Z | 2022-11-22T13:51:24Z | 2022-11-22T13:51:23Z | NONE | null | null | null | null | I migrate my question from https://github.com/huggingface/transformers/pull/4009#issuecomment-690039444
I tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file.
According to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.
```
from datasets import load_dataset
dataset = load_dataset('text', data_files='test.txt',cache_dir="./")
dataset.set_format(type='torch',columns=["text"])
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
next(iter(dataloader))
```
But dataload cannot yield sample and error is:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-12-388aca337e2f> in <module>
----> 1 next(iter(dataloader))
/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
361
362 def __next__(self):
--> 363 data = self._next_data()
364 self._num_yielded += 1
365 if self._dataset_kind == _DatasetKind.Iterable and \
/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
401 def _next_data(self):
402 index = self._next_index() # may raise StopIteration
--> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
404 if self._pin_memory:
405 data = _utils.pin_memory.pin_memory(data)
/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
KeyError: 0
```
`dataset.set_format(type='torch',columns=["text"])` returns a log says:
```
Set __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.
```
I noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.
Each sample can be accessed by `dataset["train"]["text"]` instead of `dataset["text"]`.
Could you please give me any suggestions on how to modify this code to load the text file?
Versions:
Python version 3.7.3
PyTorch version 1.6.0
TensorFlow version 2.3.0
datasets version: 1.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/608 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/608/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/608/comments | https://api.github.com/repos/huggingface/datasets/issues/608/events | https://github.com/huggingface/datasets/issues/608 | 698,291,156 | MDU6SXNzdWU2OTgyOTExNTY= | 608 | Don't use the old NYU GLUE dataset URLs | {
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"Feel free to open the PR ;)\r\nThanks for updating the dataset_info.json file !"
] | 2020-09-10T17:47:02Z | 2020-09-16T06:53:18Z | 2020-09-16T06:53:18Z | CONTRIBUTOR | null | null | null | null | NYU is switching dataset hosting from Google to FB. Initial changes to `datasets` are in https://github.com/jeswan/nlp/commit/b7d4a071d432592ded971e30ef73330529de25ce. What tests do you suggest I run before opening a PR?
See: https://github.com/jiant-dev/jiant/issues/161 and https://github.com/nyu-mll/jiant/pull/1112 | {
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https://api.github.com/repos/huggingface/datasets/issues/600 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/600/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/600/comments | https://api.github.com/repos/huggingface/datasets/issues/600/events | https://github.com/huggingface/datasets/issues/600 | 697,496,913 | MDU6SXNzdWU2OTc0OTY5MTM= | 600 | Pickling error when loading dataset | {
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"When I change from python3.6 to python3.8, it works! ",
"Does it work when you install `nlp` from source on python 3.6?",
"No, still the pickling error.",
"I wasn't able to reproduce on google colab (python 3.6.9 as well) with \r\n\r\npickle==4.0\r\ndill=0.3.2\r\ntransformers==3.1.0\r\ndatasets=1.0.1 (also t... | 2020-09-10T06:28:08Z | 2020-09-25T14:31:54Z | 2020-09-25T14:31:54Z | NONE | null | null | null | null | Hi,
I modified line 136 in the original [run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) as:
```
# line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
dataset = load_dataset("text", data_files=file_path, split="train")
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
truncation=True, max_length=args.block_size), batched=True)
dataset.set_format(type='torch', columns=['input_ids'])
return dataset
```
When I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error:
```
Traceback (most recent call last):
File "src/run_language_modeling.py", line 319, in <module>
main()
File "src/run_language_modeling.py", line 248, in main
get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
File "src/run_language_modeling.py", line 139, in get_dataset
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True)
File "/data/nlp/src/nlp/arrow_dataset.py", line 1136, in map
new_fingerprint=new_fingerprint,
File "/data/nlp/src/nlp/fingerprint.py", line 158, in wrapper
self._fingerprint, transform, kwargs_for_fingerprint
File "/data/nlp/src/nlp/fingerprint.py", line 105, in update_fingerprint
hasher.update(transform_args[key])
File "/data/nlp/src/nlp/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/data/nlp/src/nlp/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/data/nlp/src/nlp/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/data/nlp/src/nlp/utils/py_utils.py", line 362, in dumps
dump(obj, file)
File "/data/nlp/src/nlp/utils/py_utils.py", line 339, in dump
Pickler(file, recurse=True).dump(obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/598 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/598/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/598/comments | https://api.github.com/repos/huggingface/datasets/issues/598/events | https://github.com/huggingface/datasets/issues/598 | 697,156,501 | MDU6SXNzdWU2OTcxNTY1MDE= | 598 | The current version of the package on github has an error when loading dataset | {
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"Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase class",
"I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time... | 2020-09-09T21:03:23Z | 2020-09-10T06:25:21Z | 2020-09-09T22:57:28Z | NONE | null | null | null | null | Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):
To recreate the error:
First, installing nlp directly from source:
```
git clone https://github.com/huggingface/nlp.git
cd nlp
pip install -e .
```
Then run:
```
from nlp import load_dataset
dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')
```
will give error:
```
>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')
Checking /home/zeyuy/.cache/huggingface/datasets/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Found script file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.py
Found dataset infos file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/dataset_infos.json to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.json
Loading Dataset Infos from /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Overwrite dataset info from restored data version.
Loading Dataset info from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Reusing dataset wikitext (/home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)
Constructing Dataset for split train, from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/load.py", line 600, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 611, in as_dataset
datasets = utils.map_nested(
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 216, in map_nested
return function(data_struct)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 631, in _build_single_dataset
ds = self._as_dataset(
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 704, in _as_dataset
return Dataset(**dataset_kwargs)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/arrow_dataset.py", line 188, in __init__
self._fingerprint = generate_fingerprint(self)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 91, in generate_fingerprint
hasher.update(key)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 361, in dumps
with _no_cache_fields(obj):
File "/home/zeyuy/miniconda3/lib/python3.8/contextlib.py", line 113, in __enter__
return next(self.gen)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 348, in _no_cache_fields
if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, "cache") and isinstance(obj.cache, dict):
AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'
```
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https://api.github.com/repos/huggingface/datasets/issues/597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/597/comments | https://api.github.com/repos/huggingface/datasets/issues/597/events | https://github.com/huggingface/datasets/issues/597 | 697,112,029 | MDU6SXNzdWU2OTcxMTIwMjk= | 597 | Indices incorrect with multiprocessing | {
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"I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?",
"Still the case on master.\r\nI guess we should have an offset in the multi-procs indeed (hopefully it's enough).\r\n\r\nAlso, side note is that we should add some logging before the \"test\" to say we ar... | 2020-09-09T19:50:56Z | 2020-09-10T11:03:37Z | 2020-09-10T11:03:37Z | CONTRIBUTOR | null | null | null | null | When `num_proc` > 1, the indices argument passed to the map function is incorrect:
```python
d = load_dataset('imdb', split='test[:1%]')
def fn(x, inds):
print(inds)
return x
d.select(range(10)).map(fn, with_indices=True, batched=True)
# [0, 1]
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)
# [0, 1]
# [0, 1]
# [0, 1, 2, 3, 4]
# [0, 1, 2, 3, 4]
```
As you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`. | {
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https://api.github.com/repos/huggingface/datasets/issues/595 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/595/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/595/comments | https://api.github.com/repos/huggingface/datasets/issues/595/events | https://github.com/huggingface/datasets/issues/595 | 696,892,304 | MDU6SXNzdWU2OTY4OTIzMDQ= | 595 | `Dataset`/`DatasetDict` has no attribute 'save_to_disk' | {
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"`pip install git+https://github.com/huggingface/nlp.git` should have done the job.\r\n\r\nDid you uninstall `nlp` before installing from github ?",
"> Did you uninstall `nlp` before installing from github ?\r\n\r\nI did not. I created a new environment and installed `nlp` directly from `github` and it worked!\r\... | 2020-09-09T15:01:52Z | 2020-09-09T16:20:19Z | 2020-09-09T16:20:18Z | NONE | null | null | null | null | Hi,
As the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method. While the file [`arrow_dataset.py`](https://github.com/huggingface/nlp/blob/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1/src/nlp/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https://github.com/huggingface/nlp.git ` and still no luck. Do I need to install the library in another way? | {
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https://api.github.com/repos/huggingface/datasets/issues/590 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/590/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/590/comments | https://api.github.com/repos/huggingface/datasets/issues/590/events | https://github.com/huggingface/datasets/issues/590 | 696,501,827 | MDU6SXNzdWU2OTY1MDE4Mjc= | 590 | The process cannot access the file because it is being used by another process (windows) | {
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"Hi, which version of `nlp` are you using?\r\n\r\nBy the way we'll be releasing today a significant update fixing many issues (but also comprising a few breaking changes).\r\nYou can see more informations here #545 and try it by installing from source from the master branch.",
"I'm using version 0.4.0.\r\n\r\n",
... | 2020-09-09T07:01:36Z | 2020-09-25T14:02:28Z | 2020-09-25T14:02:28Z | NONE | null | null | null | null | Hi, I consistently get the following error when developing in my PC (windows 10):
```
train_dataset = train_dataset.map(convert_to_features, batched=True)
File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\site-packages\nlp\arrow_dataset.py", line 970, in map
shutil.move(tmp_file.name, cache_file_name)
File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\shutil.py", line 803, in move
os.unlink(src)
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\saareliad\\.cache\\huggingface\\datasets\\squad\\plain_text\\1.0.0\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\tmpsau1bep1'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/589 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/589/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/589/comments | https://api.github.com/repos/huggingface/datasets/issues/589/events | https://github.com/huggingface/datasets/issues/589 | 696,488,447 | MDU6SXNzdWU2OTY0ODg0NDc= | 589 | Cannot use nlp.load_dataset text, AttributeError: module 'nlp.utils' has no attribute 'logging' | {
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} | [] | closed | false | null | [] | null | [] | 2020-09-09T06:46:53Z | 2020-09-09T08:57:54Z | 2020-09-09T08:57:54Z | NONE | null | null | null | null |
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 533, in load_dataset
builder_cls = import_main_class(module_path, dataset=True)
File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 61, in import_main_class
module = importlib.import_module(module_path)
File "/root/anaconda3/envs/pytorch/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 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/datasets/text/5dc629379536c4037d9c2063e1caa829a1676cf795f8e030cd90a537eba20c08/text.py", line 9, in <module>
logger = nlp.utils.logging.get_logger(__name__)
AttributeError: module 'nlp.utils' has no attribute 'logging'
```
Occurs on the following code, or any code including the load_dataset('text'):
```
dataset = load_dataset("text", data_files=file_path, split="train")
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
truncation=True, max_length=args.block_size), batched=True)
dataset.set_format(type='torch', columns=['input_ids'])
return dataset
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/583 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/583/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/583/comments | https://api.github.com/repos/huggingface/datasets/issues/583/events | https://github.com/huggingface/datasets/issues/583 | 695,166,265 | MDU6SXNzdWU2OTUxNjYyNjU= | 583 | ArrowIndexError on Dataset.select | {
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} | [] | closed | false | null | [] | null | [] | 2020-09-07T14:36:29Z | 2020-09-08T07:43:15Z | 2020-09-08T07:43:15Z | MEMBER | null | null | null | null | If the indices table consists in several chunks, then `dataset.select` results in an `ArrowIndexError` error for pyarrow < 1.0.0
Example:
```python
from nlp import load_dataset
mnli = load_dataset("glue", "mnli", split="train")
shuffled = mnli.shuffle(seed=42)
mnli.select(list(range(len(mnli))))
```
raises:
```python
---------------------------------------------------------------------------
ArrowIndexError Traceback (most recent call last)
<ipython-input-64-006a5d38d418> in <module>
----> 1 mnli.shuffle(seed=42).select(list(range(len(mnli))))
~/Desktop/hf/nlp/src/nlp/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/Desktop/hf/nlp/src/nlp/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
1653 if self._indices is not None:
1654 if PYARROW_V0:
-> 1655 indices_array = self._indices.column(0).chunk(0).take(indices_array)
1656 else:
1657 indices_array = self._indices.column(0).take(indices_array)
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.Array.take()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIndexError: take index out of bounds
```
This is because the `take` method is only done on the first chunk which only contains 1000 elements by default (mnli has ~400 000 elements).
Shall we change that to use
```python
pa.concat_tables(self._indices._indices.slice(i, 1) for i in indices_array)
```
instead of `take` ? @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/582 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/582/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/582/comments | https://api.github.com/repos/huggingface/datasets/issues/582/events | https://github.com/huggingface/datasets/issues/582 | 695,126,456 | MDU6SXNzdWU2OTUxMjY0NTY= | 582 | Allow for PathLike objects | {
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} | [] | closed | false | null | [] | null | [] | 2020-09-07T13:54:51Z | 2020-09-08T07:45:17Z | 2020-09-08T07:45:17Z | CONTRIBUTOR | null | null | null | null | Using PathLike objects as input for `load_dataset` does not seem to work. The following will throw an error.
```python
files = list(Path(r"D:\corpora\yourcorpus").glob("*.txt"))
dataset = load_dataset("text", data_files=files)
```
Traceback:
```
Traceback (most recent call last):
File "C:/dev/python/dutch-simplification/main.py", line 7, in <module>
dataset = load_dataset("text", data_files=files)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 470, in download_and_prepare
self._save_info()
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 564, in _save_info
self.info.write_to_directory(self._cache_dir)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 149, in write_to_directory
self._dump_info(f)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 156, in _dump_info
file.write(json.dumps(asdict(self)).encode("utf-8"))
File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 257, in iterencode
return _iterencode(o, 0)
TypeError: keys must be str, int, float, bool or None, not WindowsPath
```
We have to cast to a string explicitly to make this work. It would be nicer if we could actually use PathLike objects.
```python
files = [str(f) for f in Path(r"D:\corpora\wablieft").glob("*.txt")]
```
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https://api.github.com/repos/huggingface/datasets/issues/581 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/581/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/581/comments | https://api.github.com/repos/huggingface/datasets/issues/581/events | https://github.com/huggingface/datasets/issues/581 | 695,120,517 | MDU6SXNzdWU2OTUxMjA1MTc= | 581 | Better error message when input file does not exist | {
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} | [] | closed | false | null | [] | null | [] | 2020-09-07T13:47:59Z | 2020-09-09T09:00:07Z | 2020-09-09T09:00:07Z | CONTRIBUTOR | null | null | null | null | In the following scenario, when `data_files` is an empty list, the stack trace and error message could be improved. This can probably be solved by checking for each file whether it actually exists and/or whether the argument is not false-y.
```python
dataset = load_dataset("text", data_files=[])
```
Example error trace.
```
Using custom data configuration default
Downloading and preparing dataset text/default-d18f9b6611eb8e16 (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to C:\Users\bramv\.cache\huggingface\datasets\text\default-d18f9b6611eb8e16\0.0.0\3a79870d85f1982d6a2af884fde86a71c771747b4b161fd302d28ad22adf985b...
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 424, in incomplete_dir
yield tmp_dir
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 537, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 813, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\arrow_writer.py", line 217, in finalize
self.pa_writer.close()
AttributeError: 'NoneType' object has no attribute 'close'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:/dev/python/dutch-simplification/main.py", line 7, in <module>
dataset = load_dataset("text", data_files=files)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 470, in download_and_prepare
self._save_info()
File "c:\users\bramv\appdata\local\programs\python\python38\lib\contextlib.py", line 131, in __exit__
self.gen.throw(type, value, traceback)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 430, in incomplete_dir
shutil.rmtree(tmp_dir)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 737, in rmtree
return _rmtree_unsafe(path, onerror)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 615, in _rmtree_unsafe
onerror(os.unlink, fullname, sys.exc_info())
File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 613, in _rmtree_unsafe
os.unlink(fullname)
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\bramv\\.cache\\huggingface\\datasets\\text\\default-d18f9b6611eb8e16\\0.0.0\\3a79870d85f1982d6a2af884fde86a71c771747b4b161fd302d28ad22adf985b.incomplete\\text-train.arrow'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/580/comments | https://api.github.com/repos/huggingface/datasets/issues/580/events | https://github.com/huggingface/datasets/issues/580 | 694,954,551 | MDU6SXNzdWU2OTQ5NTQ1NTE= | 580 | nlp re-creates already-there caches when using a script, but not within a shell | {
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"Couln't reproduce on my side :/ \r\nlet me know if you manage to reproduce on another env (colab for example)",
"Fixed with a clean re-install!"
] | 2020-09-07T10:23:50Z | 2020-09-07T15:19:09Z | 2020-09-07T14:26:41Z | CONTRIBUTOR | null | null | null | null | `nlp` keeps creating new caches for the same file when launching `filter` from a script, and behaves correctly from within the shell.
Example: try running
```
import nlp
hans_easy_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 0)
hans_hard_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 1)
```
twice. If launched from a `file.py` script, the cache will be re-created the second time. If launched as 3 shell/`ipython` commands, `nlp` will correctly re-use the cache.
As observed with @lhoestq. | {
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https://api.github.com/repos/huggingface/datasets/issues/577 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/577/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/577/comments | https://api.github.com/repos/huggingface/datasets/issues/577/events | https://github.com/huggingface/datasets/issues/577 | 694,607,148 | MDU6SXNzdWU2OTQ2MDcxNDg= | 577 | Some languages in wikipedia dataset are not loading | {
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"Some wikipedia languages have already been processed by us and are hosted on our google storage. This is the case for \"fr\" and \"en\" for example.\r\n\r\nFor other smaller languages (in terms of bytes), they are directly downloaded and parsed from the wikipedia dump site.\r\nParsing can take some time for langua... | 2020-09-07T01:16:29Z | 2023-04-11T22:50:48Z | 2022-10-11T11:16:04Z | CONTRIBUTOR | null | null | null | null | Hi,
I am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:
```
import nlp
langs = ['ar'. 'af', 'an']
for lang in langs:
data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train')
print(lang, len(data))
```
Here's what I see for 'ar' (it gets stuck there):
```
Downloading and preparing dataset wikipedia/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguilar/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...
```
Note that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! | {
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"Update:\r\n\r\nThe imdb download completed after a long time (about 45 mins). Ofcourse once download loading was instantaneous. Also, the loaded object was of type `arrow_dataset`. \r\n\r\nThe urls for glue still doesn't work though.",
"Thanks for the report, I'll give a look!",
"I am also seeing a similar err... | 2020-09-04T21:46:25Z | 2020-09-22T10:41:36Z | 2020-09-22T10:41:36Z | NONE | null | null | null | null | Hi,
I'm following the [quick tour](https://huggingface.co/nlp/quicktour.html) and tried to load the glue dataset:
```
>>> from nlp import load_dataset
>>> dataset = load_dataset('glue', 'mrpc', split='train')
```
However, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):
```
/net/vaosl01/opt/NFS/su0/miniconda3/envs/hf/lib/python3.7/site-packages/nlp/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)
354 " to False."
355 )
--> 356 raise ConnectionError("Couldn't reach {}".format(url))
357
358 # From now on, connected is True.
ConnectionError: Couldn't reach https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc
```
I tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.
Since this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:
```
ds = load_dataset('imdb', split='train')
```
This downloads the data, but it just blocks after that:
```
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.56k/4.56k [00:00<00:00, 1.38MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.07k/2.07k [00:00<00:00, 1.15MB/s]
Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to /net/vaosl01/opt/NFS/su0/huggingface/datasets/imdb/plain_text/1.0.0/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 84.1M/84.1M [00:07<00:00, 11.1MB/s]
```
I checked the folder `$HF_HOME/datasets/downloads/extracted/<id>/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:
1. Why is it still blocking? Is it still downloading?
2. I specified split as train, so why is the test folder being populated?
3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?
Thanks.
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https://api.github.com/repos/huggingface/datasets/issues/568 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/568/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/568/comments | https://api.github.com/repos/huggingface/datasets/issues/568/events | https://github.com/huggingface/datasets/issues/568 | 691,638,656 | MDU6SXNzdWU2OTE2Mzg2NTY= | 568 | `metric.compute` throws `ArrowInvalid` error | {
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"Hmm might be related to what we are solving in #564",
"Could you try to update to `datasets>=1.0.0` (we changed the name of the library) and try again ?\r\nIf is was related to the distributed setup settings it must be fixed.\r\nIf it was related to empty metric inputs it's going to be fixed in #654 ",
"Closin... | 2020-09-03T04:56:57Z | 2020-10-05T16:33:53Z | 2020-10-05T16:33:53Z | NONE | null | null | null | null | I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`
```
File "/home/beltagy/trainer.py", line 92, in validation_step
rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 224, in compute
self.finalize(timeout=timeout)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 213, in finalize
self.data = Dataset(**reader.read_files(node_files))
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 217, in read_files
dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 162, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 276, in _get_dataset_from_filename
f = pa.ipc.open_stream(mmap)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 173, in open_stream
return RecordBatchStreamReader(source)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 64, in __init__
self._open(source)
File "pyarrow/ipc.pxi", line 469, in pyarrow.lib._RecordBatchStreamReader._open
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/565 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/565/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/565/comments | https://api.github.com/repos/huggingface/datasets/issues/565/events | https://github.com/huggingface/datasets/issues/565 | 691,039,121 | MDU6SXNzdWU2OTEwMzkxMjE= | 565 | No module named 'nlp.logging' | {
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"Thanks for reporting.\r\n\r\nApparently this is a versioning issue: the lib downloaded the `bleurt` script from the master branch where we did this change recently. We'll fix that in a new release this week or early next week. Cc @thomwolf \r\n\r\nUntil that, I'd suggest you to download the right bleurt folder fro... | 2020-09-02T13:49:50Z | 2020-09-03T07:29:50Z | 2020-09-03T07:29:50Z | NONE | null | null | null | null | Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing?
```
>>> import nlp
2020-09-02 13:47:09.210310: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
>>> bleurt = nlp.load_metric("bleurt")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 443, in load_metric
metric_cls = import_main_class(module_path, dataset=False)
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 61, in import_main_class
module = importlib.import_module(module_path)
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 994, in _gcd_import
File "<frozen importlib._bootstrap>", line 971, in _find_and_load
File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 678, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/metrics/bleurt/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5/bleurt.py", line 20, in <module>
from nlp.logging import get_logger
ModuleNotFoundError: No module named 'nlp.logging'
```
Just to show once again that I can't import the logging module:
```
>>> import nlp
2020-09-02 13:48:38.190621: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
>>> nlp.__version__
'0.4.0'
>>> from nlp.logging import get_logger
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'nlp.logging'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/560/comments | https://api.github.com/repos/huggingface/datasets/issues/560/events | https://github.com/huggingface/datasets/issues/560 | 690,488,764 | MDU6SXNzdWU2OTA0ODg3NjQ= | 560 | Using custom DownloadConfig results in an error | {
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} | [] | closed | false | null | [] | null | [
"From my limited understanding, part of the issue seems related to the `prepare_module` and `download_and_prepare` functions each handling the case where no config is passed. For example, `prepare_module` does mutate the object passed and forces the flags `extract_compressed_file` and `force_extract` to `True`.\r\... | 2020-09-01T22:23:02Z | 2022-10-04T17:23:45Z | 2022-10-04T17:23:45Z | NONE | null | null | null | null | ## Version / Environment
Ubuntu 18.04
Python 3.6.8
nlp 0.4.0
## Description
Loading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.
## How to reproduce
### Example without DownloadConfig --> works
```python
import os
os.environ["HF_HOME"] = "/data/hf-test-without-dl-config-01/"
import logging
import nlp
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
imdb = nlp.load_dataset(path="imdb")
```
### Example with DownloadConfig --> doesn't work
```python
import os
os.environ["HF_HOME"] = "/data/hf-test-with-dl-config-01/"
import logging
import nlp
from nlp.utils import DownloadConfig
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
download_config = DownloadConfig()
imdb = nlp.load_dataset(path="imdb", download_config=download_config)
```
Error traceback:
```
Traceback (most recent call last):
File "/.../example_with_dl_config.py", line 13, in <module>
imdb = nlp.load_dataset(path="imdb", download_config=download_config)
File "/.../python3.6/python3.6/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 518, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/.../python3.6/python3.6/site-packages/nlp/datasets/imdb/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743/imdb.py", line 86, in _split_generators
arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 220, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 158, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 108, in _record_sizes_checksums
self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)
File "/.../python3.6/python3.6/site-packages/nlp/utils/info_utils.py", line 79, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/data/hf-test-with-dl-config-01/datasets/extracted/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'
```
| {
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https://api.github.com/repos/huggingface/datasets/issues/554 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/554/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/554/comments | https://api.github.com/repos/huggingface/datasets/issues/554/events | https://github.com/huggingface/datasets/issues/554 | 690,173,214 | MDU6SXNzdWU2OTAxNzMyMTQ= | 554 | nlp downloads to its module path | {
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"Indeed this is a known issue arising from the fact that we try to be compatible with cloupickle.\r\n\r\nDoes this also happen if you are installing in a virtual environment?",
"> Indeed this is a know issue with the fact that we try to be compatible with cloupickle.\r\n> \r\n> Does this also happen if you are in... | 2020-09-01T14:06:14Z | 2020-09-11T06:19:24Z | 2020-09-11T06:19:24Z | MEMBER | null | null | null | null | I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:
```>>> import nlp
>>> squad_dataset = nlp.load_dataset('squad')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 530, in load_dataset
module_path, hash = prepare_module(path, download_config=download_config, dataset=True)
File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 329, in prepare_module
os.makedirs(main_folder_path, exist_ok=True)
File "/nix/store/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5/lib/python3.8/os.py", line 223, in makedirs
mkdir(name, mode)
OSError: [Errno 30] Read-only file system: '/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/datasets/squad'
```
Do you have any suggested workaround for this issue?
Perhaps overriding the default value for `force_local_path` of `prepare_module`? | {
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"When you load a text file for the first time with `nlp`, the file is converted into Apache Arrow format. Arrow allows to use memory-mapping, which means that you can load an arbitrary large dataset.\r\n\r\nNote that as soon as the conversion has been done once, the next time you'll load the dataset it will be much... | 2020-08-31T12:57:23Z | 2024-01-02T20:26:24Z | 2020-09-08T10:19:57Z | NONE | null | null | null | null | I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.
It has been 8 hours and still, it is on the loading steps.
It does work when the text dataset size is small about 1 GB, but it doesn't scale.
It also uses a single thread during the data loading step.
```
train_files = glob.glob("xxx/*.txt",recursive=True)
random.shuffle(train_files)
print(train_files)
dataset = nlp.load_dataset('text',
data_files=train_files,
name="customDataset",
version="1.0.0",
cache_dir="xxx/nlp")
```
Is there something that I am missing ? | {
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"Update: release is planed mid-next week."
] | 2020-08-31T11:37:38Z | 2021-01-13T10:59:04Z | 2021-01-13T10:59:04Z | MEMBER | null | null | null | null | Hi all,
A few words on the roadmap for this library.
The next release will be a big one and is planed at the end of this week.
In addition to the support for indexed datasets (useful for non-parametric models like REALM, RAG, DPR, knn-LM and many other fast dataset retrieval technics), it will:
- have support for multi-modal datasets
- include various significant improvements on speed for standard processing (map, shuffling, ...)
- have a better support for metrics (better caching, and a robust API) and a bigger focus on reproductibility
- change the name to the final name (voted by the community): `datasets`
- be the 1.0.0 release as we think the API will be mostly stabilized from now on | {
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https://api.github.com/repos/huggingface/datasets/issues/543 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/543/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/543/comments | https://api.github.com/repos/huggingface/datasets/issues/543/events | https://github.com/huggingface/datasets/issues/543 | 688,644,407 | MDU6SXNzdWU2ODg2NDQ0MDc= | 543 | nlp.load_dataset is not safe for multi processes when loading from local files | {
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"I'll take a look!"
] | 2020-08-30T03:20:34Z | 2020-08-31T11:15:10Z | 2020-08-31T11:15:10Z | NONE | null | null | null | null | Loading from local files, e.g., `dataset = nlp.load_dataset('csv', data_files=['file_1.csv', 'file_2.csv'])`
concurrently from multiple processes, will raise `FileExistsError` from builder's line 430, https://github.com/huggingface/nlp/blob/6655008c738cb613c522deb3bd18e35a67b2a7e5/src/nlp/builder.py#L423-L438
Likely because multiple processes step into download_and_prepare, https://github.com/huggingface/nlp/blob/6655008c738cb613c522deb3bd18e35a67b2a7e5/src/nlp/load.py#L550-L554
This can happen when launching distributed training with commands like `python -m torch.distributed.launch --nproc_per_node 4` on a new collection of files never loaded before.
I can create a PR that puts in some file locks. It would be helpful if I can be informed of the convention for naming and placement of the lock. | {
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https://api.github.com/repos/huggingface/datasets/issues/541 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/541/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/541/comments | https://api.github.com/repos/huggingface/datasets/issues/541/events | https://github.com/huggingface/datasets/issues/541 | 688,521,224 | MDU6SXNzdWU2ODg1MjEyMjQ= | 541 | Best practices for training tokenizers with nlp | {
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"Docs that explain how to train a tokenizer with `datasets` are available here: https://huggingface.co/docs/tokenizers/training_from_memory#using-the-datasets-library"
] | 2020-08-29T12:06:49Z | 2022-10-04T17:28:04Z | 2022-10-04T17:28:04Z | NONE | null | null | null | null | Hi, thank you for developing this library.
What do you think are the best practices for training tokenizers using `nlp`? In the document and examples, I could only find pre-trained tokenizers used. | {
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https://api.github.com/repos/huggingface/datasets/issues/539 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/539/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/539/comments | https://api.github.com/repos/huggingface/datasets/issues/539/events | https://github.com/huggingface/datasets/issues/539 | 688,323,602 | MDU6SXNzdWU2ODgzMjM2MDI= | 539 | [Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data | {
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"Hi @gaguilar \r\n\r\nIf you want to take care of this, it very simple, you just need to regenerate the `dataset_infos.json` file as indicated [in the doc](https://huggingface.co/nlp/share_dataset.html#adding-metadata) by [installing from source](https://huggingface.co/nlp/installation.html#installing-from-source) ... | 2020-08-28T19:55:51Z | 2020-09-03T16:34:02Z | 2020-09-03T16:34:01Z | CONTRIBUTOR | null | null | null | null | Hi,
There is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset.
How can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https://huggingface.co/nlp/viewer/?dataset=lince&config=lid_msaea):
```python
import nlp
nlp.load_dataset('lince', 'lid_msaea')
```
Output:
```
NonMatchingChecksumError: ['https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/lid_msaea.zip']
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 196, in <module>
dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp-viewer/run.py", line 150, in get
builder_instance.download_and_prepare()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
download_config.force_download = download_mode == FORCE_REDOWNLOAD
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 469, in _download_and_prepare
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 36, in verify_checksums
raise NonMatchingChecksumError(str(bad_urls))
```
Thank you in advance!
@lhoestq | {
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"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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"`NonMatchingChecksumError` means that the checksum of the downloaded file is not the expected one.\r\nEither the file you downloaded was corrupted along the way, or the host updated the file.\r\nCould you try to clear your cache and run `load_dataset` again ? If the error is still there, it means that there was an... | 2020-08-27T23:58:16Z | 2020-09-18T12:07:04Z | 2020-09-18T12:07:04Z | CONTRIBUTOR | null | null | null | null | Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:
```
dataset = nlp.load_dataset("race")
len(dataset["train"]), len(dataset["validation"])
```
But then I got the following error:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-15-8bf7603ce0ed> in <module>
----> 1 dataset = nlp.load_dataset("race")
2 len(dataset["train"]), len(dataset["validation"])
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
546
547 # Download and prepare data
--> 548 builder_instance.download_and_prepare(
549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
550 )
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
460 logger.info("Dataset not on Hf google storage. Downloading and preparing it from source")
461 if not downloaded_from_gcs:
--> 462 self._download_and_prepare(
463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
464 )
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
519 # Checksums verification
520 if verify_infos:
--> 521 verify_checksums(
522 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
523 )
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
36 if len(bad_urls) > 0:
37 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 38 raise NonMatchingChecksumError(error_msg + str(bad_urls))
39 logger.info("All the checksums matched successfully" + for_verification_name)
40
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz']
``` | {
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"Thanks for reporting !\r\nThis has been fixed in #475 and the fix will be available in the next release",
"What you can do instead to get the list of the datasets is call\r\n\r\n```python\r\nprint([dataset.id for dataset in nlp.list_datasets()])\r\n```",
"Thanks @lhoestq . "
] | 2020-08-26T08:19:01Z | 2020-08-27T06:31:11Z | 2020-08-27T06:31:11Z | NONE | null | null | null | null | version = '0.4.0'
`list_datasets()` is broken. It results in the following error :
```
In [3]: nlp.list_datasets()
Out[3]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
375 if cls in self.type_pprinters:
376 # printer registered in self.type_pprinters
--> 377 return self.type_pprinters[cls](obj, self, cycle)
378 else:
379 # deferred printer
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in inner(obj, p, cycle)
553 p.text(',')
554 p.breakable()
--> 555 p.pretty(x)
556 if len(obj) == 1 and type(obj) is tuple:
557 # Special case for 1-item tuples.
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698 """A pprint that just redirects to the normal repr function."""
699 # Find newlines and replace them with p.break_()
--> 700 output = repr(obj)
701 lines = output.splitlines()
702 with p.group():
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/nlp/hf_api.py in __repr__(self)
110
111 def __repr__(self):
--> 112 single_line_description = self.description.replace("\n", "")
113 return f"nlp.ObjectInfo(id='{self.id}', description='{single_line_description}', files={self.siblings})"
114
AttributeError: 'NoneType' object has no attribute 'replace'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/532 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/532/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/532/comments | https://api.github.com/repos/huggingface/datasets/issues/532/events | https://github.com/huggingface/datasets/issues/532 | 685,540,614 | MDU6SXNzdWU2ODU1NDA2MTQ= | 532 | File exists error when used with TPU | {
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"I am facing probably facing similar issues with \r\n\r\n`wiki40b_en_100_0`",
"Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the d... | 2020-08-25T14:36:38Z | 2020-09-01T12:14:56Z | null | NONE | null | null | null | null | Hi,
I'm getting a "File exists" error when I use [text dataset](https://github.com/huggingface/nlp/tree/master/datasets/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).
I modified [line 131 in the original `run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py#L131) as follows:
```python
# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
dataset = load_dataset("text", data_files=file_path, split="train")
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
truncation=True, max_length=args.block_size), batched=True)
dataset.set_format(type='torch', columns=['input_ids'])
return dataset
```
When I run this with [`xla_spawn.py`](https://github.com/huggingface/transformers/blob/master/examples/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).
It seems the current version doesn't take into account distributed training processes as in [this example](https://github.com/huggingface/transformers/blob/a573777901e662ec2e565be312ffaeedef6effec/src/transformers/data/datasets/language_modeling.py#L35-L38)?
```
08/25/2020 13:59:41 - WARNING - nlp.builder - Using custom data configuration default
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Exception in device=TPU:6: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:4: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:1: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Exception in device=TPU:7: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:3: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Exception in device=TPU:2: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:0: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
Traceback (most recent call last):
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
```
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https://api.github.com/repos/huggingface/datasets/issues/525 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/525/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/525/comments | https://api.github.com/repos/huggingface/datasets/issues/525/events | https://github.com/huggingface/datasets/issues/525 | 683,875,483 | MDU6SXNzdWU2ODM4NzU0ODM= | 525 | wmt download speed example | {
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"Thanks for creating the issue :)\r\nThe download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.\r\nIs this mirror official ?\r\n\r\nAlso it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool\r\n\r... | 2020-08-21T23:29:06Z | 2022-10-04T17:45:39Z | 2022-10-04T17:45:39Z | CONTRIBUTOR | null | null | null | null | Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.
```
import nlp
nlp.load_dataset('wmt16', 'de-en')
```
Downloads at 49.1 KB/S
Whereas
```
pip install gdown # download from google drive
!gdown https://drive.google.com/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj
```
Downloads at 127 MB/s. (The file is a copy of wmt-en-de raw).
```
nlp.load_dataset('wmt16', 'ro-en')
```
goes at 27 MB/s, much faster.
if we wget the same data from s3 is the same download speed, but ¼ the file size:
```
wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro_packed_200_rand.tgz
```
Finally,
```
nlp.load_dataset('wmt19', 'zh-en')
```
Starts fast, but broken. (duplicate of #493 )
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https://api.github.com/repos/huggingface/datasets/issues/524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/524/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/524/comments | https://api.github.com/repos/huggingface/datasets/issues/524/events | https://github.com/huggingface/datasets/issues/524 | 683,686,359 | MDU6SXNzdWU2ODM2ODYzNTk= | 524 | Some docs are missing parameter names | {
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"Indeed, good catch!"
] | 2020-08-21T16:47:34Z | 2020-08-25T09:04:03Z | 2020-08-25T09:04:03Z | CONTRIBUTOR | null | null | null | null | See https://huggingface.co/nlp/master/package_reference/main_classes.html#nlp.Dataset.map. I believe this is because the parameter names are enclosed in backticks in the docstrings, maybe it's an old docstring format that doesn't work with the current Sphinx version. | {
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https://api.github.com/repos/huggingface/datasets/issues/522 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/522/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/522/comments | https://api.github.com/repos/huggingface/datasets/issues/522/events | https://github.com/huggingface/datasets/issues/522 | 682,478,833 | MDU6SXNzdWU2ODI0Nzg4MzM= | 522 | dictionnary typo in docs | {
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"Thanks!"
] | 2020-08-20T07:11:05Z | 2020-08-20T07:52:14Z | 2020-08-20T07:52:13Z | CONTRIBUTOR | null | null | null | null | Many places dictionary is spelled dictionnary, not sure if its on purpose or not.
Fixed in this pr:
https://github.com/huggingface/nlp/pull/521 | {
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https://api.github.com/repos/huggingface/datasets/issues/519 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/519/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/519/comments | https://api.github.com/repos/huggingface/datasets/issues/519/events | https://github.com/huggingface/datasets/issues/519 | 682,193,882 | MDU6SXNzdWU2ODIxOTM4ODI= | 519 | [BUG] Metrics throwing new error on master since 0.4.0 | {
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"Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric",
"Closing - seems to be just forgetting to tokenize. And found the helpful discussion in huggingface/evaluate#105 "
] | 2020-08-19T21:29:15Z | 2022-06-02T16:41:01Z | 2020-08-19T22:04:40Z | CONTRIBUTOR | null | null | null | null | The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu.
Wasn't happening on 0.4.0 but happening now on master.
```
File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 226, in compute
self.add_batch(predictions=predictions, references=references)
File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 242, in add_batch
batch = self.info.features.encode_batch(batch)
File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in encode_batch
encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]
File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in <listcomp>
encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]
File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 456, in encode_nested_example
raise ValueError("Got a string but expected a list instead: '{}'".format(obj))
``` | {
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"Any updates on this?",
"This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies."
] | 2020-08-19T14:41:59Z | 2021-08-03T05:59:33Z | null | CONTRIBUTOR | null | null | null | null | Hi,
I am recommending that someone add MLDoc, a multilingual news topic classification dataset.
- Here's a link to the Github: https://github.com/facebookresearch/MLDoc
- and the paper: http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf
Looks like the dataset contains news stories in multiple languages that can be classified into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). There are 13 languages: Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish | null | {
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"This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf ",
"Maybe I'm a bit tired but I fail to see the issue here.\r\n\r\nSince `cache_file_name` is `None` by default, if you set `keep_in_me... | 2020-08-18T18:47:40Z | 2022-10-10T12:21:58Z | 2022-10-10T12:21:58Z | CONTRIBUTOR | null | null | null | null | As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`
The commit added the lines
```python
# lines 994-996 in src/nlp/arrow_dataset.py
assert (
not keep_in_memory or cache_file_name is None
), "Please use either `keep_in_memory` or `cache_file_name` but not both."
```
This affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check.
I'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`. | {
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"Hi @vegarab yes feel free to open a discussion here.\r\n\r\nThis design choice was not very much thought about.\r\n\r\nSince `dataset.select()` (like all the method without a trailing underscore) is non-destructive and returns a new dataset it has most of its properties initialized from scratch (except the table a... | 2020-08-18T13:46:01Z | 2020-09-14T08:45:38Z | 2020-09-14T08:45:38Z | CONTRIBUTOR | null | null | null | null | Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?
When working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save("dataset.pt")`. Later loading the dataset object using `torch.load("dataset.pt")`, which conserves the defined format before saving.
I do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset.
The obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.
_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_
#### How to reproduce:
```python
import nlp
from transformers import T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("t5-base")
def create_features(batch):
context_encoding = tokenizer.batch_encode_plus(batch["context"])
return {"input_ids": context_encoding["input_ids"]}
dataset = nlp.load_dataset("cosmos_qa", split="train")
dataset = dataset.map(create_features, batched=True)
dataset.set_format(type="torch", columns=["input_ids"])
dataset[0]
# {'input_ids': tensor([ 1804, 3525, 1602, ... 0, 0])}
dataset = dataset.shuffle()
dataset[0]
# {'id': '3Q9(...)20', 'context': "Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}
``` | {
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"Seems like this method was added in 1.17. I'll add a requirement on this.",
"Thank you so much. After upgrading the numpy library, it worked."
] | 2020-08-18T08:59:13Z | 2020-08-19T18:35:56Z | 2020-08-19T18:35:56Z | NONE | null | null | null | null | Thank you so much for your excellent work! I would like to use nlp library in my project. While importing nlp, I am receiving the following error `AttributeError: module 'numpy.random' has no attribute 'Generator'` Numpy version in my project is 1.16.0. May I learn which numpy version is used for the nlp library.
Thanks in advance. | {
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"Do you want to convert a dataset script to the tfds format ?\r\nIf so, we currently have a comversion script nlp/commands/convert.py but it is a conversion script that goes from tfds to nlp.\r\nI think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).\r\nIf you manage to make it w... | 2020-08-16T08:05:20Z | 2021-08-03T06:01:18Z | 2021-08-03T06:01:17Z | NONE | null | null | null | null | Hi,
I want to use TensorFlow datasets with this repo, I noticed you made some conversion script,
can you give a simple example of using it?
Thanks
| {
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https://api.github.com/repos/huggingface/datasets/issues/508 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/508/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/508/comments | https://api.github.com/repos/huggingface/datasets/issues/508/events | https://github.com/huggingface/datasets/issues/508 | 679,705,734 | MDU6SXNzdWU2Nzk3MDU3MzQ= | 508 | TypeError: Receiver() takes no arguments | {
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"Which version of Apache Beam do you have (can you copy your full environment info here)?",
"apache-beam==2.23.0\r\nnlp==0.4.0\r\n\r\nFor me this was resolved by running the same python script on Linux (or really WSL). ",
"Do you manage to run a dummy beam pipeline with python on windows ? \r\nYou can test a du... | 2020-08-16T07:18:16Z | 2020-09-01T14:53:33Z | 2020-09-01T14:49:03Z | NONE | null | null | null | null | I am trying to load a wikipedia data set
```
import nlp
from nlp import load_dataset
dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=data_path, beam_runner='DirectRunner')
#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')
```
This fails in the apache beam runner.
```
Traceback (most recent call last):
File "D:/ML/wikiembedding/gpt2_sv.py", line 36, in <module>
dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=my_cache_dir, beam_runner='DirectRunner')
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 969, in _download_and_prepare
pipeline_results = pipeline.run()
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\pipeline.py", line 534, in run
return self.runner.run_pipeline(self, self._options)
....
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 218, in process_encoded
self.output(decoded_value)
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\operations.py", line 332, in output
cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\Cython\Shadow.py", line 167, in cast
return type(*args)
TypeError: Receiver() takes no arguments
```
This is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump. | {
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"Looks like an issue with 3.0.2 transformers version. Works fine when I use \"master\" version of transformers."
] | 2020-08-14T21:03:57Z | 2020-08-14T21:39:10Z | 2020-08-14T21:39:10Z | NONE | null | null | null | null | I tried the following example code from https://huggingface.co/deepset/roberta-base-squad2 and got errors
I am using **transformers 3.0.2** code .
from transformers.pipelines import pipeline
from transformers.modeling_auto import AutoModelForQuestionAnswering
from transformers.tokenization_auto import AutoTokenizer
model_name = "deepset/roberta-base-squad2"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)
The errors are :
res = nlp(QA_input)
File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in __call__
for s, e, score in zip(starts, ends, scores)
File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in <listcomp>
for s, e, score in zip(starts, ends, scores)
KeyError: 0
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https://api.github.com/repos/huggingface/datasets/issues/501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/501/comments | https://api.github.com/repos/huggingface/datasets/issues/501/events | https://github.com/huggingface/datasets/issues/501 | 677,952,893 | MDU6SXNzdWU2Nzc5NTI4OTM= | 501 | Caching doesn't work for map (non-deterministic) | {
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"gists... | null | [
"Thanks for reporting !\r\n\r\nTo store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.\r\nThe hash doesn't seem to stay the same over sessions for the tokenizer.\r\nApparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing fun... | 2020-08-12T20:20:07Z | 2022-08-08T11:02:23Z | 2020-08-24T16:34:35Z | NONE | null | null | null | null | The caching functionality doesn't work reliably when tokenizing a dataset. Here's a small example to reproduce it.
```python
import nlp
import transformers
def main():
ds = nlp.load_dataset("reddit", split="train[:500]")
tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2")
def convert_to_features(example_batch):
input_str = example_batch["body"]
encodings = tokenizer(input_str, add_special_tokens=True, truncation=True)
return encodings
ds = ds.map(convert_to_features, batched=True)
if __name__ == "__main__":
main()
```
Roughly 3/10 times, this example recomputes the tokenization.
Is this expected behaviour? | {
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"In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.",
"Or maybe the assertion comes from elsewhere ?",
"I'm using the master... | 2020-08-11T00:27:46Z | 2020-08-26T16:17:19Z | 2020-08-26T16:17:19Z | CONTRIBUTOR | null | null | null | null | Here's the code I'm trying to run:
```python
dset_wikipedia = nlp.load_dataset("wikipedia", "20200501.en", split="train", cache_dir=args.cache_dir)
dset_wikipedia.drop(columns=["title"])
dset_wikipedia.features.pop("title")
dset_books = nlp.load_dataset("bookcorpus", split="train", cache_dir=args.cache_dir)
dset = nlp.concatenate_datasets([dset_wikipedia, dset_books])
```
This fails because they have different schemas, despite having identical features.
```python
assert dset_wikipedia.features == dset_books.features # True
assert dset_wikipedia._data.schema == dset_books._data.schema # False
```
The Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.
```python
dset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)
```
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"I did the release on github, and updated the doc :)\r\nSorry for the delay",
"Thanks!"
] | 2020-08-10T23:59:57Z | 2020-08-11T16:50:07Z | 2020-08-11T16:50:07Z | CONTRIBUTOR | null | null | null | null | 0.4.0 was released on PyPi, but not on GitHub. This means [the documentation](https://huggingface.co/nlp/) is still displaying from 0.3.0, and that there's no tag to easily clone the 0.4.0 version of the repo. | {
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https://api.github.com/repos/huggingface/datasets/issues/490 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/490/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/490/comments | https://api.github.com/repos/huggingface/datasets/issues/490/events | https://github.com/huggingface/datasets/issues/490 | 676,482,242 | MDU6SXNzdWU2NzY0ODIyNDI= | 490 | Loading preprocessed Wikipedia dataset requires apache_beam | {
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} | [] | closed | false | null | [] | null | [] | 2020-08-10T23:46:50Z | 2020-08-14T13:17:20Z | 2020-08-14T13:17:20Z | CONTRIBUTOR | null | null | null | null | Running
`nlp.load_dataset("wikipedia", "20200501.en", split="train", dir="/tmp/wikipedia")`
gives an error if apache_beam is not installed, stemming from
https://github.com/huggingface/nlp/blob/38eb2413de54ee804b0be81781bd65ac4a748ced/src/nlp/builder.py#L981-L988
This succeeded without the dependency in version 0.3.0. This seems like an unnecessary dependency to process some dataset info if you're using the already-preprocessed version. Could it be removed? | {
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https://api.github.com/repos/huggingface/datasets/issues/489 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/489/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/489/comments | https://api.github.com/repos/huggingface/datasets/issues/489/events | https://github.com/huggingface/datasets/issues/489 | 676,456,257 | MDU6SXNzdWU2NzY0NTYyNTc= | 489 | ug | {
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"whoops",
"please delete this"
] | 2020-08-10T22:33:03Z | 2020-08-10T22:55:14Z | 2020-08-10T22:33:40Z | NONE | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/488 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/488/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/488/comments | https://api.github.com/repos/huggingface/datasets/issues/488/events | https://github.com/huggingface/datasets/issues/488 | 676,299,993 | MDU6SXNzdWU2NzYyOTk5OTM= | 488 | issues with downloading datasets for wmt16 and wmt19 | {
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"I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.... | 2020-08-10T17:32:51Z | 2022-10-04T17:46:59Z | 2022-10-04T17:46:58Z | CONTRIBUTOR | null | null | null | null | I have encountered multiple issues while trying to:
```
import nlp
dataset = nlp.load_dataset('wmt16', 'ru-en')
metric = nlp.load_metric('wmt16')
```
1. I had to do `pip install -e ".[dev]" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that `pip install -e ".[dev]" ` fixed.
2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for.
I tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)
3. my machine has crushed and when I retried I got:
```
Traceback (most recent call last):
File "./download.py", line 9, in <module>
dataset = nlp.load_dataset('wmt16', 'ru-en')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 449, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/stas/anaconda3/envs/main/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/stas/anaconda3/envs/main/lib/python3.7/os.py", line 221, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/stas/.cache/huggingface/datasets/wmt16/ru-en/1.0.0/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'
```
it can't handle resumes. but neither allows a new start. Had to delete it manually.
4. and finally when it downloaded the dataset, it then failed to fetch the metrics:
```
Traceback (most recent call last):
File "./download.py", line 15, in <module>
metric = nlp.load_metric('wmt16')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 442, in load_metric
module_path, hash = prepare_module(path, download_config=download_config, dataset=False)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 258, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 198, in cached_path
local_files_only=download_config.local_files_only,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 356, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/metrics/wmt16/wmt16.py
```
5. If I run the same code with `wmt19`, it fails too:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz
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"Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do... | 2020-08-09T06:53:24Z | 2022-10-04T17:44:33Z | 2022-10-04T17:44:33Z | CONTRIBUTOR | null | null | null | null | It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, "didn't" becomes "did" + "n't", and double quotes are changed to `` and '' for start and end quotes, respectively.
On my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https://github.com/nltk/nltk/pull/2575 | {
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import nlp
dataset = nlp.load_dataset('xtreme', 'PAWS-X.en')
dataset['test'][0]
```
prints the following
```
{'label': 'label', 'sentence1': 'sentence1', 'sentence2': 'sentence2'}
```
dataset['test'][0] should probably be the first item in the dataset, not just a dictionary mapping the column names to themselves. Probably just need to ignore the first row in the dataset by default or something like that. | {
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"found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz",
"fixed in #484 ",
"Closing this one. Thanks again @jxmorris12 for taking care of this :)"
] | 2020-08-07T15:12:01Z | 2020-09-08T09:36:34Z | 2020-09-08T09:36:33Z | CONTRIBUTOR | null | null | null | null | In an interesting twist of events, the individual who created the movie review seems to have left Cornell, and their webpage has been removed, along with the movie review dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz). It's not downloadable anymore. | {
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"This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look",
"I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip in... | 2020-08-07T08:23:35Z | 2023-04-06T09:39:59Z | 2020-08-11T23:55:15Z | NONE | null | null | null | null | Hi Huggingface Team!
Thank you guys once again for this amazing repo.
I have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)
However, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 / 0.17.0 / 1.0.0 also have the same frozen process.
Reproducible code can be found on [this colab notebook ](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.
----------------------------------------
**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)
I also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? | {
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"Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)"
] | 2020-08-05T01:08:32Z | 2020-08-05T01:21:37Z | 2020-08-05T01:21:36Z | NONE | null | null | null | null | Previously, I was writing TFRecords manually to GCP bucket with : `with tf.io.TFRecordWriter('gs://my_bucket/x.tfrecord')`
Since `0.4.0` is out with the `export()` function, I tried it. But it seems TFRecords cannot be directly written to GCP bucket.
`dataset.export('local.tfrecord')` works fine,
but `dataset.export('gs://my_bucket/x.tfrecord')` does not work.
There is no error message, I just can't find the file on my bucket...
---
Looking at the code, `nlp` is using `tf.data.experimental.TFRecordWriter`, while I was using `tf.io.TFRecordWriter`.
**What's the difference between those 2 ? How can I write TFRecords files directly to GCP bucket ?**
@jarednielsen @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/477/comments | https://api.github.com/repos/huggingface/datasets/issues/477/events | https://github.com/huggingface/datasets/issues/477 | 673,142,143 | MDU6SXNzdWU2NzMxNDIxNDM= | 477 | Overview.ipynb throws exceptions with nlp 0.4.0 | {
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"Thanks for reporting this issue\r\n\r\nThere was a bug where numpy arrays would get returned instead of tensorflow tensors.\r\nThis is fixed on master.\r\n\r\nI tried to re-run the colab and encountered this error instead:\r\n\r\n```\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no at... | 2020-08-04T23:18:15Z | 2021-08-03T06:02:15Z | 2021-08-03T06:02:15Z | NONE | null | null | null | null | with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-48907f2ad433> in <module>
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
<ipython-input-5-48907f2ad433> in <dictcomp>(.0)
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
AttributeError: 'numpy.ndarray' object has no attribute 'to_tensor' | {
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https://api.github.com/repos/huggingface/datasets/issues/474 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/474/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/474/comments | https://api.github.com/repos/huggingface/datasets/issues/474/events | https://github.com/huggingface/datasets/issues/474 | 672,407,330 | MDU6SXNzdWU2NzI0MDczMzA= | 474 | test_load_real_dataset when config has BUILDER_CONFIGS that matter | {
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"The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`\r\n\r\nAs mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in BUILDER_CONFIGS",
"This was fixed in #527 \r\n\r\nClosing this one, ... | 2020-08-03T23:46:36Z | 2020-09-07T14:53:13Z | 2020-09-07T14:53:13Z | NONE | null | null | null | null | It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.
I think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https://github.com/huggingface/nlp/blob/master/tests/test_dataset_common.py#L200)). This causes [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.
For an example, you can try running the test for lince:
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`
which yields
> E TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column' | {
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https://api.github.com/repos/huggingface/datasets/issues/469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/469/comments | https://api.github.com/repos/huggingface/datasets/issues/469/events | https://github.com/huggingface/datasets/issues/469 | 671,876,963 | MDU6SXNzdWU2NzE4NzY5NjM= | 469 | invalid data type 'str' at _convert_outputs in arrow_dataset.py | {
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} | [] | closed | false | null | [] | null | [
"Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)` (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the... | 2020-08-03T07:48:29Z | 2023-07-20T15:54:17Z | 2023-07-20T15:54:17Z | NONE | null | null | null | null | I trying to build multi label text classifier model using Transformers lib.
I'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error
File "C:\***\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
I'm using pyarrow 1.0.0. And I have simple custom data set with Text and Integer Label.
Ex: Data
Text , Label #Column Header
I'm facing an Network issue, 1
I forgot my password, 2
Error StackTrace:
File "C:\**\transformers\trainer.py", line 492, in train
for step, inputs in enumerate(epoch_iterator):
File "C:\**\tqdm\std.py", line 1104, in __iter__
for obj in iterable:
File "C:\**\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\**\torch\utils\data\dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\nlp\arrow_dataset.py", line 414, in __getitem__
output_all_columns=self._output_all_columns,
File "C:\**\nlp\arrow_dataset.py", line 403, in _getitem
outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns
File "C:\**\nlp\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
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https://api.github.com/repos/huggingface/datasets/issues/468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/468/comments | https://api.github.com/repos/huggingface/datasets/issues/468/events | https://github.com/huggingface/datasets/issues/468 | 671,622,441 | MDU6SXNzdWU2NzE2MjI0NDE= | 468 | UnicodeDecodeError while loading PAN-X task of XTREME dataset | {
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"Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the future.",
"I'm happy to tackle the broader problem - will open a ... | 2020-08-02T14:05:10Z | 2020-08-20T08:16:08Z | 2020-08-20T08:16:08Z | MEMBER | null | null | null | null | Hi 🤗 team!
## Description of the problem
I'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset:
```
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-5-1d61f439b843> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
528 ignore_verifications = ignore_verifications or save_infos
529 # Download/copy dataset processing script
--> 530 module_path, hash = prepare_module(path, download_config=download_config, dataset=True)
531
532 # Get dataset builder class from the processing script
/usr/local/lib/python3.6/dist-packages/nlp/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)
265
266 # Download external imports if needed
--> 267 imports = get_imports(local_path)
268 local_imports = []
269 library_imports = []
/usr/local/lib/python3.6/dist-packages/nlp/load.py in get_imports(file_path)
156 lines = []
157 with open(file_path, mode="r") as f:
--> 158 lines.extend(f.readlines())
159
160 logger.info("Checking %s for additional imports.", file_path)
/usr/lib/python3.6/encodings/ascii.py in decode(self, input, final)
24 class IncrementalDecoder(codecs.IncrementalDecoder):
25 def decode(self, input, final=False):
---> 26 return codecs.ascii_decode(input, self.errors)[0]
27
28 class StreamWriter(Codec,codecs.StreamWriter):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)
```
## Steps to reproduce
Install from nlp's master branch
```python
pip install git+https://github.com/huggingface/nlp.git
```
then run
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
```
## OS / platform details
- `nlp` version: latest from master
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: False
## Proposed solution
Either change [line 762](https://github.com/huggingface/nlp/blob/7ada00b1d62f94eee22a7df38c6b01e3f27194b7/datasets/xtreme/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:
```
# old
with open(filepath) as f
# new
with open(filepath, encoding='utf-8') as f
```
or raise a warning that suggests setting the locale explicitly, e.g.
```python
import locale
locale.setlocale(locale.LC_ALL, 'C.UTF-8')
```
I have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix! | {
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https://api.github.com/repos/huggingface/datasets/issues/445 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/445/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/445/comments | https://api.github.com/repos/huggingface/datasets/issues/445/events | https://github.com/huggingface/datasets/issues/445 | 666,836,658 | MDU6SXNzdWU2NjY4MzY2NTg= | 445 | DEFAULT_TOKENIZER import error in sacrebleu | {
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"This issue was resolved by #447 "
] | 2020-07-28T07:31:30Z | 2020-07-28T12:58:56Z | 2020-07-28T12:58:56Z | CONTRIBUTOR | null | null | null | null | Latest Version 0.3.0
When loading the metric "sacrebleu" there is an import error due to the wrong path
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"Same here !",
"This is the only fix I could come up with without touching the repo's code.\r\n```python\r\nfrom nlp.builder import FORCE_REDOWNLOAD\r\ndataset = load_dataset('csv', data_file='./a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')\r\n```\r\nYou'll have to change the version each time you want... | 2020-07-27T13:08:06Z | 2020-07-29T13:57:22Z | 2020-07-29T13:57:22Z | NONE | null | null | null | null | I used load a file called 'a.csv' by
```
dataset = load_dataset('csv', data_file='./a.csv')
```
And after a while, I tried to load another csv called 'b.csv'
```
dataset = load_dataset('csv', data_file='./b.csv')
```
However, the new dataset seems to remain the old 'a.csv' and not loading new csv file.
Even worse, after I load a.csv, the load_dataset function keeps loading the 'a.csv' afterward.
Is this a cache problem?
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"This seems to be fixed in a non-released version. \r\n\r\nInstalling nlp from source\r\n```\r\ngit clone https://github.com/huggingface/nlp\r\ncd nlp\r\npip install .\r\n```\r\nsolves the issue. "
] | 2020-07-27T12:13:37Z | 2020-07-27T13:05:11Z | 2020-07-27T13:05:11Z | CONTRIBUTOR | null | null | null | null | Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling:
```python
>>> import torch
>>> import nlp
>>> squad = nlp.load_dataset("squad.py", split="train")
>>> squad
Dataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype='string', id=None)}, num_rows: 87599)
>>> squad = squad.map(create_features, batched=True)
>>> squad.set_format(type="torch", columns=["source_ids", "target_ids", "attention_mask"])
>>> torch.save(squad, "squad.pt")
>>> squad_pt = torch.load("squad.pt")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 593, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 773, in _legacy_load
result = unpickler.load()
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/splits.py", line 493, in __setitem__
raise ValueError("Cannot add elem. Use .add() instead.")
ValueError: Cannot add elem. Use .add() instead.
```
where `create_features` is a function that tokenizes the data using `batch_encode_plus` and returns a Dict with `input_ids`, `target_ids` and `attention_mask`.
```python
def create_features(batch):
source_text_encoding = tokenizer.batch_encode_plus(
batch["source_text"],
max_length=max_source_length,
pad_to_max_length=True,
truncation=True)
target_text_encoding = tokenizer.batch_encode_plus(
batch["target_text"],
max_length=max_target_length,
pad_to_max_length=True,
truncation=True)
features = {
"source_ids": source_text_encoding["input_ids"],
"target_ids": target_text_encoding["input_ids"],
"attention_mask": source_text_encoding["attention_mask"]
}
return features
```
I found a similar issue in [issue 5267 in the huggingface/transformers repo](https://github.com/huggingface/transformers/issues/5267) which was solved by downgrading to `nlp==0.2.0`. That did not solve this problem, however. | {
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] | open | false | null | [] | null | [] | 2020-07-27T10:59:58Z | 2020-08-24T15:13:20Z | null | NONE | null | null | null | null | Hello! First of all, thanks for setting up this useful project!
I've just realised you provide the the [Glue Diagnostics Data](https://huggingface.co/nlp/viewer/?dataset=glue&config=ax) without labels, indicating in the `GlueConfig` that you've only a test set.
Yet, the data with labels is available, too (see also [here](https://gluebenchmark.com/diagnostics#introduction)):
https://www.dropbox.com/s/ju7d95ifb072q9f/diagnostic-full.tsv?dl=1
Have you considered incorporating it? | null | {
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"`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).... | 2020-07-27T04:25:17Z | 2020-10-28T01:46:24Z | 2020-10-28T01:46:24Z | NONE | null | null | null | null | It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https://huggingface.co/nlp/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab. Is there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ? | {
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https://api.github.com/repos/huggingface/datasets/issues/438 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/438/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/438/comments | https://api.github.com/repos/huggingface/datasets/issues/438/events | https://github.com/huggingface/datasets/issues/438 | 665,865,490 | MDU6SXNzdWU2NjU4NjU0OTA= | 438 | New Datasets: IWSLT15+, ITTB | {
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"Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?\r\nThe tutorial on writing a new dataset loading script is here: https://huggingface.co/nlp/add_dataset.html\r\nAnd the part on how to sha... | 2020-07-26T21:43:04Z | 2020-08-24T15:12:15Z | null | CONTRIBUTOR | null | null | null | null | **Links:**
[iwslt](https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/iwslt.html)
Don't know if that link is up to date.
[ittb](http://www.cfilt.iitb.ac.in/iitb_parallel/)
**Motivation**: replicate mbart finetuning results (table below)
For future readers, we already have the following language pairs in the wmt namespaces:
```
wmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']
wmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']
wmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']
wmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']
wmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']
wmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']
``` | null | {
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"Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq ",
"+1! this is the reason our tests are failing at [TextAttack](https://github.com/QData/TextAttack) \r\n\r\n(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case w... | 2020-07-25T13:05:20Z | 2020-08-20T08:08:18Z | 2020-08-20T08:08:18Z | NONE | null | null | null | null | With latest PyArrow 1.0.0 installed, I get the following exception . Restarting colab has the same issue
ImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.
The error goes only when I install version 0.16.0
i.e. !pip install pyarrow==0.16.0 | {
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"This was fixed in #434 \r\nWe'll do a release later this week to include this fix.\r\nThanks for reporting",
"I dont know if the fix was made but the problem is still present : \r\nInstaled with pip : NLP 0.3.0 // pyarrow 1.0.0 \r\nOS : archlinux with kernel zen 5.8.5",
"Yes it was fixed in `nlp>=0.4.0`\r\nYou... | 2020-07-25T03:44:39Z | 2020-09-08T17:57:15Z | 2020-08-03T16:37:32Z | NONE | null | null | null | null | The following PR raised ImportWarning at `pyarrow ==1.0.0` https://github.com/huggingface/nlp/pull/265/files | {
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"Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https://github.com/huggingface/nlp/tree/master/datasets/csv\r\n- json: https://github.com/huggingface/nlp/tree/master/datasets/json\r\n- text: https://github.com/huggingface/nlp/tree/master/... | 2020-07-24T17:27:37Z | 2022-10-04T17:59:34Z | 2022-10-04T17:59:33Z | NONE | null | null | null | null | I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https://brat.nlplab.org/standoff.html), [dataset code](https://github.com/ArneBinder/nlp/blob/brat/datasets/brat/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?
In my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library. | {
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"Yes that's definitely something we plan to add ^^",
"Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.",
"So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https://github.com/tensorflow/tensorflow/blob... | 2020-07-23T05:00:41Z | 2021-03-12T09:34:12Z | 2020-09-07T14:48:04Z | NONE | null | null | null | null | It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process/thread/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together? | {
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"Thanks for noticing ! This looks more reasonable indeed.\r\nFeel free to open a PR",
"Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file. More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\... | 2020-07-22T20:29:20Z | 2020-08-02T13:30:34Z | 2020-08-02T13:30:34Z | MEMBER | null | null | null | null | Hi 🤗 team!
## Description of the problem
Thanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
dataset_train = dataset['train']
```
However, I am not sure that `load_dataset()` is returning the correct data structure for NER.
Currently, every row in `dataset_train` is of the form
```python
{'word': str, 'ner_tag': str, 'lang': str}
```
but I think we actually want something like
```python
{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}
```
so that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.
Indeed, [this line](https://github.com/google-research/xtreme/blob/522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.
## Proposed solution
Replace
```python
with open(filepath) as f:
data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for id_, row in enumerate(data):
if row:
lang, word = row[0].split(":")[0], row[0].split(":")[1]
tag = row[1]
yield id_, {"word": word, "ner_tag": tag, "lang": lang}
```
from [these lines](https://github.com/huggingface/nlp/blob/ce7d3a1d630b78fe27188d1706f3ea980e8eec43/datasets/xtreme/xtreme.py#L881-L887) of the `_generate_examples()` function with something like
```python
guid_index = 1
with open(filepath, encoding="utf-8") as f:
words = []
ner_tags = []
langs = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words:
yield guid_index, {"words": words, "ner_tags": ner_tags, "langs": langs}
guid_index += 1
words = []
ner_tags = []
else:
# pan-x data is tab separated
splits = line.split("\t")
# strip out en: prefix
langs.append(splits[0][:2])
words.append(splits[0][3:])
if len(splits) > 1:
labels.append(splits[-1].replace("\n", ""))
else:
# examples have no label in test set
labels.append("O")
```
If you agree, me or @lvwerra would be happy to implement this and create a PR. | {
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https://api.github.com/repos/huggingface/datasets/issues/418 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/418/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/418/comments | https://api.github.com/repos/huggingface/datasets/issues/418/events | https://github.com/huggingface/datasets/issues/418 | 661,914,873 | MDU6SXNzdWU2NjE5MTQ4NzM= | 418 | Addition of google drive links to dl_manager | {
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"I think the problem is the way you wrote your urls. Try the following structure to see `https://drive.google.com/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq ",
"Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`... | 2020-07-20T14:52:02Z | 2020-07-20T15:39:32Z | 2020-07-20T15:39:32Z | CONTRIBUTOR | null | null | null | null | Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.
This is the script for me:
```python
class EmoConfig(nlp.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for EmoContext.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(EmoConfig, self).__init__(**kwargs)
_TEST_URL = "https://drive.google.com/file/d/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb/view?usp=sharing"
_TRAIN_URL = "https://drive.google.com/file/d/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X/view?usp=sharing"
class EmoDataset(nlp.GeneratorBasedBuilder):
""" SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 """
VERSION = nlp.Version("1.0.0")
force = False
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION,
features=nlp.Features(
{
"text": nlp.Value("string"),
"label": nlp.features.ClassLabel(names=["others", "happy", "sad", "angry"]),
}
),
supervised_keys=None,
homepage="https://www.aclweb.org/anthology/S19-2005/",
citation=_CITATION,
)
def _get_drive_url(self, url):
base_url = 'https://drive.google.com/uc?id='
split_url = url.split('/')
return base_url + split_url[5]
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
if(not os.path.exists("emo-train.json") or self.force):
gdown.download(self._get_drive_url(_TRAIN_URL), "emo-train.json", quiet = True)
if(not os.path.exists("emo-test.json") or self.force):
gdown.download(self._get_drive_url(_TEST_URL), "emo-test.json", quiet = True)
return [
nlp.SplitGenerator(
name=nlp.Split.TRAIN,
gen_kwargs={
"filepath": "emo-train.json",
"split": "train",
},
),
nlp.SplitGenerator(
name=nlp.Split.TEST,
gen_kwargs={"filepath": "emo-test.json", "split": "test"},
),
]
def _generate_examples(self, filepath, split):
""" Yields examples. """
with open(filepath, 'rb') as f:
data = json.load(f)
for id_, text, label in zip(data["text"].keys(), data["text"].values(), data["Label"].values()):
yield id_, {
"text": text,
"label": label,
}
```
Can someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database. | {
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https://api.github.com/repos/huggingface/datasets/issues/415 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/415/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/415/comments | https://api.github.com/repos/huggingface/datasets/issues/415/events | https://github.com/huggingface/datasets/issues/415 | 660,687,076 | MDU6SXNzdWU2NjA2ODcwNzY= | 415 | Something is wrong with WMT 19 kk-en dataset | {
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{
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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] | open | false | null | [] | null | [] | 2020-07-19T08:18:51Z | 2020-07-20T09:54:26Z | null | NONE | null | null | null | null | The translation in the `train` set does not look right:
```
>>>import nlp
>>>from nlp import load_dataset
>>>dataset = load_dataset('wmt19', 'kk-en')
>>>dataset["train"]["translation"][0]
{'kk': 'Trumpian Uncertainty', 'en': 'Трамптық белгісіздік'}
>>>dataset["validation"]["translation"][0]
{'kk': 'Ақша-несие саясатының сценарийін қайта жазсақ', 'en': 'Rewriting the Monetary-Policy Script'}
``` | null | {
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