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selfishark

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hf-issues-dataset-with-comments

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MDU6SXNzdWU3MDgzMTA5NTY=

OverflowError when slicing with an array containing negative ids

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```python 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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MDU6SXNzdWU3MDgyNTgzOTI=

Loss not decrease with Datasets and Transformers

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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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MDU6SXNzdWU3MDc2MDg1Nzg=

Does both 'bookcorpus' and 'wikipedia' belong to the same datasets which Google used for pretraining BERT?

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MDU6SXNzdWU3MDcwMzc3Mzg=

runing dataset.map, it raises TypeError: can't pickle Tokenizer objects

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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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MDU6SXNzdWU3MDcwMTc3OTE=

load_dataset from local squad.py, raise error: TypeError: 'NoneType' object is not callable

closed

NONE

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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MDExOlB1bGxSZXF1ZXN0NDkxMjI3NzUz

Created dataset card snli.md

closed

CONTRIBUTOR

First draft of a dataset card using the SNLI corpus as an example. This is mostly based on the [Google Doc draft](https://docs.google.com/document/d/1dKPGP-dA2W0QoTRGfqQ5eBp0CeSsTy7g2yM8RseHtos/edit), but I added a few sections and moved some things around. - I moved **Who Was Involved** to follow **Language**, both because I thought the authors should be presented more towards the front and because I think it makes sense to present the speakers close to the language so it doesn't have to be repeated. - I created a section I called **Data Characteristics** by pulling some things out of the other sections. I was thinking that this would be more about the language use in context of the specific task construction. That name isn't very descriptive though and could probably be improved. -- Domain and language type out of **Language**. I particularly wanted to keep the Language section as simple and as abstracted from the task as possible. -- 'How was the data collected' out of **Who Was Involved** -- Normalization out of **Features/Dataset Structure** -- I also added an annotation process section. - I kept the **Features** section mostly the same as the Google Doc, but I renamed it **Dataset Structure** to more clearly separate it from the language use, and added some links to the documentation pages. - I also kept **Tasks Supported**, **Known Limitations**, and **Licensing Information** mostly the same. Looking at it again though, maybe **Tasks Supported** should come before **Data Characteristics**? The trickiest part about writing a dataset card for the SNLI corpus specifically is that it's built on datasets which are themselves built on datasets so I had to dig in a lot of places to find information. I think this will be easier with other datasets and once there is more uptake of dataset cards so they can just link to each other. (Maybe that needs to be an added section?) I also made an effort not to repeat information across the sections or to refer to a previous section if the information was relevant in a later one. Is there too much repetition still?

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MDExOlB1bGxSZXF1ZXN0NDkxMTkyNTM3

Created dataset card snli.md

closed

CONTRIBUTOR

First draft of a dataset card using the SNLI corpus as an example

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MDExOlB1bGxSZXF1ZXN0NDkxMDA3NjEw

Replace pa.OSFile by open

closed

MEMBER

It should fix #643

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MDExOlB1bGxSZXF1ZXN0NDkwODkyMjQ0

add openwebtext

closed

CONTRIBUTOR

This adds [The OpenWebText Corpus](https://skylion007.github.io/OpenWebTextCorpus/), which is a clean and large text corpus for nlp pretraining. It is an open source effort to reproduce OpenAI’s WebText dataset used by GPT-2, and it is also needed to reproduce ELECTRA. It solves #132 . ### Besides dataset building script, I made some changes to the library. 1. Extract large amount of compressed files with multi processing I add a `num_proc` argument to `DownloadManager.extract` and pass this `num_proc` to `map_nested`. So I can decompress 20 thousands compressed files faster. `num_proc` I add is default to `None`, so it shouldn't break any other thing. 2. In `cached_path`, I change the order to deal with different kind of compressed files (zip, tar, gzip) Because there is no way to 100% detect a file is a zip file (see [this](https://stackoverflow.com/questions/18194688/how-can-i-determine-if-a-file-is-a-zip-file)), I found it wrongly detect `'./datasets/downloads/extracted/58764bd6898fa339b25d92e7fbbc3d8dbf64fb504edff1a30a1d7d99d1561027/openwebtext/urlsf_subset13-630_data.xz'` as a zip and try decompress it with zip, sure it will get error. So I made it detect wheter the file is tar or gzip first and detect zip in the last. 3. `MockDownloadManager.extract` Cuz I pass `num_proc` to `DownloadManager.extract`, I also have to make `MockDownloadManager.extract` to accept extra keywork arguments. So I make it `extract(path, *args, **kwargs)`, but just return the path as original implementation. **Note**: If there is better way for points mentioned above, thought I would like to help, unless we can solve point4 (make dataset building fast), I may not be able to afford rebuild the dataset again because of change of the dataset script (Building the dataset cost me 4 days). ### There is something I think we can improve 4. Long time to decompress compressed files Even I decompress those 20 thousands compressed files with 12 process on my 16 core 3.x Ghz server. It still took about 3 ~ 4days to complete dataset building. Most of time spent on decompress those files. ### Info about the source data The source data is an tar.xz file with following structure, files/directory beyond compressed file is what can we get after decompress it. ``` openwebtext.tar.xz |__ openwebtext |__subset000.xz | |__ ....txt | |__ ....txt | ... |__ subset001.xz | .... ``` And this the structure of dummy data, same as the original one. ``` dummy_data.zip |__ dummy_data |__ openwebtext |__fake_subset-1_data-dirxz # actually it is a directory | |__ ....txt | |__ ....txt |__ fake_subset-2_data-dirxz |__ ....txt |__ ....txt ```

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MDExOlB1bGxSZXF1ZXN0NDkwODE4NTY1

Keep new columns in transmit format

closed

MEMBER

When a dataset is formatted with a list of columns that `__getitem__` should return, then calling `map` to add new columns doesn't add the new columns to this list. It caused `KeyError` issues in #620 I changed the logic to add those new columns to the list that `__getitem__` should return.

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MDExOlB1bGxSZXF1ZXN0NDkwNzk4MDc0

Fix squad metric's Features

closed

NONE

Resolves issue [657](https://github.com/huggingface/datasets/issues/657).

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MDU6SXNzdWU3MDYyMDQzODM=

Squad Metric Description & Feature Mismatch

closed

NONE

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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MDExOlB1bGxSZXF1ZXN0NDkwNDEwODAz

Use multiprocess from pathos for multiprocessing

closed

MEMBER

[Multiprocess](https://github.com/uqfoundation/multiprocess) (from the [pathos](https://github.com/uqfoundation/pathos) project) allows to use lambda functions in multiprocessed map. It was suggested to use it by @kandorm. We're already using dill which is its only dependency.

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MDExOlB1bGxSZXF1ZXN0NDkwMzU4OTQ3

added Winogrande debiased subset

closed

MEMBER

The [Winogrande](https://arxiv.org/abs/1907.10641) paper mentions a `debiased` subset that wasn't in the first release; this PR adds it.

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MDExOlB1bGxSZXF1ZXN0NDkwMjI1Nzk3

Allow empty inputs in metrics

closed

MEMBER

There was an arrow error when trying to compute a metric with empty inputs. The error was occurring when reading the arrow file, before calling metric._compute.

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MDExOlB1bGxSZXF1ZXN0NDkwMjAxOTg4

handle data alteration when trying type

closed

MEMBER

Fix #649 The bug came from the type inference that didn't handle a weird case in Pyarrow. Indeed this code runs without error but alters the data in arrow: ```python import pyarrow as pa type = pa.struct({"a": pa.struct({"b": pa.string()})}) array_with_altered_data = pa.array([{"a": {"b": "foo", "c": "bar"}}] * 10, type=type) print(array_with_altered_data[0].as_py()) # {'a': {'b': 'foo'}} -> the sub-field "c" is missing ``` (I don't know if this is intended in pyarrow tbh) We didn't take this case into account during type inference. By default it was keeping old features and maybe alter data. To fix that I added a line that checks that the first element of the array is not altered.

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MDExOlB1bGxSZXF1ZXN0NDkwMTI3MjIx

handle connection error in download_prepared_from_hf_gcs

closed

MEMBER

Fix #647

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MDU6SXNzdWU3MDUyMTIwMzQ=

Problem with JSON dataset format

open

NONE

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?

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MDU6SXNzdWU3MDQ4NjE4NDQ=

dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators`

closed

CONTRIBUTOR

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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MDU6SXNzdWU3MDQ4Mzg0MTU=

Inconsistent behavior in map

closed

NONE

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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MDU6SXNzdWU3MDQ3NTMxMjM=

offset overflow when multiprocessing batched map on large datasets.

closed

CONTRIBUTOR

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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MDU6SXNzdWU3MDQ3MzQ3NjQ=

Cannot download dataset_info.json

closed

NONE

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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MDExOlB1bGxSZXF1ZXN0NDg5NTAyMTM3

Fix docs typos

closed

CONTRIBUTOR

This PR fixes few typos in the docs and the error in the code snippet in the set_format section in docs/source/torch_tensorflow.rst. `torch.utils.data.Dataloader` expects padded batches so it throws an error due to not being able to stack the unpadded tensors. If we follow the Quick tour from the docs where they add the `truncation=True, padding='max_length'` arguments to the tokenizer before passing data to Dataloader, we can easily fix the issue.

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MDExOlB1bGxSZXF1ZXN0NDg5NDQ5MjAx

Don't use take on dataset table in pyarrow 1.0.x

closed

MEMBER

Fix #615

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MDExOlB1bGxSZXF1ZXN0NDg5NDQzMTk1

Better windows support

closed

MEMBER

There are a few differences in the behavior of python and pyarrow on windows. For example there are restrictions when accessing/deleting files that are open Fix #590

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MDU6SXNzdWU3MDQ0NzcxNjQ=

Caching processed dataset at wrong folder

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NDg5MzMwMDAx

Rename wnut fields

closed

MEMBER

As mentioned in #641 it would be cool to have it follow the naming of the other NER datasets

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MDExOlB1bGxSZXF1ZXN0NDg5MzExOTU3

Add Polyglot-NER Dataset

closed

CONTRIBUTOR

Adds the [Polyglot-NER dataset](https://sites.google.com/site/rmyeid/projects/polylgot-ner) with named entity tags for 40 languages. I include separate configs for each language as well as a `combined` config which lumps them all together.

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MDExOlB1bGxSZXF1ZXN0NDg5MjYwNTc1

Make shuffle compatible with temp_seed

closed

MEMBER

This code used to return different dataset at each run ```python import dataset as ds dataset = ... with ds.temp_seed(42): shuffled = dataset.shuffle() ``` Now it returns the same one since the seed is set

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MDExOlB1bGxSZXF1ZXN0NDg5MTgxOTY3

Update glue QQP checksum

closed

MEMBER

Fix #638

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MDU6SXNzdWU3MDQxNDY5NTY=

GLUE/QQP dataset: NonMatchingChecksumError

closed

CONTRIBUTOR

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://github.com/huggingface/datasets/pull/637

MDExOlB1bGxSZXF1ZXN0NDg4NjMwNzk4

Add MATINF

closed

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https://github.com/huggingface/datasets/pull/636

MDExOlB1bGxSZXF1ZXN0NDg4MDg3OTA5

Consistent ner features

closed

MEMBER

As discussed in #613 , this PR aims at making NER feature names consistent across datasets. I changed the feature names of LinCE and XTREME/PAN-X

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MDExOlB1bGxSZXF1ZXN0NDg4MDM2OTE5

Loglevel

closed

MEMBER

Continuation of #618

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https://github.com/huggingface/datasets/pull/634

MDExOlB1bGxSZXF1ZXN0NDg3OTEzOTk4

Add ConLL-2000 dataset

closed

CONTRIBUTOR

Adds ConLL-2000 dataset used for text chunking. See https://www.clips.uantwerpen.be/conll2000/chunking/ for details and [motivation](https://github.com/huggingface/transformers/pull/7041#issuecomment-692710948) behind this PR

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MDU6SXNzdWU3MDI0NDA0ODQ=

Load large text file for LM pre-training resulting in OOM

open

NONE

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.

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https://github.com/huggingface/datasets/pull/632

MDExOlB1bGxSZXF1ZXN0NDg3NjQ5OTQ2

Fix typos in the loading datasets docs

closed

CONTRIBUTOR

This PR fixes two typos in the loading datasets docs, one of them being a broken link to the `load_dataset` function.

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https://github.com/huggingface/datasets/pull/631

MDExOlB1bGxSZXF1ZXN0NDg3MTE3OTA0

Fix text delimiter

closed

MEMBER

I changed the delimiter in the `text` dataset script. It should fix the `pyarrow.lib.ArrowInvalid: CSV parse error` from #622 I changed the delimiter to an unused ascii character that is not present in text files : `\b`

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MDU6SXNzdWU3MDE2MzYzNTA=

Text dataset not working with large files

closed

NONE

``` 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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MDU6SXNzdWU3MDE1MTc1NTA=

straddling object straddles two block boundaries

closed

NONE

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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MDExOlB1bGxSZXF1ZXN0NDg2OTQyNzgx

Update docs links in the contribution guideline

closed

CONTRIBUTOR

Fixed the `add a dataset` and `share a dataset` links in the contribution guideline to refer to the new docs website.

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MDExOlB1bGxSZXF1ZXN0NDg2ODcxMTg2

fix (#619) MLQA features names

closed

CONTRIBUTOR

Fixed the features names as suggested in (#619) in the `_generate_examples` and `_info` methods in the MLQA loading script and also changed the names in the `dataset_infos.json` file.

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MDExOlB1bGxSZXF1ZXN0NDg2ODIzMTY1

Update GLUE URLs (now hosted on FB)

closed

CONTRIBUTOR

NYU is switching dataset hosting from Google to FB. This PR closes https://github.com/huggingface/datasets/issues/608 and is necessary for https://github.com/jiant-dev/jiant/issues/161. This PR updates the data URLs based on changes made in https://github.com/nyu-mll/jiant/pull/1112. Note: rebased on huggingface/datasets

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MDU6SXNzdWU3MDEwNTc3OTk=

dtype of tensors should be preserved

closed

CONTRIBUTOR

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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MDU6SXNzdWU3MDA1NDE2Mjg=

Add learningq dataset

open

NONE

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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MDU6SXNzdWU3MDAyMzUzMDg=

Custom feature types in `load_dataset` from CSV

closed

MEMBER

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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MDU6SXNzdWU3MDAyMjU4MjY=

load_dataset for text files not working

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NDg1ODQ3ODYz

[docs] Index: The native emoji looks kinda ugly in large size

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MDU6SXNzdWU2OTk4MTUxMzU=

map/filter multiprocessing raises errors and corrupts datasets

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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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MDU6SXNzdWU2OTk3MzM2MTI=

Mistakes in MLQA features names

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NDg1NDAxMzI5

sync logging utils with transformers

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sync the docs/code with the recent changes in transformers' `logging` utils: 1. change the default level to `WARNING` 2. add `DATASETS_VERBOSITY` env var 3. expand docs

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MDU6SXNzdWU2OTk0NzI1OTY=

Compare different Rouge implementations

closed

NONE

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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MDU6SXNzdWU2OTk0NjIyOTM=

UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors

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CONTRIBUTOR

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.

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MDU6SXNzdWU2OTk0MTA3NzM=

Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0

closed

MEMBER

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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MDExOlB1bGxSZXF1ZXN0NDg0OTQ2MzA1

[doc] Update deploy.sh

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MDExOlB1bGxSZXF1ZXN0NDg0ODkyMTUx

Add CoNLL-2003 shared task dataset

closed

CONTRIBUTOR

Please consider adding CoNLL-2003 shared task dataset as it's beneficial for token classification tasks. The motivation behind this PR is the [PR](https://github.com/huggingface/transformers/pull/7041) in the transformers project. This dataset would be not only useful for the usual run-of-the-mill NER tasks but also for syntactic chunking and part-of-speech (POS) tagging.

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MDExOlB1bGxSZXF1ZXN0NDg0Nzk2Mjg5

add multi-proc to dataset dict

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Add multi-proc to `DatasetDict`

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MDU6SXNzdWU2OTg4NjM5ODg=

ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648

open

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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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MDU6SXNzdWU2OTgzNDkzODg=

Load text file for RoBERTa pre-training.

open

NONE

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://github.com/huggingface/datasets/pull/609

MDExOlB1bGxSZXF1ZXN0NDg0MTc4Nzky

Update GLUE URLs (now hosted on FB)

closed

CONTRIBUTOR

NYU is switching dataset hosting from Google to FB. This PR closes https://github.com/huggingface/datasets/issues/608 and is necessary for https://github.com/jiant-dev/jiant/issues/161. This PR updates the data URLs based on changes made in https://github.com/nyu-mll/jiant/pull/1112.

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https://github.com/huggingface/datasets/issues/608

MDU6SXNzdWU2OTgyOTExNTY=

Don't use the old NYU GLUE dataset URLs

closed

CONTRIBUTOR

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://github.com/huggingface/datasets/pull/607

MDExOlB1bGxSZXF1ZXN0NDgzOTcyMDg4

Add transmit_format wrapper and tests

closed

MEMBER

Same as #605 but using a decorator on-top of dataset transforms that are not in place

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MDExOlB1bGxSZXF1ZXN0NDgzOTMzMDA1

Quick fix :)

closed

MEMBER

`nlp` => `datasets`

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https://github.com/huggingface/datasets/pull/605

MDExOlB1bGxSZXF1ZXN0NDgzNzg1Mjc1

[Datasets] Transmit format to children

closed

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Transmit format to children obtained when processing a dataset. Added a test. When concatenating datasets, if the formats are disparate, the concatenated dataset has a format reset to defaults.

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https://github.com/huggingface/datasets/pull/604

MDExOlB1bGxSZXF1ZXN0NDgzNjgxNTc0

Update bucket prefix

closed

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cc @julien-c

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https://github.com/huggingface/datasets/pull/603

MDExOlB1bGxSZXF1ZXN0NDgzNjY2ODk5

Set scripts version to master

closed

MEMBER

By default the scripts version is master, so that if the library is installed with ``` pip install git+http://github.com/huggingface/nlp.git ``` or ``` git clone http://github.com/huggingface/nlp.git pip install -e ./nlp ``` will use the latest scripts, and not the ones from the previous version.

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https://github.com/huggingface/datasets/pull/602

MDExOlB1bGxSZXF1ZXN0NDgzNTU3NDM0

apply offset to indices in multiprocessed map

closed

MEMBER

Fix #597 I fixed the indices by applying an offset. I added the case to our tests to make sure it doesn't happen again. I also added the message proposed by @thomwolf in #597 ```python >>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2, load_from_cache_file=False) Done writing 10 indices in 80 bytes . Testing the mapped function outputs [0, 1] Testing finished, running the mapping function on the dataset Done writing 5 indices in 41 bytes . Done writing 5 indices in 41 bytes . Spawning 2 processes [0, 1, 2, 3, 4] [5, 6, 7, 8, 9] #0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 377.90ba/s] #1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 378.92ba/s] Concatenating 2 shards from multiprocessing # Dataset(features: {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}, num_rows: 10) ```

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https://github.com/huggingface/datasets/pull/601

MDExOlB1bGxSZXF1ZXN0NDgzNTAzMjAw

check if trasnformers has PreTrainedTokenizerBase

closed

MEMBER

Fix #598

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MDU6SXNzdWU2OTc0OTY5MTM=

Pickling error when loading dataset

closed

NONE

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://github.com/huggingface/datasets/pull/599

MDExOlB1bGxSZXF1ZXN0NDgzMzI3ODQ5

Add MATINF dataset

closed

MEMBER

@lhoestq The command to create metadata failed. I guess it's because the zip is not downloaded from a remote address? How to solve that? Also the CI fails and I don't know how to fix that :(

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https://github.com/huggingface/datasets/issues/598

MDU6SXNzdWU2OTcxNTY1MDE=

The current version of the package on github has an error when loading dataset

closed

NONE

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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MDU6SXNzdWU2OTcxMTIwMjk=

Indices incorrect with multiprocessing

closed

CONTRIBUTOR

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://github.com/huggingface/datasets/pull/596

MDExOlB1bGxSZXF1ZXN0NDgyOTM5MTgw

[style/quality] Moving to isort 5.0.0 + style/quality on datasets and metrics

closed

MEMBER

Move the repo to isort 5.0.0. Also start testing style/quality on datasets and metrics. Specific rule: we allow F401 (unused imports) in metrics to be able to add imports to detect early on missing dependencies. Maybe we could add this in datasets but while cleaning this I've seen many example of really unused imports in dataset so maybe it's better to have it as a line-by-line nova instead of a general rule like in metrics.

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