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`metric.compute` throws `ArrowInvalid` error
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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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Fix BLEURT metrics for backward compatibility
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Fix #565
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Remove logger pickling to fix gg colab issues
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A `logger` objects are not picklable in google colab, contrary to `logger` objects in jupyter notebooks or in python shells. It creates some issues in google colab right now. Indeed by calling any `Dataset` method, the fingerprint update pickles the transform function, and as the logger comes with it, it results in an error (full stacktrace [here](http://pastebin.fr/64330)): ```python /usr/local/lib/python3.6/dist-packages/zmq/backend/cython/socket.cpython-36m-x86_64-linux-gnu.so in zmq.backend.cython.socket.Socket.__reduce_cython__() TypeError: no default __reduce__ due to non-trivial __cinit__ ``` To fix that I no longer dump the transform (`_map_single`, `select`, etc.), but the full name only (`nlp.arrow_dataset.Dataset._map_single`, `nlp.arrow_dataset.Dataset.select`, etc.)
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No module named 'nlp.logging'
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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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564
Wait for writing in distributed metrics
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There were CI bugs where a distributed metric would try to read all the files in process 0 while the other processes haven't started writing. To fix that I added a custom locking mechanism that waits for the file to exist before trying to read it
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[Large datasets] Speed up download and processing
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Various improvements to speed-up creation and processing of large scale datasets. Currently: - distributed downloads - remove etag from datafiles hashes to spare a request when restarting a failed download
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[Reproductibility] Allow to pin versions of datasets/metrics
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Repurpose the `version` attribute in datasets and metrics to let the user pin a specific version of datasets and metric scripts: ``` dataset = nlp.load_dataset('squad', version='1.0.0') metric = nlp.load_metric('squad', version='1.0.0') ``` Notes: - version number are the release version of the library - currently only possible for canonical datasets/metrics, ie. integrated in the GitHub repo of the library
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Made `share_dataset` more readable
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Using custom DownloadConfig results in an error
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## 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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Adding the KILT knowledge source and tasks
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This adds Wikipedia pre-processed for KILT, as well as the task data. Only the question IDs are provided for TriviaQA, but they can easily be mapped back with: ``` import nlp kilt_wikipedia = nlp.load_dataset('kilt_wikipedia') kilt_tasks = nlp.load_dataset('kilt_tasks') triviaqa = nlp.load_dataset('trivia_qa', 'unfiltered.nocontext') triviaqa_map = {} for k in ['train', 'validation', 'test']: triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(triviaqa[k]['question_id'])]) kilt_tasks[k + '_triviaqa'] = kilt_tasks[k + '_triviaqa'].filter(lambda x: x['id'] in triviaqa_map) kilt_tasks[k + '_triviaqa'].map(lambda x: {'input': triviaqa[split][triviaqa_map[x['id']]]['question']}) ``` It would be great to have the dataset by Monday, which is when the paper should land on Arxiv and @fabiopetroni is planning on tweeting about the paper and `facebookresearch` repository for the datasett
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Rerun pip install -e
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Hopefully it fixes the github actions
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Fix a few typos
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Add DailyDialog
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http://yanran.li/dailydialog.html https://arxiv.org/pdf/1710.03957.pdf
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Upgrade pip in benchmark github action
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It looks like it fixes the `import nlp` issue we have
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nlp downloads to its module path
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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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[Fix GitHub Actions] test adding tmate
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Add multiprocessing
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Adding multiprocessing to `.map` It works in 3 steps: - shard the dataset in `num_proc` shards - spawn one process per shard and call `map` on them - concatenate the resulting datasets Example of usage: ```python from nlp import load_dataset dataset = load_dataset("squad", split="train") def function(x): return {"lowered": x.lower()} processed = d.map( function, input_columns=["context"], num_proc=4, cache_file_name="playground/tmp.arrow", load_from_cache_file=False ) ``` Here it writes 4 files depending on the process rank: - `playground/tmp_00000_of_00004.arrow` - `playground/tmp_00001_of_00004.arrow` - `playground/tmp_00002_of_00004.arrow` - `playground/tmp_00003_of_00004.arrow` The suffix format can be specified by the user. If the `cache_file_name` is not specified, it writes into separated files depending on the fingerprint, as usual. I still need to: - write tests for this - try to improve the logging (currently it shows 4 progress bars, but if one finishes before the others, then the following messages are written over the progress bars)
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added HANS dataset
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Adds the [HANS](https://github.com/tommccoy1/hans) dataset to evaluate NLI systems.
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[BUGFIX] Solving mismatched checksum issue for the LinCE dataset (#539)
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Hi, I have added the updated `dataset_infos.json` file for the LinCE benchmark. This update is to fix the mismatched checksum bug #539 for one of the datasets in the LinCE benchmark. To update the file, I run this command from the nlp root directory: ``` python nlp-cli test ./datasets/lince --save_infos --all_configs ``` **NOTE**: I needed to change [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/commands/dummy_data.py#L8) from: `from .utils.logging import get_logger` to `from nlp.utils.logging import get_logger`, otherwise the script was not able to import `get_logger`. However, I did not include that in this PR since that could have been just my environment (and another PR could be fixing this already if it is actually an issue).
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Fix bleurt logging import
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Bleurt started throwing an error in some code we have. This looks like the fix but... It's also unnerving that even a prebuilt docker image with pinned versions can be working 1 day and then fail the next (especially for production systems). Any way for us to pin your metrics code so that they are guaranteed not to to change and possibly fail on repository changes? Thanks (and also for your continued work on the lib...)
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[Breaking] Switch text loading to multi-threaded PyArrow loading
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Test if we can get better performances for large-scale text datasets by using multi-threaded text file loading based on Apache Arrow multi-threaded CSV loader. If it works ok, it would fix #546. **Breaking change**: The text lines now do not include final line-breaks anymore.
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[Distributed] Making loading distributed datasets a bit safer
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Add some file-locks during dataset loading
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Very slow data loading on large dataset
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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New release coming up for this library
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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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[Distributed] Fix load_dataset error when multiprocessing + add test
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Fix #543 + add test
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nlp.load_dataset is not safe for multi processes when loading from local files
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Add TensorFlow example
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Update the Quick Tour documentation in order to add the TensorFlow equivalent source code for the classification example. Now it is possible to select either the code in PyTorch or in TensorFlow in the Quick tour.
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Best practices for training tokenizers with nlp
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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[BUGFIX] Fix Race Dataset Checksum bug
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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In #537 I noticed that there was a bug in checksum checking when I have tried to download the race dataset. The reason for this is that the current preprocessing was just considering the `high school` data and it was ignoring the `middle` one. This PR just fixes it :) Moreover, I have added some descriptions.
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[Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data
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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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[logging] Add centralized logging - Bump-up cache loads to warnings
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Add a `nlp.logging` module to set the global logging level easily. The verbosity level also controls the tqdm bars (disabled when set higher than INFO). You can use: ``` nlp.logging.set_verbosity(verbosity: int) nlp.logging.set_verbosity_info() nlp.logging.set_verbosity_warning() nlp.logging.set_verbosity_debug() nlp.logging.set_verbosity_error() nlp.logging.get_verbosity() -> int ``` And use the levels: ``` nlp.logging.CRITICAL nlp.logging.DEBUG nlp.logging.ERROR nlp.logging.FATAL nlp.logging.INFO nlp.logging.NOTSET nlp.logging.WARN nlp.logging.WARNING ```
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[Dataset] RACE dataset Checksums error
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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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Fingerprint
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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This PR is a continuation of #513 , in which many in-place functions were introduced or updated (cast_, flatten_) etc. However the caching didn't handle these changes. Indeed the caching took into account only the previous cache file name of the table, and not the possible in-place transforms of the table. To fix that, I added the concept of dataset fingerprint, that is updated after each transform (in place or not), and stored inside the table metadata. When a dataset is created, an initial fingerprint is computed. If the dataset is memory-mapped, then the fingerprint generator doesn't read the table and only looks at the filename. However if the table is in-memory, then the fingerprint generator reads the content of the table using a batched non-crypto hashing. I added a utility class to compute hashes of arbitrary python objects in `fingerprint.py` : `Hasher`. The API is close to standard hashing tools (`.update`, `.hexdigest`). It also supports custom hashing functions depending on object types using a registry like pickle. I added a custom hashing function to hash a `pa.Table` in a batched way, and also for `nlp.DatasetInfo` to leverage its json serialization feature. Note about this PR: This is a draft PR because #513 needs to be merged first. The diff that is shown is for branches fingerprint -> indices (and not master, for now)
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Benchmarks
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Adding some benchmarks with DVC/CML To add a new tracked benchmark: - create a new python benchmarking script in `./benchmarks/`. The script can use the utilities in `./benchmarks/utils.py` and should output a JSON file with results in `./benchmarks/results/`. - add a new pipeline stage in [dvc.yaml](./dvc.yaml) with the name of your new benchmark. That's it
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`list_datasets()` is broken.
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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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Fix ArrayXD for pyarrow 0.17.1 by using non fixed length list arrays
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It should fix the CI problems in #513
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532
File exists error when used with TPU
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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531
add concatenate_datasets to the docs
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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use ragged tensor by default
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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I think it's better if it's clear whether the returned tensor is ragged or not when the type is set to tensorflow. Previously it was a tensor (not ragged) if numpy could stack the output (which can change depending on the batch of example you take), which make things difficult to handle, as it may sometimes return a ragged tensor and sometimes not. Therefore I reverted this behavior to always return a ragged tensor as we used to do.
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Add MLSUM
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Hello (again :) !), So, I started a new branch because of a [rebase issue](https://github.com/huggingface/nlp/pull/463), sorry for the mess. However, the command `pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_mlsum` still fails because there is no default language dataset : the script throws an error as a specific config language is necessary. I think that setting a default language would be a bad workaround for this so I kept it as it is. Putting all the train files across languages together would also be a bad idea because of the size. Thanks for your help, Rachel
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528
fix missing variable names in docs
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fix #524
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527
Fix config used for slow test on real dataset
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As noticed in #470, #474, #476, #504 , the slow test `test_load_real_dataset` couldn't run on datasets that require config parameters. To fix that I replaced it with one test with the first config of BUILDER_CONFIGS `test_load_real_dataset`, and another test that runs all of the configs in BUILDER_CONFIGS `test_load_real_dataset_all_configs`
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526
Returning None instead of "python" if dataset is unformatted
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Following the discussion on Slack, this small fix ensures that calling `dataset.set_format(type=dataset.format["type"])` works properly. Slightly breaking as calling `dataset.format` when the dataset is unformatted will return `None` instead of `python`.
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wmt download speed example
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Some docs are missing parameter names
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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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Speed up Tokenization by optimizing cast_to_python_objects
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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I changed how `cast_to_python_objects` works to make it faster. It is used to cast numpy/pytorch/tensorflow/pandas objects to python lists, and it works recursively. To avoid iterating over possibly long lists, it first checks if the first element that is not None has to be casted. If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same. This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example. Speed improvement: ```python import transformers import nlp tok = transformers.BertTokenizerFast.from_pretrained("bert-base-uncased") txt = ["a " * 512] * 1000 dataset = nlp.Dataset.from_dict({"txt": txt}) # Tokenization using .map is now faster. Previously it was taking 3.5s %time _ = dataset.map(lambda x: tok(x["txt"]), batched=True, load_from_cache_file=False) # 450ms # for comparison %time _ = tok(txt) # 280ms ```
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dictionnary typo in docs
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Fix dictionnary (dictionary) typo
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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This error happens many times I'm thinking maybe its spelled like this on purpose?
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Transform references for sacrebleu
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Currently it is impossible to use sacrebleu when len(predictions) != the number of references per prediction (very uncommon), due to a strange format expected by sacrebleu. If one passes in the data to `nlp.metric.compute()` in sacrebleu format, `nlp` throws an error due to mismatching lengths between predictions and references. If one uses a more standard format where predictions and references are lists of the same length, sacrebleu throws an error. This PR transforms reference data in a more standard format into the [unusual format](https://github.com/mjpost/sacreBLEU#using-sacrebleu-from-python) expected by sacrebleu.
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[BUG] Metrics throwing new error on master since 0.4.0
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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[METRICS, breaking] Refactor caching behavior, pickle/cloudpickle metrics and dataset, add tests on metrics
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Move the acquisition of the filelock at a later stage during metrics processing so it can be pickled/cloudpickled after instantiation. Also add some tests on pickling, concurrent but separate metric instances and concurrent and distributed metric instances. Changes significantly the caching behavior for the metrics: - if the metric is used in a non-distributed setup (most common case) we try to find a free cache file using UUID instead of asking for an `experiment_id` if we can't lock the cache file this allows to use several instances of the same metrics in parallel. - if the metrics is used in a distributed setup we ask for an `experiment_id` if we can't lock the cache file (because all the nodes need to have related cache file names for the final sync. - after the computation, we free the locks and delete all the cache files. Breaking: Some arguments for Metrics initialization have been removed for simplicity (`version`...) and some have been renamed for consistency with the rest of the library (`in_memory` => `keep_in_memory`). Also remove the `_has_transformers` detection in utils to avoid importing transformers everytime during loading.
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add MLDoc dataset
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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
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[Breaking] Rename formated to formatted
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`formated` is not correct but `formatted` is
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Fix batched map for formatted dataset
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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If you had a dataset formatted as numpy for example, and tried to do a batched map, then it would crash because one of the elements from the inputs was missing for unchanged columns (ex: batch of length 999 instead of 1000). The happened during the creation of the `pa.Table`, since columns had different lengths.
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dataset.shuffle(keep_in_memory=True) is never allowed
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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[speedup] Use indices mappings instead of deepcopy for all the samples reordering methods
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Use an indices mapping instead of rewriting the dataset for all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`). Added a `flatten_indices` method which copy the dataset to a new table to remove the indices mapping with tests. All the samples re-ordering/selection methods should be a lot faster. The downside is that iterating on very large batch of the dataset might be a little slower when we have changed the order of the samples since with in these case we use `pyarrow.Table.take` instead of `pyarrow.Table.slice`. There is no free lunch but the speed of iterating over the dataset is rarely the bottleneck. *Backward breaking change*: the `cache_file_name` argument in all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`) is now called `indices_cache_file_name` on purpose to make it explicit to the user that this caching file is used for caching the indices mapping and not the dataset itself.
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Delete CONTRIBUTING.md
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dataset.shuffle() and select() resets format. Intended?
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Version of numpy to use the library
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Converting TensorFlow dataset example
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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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TypeError: Receiver() takes no arguments
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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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Errors when I use
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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fix dataset.map for function without outputs
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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As noticed in #505 , giving a function that doesn't return anything in `.map` raises an error because of an unreferenced variable. I fixed that and added tests. Thanks @avloss for reporting
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tmp_file referenced before assignment
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Just learning about this library - so might've not set up all the flags correctly, but was getting this error about "tmp_file".
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Added downloading to Hyperpartisan news detection
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Following the discussion on Slack and #349, I've updated the hyperpartisan dataset to pull directly from Zenodo rather than manual install, which should make this dataset much more accessible. Many thanks to @johanneskiesel ! Currently doesn't pass `test_load_real_dataset` - I'm using `self.config.name` which is `default` in this test. Might be related to #474
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CompGuessWhat?! 0.2.0
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We updated some metadata information associated with the dataset. In addition, we've updated the `create_dummy_data.py` script to generate data samples for the dataset.
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Fix tokenizers caching
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I've found some cases where the caching didn't work properly for tokenizers: 1. if a tokenizer has a regex pattern, then the caching would be inconsistent across sessions 2. if a tokenizer has a cache attribute that changes after some calls, the the caching would not work after cache updates 3. if a tokenizer is used inside a function, the caching of this function would result in the same cache file for different tokenizers 4. if `unique_no_split_tokens`'s attribute is not the same across sessions (after loading a tokenizer) then the caching could be inconsistent To fix that, this is what I did: 1. register a specific `save_regex` function for pickle that makes regex dumps deterministic 2. ignore cache attribute of some tokenizers before dumping 3. enable recursive dump by default for all dumps 4. make `unique_no_split_tokens` deterministic in https://github.com/huggingface/transformers/pull/6461 I also added tests to make sure that tokenizers hashing works as expected. In the future we should find a way to test if hashing also works across session (maybe using two CI jobs ? or by hardcoding a tokenizer's hash ?)
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Caching doesn't work for map (non-deterministic)
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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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Use hnsw in wiki_dpr
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The HNSW faiss index is much faster that regular Flat index.
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Narrativeqa (with full text)
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Following the uploading of the full text data in #309, I've added the full text to the narrativeqa dataset. Few notes: - Had some encoding issues using the default `open` so am using `open(encoding="latin-1"...` which seems to fix it. Looks fine. - Can't get the dummy data to work. Currently putting stuff at: ``` dummy |---- 0.0.0 |- dummy_data.zip |-master.zip | |- narrativeqa-master | |- documents.csv | |- qaps.csv | |- third_party ...... | | - narrativeqa_full_text.zip | | - 001.content | | - .... ``` Not sure what I'm messing up here (probably something obvious).
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dont use beam fs to save info for local cache dir
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If the cache dir is local, then we shouldn't use beam's filesystem to save the dataset info Fix #490
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skip header in PAWS-X
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This should fix #485 I also updated the `dataset_infos.json` file that is used to verify the integrity of the generated splits (the number of examples was reduced by one). Note that there are new fields in `dataset_infos.json` introduced in the latest release 0.4.0 corresponding to post processing info. I removed them in this case when I ran `nlp-cli ./datasets/xtreme --save_infos` to keep backward compatibility (versions 0.3.0 can't load these fields). I think I'll change the logic so that `nlp-cli test` doesn't create these fields for dataset with no post processing
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fix bad type in overflow check
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When writing an arrow file and inferring the features, the overflow check could fail if the first example had a `null` field. This is because we were not using the inferred features to do this check, and we could end up with arrays that don't match because of a type mismatch (`null` vs `string` for example). This should fix #482
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stack vectors in pytorch and tensorflow
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When the format of a dataset is set to pytorch or tensorflow, and if the dataset has vectors in it, they were not stacked together as tensors when calling `dataset[i:i + batch_size][column]` or `dataset[column]`. I added support for stacked tensors for both pytorch and tensorflow. For ragged tensors, they are stacked only for tensorflow as pytorch doesn't support ragged tensors.
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Fix numpy stacking
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When getting items using a column name as a key, numpy arrays were not stacked. I fixed that and added some tests. There is another issue that still needs to be fixed though: when getting items using a column name as a key, pytorch tensors are not stacked (it outputs a list of tensors). This PR should help with the to fix this issue.
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493
Fix wmt zh-en url
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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I verified that ``` wget https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 ``` runs in 2 minutes.
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nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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No 0.4.0 release on GitHub
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Loading preprocessed Wikipedia dataset requires apache_beam
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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issues with downloading datasets for wmt16 and wmt19
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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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Fix elasticsearch result ids returning as strings
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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I am using the latest elasticsearch binary and master of nlp. For me elasticsearch searches failed because the resultant "id_" returned for searches are strings, but our library assumes them to be integers.
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Bookcorpus data contains pretokenized text
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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PAWS dataset first item is header
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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update mirror for RT dataset
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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rotten tomatoes movie review dataset taken down
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Bugs : dataset.map() is frozen on ELI5
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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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Apply utf-8 encoding to all datasets
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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## Description This PR applies utf-8 encoding for all instances of `with open(...) as f` to all Python files in `datasets/`. As suggested by @thomwolf in #468 , we use regular expressions and the following function ```python def apply_encoding_on_file_open(filepath: str): """Apply UTF-8 encoding for all instances where a non-binary file is opened.""" with open(filepath, 'r', encoding='utf-8') as input_file: regexp = re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)") input_text = input_file.read() match = regexp.search(input_text) if match: output = regexp.sub(lambda m: m.group()[:-1]+', encoding="utf-8")', input_text) with open(filepath, 'w', encoding='utf-8') as output_file: output_file.write(output) ``` to perform the replacement. Note: 1. I excluded all _**binary files**_ from the search since it's possible some objects are opened for which the encoding doesn't make sense. Please correct me if I'm wrong and I'll tweak the regexp accordingly 2. There were two edge cases where the regexp failed (e.g. two `open` instances on a single line), but I decided to just fix these manually in the interest of time. 3. I only applied the replacement to files in `datasets/`. Let me know if this should be extended to other places like `metrics/` 4. I have implemented a unit test that should catch missing encodings in future CI runs Closes #468 and possibly #347
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Column indexing hotfix
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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As observed for example in #469 , currently `__getitem__` does not convert the data to the dataset format when indexing by column. This is a hotfix that imitates functional 0.3.0. code. In the future it'd probably be nice to have a test there.
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Added the METEOR metric. Can be used like this: ```python import nlp meteor = nlp.load_metric('metrics/meteor') meteor.compute(["some string", "some string"], ["some string", "some similar string"]) # {'meteor': 0.6411637931034483} meteor.add("some string", "some string") meteor.add('some string", "some similar string") meteor.compute() # {'meteor': 0.6411637931034483} ``` Uses [NLTK's implementation](https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score), [(source)](https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py)
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Export TFRecord to GCP bucket
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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Overview.ipynb throws exceptions with nlp 0.4.0
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Sorry for the large pull request. - Added checklists as datasets. I can't run `test_load_real_dataset` (see #474), but I can load the datasets successfully as shown in the example notebook - Added a checklist wrapper
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misc. bugs and quality of life
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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A few misc. bugs and QOL improvements that I've come across in using the library. Let me know if you don't like any of them and I can adjust/remove them. 1. Printing datasets without a description field throws an error when formatting the `single_line_description`. This fixes that, and also adds some formatting to the repr to make it slightly more readable. ``` >>> print(list_datasets()[0]) nlp.ObjectInfo( id='aeslc', description='A collection of email messages of employees in the Enron Corporation.There are two features: - email_body: email body text. - subject_line: email subject text.', files=[nlp.S3Object('aeslc.py'), nlp.S3Object('dataset_infos.json'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/dev/allen-p_inbox_29.subject'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/test/allen-p_inbox_24.subject'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/train/allen-p_inbox_20.subject'), nlp.S3Object('dummy/1.0.0/dummy_data.zip'), nlp.S3Object('urls_checksums/checksums.txt')] ) ``` 2. Add id-only option to `list_datasets` and `list_metrics` to allow the user to easily print out just the names of the datasets & metrics. I often found myself annoyed that this took so many strokes to do. ```python [dataset.id for dataset in list_datasets()] # before list_datasets(id_only=True) # after ``` 3. Fix null-seed randomization caching. When using `train_test_split` and `shuffle`, the computation was being cached even without a seed or generator being passed. The result was that calling `.shuffle` more than once on the same dataset didn't do anything without passing a distinct seed or generator. Likewise with `train_test_split`. 4. Indexing by iterables of bool. I added support for passing an iterable of type bool to `_getitem` as a numpy/pandas-like indexing method. Let me know if you think it's redundant with `filter` (I know it's not optimal memory-wise), but I think it's nice to have as a lightweight alternative to do simple things without having to create a copy of the entire dataset, e.g. ```python dataset[dataset['label'] == 0] # numpy-like bool indexing to look at instances with labels of 0 ``` 5. Add an `input_column` argument to `map` and `filter`, which allows you to filter/map on a particular column rather than passing the whole dict to the function. Also adds `fn_kwargs` to be passed to the function. I think these together make mapping much cleaner in many cases such as mono-column tokenization: ```python # before dataset = dataset.map(lambda batch: tokenizer(batch["text"]) # after dataset = dataset.map(tokenizer, input_column="text") dataset = dataset.map(tokenizer, input_column="text", fn_kwargs={"truncation": True, "padding": True}) ```
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test_load_real_dataset when config has BUILDER_CONFIGS that matter
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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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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add DoQA dataset (ACL 2020)
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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add DoQA dataset (ACL 2020) http://ixa.eus/node/12931
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add crd3 dataset
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opening new PR for CRD3 dataset (ACL2020) to fix the circle CI problems
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add reuters21578 dataset
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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new PR to add the reuters21578 dataset and fix the circle CI problems.
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Adding IWSLT 2017 dataset.
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[ "Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)." ]
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Created a [IWSLT 2017](https://sites.google.com/site/iwsltevaluation2017/TED-tasks) dataset script for the *multilingual data*. ``` Bilingual data: {Arabic, German, French, Japanese, Korean, Chinese} <-> English Multilingual data: German, English, Italian, Dutch, Romanian. (Any pair) ``` I'm unsure how to handle bilingual vs multilingual. Given `nlp` architecture a Config option seems to be the way to go, however, it might be a bit confusing to have different language pairs with different option. Using just language pairs is not viable as English to German exists in both. Any opinion on how that should be done ? EDIT: I decided to just omit de-en from multilingual as it's only a subset of the bilingual one. That way only language pairs exist. EDIT : Could be interesting for #438
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invalid data type 'str' at _convert_outputs in arrow_dataset.py
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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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