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hf-issues-dataset-with-comments

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MDU6SXNzdWU3MzEyNTcxMDQ=

How to choose proper download_mode in function load_dataset?

closed

NONE

Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue?

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MDU6SXNzdWU3MzA5MDgwNjA=

Add a `lazy_map` method to `Dataset` and `DatasetDict`

open

MEMBER

The library is great, but it would be even more awesome with a `lazy_map` method implemented on `Dataset` and `DatasetDict`. This would apply a function on a give item but when the item is requested. Two use cases: 1. load image on the fly 2. apply a random function and get different outputs at each epoch (like data augmentation or randomly masking a part of a sentence for BERT-like objectives).

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MDU6SXNzdWU3MzA3NzE2MTA=

Add option for named splits when using ds.train_test_split

open

CONTRIBUTOR

### Feature Request 🚀 Can we add a way to name your splits when using the `.train_test_split` function? In almost every use case I've come across, I have a `train` and a `test` split in my `DatasetDict`, and I want to create a `validation` split. Therefore, its kinda useless to get a `test` split back from `train_test_split`, as it'll just overwrite my real `test` split that I intended to keep. ### Workaround this is my hack for dealin with this, for now :slightly_smiling_face: ```python from datasets import load_dataset ​ ​ ds = load_dataset('imdb') ds['train'], ds['validation'] = ds['train'].train_test_split(.1).values() ```

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MDU6SXNzdWU3MzA2Njk1OTY=

[GEM] add DART data-to-text generation dataset

closed

MEMBER

## Adding a Dataset - **Name:** DART - **Description:** DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. - **Paper:** https://arxiv.org/abs/2007.02871v1 - **Data:** https://github.com/Yale-LILY/dart - **Motivation:** the dataset will likely be included in the GEM benchmark Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDU6SXNzdWU3MzA2NjgzMzI=

[GEM] Add DART data-to-text generation dataset

closed

MEMBER

## Adding a Dataset - **Name:** DART - **Description:** DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. - **Paper:** https://arxiv.org/abs/2007.02871v1 - **Data:** https://github.com/Yale-LILY/dart - **Motivation:** It will likely be included in the GEM generation evaluation benchmark Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDExOlB1bGxSZXF1ZXN0NTEwODkyMTk2

Adding Issue Template for Dataset Requests

closed

MEMBER

adding .github/ISSUE_TEMPLATE/add-dataset.md

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MDExOlB1bGxSZXF1ZXN0NTEwODcyMDYx

Fixed errors in bertscore related to custom baseline

closed

CONTRIBUTOR

[bertscore version 0.3.6 ](https://github.com/Tiiiger/bert_score) added support for custom baseline files. This update added extra argument `baseline_path` to BERTScorer class as well as some extra boolean parameters `use_custom_baseline` in functions like `get_hash(model, num_layers, idf, rescale_with_baseline, use_custom_baseline)`. This PR fix those matching errors in bertscore metric implementation.

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MDU6SXNzdWU3MzA1ODY5NzI=

[GEM] Add Czech Restaurant data-to-text generation dataset

closed

MEMBER

- Paper: https://www.aclweb.org/anthology/W19-8670.pdf - Data: https://github.com/UFAL-DSG/cs_restaurant_dataset - The dataset will likely be part of the GEM benchmark

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MDU6SXNzdWU3Mjk4OTg4Njc=

Downloaded datasets are not usable offline

closed

CONTRIBUTOR

I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises an error from the ```requests``` library trying to reach for the online dataset. Is this the intended behavior ? (Sorry, I wrote the the first version of this issue while still on nlp 0.3.0).

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MDU6SXNzdWU3Mjk2Mzc5MTc=

Add meta-data to the HANS dataset

closed

MEMBER

The current version of the [HANS dataset](https://github.com/huggingface/datasets/blob/master/datasets/hans/hans.py) is missing the additional information provided for each example, including the sentence parses, heuristic and subcase.

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MDU6SXNzdWU3MjkwNDY5MTY=

(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py

closed

NONE

Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?

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MDU6SXNzdWU3Mjg2Mzg1NTk=

Process 0 very slow when using num_procs with map to tokenizer

closed

NONE

<img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` 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), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ```

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MDU6SXNzdWU3MjgyNDE0OTQ=

CUDA out of memory

closed

NONE

In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.

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MDExOlB1bGxSZXF1ZXN0NTA4OTYwNTc3

Start community-provided dataset docs

closed

CONTRIBUTOR

Continuation of #736 with clean fork. #### Old description This is what I did to get the pseudo-labels updated. Not sure if it generalizes, but I figured I would write it down. It was pretty easy because all I had to do was make properly formatted directories and change URLs. In slack @thomwolf called it a user-namespace dataset, but the docs call it community dataset. I think the first naming is clearer, but I didn't address that here. I didn't add metadata, will try that.

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MDExOlB1bGxSZXF1ZXN0NTA4OTU0NDI2

Start community-provided dataset docs V2

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NTA4NjczNzM2

Use full released xsum dataset

closed

CONTRIBUTOR

#672 Fix xsum to expand coverage and include IDs Code based on parser from older version of `datasets/xsum/xsum.py` @lhoestq

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MDExOlB1bGxSZXF1ZXN0NTA4MzI4ODM0

Fix doc links to viewer

closed

MEMBER

It seems #733 forgot some links in the doc :)

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MDU6SXNzdWU3MjY5MTc4MDE=

Clicking on a metric in the search page points to datasets page giving "Missing dataset" warning

closed

NONE

Hi! Sorry if this isn't the right place to talk about the website, I just didn't exactly where to write this. Searching a metric in https://huggingface.co/metrics gives the right results but clicking on a metric (E.g ROUGE) points to https://huggingface.co/datasets/rouge. Clicking on a metric without searching points to the right page. Thanks for all the great work!

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MDU6SXNzdWU3MjY4MjAxOTE=

Error loading ms_marco v2.1 using load_dataset()

closed

NONE

Code: `dataset = load_dataset('ms_marco', 'v2.1')` Error: ``` `--------------------------------------------------------------------------- JSONDecodeError Traceback (most recent call last) <ipython-input-16-34378c057212> in <module>() 9 10 # Downloading and loading a dataset ---> 11 dataset = load_dataset('ms_marco', 'v2.1') 10 frames /usr/lib/python3.6/json/decoder.py in raw_decode(self, s, idx) 353 """ 354 try: --> 355 obj, end = self.scan_once(s, idx) 356 except StopIteration as err: 357 raise JSONDecodeError("Expecting value", s, err.value) from None JSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660) ` ```

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MDU6SXNzdWU3MjY1ODk0NDY=

load_dataset doesn't include `features` in its hash

closed

MEMBER

It looks like the function `load_dataset` does not include what's passed in the `features` argument when creating a hash for a given dataset. As a result, if a user includes new features from an already downloaded dataset, those are ignored. Example: some models on the hub have a different ordering for the labels than what `datasets` uses for MNLI so I'd like to do something along the lines of: ``` dataset = load_dataset("glue", "mnli") features = dataset["train"].features features["label"] = ClassLabel(names = ['entailment', 'contradiction', 'neutral']) # new label order dataset = load_dataset("glue", "mnli", features=features) ```

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MDU6SXNzdWU3MjYzNjYwNjI=

[XGLUE] Adding new dataset

closed

MEMBER

XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance

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MDExOlB1bGxSZXF1ZXN0NTA3MzAyNjE3

New version of CompGuessWhat?! with refined annotations

closed

CONTRIBUTOR

This pull request introduces a few fixes to the annotations for VisualGenome in the CompGuessWhat?! original split.

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MDExOlB1bGxSZXF1ZXN0NTA3MDQ3MDE4

Add Quail question answering dataset

closed

CONTRIBUTOR

QuAIL is a multi-domain RC dataset featuring news, blogs, fiction and user stories. Each domain is represented by 200 texts, which gives us a 4-way data split. The texts are 300-350 word excerpts from CC-licensed texts that were hand-picked so as to make sense to human readers without larger context. Domain diversity mitigates the issue of possible overlap between training and test data of large pre-trained models, which the current SOTA systems are based on. For instance, BERT is trained on Wikipedia + BookCorpus, and was tested on Wikipedia-based SQuAD (Devlin, Chang, Lee, & Toutanova, 2019). https://text-machine-lab.github.io/blog/2020/quail/ @annargrs

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MDExOlB1bGxSZXF1ZXN0NTA2ODMzNDMw

dataset(ngt): add ngt dataset initial loading script

closed

CONTRIBUTOR

Currently only making the paths to the annotation ELAN (eaf) file and videos available. This is the first accessible way to download this dataset, which is not manual file-by-file. Only downloading the necessary files, the annotation files are very small, 20MB for all of them, but the video files are large, 100GB in total, saved in `mpg` format. I do not intend to actually store these as an uncompressed array of frames, because it will be huge. Future updates may add pose estimation files for all videos, making it easier to work with this data

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MDExOlB1bGxSZXF1ZXN0NTA2ODAxMTI0

Fix emotion description

closed

MEMBER

Fixes the description of the emotion dataset to reflect the class names observed in the data, not the ones described in the paper. I also took the liberty to make use of `ClassLabel` for the emotion labels.

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MDU6SXNzdWU3MjQ5MTg0NDg=

Dataset Explorer Doesn't Work for squad_es and squad_it

closed

NONE

https://huggingface.co/nlp/viewer/?dataset=squad_es https://huggingface.co/nlp/viewer/?dataset=squad_it Both pages show "OSError: [Errno 28] No space left on device".

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MDU6SXNzdWU3MjQ3MDM5ODA=

load_dataset for CSV files not working

open

CONTRIBUTOR

Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you

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MDExOlB1bGxSZXF1ZXN0NTA1ODgzNjI3

Add OCNLI, a new CLUE dataset

closed

MEMBER

OCNLI stands for Original Chinese Natural Language Inference. It is a corpus for Chinese Natural Language Inference, collected following closely the procedures of MNLI, but with enhanced strategies aiming for more challenging inference pairs. We want to emphasize we did not use human/machine translation in creating the dataset, and thus our Chinese texts are original and not translated.

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MDU6SXNzdWU3MjM5MjQyNzU=

Creating dataset consumes too much memory

closed

CONTRIBUTOR

Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow.  If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory:  And it is only using one CPU core, which is probably why it's so slow: 

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MDExOlB1bGxSZXF1ZXN0NTA0NzAyNTc0

Fix TREC urls

closed

MEMBER

The old TREC urls are now redirections. I updated the urls to the new ones, since we don't support redirections for downloads. Fix #737

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MDExOlB1bGxSZXF1ZXN0NTA0Njk5NTY3

Add wiki dpr multiset embeddings

closed

MEMBER

There are two DPR encoders, one trained on Natural Questions and one trained on a multiset/hybrid dataset. Previously only the embeddings from the encoder trained on NQ were available. I'm adding the ones from the encoder trained on the multiset/hybrid dataset. In the configuration you can now specify `embeddings_name="nq"` or `embeddings_name="multiset"`

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MDExOlB1bGxSZXF1ZXN0NTA0NjkxNjM4

Replace seqeval code with original classification_report for simplicity

closed

CONTRIBUTOR

Recently, the original seqeval has enabled us to get per type scores and overall scores as a dictionary. This PR replaces the current code with the original function(`classification_report`) to simplify it. Also, the original code has been updated to fix #352. - Related issue: https://github.com/chakki-works/seqeval/pull/38 ```python from datasets import load_metric metric = load_metric("seqeval") y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] metric.compute(predictions=y_pred, references=y_true) # Output: {'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8} ```

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MDU6SXNzdWU3MjI0NjM5MjM=

Trec Dataset Connection Error

closed

NONE

**Datasets Version:** 1.1.2 **Python Version:** 3.6/3.7 **Code:** ```python from datasets import load_dataset load_dataset("trec") ``` **Expected behavior:** Download Trec dataset and load Dataset object **Current Behavior:** Get a connection error saying it couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label (but the link doesn't seem broken) <details> <summary>Error Logs</summary> Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /root/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-8-66bf1242096e> in <module>() ----> 1 load_dataset("trec") 10 frames /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label </details>

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MDExOlB1bGxSZXF1ZXN0NTA0MTE0MjMy

Start community-provided dataset docs

closed

CONTRIBUTOR

This is one I did to get the pseudo-labels updated. Not sure if it generalizes, but I figured I would write it down. It was pretty easy because all I had to do was make properly formatted directories and change URLs. + In slack @thomwolf called it a `user-namespace` dataset, but the docs call it `community dataset`. I think the first naming is clearer, but I didn't address that here. + I didn't add metadata, will try that.

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MDU6SXNzdWU3MjIyMjUyNzA=

Throw error when an unexpected key is used in data_files

closed

CONTRIBUTOR

I have found that only "train", "validation" and "test" are valid keys in the `data_files` argument. When you use any other ones, those attached files are silently ignored - leading to unexpected behaviour for the users. So the following, unintuitively, returns only one key (namely `train`). ```python datasets = load_dataset("text", data_files={"train": train_f, "valid": valid_f}) print(datasets.keys()) # dict_keys(['train']) ``` whereas using `validation` instead, does return the expected result: ```python datasets = load_dataset("text", data_files={"train": train_f, "validation": valid_f}) print(datasets.keys()) # dict_keys(['train', 'validation']) ``` I would like to see more freedom in which keys one can use, but if that is not possible at least an error should be thrown when using an unexpected key.

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MDExOlB1bGxSZXF1ZXN0NTAzNjMwMDcz

Fix GLUE metric description

closed

MEMBER

Small typo: the description says translation instead of prediction.

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MDExOlB1bGxSZXF1ZXN0NTAzMjk2NDQw

Update link to dataset viewer

closed

CONTRIBUTOR

Change 404 error links in quick tour to working ones

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MDExOlB1bGxSZXF1ZXN0NTAzMjkwMjEy

dataset(wlasl): initial loading script

closed

CONTRIBUTOR

takes like 9-10 hours to download all of the videos for the dataset, but it does finish :)

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MDExOlB1bGxSZXF1ZXN0NTAzMTExNzc4

dataset(aslg_pc12): initial loading script

closed

CONTRIBUTOR

This contains the only current public part of this corpus. The rest of the corpus is not yet been made public, but this sample is still being used by researchers.

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MDU6SXNzdWU3MjEwNzM4MTI=

Possible caching bug

closed

NONE

The following code with `test1.txt` containing just "🤗🤗🤗": ``` dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="latin_1") print(dataset[0]) dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="utf-8") print(dataset[0]) ``` produces this output: ``` Downloading and preparing dataset text/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155... Dataset text downloaded and prepared to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data. {'text': 'ð\x9f¤\x97ð\x9f¤\x97ð\x9f¤\x97'} Using custom data configuration default Reusing dataset text (/home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155) {'text': 'ð\x9f¤\x97ð\x9f¤\x97ð\x9f¤\x97'} ``` Just changing the order (and deleting the temp files): ``` dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="utf-8") print(dataset[0]) dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="latin_1") print(dataset[0]) ``` produces this: ``` Using custom data configuration default Downloading and preparing dataset text/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155... Dataset text downloaded and prepared to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data. {'text': '🤗🤗🤗'} Using custom data configuration default Reusing dataset text (/home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155) {'text': '🤗🤗🤗'} ``` Is it intended that the cache path does not depend on the config entries? tested with datasets==1.1.2 and python==3.8.5

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MDU6SXNzdWU3MTk1NTg4NzY=

Better error message when one forgets to call `add_batch` before `compute`

closed

MEMBER

When using metrics, if for some reason a user forgets to call `add_batch` to a metric before `compute` (with no arguments), the error message is a bit cryptic and could probably be made clearer. ## Reproducer ```python import datasets import torch from datasets import Metric class GatherMetric(Metric): def _info(self): return datasets.MetricInfo( description="description", citation="citation", inputs_description="kwargs", features=datasets.Features({ 'predictions': datasets.Value('int64'), 'references': datasets.Value('int64'), }), codebase_urls=[], reference_urls=[], format='numpy' ) def _compute(self, predictions, references): return {"predictions": predictions, "labels": references} metric = GatherMetric(cache_dir="test-metric") inputs = torch.randint(0, 2, (1024,)) targets = torch.randint(0, 2, (1024,)) batch_size = 8 for i in range(0, 1024, batch_size): pass # User forgets to call `add_batch` result = metric.compute() ``` ## Stack trace: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-13-267729d187fa> in <module> 3 pass 4 # metric.add_batch(predictions=inputs[i:i+batch_size], references=targets[i:i+batch_size]) ----> 5 result = metric.compute() ~/git/datasets/src/datasets/metric.py in compute(self, *args, **kwargs) 380 if predictions is not None: 381 self.add_batch(predictions=predictions, references=references) --> 382 self._finalize() 383 384 self.cache_file_name = None ~/git/datasets/src/datasets/metric.py in _finalize(self) 343 elif self.process_id == 0: 344 # Let's acquire a lock on each node files to be sure they are finished writing --> 345 file_paths, filelocks = self._get_all_cache_files() 346 347 # Read the predictions and references ~/git/datasets/src/datasets/metric.py in _get_all_cache_files(self) 280 filelocks = [] 281 for process_id, file_path in enumerate(file_paths): --> 282 filelock = FileLock(file_path + ".lock") 283 try: 284 filelock.acquire(timeout=self.timeout) TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' ```

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MDU6SXNzdWU3MTk1NTU3ODA=

Passing `cache_dir` to a metric does not work

closed

MEMBER

When passing `cache_dir` to a custom metric, the folder is concatenated to itself at some point and this results in a FileNotFoundError: ## Reproducer ```python import datasets import torch from datasets import Metric class GatherMetric(Metric): def _info(self): return datasets.MetricInfo( description="description", citation="citation", inputs_description="kwargs", features=datasets.Features({ 'predictions': datasets.Value('int64'), 'references': datasets.Value('int64'), }), codebase_urls=[], reference_urls=[], format='numpy' ) def _compute(self, predictions, references): return {"predictions": predictions, "labels": references} metric = GatherMetric(cache_dir="test-metric") inputs = torch.randint(0, 2, (1024,)) targets = torch.randint(0, 2, (1024,)) batch_size = 8 for i in range(0, 1024, batch_size): metric.add_batch(predictions=inputs[i:i+batch_size], references=targets[i:i+batch_size]) result = metric.compute() ``` ## Stack trace: ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) ~/git/datasets/src/datasets/metric.py in _finalize(self) 349 reader = ArrowReader(path=self.data_dir, info=DatasetInfo(features=self.features)) --> 350 self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths])) 351 except FileNotFoundError: ~/git/datasets/src/datasets/arrow_reader.py in read_files(self, files, original_instructions) 227 # Prepend path to filename --> 228 pa_table = self._read_files(files) 229 files = copy.deepcopy(files) ~/git/datasets/src/datasets/arrow_reader.py in _read_files(self, files) 166 for f_dict in files: --> 167 pa_table: pa.Table = self._get_dataset_from_filename(f_dict) 168 pa_tables.append(pa_table) ~/git/datasets/src/datasets/arrow_reader.py in _get_dataset_from_filename(self, filename_skip_take) 291 ) --> 292 mmap = pa.memory_map(filename) 293 f = pa.ipc.open_stream(mmap) ~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.memory_map() ~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.MemoryMappedFile._open() ~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() FileNotFoundError: [Errno 2] Failed to open local file 'test-metric/gather_metric/default/test-metric/gather_metric/default/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) <ipython-input-17-e42d43cc981f> in <module> 2 for i in range(0, 1024, batch_size): 3 metric.add_batch(predictions=inputs[i:i+batch_size], references=targets[i:i+batch_size]) ----> 4 result = metric.compute() ~/git/datasets/src/datasets/metric.py in compute(self, *args, **kwargs) 380 if predictions is not None: 381 self.add_batch(predictions=predictions, references=references) --> 382 self._finalize() 383 384 self.cache_file_name = None ~/git/datasets/src/datasets/metric.py in _finalize(self) 351 except FileNotFoundError: 352 raise ValueError( --> 353 "Error in finalize: another metric instance is already using the local cache file. " 354 "Please specify an experiment_id to avoid colision between distributed metric instances." 355 ) ValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances. ``` The code works when we remove the `cache_dir=...` from the metric.

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MDU6SXNzdWU3MTkzODYzNjY=

Parallel downloads progress bar flickers

open

MEMBER

When there are parallel downloads using the download manager, the tqdm progress bar flickers since all the progress bars are on the same line. To fix that we could simply specify `position=i` for i=0 to n the number of files to download when instantiating the tqdm progress bar. Another way would be to have one "master" progress bar that tracks the number of finished downloads, and then one progress bar per process that show the current downloads.

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MDU6SXNzdWU3MTkzMTM3NTQ=

"Checksums didn't match for dataset source files" error while loading openwebtext dataset

closed

NONE

Hi, I have encountered this problem during loading the openwebtext dataset: ``` >>> dataset = load_dataset('openwebtext') Downloading and preparing dataset openwebtext/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to /home/admin/.cache/huggingface/datasets/openwebtext/plain_text/1.0.0/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 536, in _download_and_prepare self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://zenodo.org/record/3834942/files/openwebtext.tar.xz'] ``` I think this problem is caused because the released dataset has changed. Or I should download the dataset manually? Sorry for release the unfinised issue by mistake.

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https://github.com/huggingface/datasets/pull/725

MDExOlB1bGxSZXF1ZXN0NTAxMjUxODI1

pretty print dataset objects

closed

MEMBER

Currently, if I do: ``` from datasets import load_dataset load_dataset("wikihow", 'all', data_dir="/hf/pegasus-datasets/wikihow/") ``` I get: ``` DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None), 'headline': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None)}, num_rows: 157252), 'validation': Dataset(features: {'text': Value(dtype='string', id=None), 'headline': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None)}, num_rows: 5599), 'test': Dataset(features: {'text': Value(dtype='string', id=None), 'headline': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None)}, num_rows: 5577)}) ``` This is not very readable. Can we either have a better `__repr__` or have a custom method to nicely pprint the dataset object? Here is my very simple attempt. With this PR, it produces: ``` DatasetDict({ train: Dataset({ features: ['text', 'headline', 'title'], num_rows: 157252 }) validation: Dataset({ features: ['text', 'headline', 'title'], num_rows: 5599 }) test: Dataset({ features: ['text', 'headline', 'title'], num_rows: 5577 }) }) ``` I did omit the data types on purpose to make it more readable, but it shouldn't be too difficult to integrate those too. note that this PR also fixes the inconsistency in output that in master misses enclosing `{}` for Dataset, but it is there for `DatasetDict` - or perhaps it was by design. I'm totally not attached to this format, just wanting something more readable. One approach could be to serialize to `json.dumps` or something similar. It'd make the indentation simpler. Thank you.

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MDU6SXNzdWU3MTg5NDc3MDA=

need to redirect /nlp to /datasets and remove outdated info

closed

MEMBER

It looks like the website still has all the `nlp` data, e.g.: https://huggingface.co/nlp/viewer/?dataset=wikihow&config=all should probably redirect to: https://huggingface.co/datasets/wikihow also for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had the links marked up, the new one is just a jumble of text in one chunk and no markup for links (i.e. not clickable).

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MDU6SXNzdWU3MTg5MjY3MjM=

Adding pseudo-labels to datasets

closed

CONTRIBUTOR

I recently [uploaded pseudo-labels](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/precomputed_pseudo_labels.md) for CNN/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo. Since pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution. I read https://huggingface.co/docs/datasets/add_dataset.html, but it doesn't really cover this type of contribution. I could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https://github.com/huggingface/datasets/blob/5f4c6e830f603830117877b8990a0e65a2386aa6/datasets/xsum/xsum.py What do you think @lhoestq ?

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MDExOlB1bGxSZXF1ZXN0NTAxMDI3NjAw

datasets(RWTH-PHOENIX-Weather 2014 T): add initial loading script

open

CONTRIBUTOR

This is the first sign language dataset in this repo as far as I know. Following an old issue I opened https://github.com/huggingface/datasets/issues/302. I added the dataset official REAMDE file, but I see it's not very standard, so it can be removed.

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MDU6SXNzdWU3MTg2NDcxNDc=

feat(dl_manager): add support for ftp downloads

closed

CONTRIBUTOR

I am working on a new dataset (#302) and encounter a problem downloading it. ```python # This is the official download link from https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/ _URL = "ftp://wasserstoff.informatik.rwth-aachen.de/pub/rwth-phoenix/2016/phoenix-2014-T.v3.tar.gz" dl_manager.download_and_extract(_URL) ``` I get an error: > ValueError: unable to parse ftp://wasserstoff.informatik.rwth-aachen.de/pub/rwth-phoenix/2016/phoenix-2014-T.v3.tar.gz as a URL or as a local path I checked, and indeed you don't consider `ftp` as a remote file. https://github.com/huggingface/datasets/blob/4c2af707a6955cf4b45f83ac67990395327c5725/src/datasets/utils/file_utils.py#L188 Adding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.

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MDU6SXNzdWU3MTY1ODEyNjY=

OSError: Cannot find data file when not using the dummy dataset in RAG

closed

NONE

## Environment info transformers version: 3.3.1 Platform: Linux-4.19 Python version: 3.7.7 PyTorch version (GPU?): 1.6.0 Tensorflow version (GPU?): No Using GPU in script?: Yes Using distributed or parallel set-up in script?: No ## To reproduce Steps to reproduce the behaviour: ``` import os os.environ['HF_DATASETS_CACHE'] = '/workspace/notebooks/POCs/cache' from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq") retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=False) ``` Plese note that I'm using the whole dataset: **use_dummy_dataset=False** After around 4 hours (downloading and some other things) this is returned: ``` Downloading and preparing dataset wiki_dpr/psgs_w100.nq.exact (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /workspace/notebooks/POCs/cache/wiki_dpr/psgs_w100.nq.exact/0.0.0/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2... --------------------------------------------------------------------------- UnpicklingError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding) 459 try: --> 460 return pickle.load(fid, **pickle_kwargs) 461 except Exception: UnpicklingError: pickle data was truncated During handling of the above exception, another exception occurred: OSError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 552 # Prepare split will record examples associated to the split --> 553 self._prepare_split(split_generator, **prepare_split_kwargs) 554 except OSError: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 840 for key, record in utils.tqdm( --> 841 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose 842 ): /opt/conda/lib/python3.7/site-packages/tqdm/notebook.py in __iter__(self, *args, **kwargs) 217 try: --> 218 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs): 219 # return super(tqdm...) will not catch exception /opt/conda/lib/python3.7/site-packages/tqdm/std.py in __iter__(self) 1128 try: -> 1129 for obj in iterable: 1130 yield obj ~/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2/wiki_dpr.py in _generate_examples(self, data_file, vectors_files) 131 break --> 132 vecs = np.load(open(vectors_files.pop(0), "rb"), allow_pickle=True) 133 vec_idx = 0 /opt/conda/lib/python3.7/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding) 462 raise IOError( --> 463 "Failed to interpret file %s as a pickle" % repr(file)) 464 finally: OSError: Failed to interpret file <_io.BufferedReader name='/workspace/notebooks/POCs/cache/downloads/f34d5f091294259b4ca90e813631e69a6ded660d71b6cbedf89ddba50df94448'> as a pickle During handling of the above exception, another exception occurred: OSError Traceback (most recent call last) <ipython-input-10-f28df370ac47> in <module> 1 # ln -s /workspace/notebooks/POCs/cache /root/.cache/huggingface/datasets ----> 2 retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=False) /opt/conda/lib/python3.7/site-packages/transformers/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs) 307 generator_tokenizer = rag_tokenizer.generator 308 return cls( --> 309 config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer 310 ) 311 /opt/conda/lib/python3.7/site-packages/transformers/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer) 298 self.config = config 299 if self._init_retrieval: --> 300 self.init_retrieval() 301 302 @classmethod /opt/conda/lib/python3.7/site-packages/transformers/retrieval_rag.py in init_retrieval(self) 324 325 logger.info("initializing retrieval") --> 326 self.index.init_index() 327 328 def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None): /opt/conda/lib/python3.7/site-packages/transformers/retrieval_rag.py in init_index(self) 238 split=self.dataset_split, 239 index_name=self.index_name, --> 240 dummy=self.use_dummy_dataset, 241 ) 242 self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 609 download_config=download_config, 610 download_mode=download_mode, --> 611 ignore_verifications=ignore_verifications, 612 ) 613 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 474 if not downloaded_from_gcs: 475 self._download_and_prepare( --> 476 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 477 ) 478 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 553 self._prepare_split(split_generator, **prepare_split_kwargs) 554 except OSError: --> 555 raise OSError("Cannot find data file. " + (self.manual_download_instructions or "")) 556 557 if verify_infos: OSError: Cannot find data file. ``` Thanks

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MDExOlB1bGxSZXF1ZXN0NDk5MjE5Mjg2

Fix train_test_split output format

closed

MEMBER

There was an issue in the `transmit_format` wrapper that returned bad formats when using train_test_split. This was due to `column_names` being handled as a List[str] instead of Dict[str, List[str]] when the dataset transform (train_test_split) returns a DatasetDict (one set of column names per split). This should fix @timothyjlaurent 's issue in #620 and fix #676 I added tests for `transmit_format` so that it doesn't happen again

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MDExOlB1bGxSZXF1ZXN0NDk4NTU5MDcw

Don't use tqdm 4.50.0

closed

MEMBER

tqdm 4.50.0 introduced permission errors on windows see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/235/workflows/cfb6a39f-68eb-4802-8b17-2cd5e8ea7369/jobs/1111) for the error details. For now I just added `<4.50.0` in the setup.py Hopefully we can find what's wrong with this version soon

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MDExOlB1bGxSZXF1ZXN0NDk3OTUwOTA2

Fixes #712 Error in the Overview.ipynb notebook

closed

CONTRIBUTOR

Fixes #712 Error in the Overview.ipynb notebook by adding `with_details=True` parameter to `list_datasets` function in Cell 3 of **overview** notebook

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https://github.com/huggingface/datasets/pull/716

MDExOlB1bGxSZXF1ZXN0NDk3OTQ1ODAw

Fixes #712 Attribute error in cell 3 of the overview notebook

closed

CONTRIBUTOR

Fixes the Attribute error in cell 3 of the overview notebook

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https://github.com/huggingface/datasets/pull/715

MDExOlB1bGxSZXF1ZXN0NDk3NzMwMDQ2

Use python read for text dataset

closed

MEMBER

As mentioned in #622 the pandas reader used for text dataset doesn't work properly when there are \r characters in the text file. Instead I switched to pure python using `open` and `read`. From my benchmark on a 100MB text file, it's the same speed as the previous pandas reader.

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MDExOlB1bGxSZXF1ZXN0NDk3NTYzNjAx

Add the official dependabot implementation

closed

NONE

This will keep dependencies up to date. This will require a pr label `dependencies` being created in order to function correctly.

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https://github.com/huggingface/datasets/pull/713

MDExOlB1bGxSZXF1ZXN0NDk3NTUzOTUy

Fix reading text files with carriage return symbols

closed

NONE

The new pandas-based text reader isn't able to work properly with files that contain carriage return symbols (`\r`). It fails with the following error message: ``` ... File "pandas/_libs/parsers.pyx", line 847, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 918, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 2042, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file. ``` ___ I figured out the pandas uses those symbols as line terminators and this eventually causes the error. Explicitly specifying the `lineterminator` fixes that issue and everything works fine. Please, consider this PR as it seems to be a common issue to solve.

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MDU6SXNzdWU3MTQyNDIzMTY=

Error in the notebooks/Overview.ipynb notebook

closed

CONTRIBUTOR

Hi, I got the following error in **cell number 3** while exploring the **Overview.ipynb** notebook in google colab. I used the [link ](https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb) provided in the main README file to open it in colab. ```python # You can access various attributes of the datasets before downloading them squad_dataset = list_datasets()[datasets.index('squad')] pprint(squad_dataset.__dict__) # It's a simple python dataclass ``` Error message ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-5-8dc805c4949c> in <module>() 2 squad_dataset = list_datasets()[datasets.index('squad')] 3 ----> 4 pprint(squad_dataset.__dict__) # It's a simple python dataclass AttributeError: 'str' object has no attribute '__dict__' ``` The object `squad_dataset` is a `str` not a `dataclass` .

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MDExOlB1bGxSZXF1ZXN0NDk3Mzc3NzU3

New Update bertscore.py

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MDExOlB1bGxSZXF1ZXN0NDk3MzQ1NjQ0

fix README typos/ consistency

closed

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MDU6SXNzdWU3MTQwNjc5MDI=

How to use similarity settings other then "BM25" in Elasticsearch index ?

open

NONE

**QUESTION : How should we use other similarity algorithms supported by Elasticsearch other than "BM25" ?** **ES Reference** https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-similarity.html **HF doc reference:** https://huggingface.co/docs/datasets/faiss_and_ea.html **context :** ======== I used the latest Elasticsearch server version 7.9.2 When I set DFR which is one of the other similarity algorithms supported by elasticsearch in the mapping, I get an error For example DFR that I had tried in the first instance in mappings as below., `"mappings": {"properties": {"text": {"type": "text", "analyzer": "standard", "similarity": "DFR"}}},` I get the following error RequestError: RequestError(400, 'mapper_parsing_exception', 'Unknown Similarity type [DFR] for field [text]') The other thing as another option I had tried was to declare "similarity": "my_similarity" within settings and then assigning "my_similarity" inside the mappings as below `es_config = { "settings": { "number_of_shards": 1, **"similarity": "my_similarity"**: { "type": "DFR", "basic_model": "g", "after_effect": "l", "normalization": "h2", "normalization.h2.c": "3.0" } , "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}}, }, "mappings": {"properties": {"text": {"type": "text", "analyzer": "standard", "similarity": "my_similarity"}}}, }` For this , I got the following error RequestError: RequestError(400, 'illegal_argument_exception', 'unknown setting [index.similarity] please check that any required plugins are installed, or check the breaking changes documentation for removed settings')

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MDU6SXNzdWU3MTQwMjA5NTM=

Datasets performance slow? - 6.4x slower than in memory dataset

closed

NONE

I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset. Now, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower. For example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33. Is this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss. For reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU. I can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower. What am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance? At 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice? In any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test. ``` py import sys from datasets import load_dataset from transformers import DataCollatorWithPadding, BertTokenizerFast from torch.utils.data import DataLoader from tqdm import tqdm if __name__ == '__main__': tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased') collate_fn = DataCollatorWithPadding(tokenizer, padding=True) ds = load_dataset('yelp_polarity') def do_tokenize(x): return tokenizer(x['text'], truncation=True) ds = ds.map(do_tokenize, batched=True) ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask']) if len(sys.argv) == 2 and sys.argv[1] == 'memory': # copy to memory - probably a faster way to do this - but demonstrates the point # approximately 530 batches per second - 17500 batches in 0:33 print('using memory') _ds = [data for data in tqdm(ds['train'])] else: # approximately 83 batches per second - 17500 batches in 3:31 print('using datasets') _ds = ds['train'] dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4) for data in tqdm(dl): for k, v in data.items(): data[k] = v.to('cuda') ``` For reference, my conda environment is [here](https://gist.github.com/05b6101518ff70ed42a858b302a0405d) Once again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints. Thanks for all your great work.

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MDU6SXNzdWU3MTM5NTQ2NjY=

Requirements should specify pyarrow<1

closed

NONE

I was looking at the docs on [Perplexity](https://huggingface.co/transformers/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error, ``` module 'pyarrow' has no attribute 'PyExtensionType' ``` I traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. https://github.com/huggingface/datasets/blob/e86a2a8f869b91654e782c9133d810bb82783200/setup.py#L68 Downgrading by installing `pip install "pyarrow<1"` resolved the issue.

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MDExOlB1bGxSZXF1ZXN0NDk2OTkwMDA0

Fix config creation for data files with NamedSplit

closed

MEMBER

During config creation, we need to iterate through the data files of all the splits to compute a hash. To make sure the hash is unique given a certain combination of files/splits, we sort the split names. However the `NamedSplit` objects can't be passed to `sorted` and currently it raises an error: we need to sort the string of their names instead. Fix #705

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MDU6SXNzdWU3MTM3MDkxMDA=

TypeError: '<' not supported between instances of 'NamedSplit' and 'NamedSplit'

closed

NONE

## Environment info <!-- You can run the command `transformers-cli env` and copy-and-paste its output below. Don't forget to fill out the missing fields in that output! --> - `transformers` version: 3.3.1 (installed from master) - `datasets` version: 1.0.2 (installed as a dependency from transformers) - Platform: Linux-4.15.0-118-generic-x86_64-with-debian-stretch-sid - Python version: 3.7.9 I'm testing my own text classification dataset using [this example](https://github.com/huggingface/transformers/tree/master/examples/text-classification#run-generic-text-classification-script-in-tensorflow) from transformers. The dataset is split into train / dev / test, and in csv format, containing just a text and a label columns, using comma as sep. Here's a sample: ``` text,label "Registra-se a presença do acadêmico <name> . <REL_SEP> Ao me deparar com a descrição de dois autores no polo ativo da ação junto ao PJe , margem esquerda foi informado pela procuradora do reclamante que se trata de uma reclamação trabalhista individual . <REL_SEP> Diante disso , face a ausência injustificada do autor <name> , determina-se o ARQUIVAMENTO do presente processo , com relação a este , nos termos do [[ art . 844 da CLT ]] . <REL_SEP> CUSTAS AUTOR - DISPENSADO <REL_SEP> Custas pelo autor no importe de R $326,82 , calculadas sobre R $16.341,03 , dispensadas na forma da lei , em virtude da concessão dos benefícios da Justiça Gratuita , ora deferida . <REL_SEP> Cientes os presentes . <REL_SEP> Audiência encerrada às 8h42min . <REL_SEP> <name> <REL_SEP> Juíza do Trabalho <REL_SEP> Ata redigida por << <name> >> , Secretário de Audiência .",NO_RELATION ``` However, @Santosh-Gupta reported in #7351 that he had the exact same problem using the ChemProt dataset. His colab notebook is referenced in the following section. ## To reproduce Steps to reproduce the behavior: 1. Created a new conda environment using conda env -n transformers python=3.7 2. Cloned transformers master, `cd` into it and installed using pip install --editable . -r examples/requirements.txt 3. Installed tensorflow with `pip install tensorflow` 3. Ran `run_tf_text_classification.py` with the following parameters: ``` --train_file <DATASET_PATH>/train.csv \ --dev_file <DATASET_PATH>/dev.csv \ --test_file <DATASET_PATH>/test.csv \ --label_column_id 1 \ --model_name_or_path neuralmind/bert-base-portuguese-cased \ --output_dir <OUTPUT_PATH> \ --num_train_epochs 4 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --do_train \ --do_eval \ --do_predict \ --logging_steps 1000 \ --evaluate_during_training \ --save_steps 1000 \ --overwrite_output_dir \ --overwrite_cache ``` I have also copied [@Santosh-Gupta 's colab notebook](https://colab.research.google.com/drive/11APei6GjphCZbH5wD9yVlfGvpIkh8pwr?usp=sharing) as a reference. <!-- If you have code snippets, error messages, stack traces please provide them here as well. Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.--> Here is the stack trace: ``` 2020-10-02 07:33:41.622011: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 /media/discoD/repositorios/transformers_pedro/src/transformers/training_args.py:333: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options) FutureWarning, 2020-10-02 07:33:43.471648: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1 2020-10-02 07:33:43.471791: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:43.472664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1 coreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.92GiB deviceMemoryBandwidth: 238.66GiB/s 2020-10-02 07:33:43.472684: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 2020-10-02 07:33:43.472765: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 2020-10-02 07:33:43.472809: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10 2020-10-02 07:33:43.472848: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10 2020-10-02 07:33:43.474209: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10 2020-10-02 07:33:43.474276: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10 2020-10-02 07:33:43.561219: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 2020-10-02 07:33:43.561397: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:43.562345: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:43.563219: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0 2020-10-02 07:33:43.563595: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2020-10-02 07:33:43.570091: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 3591830000 Hz 2020-10-02 07:33:43.570494: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x560842432400 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2020-10-02 07:33:43.570511: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2020-10-02 07:33:43.570702: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:43.571599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1 coreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.92GiB deviceMemoryBandwidth: 238.66GiB/s 2020-10-02 07:33:43.571633: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 2020-10-02 07:33:43.571645: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 2020-10-02 07:33:43.571654: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10 2020-10-02 07:33:43.571664: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10 2020-10-02 07:33:43.571691: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10 2020-10-02 07:33:43.571704: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10 2020-10-02 07:33:43.571718: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 2020-10-02 07:33:43.571770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:43.572641: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:43.573475: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0 2020-10-02 07:33:47.139227: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-10-02 07:33:47.139265: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0 2020-10-02 07:33:47.139272: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N 2020-10-02 07:33:47.140323: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:47.141248: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:47.142085: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-10-02 07:33:47.142854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5371 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1) 2020-10-02 07:33:47.146317: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5608b95dc5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2020-10-02 07:33:47.146336: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1 10/02/2020 07:33:47 - INFO - __main__ - n_replicas: 1, distributed training: False, 16-bits training: False 10/02/2020 07:33:47 - INFO - __main__ - Training/evaluation parameters TFTrainingArguments(output_dir='/media/discoD/models/datalawyer/pedidos/transformers_tf', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=True, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=4, per_device_eval_batch_size=4, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=4.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Oct02_07-33-43_user-XPS-8700', logging_first_step=False, logging_steps=1000, save_steps=1000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=1000, dataloader_num_workers=0, past_index=-1, run_name='/media/discoD/models/datalawyer/pedidos/transformers_tf', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=False, tpu_name=None, xla=False) 10/02/2020 07:33:53 - INFO - filelock - Lock 140407857405776 acquired on /home/user/.cache/huggingface/datasets/e0f1e9ed46db1e2429189f06b479cbd4075c0976104c1aacf8f77d9a53d2ad87.03756fef6da334f50a7ff73608e21b5018229944ca250416ce7352e25d84a552.py.lock 10/02/2020 07:33:53 - INFO - filelock - Lock 140407857405776 released on /home/user/.cache/huggingface/datasets/e0f1e9ed46db1e2429189f06b479cbd4075c0976104c1aacf8f77d9a53d2ad87.03756fef6da334f50a7ff73608e21b5018229944ca250416ce7352e25d84a552.py.lock Using custom data configuration default Traceback (most recent call last): File "run_tf_text_classification.py", line 283, in <module> main() File "run_tf_text_classification.py", line 222, in main max_seq_length=data_args.max_seq_length, File "run_tf_text_classification.py", line 43, in get_tfds ds = datasets.load_dataset("csv", data_files=files) File "/media/discoD/anaconda3/envs/transformers/lib/python3.7/site-packages/datasets/load.py", line 604, in load_dataset **config_kwargs, File "/media/discoD/anaconda3/envs/transformers/lib/python3.7/site-packages/datasets/builder.py", line 158, in __init__ **config_kwargs, File "/media/discoD/anaconda3/envs/transformers/lib/python3.7/site-packages/datasets/builder.py", line 269, in _create_builder_config for key in sorted(data_files.keys()): TypeError: '<' not supported between instances of 'NamedSplit' and 'NamedSplit' ``` ## Expected behavior Should be able to run the text-classification example as described in [https://github.com/huggingface/transformers/tree/master/examples/text-classification#run-generic-text-classification-script-in-tensorflow](https://github.com/huggingface/transformers/tree/master/examples/text-classification#run-generic-text-classification-script-in-tensorflow) Originally opened this issue at transformers' repository: [https://github.com/huggingface/transformers/issues/7535](https://github.com/huggingface/transformers/issues/7535). @jplu instructed me to open here, since according to [this](https://github.com/huggingface/transformers/issues/7535#issuecomment-702778885) evidence, the problem is from datasets. Thanks!

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https://github.com/huggingface/datasets/pull/704

MDExOlB1bGxSZXF1ZXN0NDk2ODY2NTQ0

Fix remote tests for new datasets

closed

MEMBER

When adding a new dataset, the remote tests fail because they try to get the new dataset from the master branch (i.e., where the dataset doesn't exist yet) To fix that I reverted to the use of the HF API that fetch the available datasets on S3 that is synced with the master branch

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https://github.com/huggingface/datasets/pull/703

MDExOlB1bGxSZXF1ZXN0NDk2ODU1OTQ5

Add hotpot QA

closed

CONTRIBUTOR

Added the [HotpotQA](https://github.com/hotpotqa/hotpot) multi-hop question answering dataset.

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https://github.com/huggingface/datasets/pull/702

MDExOlB1bGxSZXF1ZXN0NDk2ODA3Mjg4

Complete rouge kwargs

closed

MEMBER

In #701 we noticed that some kwargs were missing for rouge

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https://github.com/huggingface/datasets/pull/701

MDExOlB1bGxSZXF1ZXN0NDk2Nzk2MTQ1

Add rouge 2 and rouge Lsum to rouge metric outputs

closed

MEMBER

Continuation of #700 Rouge 2 and Rouge Lsum were missing in Rouge's outputs. Rouge Lsum is also useful to evaluate Rouge L for sentences with `\n` Fix #617

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https://github.com/huggingface/datasets/pull/700

MDExOlB1bGxSZXF1ZXN0NDk2NzY3MTMz

Add rouge-2 in rouge_types for metric calculation

closed

NONE

The description of the ROUGE metric says, ``` _KWARGS_DESCRIPTION = """ Calculates average rouge scores for a list of hypotheses and references Args: predictions: list of predictions to score. Each predictions should be a string with tokens separated by spaces. references: list of reference for each prediction. Each reference should be a string with tokens separated by spaces. Returns: rouge1: rouge_1 f1, rouge2: rouge_2 f1, rougeL: rouge_l f1, rougeLsum: rouge_l precision """ ``` but the `rouge_types` argument defaults to `rouge_types = ["rouge1", "rougeL"]`, this PR updates and add `rouge2` to the list so as to reflect the description card.

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https://github.com/huggingface/datasets/issues/699

MDU6SXNzdWU3MTMzOTU2NDI=

XNLI dataset is not loading

closed

NONE

`dataset = datasets.load_dataset(path='xnli')` showing below error ``` /opt/conda/lib/python3.7/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: ['https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip'] ``` I think URL is now changed to "https://cims.nyu.edu/~sbowman/xnli/XNLI-MT-1.0.zip"

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MDExOlB1bGxSZXF1ZXN0NDk2MzczNDU5

Update README.md

closed

NONE

Hey I was just telling my subscribers to check out your repositories Thank you

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MDExOlB1bGxSZXF1ZXN0NDk2MzQzMjEy

Elasticsearch index docs

closed

MEMBER

I added the docs for ES indexes. I also added a `load_elasticsearch_index` method to load an index that has already been built. I checked the tests for the ES index and we have tests that mock ElasticSearch. I think this is good for now but at some point it would be cool to have an end-to-end test with a real ES running.

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