Datasets:

q-allen

/

github-issues

like 0

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[ "Very cool to have this dataset, thank you for adding it :)" ]

"2020-06-29T19:11:16Z"

"2020-06-30T17:07:31Z"

"2020-06-30T17:07:31Z"

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[ "The glue metric for cola is a metric for classification. It expects label ids as integers as inputs.", "I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?\r\n\r\nusing BertTokenizer;\r\n```\r\nencoded_reference=tokenizer.encode(reference, add_special_tokens=False)\r\nencoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)\r\n```\r\n\r\n`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`\r\n```\r\n\r\nValueError Traceback (most recent call last)\r\n<ipython-input-9-4c3a3ce7b583> in <module>()\r\n----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)\r\n\r\n6 frames\r\n/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n 198 predictions = self.data[\"predictions\"]\r\n 199 references = self.data[\"references\"]\r\n--> 200 output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n 201 return output\r\n 202 \r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in _compute(self, predictions, references)\r\n 101 return pearson_and_spearman(predictions, references)\r\n 102 elif self.config_name in [\"mrpc\", \"qqp\"]:\r\n--> 103 return acc_and_f1(predictions, references)\r\n 104 elif self.config_name in [\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]:\r\n 105 return {\"accuracy\": simple_accuracy(predictions, references)}\r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in acc_and_f1(preds, labels)\r\n 60 def acc_and_f1(preds, labels):\r\n 61 acc = simple_accuracy(preds, labels)\r\n---> 62 f1 = f1_score(y_true=labels, y_pred=preds)\r\n 63 return {\r\n 64 \"accuracy\": acc,\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\r\n 1097 pos_label=pos_label, average=average,\r\n 1098 sample_weight=sample_weight,\r\n-> 1099 zero_division=zero_division)\r\n 1100 \r\n 1101 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\r\n 1224 warn_for=('f-score',),\r\n 1225 sample_weight=sample_weight,\r\n-> 1226 zero_division=zero_division)\r\n 1227 return f\r\n 1228 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\r\n 1482 raise ValueError(\"beta should be >=0 in the F-beta score\")\r\n 1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n-> 1484 pos_label)\r\n 1485 \r\n 1486 # Calculate tp_sum, pred_sum, true_sum ###\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\r\n 1314 raise ValueError(\"Target is %s but average='binary'. Please \"\r\n 1315 \"choose another average setting, one of %r.\"\r\n-> 1316 % (y_type, average_options))\r\n 1317 elif pos_label not in (None, 1):\r\n 1318 warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\r\n\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n\r\n```", "MRPC is also a binary classification task, so its metric is a binary classification metric.\r\n\r\nTo evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).", "Closing this one. Feel free to re-open if you have other questions :)" ]

"2020-06-29T16:53:48Z"

"2020-07-09T09:13:34Z"

"2020-07-09T09:13:34Z"

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[ "Sorry for the long diff, everything after the imports comes from `black` for code quality :/ ", " I think it's fine and I can't think of another way to make the import work anyways.\r\n\r\nMaybe we can have the `sys.path` behavior inside `prepare_module` instead ? Currently it seems to come out of nowhere in the code ^^'\r\nWe could check if external imports have a `__init__.py` and if it is the case then we can add to directory to the `PYTHONPATH`" ]

"2020-06-29T16:46:01Z"

"2020-07-30T14:00:23Z"

"2020-07-30T14:00:23Z"

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"2020-06-29T15:47:47Z"

"2020-07-02T08:33:33Z"

"2020-07-02T08:33:32Z"

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[ "It looks like it comes from `mwparserfromhell`.\r\n\r\nWould it be possible to get the bad `section` that causes this issue ? The `section` string is from `datasets/wikipedia.py:L548` ? You could just add a `try` statement and print the section if the line `section_text.append(section.strip_code().strip())` crashes.\r\n\r\nIt will help us know if we have to fix it on our side or if it is a `mwparserfromhell` issue.", "Hi, \r\n\r\nThank you for you answer.\r\nI have try to print the bad section using `try` and `except`, but it is a bit weird as the error seems to appear 3 times for instance, but the two first error does not print anything (as if the function did not go in the `except` part).\r\nFor the third one, I got that (I haven't display the entire text) :\r\n\r\n> error : ==== Parque nacional Cajas ====\r\n> {{AP|Parque nacional Cajas}}\r\n> [[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\n> El parque nacional Cajas está situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n> [[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos más comunes al parque inician todos en Cuenca: Desde allí, la vía Cuenca-Mol\r\n> leturo atraviesa en Control de [[Surocucho]] en poco más de 30 minutos de viaje; más adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde están el Centro Administrativo y de Información del parque. Siguiendo de largo hacia [[Molleturo]], por esta vía se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n> Para acceder al parque desde la costa, la vía Molleturo-Cuenca es también la mejor opción.\r\n\r\nHow can I display the link instead of the text ? I suppose it will help you more ", "The error appears several times as Apache Beam retries to process examples up to 4 times irc.\r\n\r\nI just tried to run this text into `mwparserfromhell` but it worked without the issue.\r\n\r\nI used this code (from the `wikipedia.py` script):\r\n```python\r\nimport mwparserfromhell as parser\r\nimport re\r\nimport six\r\n\r\nraw_content = r\"\"\"==== Parque nacional Cajas ====\r\n{{AP|Parque nacional Cajas}}\r\n[[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\nEl parque nacional Cajas está situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n[[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos más comunes al parque inician todos en Cuenca: Desde allí, la vía Cuenca-Mol\r\nleturo atraviesa en Control de [[Surocucho]] en poco más de 30 minutos de viaje; más adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde están el Centro Administrativo y de Información del parque. Siguiendo de largo hacia [[Molleturo]], por esta vía se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n\"\"\"\r\n\r\nwikicode = parser.parse(raw_content)\r\n\r\n# Filters for references, tables, and file/image links.\r\nre_rm_wikilink = re.compile(\"^(?:File|Image|Media):\", flags=re.IGNORECASE | re.UNICODE)\r\n\r\ndef rm_wikilink(obj):\r\n return bool(re_rm_wikilink.match(six.text_type(obj.title)))\r\n\r\ndef rm_tag(obj):\r\n return six.text_type(obj.tag) in {\"ref\", \"table\"}\r\n\r\ndef rm_template(obj):\r\n return obj.name.lower() in {\"reflist\", \"notelist\", \"notelist-ua\", \"notelist-lr\", \"notelist-ur\", \"notelist-lg\"}\r\n\r\ndef try_remove_obj(obj, section):\r\n try:\r\n section.remove(obj)\r\n except ValueError:\r\n # For unknown reasons, objects are sometimes not found.\r\n pass\r\n\r\nsection_text = []\r\nfor section in wikicode.get_sections(flat=True, include_lead=True, include_headings=True):\r\n for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):\r\n try_remove_obj(obj, section)\r\n for obj in section.ifilter_templates(matches=rm_template, recursive=True):\r\n try_remove_obj(obj, section)\r\n for obj in section.ifilter_tags(matches=rm_tag, recursive=True):\r\n try_remove_obj(obj, section)\r\n\r\n section_text.append(section.strip_code().strip())\r\n```", "Not sure why we're having this issue. Maybe could you get also the file that's causing that ?", "thanks for your answer.\r\nHow can I know which file is causing the issue ? \r\nI am trying to load the spanish wikipedia data. ", "Because of the way Apache Beam works we indeed don't have access to the file name at this point in the code.\r\nWe'll have to use some tricks I think :p \r\n\r\nYou can append `filepath` to `title` in `wikipedia.py:L512` for example. [[EDIT: it's L494 my bad]]\r\nThen just do `try:...except:` on the call of `_parse_and_clean_wikicode` L500 I guess.\r\n\r\nThanks for diving into this ! I tried it myself but I run out of memory on my laptop\r\nAs soon as we have the name of the file it should be easier to find what's wrong.", "Thanks for your help.\r\n\r\nI tried to print the \"title\" of the document inside the` except (mwparserfromhell.parser.ParserError) as e`,the title displayed was : \"Campeonato Mundial de futsal de la AMF 2015\". (Wikipedia ES) Is it what you were looking for ?", "Thanks a lot @Shiro-LK !\r\n\r\nI was able to reproduce the issue. It comes from [this table on wikipedia](https://es.wikipedia.org/wiki/Campeonato_Mundial_de_futsal_de_la_AMF_2015#Clasificados) that can't be parsed.\r\n\r\nThe file in which the problem occurs comes from the wikipedia dumps, and it can be downloaded [here](https://dumps.wikimedia.org/eswiki/20200501/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2)\r\n\r\nParsing the file this way raises the parsing issue:\r\n\r\n```python\r\nimport mwparserfromhell as parser\r\nfrom tqdm.auto import tqdm\r\nimport bz2\r\nimport six\r\nimport logging\r\nimport codecs\r\nimport xml.etree.cElementTree as etree\r\n\r\nfilepath = \"path/to/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2\"\r\n\r\ndef _extract_content(filepath):\r\n \"\"\"Extracts article content from a single WikiMedia XML file.\"\"\"\r\n logging.info(\"generating examples from = %s\", filepath)\r\n with open(filepath, \"rb\") as f:\r\n f = bz2.BZ2File(filename=f)\r\n if six.PY3:\r\n # Workaround due to:\r\n # https://github.com/tensorflow/tensorflow/issues/33563\r\n utf_f = codecs.getreader(\"utf-8\")(f)\r\n else:\r\n utf_f = f\r\n # To clear root, to free-up more memory than just `elem.clear()`.\r\n context = etree.iterparse(utf_f, events=(\"end\",))\r\n context = iter(context)\r\n unused_event, root = next(context)\r\n for unused_event, elem in tqdm(context, total=949087):\r\n if not elem.tag.endswith(\"page\"):\r\n continue\r\n namespace = elem.tag[:-4]\r\n title = elem.find(\"./{0}title\".format(namespace)).text\r\n ns = elem.find(\"./{0}ns\".format(namespace)).text\r\n id_ = elem.find(\"./{0}id\".format(namespace)).text\r\n # Filter pages that are not in the \"main\" namespace.\r\n if ns != \"0\":\r\n root.clear()\r\n continue\r\n raw_content = elem.find(\"./{0}revision/{0}text\".format(namespace)).text\r\n root.clear()\r\n\r\n if \"Campeonato Mundial de futsal de la AMF 2015\" in title:\r\n yield (id_, title, raw_content)\r\n\r\nfor id_, title, raw_content in _extract_content(filepath):\r\n wikicode = parser.parse(raw_content)\r\n```\r\n\r\nThe copied the raw content that can't be parsed [here](https://pastebin.com/raw/ZbmevLyH).\r\n\r\nThe minimal code to reproduce is:\r\n```python\r\nimport mwparserfromhell as parser\r\nimport requests\r\n\r\nraw_content = requests.get(\"https://pastebin.com/raw/ZbmevLyH\").content.decode(\"utf-8\")\r\nwikicode = parser.parse(raw_content)\r\n\r\n```\r\n\r\nI will create an issue on mwparserfromhell's repo to see if we can fix that\r\n", "This going to be fixed in the next `mwparserfromhell` release :)", "Fixed in `mwparserfromhell` version 0.6." ]

"2020-06-29T11:10:43Z"

"2022-02-14T15:21:46Z"

"2022-02-14T15:21:46Z"

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[ "I wonder if this means downloading failed? That corpus has a really slow server.", "This dataset seems to have a decoding problem that results in inconsistencies in the number of generated examples.\r\nSee #215.\r\nThat's why we end up with a `NonMatchingSplitsSizesError `." ]

"2020-06-29T07:36:35Z"

"2020-06-29T14:44:42Z"

"2020-06-29T14:44:42Z"

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[ "Oh yes, this is a backward compatibility feature with tensorflow_dataset in which a `Sequence` or `dict` is converted in a `dict` of `lists`, unfortunately it is not very intuitive, see here: https://github.com/huggingface/nlp/blob/master/src/nlp/features.py#L409\r\n\r\nTo avoid this behavior, you can just define the list in the feature with a simple list or a tuple (which is also simpler to write).\r\nIn your case, the features could be as follow:\r\n``` python\r\n...\r\nfeatures=nlp.Features({\r\n \"title\": nlp.Value(\"string\"),\r\n \"vertexSet\": [[{\r\n \"name\": nlp.Value(\"string\"),\r\n \"sent_id\": nlp.Value(\"int32\"),\r\n \"pos\": nlp.features.Sequence(nlp.Value(\"int32\")),\r\n \"type\": nlp.Value(\"string\"),\r\n }]],\r\n ...\r\n }),\r\n...\r\n```" ]

"2020-06-27T23:45:17Z"

"2020-07-03T10:22:00Z"

"2020-07-03T10:22:00Z"

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[ "It's definitely going in the right direction ! Thanks for giving it a try\r\n\r\nI really like the API.\r\nIMO it's fine right now if we don't have all the dataset transforms (map, filter, etc.) as it can be done before building the multitask dataset, but it will be important to have them in the end.\r\nAll the formatting methods could easily be added though.\r\n\r\nI think there are some parts that will require some work with apache arrow like slicing. I can find a way to do it using pyarrow tables concatenation (I did something similar when implementing `__getitem__` with an input that is a list of indices [here](https://github.com/huggingface/nlp/pull/322/files#diff-73270df8d7f08c62a27e40806e1a5fb0R463-R469)). It is very fast and it allows to have the same output format as a normal Dataset.\r\n\r\nAlso maybe we should check that not only the columns but also the schemas match ?\r\nAnd maybe add the `seed` of the shuffling step as an argument ?\r\n\r\n", "Maybe we should remove the methods that are not implemented for now, WDYT @thomwolf ?", "That's an interesting first draft, thanks a lot for that and the user facing API is really nice.\r\n\r\nI think we should dive more into this and the questions of #217 before merging the first version though.\r\n\r\nIn particular, the typical way to do multi-tasking is usually to sample a task and then sample a batch within the selected task. I think we should probably stay be closer to this traditional approach, or at least make it very easy to do, rather than go to close to the T5 approach which is very specific to this paper.\r\n\r\nIn this regard, it seems important to find some way to address the remarks of @zphang. I'm still wondering if we should not adopt more of a sampling approach rather than an iteration approach.", "@thomwolf Thanks! I mainly wanted to get something working quickly for my own MTL research. I agree with a lot of the points you made so I'll convert this pull request back to a draft.\r\n\r\nFor your specific point about 'batch-level' multitask mixing, it would be a pretty trivial change to add a `batch_size` parameter and ensure every `batch_size` examples are from the same task. This would certainly work, but would add a notion of 'batches' to a Dataset, which does feel like a 'Sampler-level' concept and not a Dataset one. There's also the possibility of wanting some specific task-level sampling functionality (e.g. applying `SortishSampler` to each task) which would only work with this kind of 2 step sampling approach. My first proposal in the transformers repo was actually a Sampler https://github.com/huggingface/transformers/issues/4340. I wonder whether functionality at the sampler-level has a place in the vision for the `nlp` repo?\r\n\r\nI imagine following a sampling approach you'd have to abandon maintaining the same user-facing API as a standard dataset (A shame because replacing a single dataset seamlessly with a multitask one is a really nice user-experience).\r\n\r\nRandom half-Idea: You could have a class which accepts a list of any iterables (either a Dataset or a DataLoader which already is doing the batching). Not sure what interface you'd present though. hmmm. \r\n\r\nThere's definitely more discussion to have. \r\n", "Are there any updates on making multi-task learning more officially supported in the datasets/transformers libraries? \r\nGiven that many papers use more than one task, it would be great to have multi-task learning more officially supported and easier to use. There are a few notebooks/blogs about using HF Transformers for this, but they all mention that it's more of a hack and not really officially supported (e.g. [this notebook](https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb#scrollTo=xW8bnTgCsx5c), or [this blog](https://medium.com/@shahrukhx01/multi-task-learning-with-transformers-part-1-multi-prediction-heads-b7001cf014bf)). \r\n\r\n[jiant](https://github.com/nyu-mll/jiant) was a framework built on transformers that made multi-task learning a first class feature of the library until recently, but they stopped maintaining their library a month ago ([see here](https://github.com/nyu-mll/jiant)). \r\nThis could be a good reason to increase support from the HF team? @lhoestq @thomwolf \r\n\r\nI'm not advanced enough to contribute on this, but an up-to-date notebook showing how to train a model e.g. on both MLM and next-sentence-prediction would already be very useful!", "> Are there any updates on making multi-task learning more officially supported in the datasets/transformers libraries? Given that many papers use more than one task, it would be great to have multi-task learning more officially supported and easier to use. There are a few notebooks/blogs about using HF Transformers for this, but they all mention that it's more of a hack and not officially supported (e.g. [this notebook](https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb#scrollTo=xW8bnTgCsx5c), or [this blog](https://medium.com/@shahrukhx01/multi-task-learning-with-transformers-part-1-multi-prediction-heads-b7001cf014bf)).\r\n> \r\n> [jiant](https://github.com/nyu-mll/jiant) was a framework built on transformers that made multi-task learning a first class feature of the library until recently, but they stopped maintaining their library a month ago. This could be a good reason to increase support from the HF team? @lhoestq\r\n> \r\n> I'm not advanced enough to contribute on this, but an up-to-date notebook showing how to train a model e.g. on both MLM and NSP would already be very useful!\r\n\r\nI kinda stopped working on this as I didn't really get any response on an actual workable solution.\r\n\r\nThe problem that I came up against after initially being redirected here after [proposing this in the transformers repo](https://github.com/huggingface/transformers/issues/4340) ([among](https://github.com/huggingface/transformers/issues/6872) [others](https://github.com/huggingface/transformers/issues/1856)) , was the request be able to do the multitask mixing at the batch level as well as at the level of individual examples. As this repo doesn't really have the concept of 'batches' it would need to be implemented in the transformers repo, rather than here. You could then pick which level to do your multitask learning on.\r\n\r\nWork on T5 and as of last week, on [exT5](https://arxiv.org/pdf/2111.10952.pdf), have shown that multitask mixing on the example level works incredibly well (with a big enough batch size), so if you're ok doing that, then this pull request works.\r\n\r\nI completely agree that multitask learning is a vital part of modern NLP, nearly every piece of research code I write has at least some aspect of multitask learning (currently using this patch). Many of the top GLUE and SuperGLUE submissions are using some aspect of mutlitask learning. We need to support it.", "Fully agree. Batching and data loading is one important thing. The part I'm struggling with right now is the classification head (which is more part of the Transformers repo, but also essential for multi-task learning). @ghomasHudson, how do you tune two classification heads simultaneously? Say, when I want to fine-tune an existing base-model on some classification task (like NLI, or next-sentence-prediction) and at the same time add some MLM for regularisation & domain adaptation. In this case I need two classification heads, but I don't know how to switch them between the batches. ", "> Fully agree. Batching and data loading is one important thing. The part I'm struggling with right now is the classification head (which is more part of the Transformers repo, but also essential for multi-task learning). @ghomasHudson, how do you tune two classification heads simultaneously? Say, when I want to fine-tune an existing base-model on some classification task (like NLI, or next-sentence-prediction) and at the same time add some MLM for regularisation & domain adaptation. In this case I need two classification heads, but I don't know how to switch them between the batches.\r\n\r\nThis pull request is mainly focused on getting the data in the right format, but you're right that there's no easy way to pick between the heads without something like jiant. You could of course replicate this functionality yourself - probably by making a class that implements the functionality of both `ModelNameForSequenceClassification` or `ModelNameForMaskedLM` picking between them depending on some task parameter you add to the forward pass. \r\n\r\njiant make this approach model agnostic by [ignoring the custom per-model head implementations of huggingface](https://github.com/nyu-mll/jiant/blob/386d4e726a27becda1b03c241f064eb13c54860f/jiant/proj/main/modeling/heads.py#L17-L18), instead making generic versions. Then the jiant code [passes a `task` parameter](https://github.com/nyu-mll/jiant/blob/386d4e726a27becda1b03c241f064eb13c54860f/jiant/proj/main/modeling/primary.py#L107-L109) into their [JiantModel](https://github.com/nyu-mll/jiant/blob/386d4e726a27becda1b03c241f064eb13c54860f/jiant/proj/main/modeling/primary.py#L36-L79) wrapper. To implement this in huggingface transformers would require quite a few modifications to the current approach (potentially interfering with some other project aims e.g. code readability), so you might find it tricky to get a change like that accepted. It would be super cool though.\r\n\r\nAnd there's of course the exT5 way of doing things too where you sidestep this issue entirely by treating both tasks as text-to-text problems so you can end up with 100% shared parameters, e.g.\r\nMLM: `Lorem <mask_0> amet, consectetur <mask_1> do eiusmod tempor incididunt ut labore <mask_2>`\r\nNLI: `Premise: The Old One always comforted Ca'daan, except today. hypothesis: Ca'daan knew the Old One very well.`\r\nThis also allows you to do mixed batches of both tasks.\r\n\r\nPersonally, my research mainly focuses on this last approach, using the structure of the data itself to indicate the task rather than swapping in and out different parts of the network.", "Hi! `jiant` maintainer here, don't have much to add to the conversation yet but I'm happy to share my experience/thoughts on working with Multitask models if people have questions.", "Hi ! I think it could be easier to simply share as examples in `transformers` some code that uses `jiant` and/or subclass/reimplement some part of `transformers` for multitask ?", "> Hi ! I think it could be easier to simply share as examples in `transformers` some code that uses `jiant` and/or subclass/reimplement some part of `transformers` for multitask ?\r\n\r\nWell since `jiant` requires new huggingface models to be explicitly added (as there are [\"subtle differences in the models that jiant must abstract\"](https://github.com/nyu-mll/jiant/blob/master/guides/models/adding_models.md)), and isn't being maintained anymore, then the first option might be out of date quickly.\r\n\r\nIf `transformers` could move towards making the task-specific heads more generic and as well as [creating a new base model in the `__init__` method](https://github.com/huggingface/transformers/blob/43f953cc2eec804eba04e2a9ae164d1a33fd97a8/src/transformers/models/bert/modeling_bert.py#L1502), allowing it to be passed as an argument (along with other little tweaks to standardize the approach), then this functionality could be moved into `transformers` itself.\r\n\r\nIt does seem a little redundant to have `jiant` as a library abstracting all the idiosyncrasies of each model type, where this could be done directly in the `transformers` repo in a single place alongside the model.\r\n\r\nIt's not an easy problem to solve though, especially balanced with the desire to expose models with minimal abstraction. @zphang probably knows more about this than me though.", "As mentioned, one of the main obstacles is that HF/T doesn't support generic heads. At first glance, this should be easy, since the interface is quite simple: models output both a token-wise and a sequence representation (e.g. `[CLS]`), and heads use either one and output the corresponding predictions/losses.\r\n\r\nHowever, there are a number of cases where this doesn't work. One of them is multiple-choice tasks like HellaSwag, which is a multiple choice task with 4 text options. The way this is normally formatted is that you encode `context + question + option_X` for X=1..4, and then score all four options based on a scoring head and pick the highest scoring option as the prediction. This requires you to run the encoder on 4 separate inputs, which breaks the above abstraction (the task-specific model might need to call the encoder multiple times).\r\n\r\nAnother thing is batching. You can imagine with the above that you might want a different batch size for multiple-choice tasks compared to simpler classification tasks. This means you need task-specific batching as well. In addition, [it's been shown](https://arxiv.org/abs/2101.11038) that you really want to mix tasks within a single batch. This also leads into issues like how you want to sample different task examples, early stopping on them, how to mix the validation scores, etc. (`jiant` addressed these, through probably more-complicated-than-necessary configurations.)\r\n\r\nNone of these are insurmountable problems, but it requires some tweaking of the current code layout to get it to work. I would guess that it wouldn't take much work to get a 90% implementation.", "> Another thing is batching. You can imagine with the above that you might want a different batch size for multiple-choice tasks compared to simpler classification tasks. This means you need task-specific batching as well. In addition, [it's been shown](https://arxiv.org/abs/2101.11038) that you really want to mix tasks within a single batch. This also leads into issues like how you want to sample different task examples, early stopping on them, how to mix the validation scores, etc. (`jiant` addressed these, through probably more-complicated-than-necessary configurations.)\r\n\r\nThat's reassuring. exT5 find the same thing - that mixing tasks together in a batch gives better performance (provided the batch size is big enough that each batch contains a mix of different tasks). Assuming this, we can ignore doing things at the batch-level and just do this at the individual example level - in which case this pull request already does the data mixing part of the problem! Balancing different tasks could easily be added here by implementing temperature-scaled mixing, custom weights, etc...\r\n\r\nTo make a generic implementation of this using different heads would be hard (impossible?) without doing the sub-batching that Muppet do - in which case we're back at dealing with the 'batch' (sub-batch) level which would need an implementation in `transformers` not here.\r\n\r\n", "Mixing at example should work fine. One issue though is that, as mentioned above, different tasks maybe actually require different amounts of memory, so downstream the user would have to find some way to handle that. But this might be one of those \"the last/edge-case 10% is the hardest\" to handle kind of deals.", "Very true - there's always going to be those cases. I also feel that the way things are going, if we just leave this for a few years no one will be wanting to use task-specific heads anymore - it'll all be task prompts included in the input a-la GPT, T5, etc... which will make this substantially simpler to implement.\r\n\r\nIt's quite tricky to make a suitably non-opinionated generic version of this at the moment.", "> Is there an advantage to varying the proportions of each task in each batch\r\n\r\nSome tasks have much less data than others. E.g. SNLI vs. CoLA is almost a 100x difference, so people often sample differently-sized tasks differently.", "As a short-term solution, I like @lhoestq's suggestion to create a notebook that shows how to implement multi-task learning by subclassing some transformer & dataset classes in a general way. I've been trying to get @zphang's [great but old notebook](https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb#scrollTo=CQ39AbTAPAUi) on multi-task learning running today and I didn't get it to work, probably because it was implemented a long time ago with `transformers==2.11`, `torch==1.2`~ etc and installing older versions still caused errors.\r\nThere is also this [interesting new repo](https://github.com/shahrukhx01/multitask-learning-transformers), which has a cool way of enabling you to save and load a model with two classification heads ([see model here](https://huggingface.co/shahrukhx01/bert-multitask-query-classifiers) and blog post [here](https://medium.com/@shahrukhx01/multi-task-learning-with-transformers-part-1-multi-prediction-heads-b7001cf014bf)). Haven't tried it yet, but it only uses `BertForSequenceClassification` instead of the more general AutoModelForXYZ\r\n\r\n@zphang, would you maybe be up for contributing an updated version of your older notebook with the latest version of `transformers` and `datasets` which runs in today's colabs? I feel like this would be very helpful for the community and if you keep the classes/functions somewhat general, people can easily adapt it to their use cases! 🙏 :) \r\nWould be a great addition to the [HF notebooks](https://huggingface.co/docs/transformers/notebooks).\r\n\r\nIn the medium-term, I agree that it would be great to have more native support for this via the HF libraries. I feels weird that you can neither train the old BERT (trained on two tasks) nor any of the newer models, without some hacks. ", "@zphang would love to see the newer notebook as suggested by @MoritzLaurer " ]

"2020-06-27T13:27:29Z"

"2022-07-06T15:19:57Z"

"2022-07-06T15:19:57Z"

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[ "For one dataset you can have different configurations that each have their own `nlp.Features`.\r\nWe imagine having one configuration per subtask for example.\r\nThey are loaded with `nlp.load_dataset(\"my_dataset\", \"my_config\")`.\r\n\r\nFor example the `glue` dataset has many configurations. It is a bit different from your case though because each configuration is a dataset by itself (sst2, mnli).\r\nAnother example is `wikipedia` that has one configuration per language." ]

"2020-06-26T23:14:19Z"

"2020-10-27T15:36:52Z"

"2020-10-27T15:36:52Z"

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[ "Thanks @jxmorris12 for adding this adding. \r\nCan you please add a small description of the PR?" ]

"2020-06-26T16:11:58Z"

"2020-06-30T09:58:08Z"

"2020-06-30T08:31:55Z"

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[ "Update: I think I've found a solution.\r\n\r\n```python\r\noutput_types = {\"input_ids\": tf.int64, \"token_type_ids\": tf.int64, \"attention_mask\": tf.int64}\r\ndef train_dataset_gen():\r\n for i in range(len(train_dataset)):\r\n yield train_dataset[i]\r\ntf_dataset = tf.data.Dataset.from_generator(train_dataset_gen, output_types=output_types)\r\n```\r\n\r\nloads WikiText-2 in 20 ms, and WikiText-103 in 20 ms. It appears to be lazily loading via indexing train_dataset.", "Yes this is the current best solution. We should probably show it in the tutorial notebook.\r\n\r\nNote that this solution unfortunately doesn't allow to train on TPUs (yet). See #193 ", "This approach still seems quite slow. When using TFRecords with a similar training loop, I get ~3.0-3.5 it/s on multi-node, multi-GPU training. I notice a pretty severe performance regression when scaling, with observed performance numbers. Since the allreduce step takes less than 100ms/it and I've achieved 80% scaling efficiency up to 64 GPUs, it must be the data pipeline.\r\n\r\n| Nodes | GPUs | Iterations/Second |\r\n| --- | --- | --- |\r\n| 1 | 2 | 2.01 |\r\n| 1 | 8 | 0.81 |\r\n| 2 | 16 | 0.37 |\r\n\r\nHere are performance metrics over 10k steps. The iteration speed appears to follow some sort of caching pattern. I would love to use `nlp` in my project, but a slowdown from 3.0 it/s to 0.3 it/s is too great to stomach.\r\n\r\n<img width=\"1361\" alt=\"Screen Shot 2020-07-02 at 8 29 22 AM\" src=\"https://user-images.githubusercontent.com/4564897/86378156-2f8d3900-bc3e-11ea-918b-c395c3df5377.png\">\r\n", "An interesting alternative to investigate here would be to use the tf.io library which has some support for Arrow to TF conversion: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset\r\n\r\nThere are quite a few types supported, including lists so if the unsupported columns are dropped then we could maybe have a zero-copy mapping from Arrow to TensorFlow, including tokenized inputs and 1D tensors like the ones we mostly use in NLP: https://github.com/tensorflow/io/blob/322b3170c43ecac5c6af9e39dbd18fd747913e5a/tensorflow_io/arrow/python/ops/arrow_dataset_ops.py#L44-L72\r\n\r\nHere is an introduction on Arrow to TF using tf.io: https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f", "Interesting. There's no support for strings, but it does enable int and floats so that would work for tokenized inputs. \r\n\r\nArrowStreamDataset requires loading from a \"record batch iterator\", which can be instantiated from in-memory arrays as described here: https://arrow.apache.org/docs/python/ipc.html. \r\n\r\nBut the nlp.Dataset stores its data as a `pyarrow.lib.Table`, and the underlying features are `pyarrow.lib.ChunkedArray`. I can't find any documentation about lazily creating a record batch iterator from a ChunkedArray or a Table. Have you had any success?\r\n\r\nI can't find [any uses](https://grep.app/search?q=ArrowDataset&filter[lang][0]=Python) of tfio.arrow.ArrowDataset on GitHub.", "You can use `to_batches` maybe?\r\nhttps://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_batches", "Also note that since #322 it is now possible to do\r\n```python\r\nids = [1, 10, 42, 100]\r\nbatch = dataset[ids]\r\n```\r\nFrom my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).\r\nLet me know if you think there could be a better way to implement it. (current code is [here](https://github.com/huggingface/nlp/blob/78628649962671b4aaa31a6b24e7275533416845/src/nlp/arrow_dataset.py#L463))", "Thanks @lhoestq! That format is much better to work with.\r\n\r\nI put together a benchmarking script. This doesn't measure the CPU-to-GPU efficiency, nor how it scales with multi-GPU multi-node training where many processes are making the same demands on the same dataset. But it does show some interesting results:\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport time\r\n\r\ndset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\")\r\ndset = dset.filter(lambda ex: len(ex[\"text\"]) > 0)\r\nbsz = 1024\r\nn_batches = 100\r\n\r\ndef single_item_gen():\r\n for i in range(len(dset)):\r\n yield dset[i]\r\n\r\ndef sequential_batch_gen():\r\n for i in range(0, len(dset), bsz):\r\n yield dset[i:i+bsz]\r\n\r\ndef random_batch_gen():\r\n for i in range(len(dset)):\r\n indices = list(np.random.randint(len(dset), size=(bsz,)))\r\n yield dset[indices]\r\n\r\noutput_types = {\"text\": tf.string}\r\nsingle_item = tf.data.Dataset.from_generator(single_item_gen, output_types=output_types).batch(bsz)\r\ninterleaved = tf.data.Dataset.range(10).interleave(\r\n lambda idx: tf.data.Dataset.from_generator(single_item_gen, output_types=output_types),\r\n cycle_length=10,\r\n)\r\nsequential_batch = tf.data.Dataset.from_generator(sequential_batch_gen, output_types=output_types)\r\nrandom_batch = tf.data.Dataset.from_generator(random_batch_gen, output_types=output_types)\r\n\r\ndef iterate(tf_dset):\r\n start = time.perf_counter()\r\n for i, batch in enumerate(tf_dset.take(n_batches)):\r\n pass\r\n elapsed = time.perf_counter() - start\r\n print(f\"{tf_dset} took {elapsed:.3f} secs\")\r\n\r\niterate(single_item)\r\niterate(interleaved)\r\niterate(sequential_batch)\r\niterate(random_batch)\r\n```\r\n\r\nResults:\r\n```\r\n<BatchDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 23.005 secs\r\n<InterleaveDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.135 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.074 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.550 secs\r\n```\r\n\r\n- Batching a generator which fetches a single item is terrible.\r\n- Interleaving performs well on a single process, but doesn't scale well to multi-GPU training. I believe the bottleneck here is in Arrow dataset locking or something similar. The numbers from the table above are with interleaving.\r\n- The sequential access dominates the random access (7x faster). Is there any way to bring random access times closer to sequential access? Maybe re-indexing the dataset after shuffling each pass over the data.", "Hey @jarednielsen \r\n\r\nThanks for this very interesting analysis!! IMHO to read text data one should use `tf.data.TextLineDataset`. It would be interesting to compare what you have done with simply load with a `TextLineDataset` and see if there is a difference.\r\n\r\nA good example can be found here https://www.tensorflow.org/tutorials/load_data/text", "Thanks! I'm not actually loading in raw text data, that was just the synthetic data I created for this benchmark. A more realistic use case would be a dataset of tokenized examples, which would be a dict of lists of integers. TensorFlow's TextLineDataset greedily loads the dataset into the graph itself, which can lead to out-of-memory errors - one of the main reason I'm so drawn to the `nlp` library is its zero-copy no-RAM approach to dataset loading and mapping. \r\n\r\nIt's quite helpful for running a preprocessing pipeline - a sample ELECTRA pipeline I've built is here: https://github.com/jarednielsen/deep-learning-models/blob/nlp/models/nlp/common/preprocess.py.", "Sorry, I think I badly expressed myself, my bad. What I suggested is to compare with the usual loading textual data in pure TF with `TextLineDataset` with `nlp`. I know it is not recommended with very large datasets to use it, but I was curious to see how it behaves compared to a processing with `nlp` on smaller datasets.\r\n\r\nBTW your script looks very interesting, thanks for sharing!!" ]

"2020-06-25T22:30:20Z"

"2020-10-27T15:38:17Z"

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[ "Thank you BatJeti! The storm-joker, aka the typo, finally got caught!" ]

"2020-06-25T10:45:59Z"

"2020-06-26T08:46:41Z"

"2020-06-25T12:43:59Z"

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[ "Looks good to me" ]

"2020-06-25T09:22:02Z"

"2020-06-30T08:28:11Z"

"2020-06-30T08:28:11Z"

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[ "Hi Jared,\r\nInteresting, thanks for raising this question. You can also do that after loading with `dataset.select()` or `dataset.filter()` which let you keep only a specific subset of rows in a dataset.\r\nWhat is your use-case for sharding?", "Thanks for the pointer to those functions! It's still a little more verbose since you have to manually calculate which ids each rank would keep, but definitely works.\r\n\r\nMy use case is multi-node, multi-GPU training and avoiding global batches of duplicate elements. I'm using horovod. You can shuffle indices, or set random seeds, but explicitly sharding the dataset up front is the safest and clearest way I've found to do so." ]

"2020-06-24T22:48:33Z"

"2020-07-06T12:35:36Z"

"2020-07-06T12:35:36Z"

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"2020-06-24T22:17:22Z"

"2020-06-29T16:37:38Z"

"2020-06-29T16:37:38Z"

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[ "That's great work @ghomasHudson !" ]

"2020-06-24T18:00:35Z"

"2020-06-25T12:32:25Z"

"2020-06-25T12:32:25Z"

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[ "Does it make sense to download the full stories? I remember attempting to implement this dataset a while ago and ended up with something like:\r\n```python\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n data_dir = os.path.join(dl_dir, \"narrativeqa-master\")\r\n\r\n urls = {\"test\":{}, \"train\": {},\"valid\":{}}\r\n with open(os.path.join(data_dir,\"documents.csv\")) as f_in:\r\n csv_reader = csv.reader(f_in)\r\n next(csv_reader) # discard header row\r\n for i,row in enumerate(csv_reader):\r\n if i > 1572:\r\n break\r\n if row != []:\r\n urls[row[1]][row[0]] = row[3]\r\n\r\n url_files = {}\r\n for key in urls.keys():\r\n url_files[key] = dl_manager.download_and_extract(urls[key])\r\n\r\n return [\r\n nlp.SplitGenerator(\r\n name=nlp.Split.TRAIN,\r\n gen_kwargs={\r\n \"data_dir\":data_dir,\r\n \"split\":\"train\",\r\n \"doc_id_to_path\":url_files[\"train\"]\r\n }\r\n ),\r\n ....\r\n```\r\nIt does end up cluttering your huggingface cache dir though.", "Also since there doesn't seem to be any meaning in the order of answer_1 and answer_2, it might make sense to combine them (see [squad.py](https://github.com/huggingface/nlp/blob/8b0ffc85e4e52ae1f18d31be99b6c70b82c991ca/datasets/squad/squad.py#L86-L88)):\r\n```python\r\n\"answers\": nlp.features.Sequence({\r\n \"text\": nlp.Value(\"string\"),\r\n \"tokenized\": nlp.features.Sequence(nlp.Value(\"string\"))\r\n})\r\n```\r\n(the tokenized features should also probably be lists of strings not just strings - see [natural_questions.py](https://github.com/huggingface/nlp/blob/4cd34287300a1135ce7b22f6dd209ca305c71b3a/datasets/natural_questions/natural_questions.py#L83))\r\n\r\nAgain, this is a personal preference thing, but it might be useful to combine the document-related features:\r\n```python\r\n{\r\n \"document\": {\r\n \"id\": nlp.Value(\"string\"),\r\n \"kind\": nlp.Value(\"string\"),\r\n \"url\": nlp.Value(\"string\"),\r\n \"file_size\": nlp.Value(\"int32\"),\r\n \"word_count\": nlp.Value(\"int32\"),\r\n \"start\": nlp.Value(\"string\"),\r\n \"end\": nlp.Value(\"string\"),\r\n \"wiki_url\": nlp.Value(\"string\"),\r\n \"wiki_title\": nlp.Value(\"string\"),\r\n \"summary\": nlp.features.Sequence({\r\n \"text\": nlp.Value(\"string\"),\r\n \"tokens\": nlp.features.Sequence(nlp.Value(\"string\"))\r\n }),\r\n \"text\": nlp.Value(\"string\"),\r\n },\r\n \"question\": nlp.features.Sequence({\r\n \"text\": nlp.Value(\"string\"),\r\n \"tokens\": nlp.features.Sequence(nlp.Value(\"string\"))\r\n }),\r\n \"answers\": nlp.features.Sequence({\r\n \"text\": nlp.Value(\"string\"),\r\n \"tokens\": nlp.features.Sequence(nlp.Value(\"string\"))\r\n })\r\n}\r\n```", "Did you manage to fix the dummy data @Varal7 ?", "@lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.\r\n\r\nThe real value (and original purpose) of this dataset is doing question answering on the full text.", "> @lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.\r\n> \r\n> The real value (and original purpose) of this dataset is doing question answering on the full text.\r\n\r\nWhat's the problem exactly with the cache ?", "Nothing, just that because each story is a separate download it gets a bit messy as all 1573 files are under `~/.cache/hugginface/datasets` rather than organized under a subdir.\r\n\r\nProbably doesn't matter to the end user though.", "Yea I agree it's a mess. I just created #393 to make things easier.", "I got the PR merged to have a cleaner the cache directory (everything is downloaded inside the 'downloads' sub-directory).\r\nFeel free to download all the stories then @ghomasHudson @Varal7 x)\r\nIf you have the possibility of downloading a compressed file with most of the stories at once it would be better though.", "Looks good @lhoestq . The problem I'm having at the moment is that stories from project Gutenberg occasionally fail. All books are out of copyright so we should be able to host them. \r\n\r\nHere's a zip file of the full text if we have anywhere to put them: https://drive.google.com/file/d/17jOR7NqvzDwSlPXrlHaYV-PGI8JG-KY5/view?usp=sharing\r\n", "I put the zip file here @ghomasHudson \r\nhttps://storage.googleapis.com/huggingface-nlp/datasets/narrative_qa/narrativeqa_full_text.zip\r\n\r\nSorry for the delay", "Closing in favor of #499" ]

"2020-06-24T17:26:18Z"

"2020-09-03T09:02:10Z"

"2020-09-03T09:02:09Z"

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"2020-06-23T22:44:36Z"

"2020-06-25T12:52:21Z"

"2020-06-25T12:16:10Z"

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"2020-06-23T22:24:49Z"

"2020-06-25T12:16:09Z"

"2020-06-25T12:16:09Z"

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[ "@lucidrains - Thanks a lot for making the PR - PG19 is a super important dataset! Thanks for making it. Many people are asking for PG-19, so it would be great to have that in the library as soon as possible @thomwolf .", "@mariamabarham yup! around 11GB!", "I'm looking forward to our first deep learning written novel already lol. It's definitely happening", "Good to merge IMO.", "Oh I just noticed but as we changed the urls to download the files, we have to update `dataset_infos.json`.\r\nCould you re-rurn `nlp-cli test ./datasets/pg19 --save_infos` ?", "@lhoestq on it!", "should be good!", "@lhoestq - I think it's good to merge no?", "`dataset_infos.json` is still not up to date with the new urls (we can see that there are urls like `gs://deepmind-gutenberg/train/*` instead of `https://storage.googleapis.com/deepmind-gutenberg/train/*` in the json file)\r\n\r\nCan you check that you re-ran the command to update the json file, and that you pushed the changes @lucidrains ?", "@lhoestq ohhh, I made the change in this commit https://github.com/lucidrains/nlp/commit/f3e23d823ad9942031be80b7c4e4212c592cd90c , that's interesting that the pull request didn't pick it up. maybe it's because I did it on another machine, let me check and get back to you!", "@lhoestq wrong branch 😅 thanks for catching! ", "Awesome thanks 🎉" ]

"2020-06-23T22:03:52Z"

"2020-07-06T07:55:59Z"

"2020-07-06T07:55:59Z"

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"2020-06-23T21:09:05Z"

"2020-07-30T16:44:23Z"

"2020-07-30T16:44:23Z"

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"2020-06-23T19:32:05Z"

"2020-07-22T09:50:58Z"

"2020-07-22T09:50:58Z"

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"2020-06-23T14:56:08Z"

"2020-06-23T15:08:44Z"

"2020-06-23T15:08:43Z"

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[ "Even more complicating - \r\n\r\nAs I see it, datasets can have \"addons\".\r\nFor example, the WebNLG dataset is a dataset for data-to-text. However, a work of mine and other works enriched this dataset with text plans / underlying text structures. In that case, I see a need to load the dataset \"WebNLG\" with \"plans\" addon.\r\n\r\nSame for sign language - if there is a dataset of videos, one addon can be to run OpenPose, another to run ARKit4 pose estimation, and another to run PoseNet, or even just a video embedding addon. (which are expensive to run individually for everyone who wants to use these data)\r\n\r\nThis is something I dabbled with my own implementation to a [research datasets library](https://github.com/AmitMY/meta-scholar/) and I love to get the discussion going on these topics.", "This is a really cool idea !\r\nThe example for data objects you gave for the RWTH-PHOENIX-Weather 2014 T dataset can totally fit inside the library.\r\n\r\nFor your point about formats like `ilex`, `eaf`, or `srt`, it is possible to use any library in your dataset script.\r\nHowever most user probably won't need these libraries, as most datasets don't need them, and therefore it's unlikely that we will have them in the minimum requirements to use `nlp` (we want to keep it as light-weight as possible). If a user wants to load your dataset and doesn't have the libraries you need, an error is raised asking the user to install them.\r\n\r\nMore generally, we plan to have something like a `requirements.txt` per dataset. This could also be a place for addons as you said. What do you think ?", "Thanks, Quentin, I think a `requirements.txt` per dataset will be a good thing.\r\nI will work on adding this dataset next week, and once we sort all of the kinks, I'll add more." ]

"2020-06-23T14:53:40Z"

"2020-11-25T11:25:33Z"

"2020-11-25T11:25:33Z"

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[ "Whoops didn't mean to close this one.\r\nI did some changes, could you try to run it from the master branch ?", "Now it works, thanks!" ]

"2020-06-23T11:31:44Z"

"2020-06-24T07:05:07Z"

"2020-06-24T07:05:07Z"

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"2020-06-23T09:38:59Z"

"2020-06-23T14:47:38Z"

"2020-06-23T14:47:37Z"

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[ "I guess you can just rebase from master to fix the CI" ]

"2020-06-23T07:46:06Z"

"2020-06-23T08:10:46Z"

"2020-06-23T08:10:44Z"

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[ "Looks very cool! Only looked at it superficially though", "Alright I think I've checked all your comments, thanks :)\r\n\r\nMoreover I just added a way to serialize faiss indexes.\r\nThis is important because for big datasets the index construction can take some time.\r\n\r\nExamples:\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\nds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))\r\nds_with_embeddings.add_faiss_index(column='embeddings')\r\n# query\r\nscores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n# save index\r\nds_with_embeddings.get_index('embeddings').save('my_index.faiss')\r\n```\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\n# load index\r\nfaiss_index = nlp.search.FaissIndex.load('my_index.faiss')\r\nds.add_faiss_index('embeddings', faiss_index=faiss_index)\r\n# query\r\nscores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n```\r\n\r\nLet me know what you think", "Nice!\r\n\r\nHere are a few comments:\r\n\r\nI think it would be good to separate (1) the name of the column we use for indexing and (2) the name of the index itself, at least in our head. As I understand it, once the index is created, the column we used to create it is irrelevant so the column name will only be relevant in the `add_faiss_index` and we should be able to supply a different index name, e.g. `my_faiss_index`. When we reload an index, we don't really care about the column that was used to create it, right? so it's maybe better to have an `index_name` (which default to the column name for a simple user experience but it can also be something else and this should be clear in our head when we define the API).\r\n\r\nI'm wondering if we should not have a triple of methods for each retrieval engine: `add_xxx_index`, `save_xxx_index` and `load_xxx_index` when `xxx` can be `faiss` or `elasticsearch`. I'm not a fan of exposing `nlp.search.FaissIndex` unless you think there is a strong reason to have the user learn this abstraction.\r\n\r\nLast but not least, I think we should already think about hosting index on our S3. I would maybe go for something like this: host the index serialized with the cached dataset on user-provided namespaces:\r\n```python\r\nwiki_indexed = load_dataset('thom/wiki_indexed_with_dpr_faiss')\r\n```", "I agree, I just changed to using `index_name` and having add/save/load methods", "To summarize:\r\n\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\nds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))\r\nds_with_embeddings.add_faiss_index(column='embeddings')\r\n# query\r\nscores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n# save index\r\nds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')\r\n```\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\n# load index\r\nds.load_faiss_index('embeddings', 'my_index.faiss')\r\n# query\r\nscores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n```", "Good to me. I understand that for now there is no check that the index matches the dataset on loading.\r\nMaybe just add a basic test on the number of examples?", "Ok I think this one is ready now", "Looks like the CI is having troubles to pass because of `tests/test_dataset_common.py::AWSDatasetTest::test_builder_configs_{<insert_rando_dataset_name_here>}`, `requests.exceptions.ConnectionError` :/" ]

"2020-06-23T07:33:03Z"

"2020-06-26T07:50:44Z"

"2020-06-26T07:50:43Z"

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[ "Thanks for reporting these errors :)\r\n\r\nI can actually see two issues here.\r\n\r\nFirst, datasets like `natural_questions` require apache_beam to be processed. Right now the import is not at the right place so we have this error message. However, even the imports are fixed, the nlp viewer doesn't actually have the resources to process NQ right now so we'll have to wait until we have a version that we've already processed on our google storage (that's what we've done for wikipedia for example).\r\n\r\nSecond, datasets like `newsroom` require manual downloads as we're not allowed to redistribute the data ourselves (if I'm not wrong). An error message should be displayed saying that we're not allowed to show the dataset.\r\n\r\nI can fix the first issue with the imports but for the second one I think we'll have to see with @srush to show a message for datasets that require manual downloads (it can be checked whether a dataset requires manual downloads if `dataset_builder_instance.manual_download_instructions is not None`).\r\n\r\n", "I added apache-beam to the viewer. We can think about how to add newsroom. ", "We don't plan to host the source files of newsroom ourselves for now.\r\nYou can still get the dataset if you follow the download instructions given by `dataset = load_dataset('newsroom')` though.\r\nThe viewer also shows the instructions now.\r\n\r\nClosing this one. If you have other questions, feel free to re-open :)" ]

"2020-06-23T00:38:42Z"

"2020-07-17T17:43:06Z"

"2020-07-17T17:43:06Z"

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[ "@jxmorris12 , we use `-1` to label examples for which `gold label` is missing (`gold label = -` in the original dataset). ", "Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training/eval?", "Yes the original dataset is missing some labels maybe @sleepinyourhat , @gangeli can correct me if I'm wrong \r\nFor my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them. ", "thanks @mariamabarham :)" ]

"2020-06-22T23:33:30Z"

"2020-06-23T14:41:59Z"

"2020-06-23T14:41:58Z"

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"2020-06-22T17:28:57Z"

"2020-06-23T14:47:37Z"

"2020-06-23T14:47:37Z"

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[ "I couldn't replicate this issue on my macbook :/\r\nCould you try to play with different encodings in `with open(path, encoding=...) as f` in scientific_papers.py:L108 ?", "I was able to track down the file causing the problem by adding the following to `scientific_papers.py` (starting at line 116):\r\n\r\n```python\r\n from json import JSONDecodeError\r\n try:\r\n d = json.loads(line)\r\n summary = \"\\n\".join(d[\"abstract_text\"])\r\n except JSONDecodeError:\r\n print(path, line)\r\n```\r\n\r\n\r\n\r\nFor me it was at: `/Users/johngiorgi/.cache/huggingface/datasets/f87fd498c5003cbe253a2af422caa1e58f87a4fd74cb3e67350c635c8903b259/arxiv-dataset/train.txt` with `\"article_id\": \"1407.3051\"`.\r\n\r\nNot really 100% sure at the moment, but it looks like this specific substring from `\"article_text\"` may be causing the problem?\r\n\r\n```\r\n\"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev/@xmath1 . in fig . [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev/@xmath1 . a fitting result with a lorentzian function for the @xmath86 ( dashed line ) and the three - body phas\r\n```\r\n\r\nperhaps because it appears to be truncated. I (think) I can recreate the problem by doing the following:\r\n\r\n```python\r\nimport json\r\n\r\n# A minimal example of the json file that causes the error\r\ninvalid_json = '{\"article_id\": \"1407.3051\", \"article_text\": [\"the missing - mass resolution was obtained to be 2.8 @xmath3 0.1 mev/@xmath4 ( fwhm ) , which corresponds to the missing - mass resolution of 3.2 @xmath3 0.2 mev/@xmath4 ( fwhm ) at the @xmath6 cusp region in the @xmath0 reaction .\", \"this resolution is at least by a factor of 2 better than the previous measurement with the same reaction ( 3.2@xmath595.5 mev/@xmath4 in @xmath84 ) @xcite .\", \"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev/@xmath1 . in fig . [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev/@xmath1 . a fitting result with a lorentzian function for the @xmath86 ( dashed line ) and the three - body phas' \r\n# The line of code from `scientific_papers.py` which appears to cause the error\r\njson.loads(invalid_json)\r\n```\r\n\r\nThis is as far as I get before I am stumped.", "I just checked inside `train.txt` and this line isn't truncated for me (line 163577).\r\nCould you try to clear your cache and re-download the dataset ?", "Ah the turn-it-off-turn-it-on again solution! That did it, thanks a lot :) " ]

"2020-06-22T15:46:55Z"

"2020-06-30T15:25:10Z"

"2020-06-30T15:25:10Z"

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"2020-06-22T10:15:33Z"

"2020-06-22T11:07:00Z"

"2020-06-22T11:06:59Z"

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[ "Great! Thanks @jvamvas for these updates.\r\n", "I have fixed a warning. The remaining test failure is due to an unrelated dataset.", "We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ?" ]

"2020-06-22T09:13:26Z"

"2020-06-23T08:07:24Z"

"2020-06-23T08:07:24Z"

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[ "I guess,test failing due to connection error?", "We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ?", "If I'm not wrong this function returns None if no main class was found.\r\nI think it makes things less clear not to have a return at the end of the function.\r\nI guess we can have one return in the for loop instead of the break statement, AND one return at the end to explicitly return None.\r\nWhat do you think ?" ]

"2020-06-22T01:40:55Z"

"2020-06-23T17:57:58Z"

"2020-06-23T09:37:02Z"

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[ "It should ne fixed now, thanks for reporting this one :)\r\nIt was an issue on our google storage.\r\n\r\nLet me now if you're still facing this issue.", "It works now, thanks for prompt help!" ]

"2020-06-19T13:40:33Z"

"2020-06-20T13:22:24Z"

"2020-06-20T13:22:24Z"

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[ "Looks cool!\r\n@mariamabarham can you add a detailed description here what exactly is changed and how the user can load xsum now?", "And a rebase should solve the conflicts", "This is a super useful PR :-) @sshleifer - maybe you can take a look at the updated version of xsum if you can use it for your use case. Now, one should be able to just load it with:\r\n\r\n```python \r\nnlp.load_datasets(\"xsum\", ....) # no manual dir required anymore\r\n```\r\n" ]

"2020-06-19T12:28:32Z"

"2020-06-22T13:27:26Z"

"2020-06-22T07:20:07Z"

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[ "It looks like the bug comes from `dill`. Which version of `dill` are you using ?", "Thank you. It is version 0.2.6, which version is better?", "0.2.6 is three years old now, maybe try a more recent one, e.g. the current 0.3.2 if you can?", "Thanks guys! I upgraded dill and it works.", "Awesome" ]

"2020-06-19T11:01:22Z"

"2020-06-21T09:05:11Z"

"2020-06-21T09:05:11Z"

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"2020-06-19T08:24:46Z"

"2020-06-19T08:33:43Z"

"2020-06-19T08:33:41Z"

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[ "Awesome!! Thanks @easonnie.\r\nLet's wait for additional reviews maybe from @lhoestq @patrickvonplaten @jplu" ]

"2020-06-18T22:27:30Z"

"2020-06-22T12:23:27Z"

"2020-06-22T12:23:27Z"

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[ "Circle CI shuold be green :-) " ]

"2020-06-18T16:25:23Z"

"2020-06-22T08:09:25Z"

"2020-06-22T08:09:24Z"

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[ "Verified that this works, thanks!", "But I get\r\n```python\r\nConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py\r\n```\r\nWhen I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).\r\n\r\n\r\nBoth machines can run\r\n```bash\r\naws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/\r\n```\r\nbut it seems one must be in the nlp directory to run the command?\r\n\r\n(I ran `pip install -e . ` on this branch in both situations.)\r\n\r\n\r\n", "`https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py` looks very weird.\r\n\r\n(Also, S3 is not a file-system, it's a flat key-value store)", "Good to merge I think @lhoestq ", "> But I get\r\n> \r\n> ```python\r\n> ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py\r\n> ```\r\n> \r\n> When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).\r\n> \r\n> Both machines can run\r\n> \r\n> ```shell\r\n> aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/\r\n> ```\r\n> \r\n> but it seems one must be in the nlp directory to run the command?\r\n> \r\n> (I ran `pip install -e . ` on this branch in both situations.)\r\n\r\nAs soon as it is on master, the dataset script wmt16.py will be synced on S3 and you'll be able to do `load_dataset(\"wmt16\")`" ]

"2020-06-18T15:59:57Z"

"2020-06-19T08:24:21Z"

"2020-06-19T08:24:19Z"

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"2020-06-18T14:48:45Z"

"2020-06-22T17:30:46Z"

"2020-06-22T17:30:46Z"

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"2020-06-18T13:41:15Z"

"2020-06-18T16:24:52Z"

"2020-06-18T16:24:51Z"

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[ "Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)\r\n\r\nWe're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road.", "Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV/TSV and JSON are supported. To load a dataset in JSON format:\r\n\r\n```\r\nnlp.load_dataset(path=\"json\", data_files={nlp.Split.TRAIN: [\"path/to/train.json\"], nlp.Split.TEST: [\"path/to/test.json\"]})\r\n```\r\n\r\nFor CSV/TSV datasets, you have to replace `json` by `csv`.", "Hi @julien-c @jplu,\r\nThanks for sharing this solution with me, it helps, this is what I was looking for. \r\nIf not already there and only missed by me, this could be a great addition in the docs.\r\n\r\nClosing my issue as resolved, thanks again." ]

"2020-06-18T09:47:27Z"

"2020-06-20T13:15:12Z"

"2020-06-20T13:15:12Z"

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"2020-06-17T19:10:54Z"

"2020-06-19T08:33:41Z"

"2020-06-19T08:33:41Z"

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[ "When you're processing a dataset with `.map`, it checks whether it has already done this computation using a hash based on the function and the input (using some fancy serialization with `dill`). If you found that it doesn't work as expected in some cases, let us know !\r\n\r\nGiven that, you can still force to re-process using `.map(my_func, load_from_cache_file=False)` if you want to.\r\n\r\nI am curious about the problem you have with splits. It makes me think about #160 that was an issue of version 0.1.0. What version of `nlp` are you running ? Could you give me more details ?", "Thanks, that's helpful! I was running 0.1.0, but since upgraded to 0.2.1. I can't reproduce the issue anymore as I've cleared the cache & everything now seems to be running fine since the upgrade. I've added some checks to my code, so if I do encounter it again I will reopen this issue.", "Just checking in, the cache sometimes still does not work when I make changes in my processing function in version `1.2.1`. The changes made to my data processing function only propagate to the dataset when I use `load_from_cache_file=False` or clear the cache. Is this a system-specific issue?", "Hi @sarahwie \r\nThe data are reloaded from the cache if the hash of the function you provide is the same as a computation you've done before. The hash is computed by recursively looking at the python objects of the function you provide.\r\n\r\nIf you think there's an issue, can you share the function you used or a google colab please ?", "I can't reproduce it, so I'll close for now." ]

"2020-06-17T17:17:21Z"

"2021-07-06T21:43:28Z"

"2021-04-18T23:43:49Z"

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[ "Hi !\r\n\r\nAs you noticed, \"big\" datasets like Wikipedia require apache beam to be processed.\r\nHowever users usually don't have an apache beam runtime available (spark, dataflow, etc.) so our goal for this library is to also make available processed versions of these datasets, so that users can just download and use them right away.\r\n\r\nThis is the case for english and french wikipedia right now: we've processed them ourselves and now they are available from our google storage. However we've not processed the german one (yet).", "Hi @lhoestq \r\n\r\nThank you for your quick reply. I thought this might be the case, that the processing was done for some languages and not for others. Is there any set timeline for when other languages (German, Italian) will be processed?\r\n\r\nGiven enough memory, is it possible to process the data ourselves by specifying the `beam_runner`?", "Adding them is definitely in our short term objectives. I'll be working on this early next week :)\r\n\r\nAlthough if you have an apache beam runtime feel free to specify the beam runner. You can find more info [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md) on how to make it work on Dataflow but you can adapt it for Spark or any other beam runtime (by changing the `runner`).\r\n\r\nHowever if you don't have a beam runtime and even if you have enough memory, I discourage you to use the `DirectRunner` on the german or italian wikipedia. According to Apache Beam documentation it was made for testing purposes and therefore it is memory-inefficient.", "German is [almost] done @gregburman", "I added the German and the Italian Wikipedia to our google cloud storage:\r\nFirst update the `nlp` package to 0.3.0:\r\n```bash\r\npip install nlp --upgrade\r\n```\r\nand then\r\n```python\r\nfrom nlp import load_dataset\r\nwiki_de = load_dataset(\"wikipedia\", \"20200501.de\")\r\nwiki_it = load_dataset(\"wikipedia\", \"20200501.it\")\r\n```\r\nThe datasets are downloaded and directly ready to use (no processing).", "Hi @lhoestq \r\n\r\nWow, thanks so much, that's **really** incredible! I was considering looking at creating my own Beam Dataset, as per the doc you linked, but instead opted to process the data myself using `wikiextractor`. However, now that this is available, I'll definitely switch across and use it.\r\n\r\nThanks so much for the incredible work, this really helps out our team considerably!\r\n\r\nHave a great (and well-deserved ;) weekend ahead!\r\n\r\nP.S. I'm not sure if I should close the issue here - if so I'm happy to do so.", "Thanks for your message, glad I could help :)\r\nClosing this one." ]

"2020-06-17T15:06:21Z"

"2020-06-19T12:53:02Z"

"2020-06-19T12:53:02Z"

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[ "We are only wrapping the original dataset.\r\n\r\nMaybe try to ask on the GLUE mailing list or reach out to the original authors?", "Tanks for the suggestion, I'll try to ask GLUE benchmark.\r\nI'll first close the issue, post the following up here afterwards, and reopen the issue if needed. " ]

"2020-06-17T05:54:52Z"

"2020-06-21T00:21:45Z"

"2020-06-21T00:21:45Z"

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[ "Awesome! This is working now:\r\n\r\n```python\r\nimport nlp \r\nseqeval = nlp.load_metric(\"seqeval\") \r\ny_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] \r\ny_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] \r\n\r\nresults = seqeval.compute(y_true, y_pred)\r\n```\r\n\r\nI heavily need this fix for an upcoming `nlp` integration PR for Transformers (token classification example) 😅", "Haha nice ! We'll ship this fix with the next release that will probably come out on thursday :)" ]

"2020-06-16T08:52:01Z"

"2020-06-18T07:41:45Z"

"2020-06-18T07:41:44Z"

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[ "For some reason the files are not available for unauthenticated users right now (like the download service of this package). Instead of downloading the right files, it downloads the html of the error.\r\nAccording to the error it should be back again in 24h.\r\n\r\n\r\n" ]

"2020-06-16T07:31:51Z"

"2020-06-19T07:37:07Z"

"2020-06-19T07:37:07Z"

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[ "Sounds good! Do you want to give it a try?", "Ok, I'll see if I can figure it out tomorrow!", "Got around to this today, and so far so good, I'm able to download and load pg19 locally. However, I think there may be an issue with the dummy data, and testing in general.\r\n\r\nThe problem lies in the fact that each book from pg19 actually resides as its own text file in a google cloud folder that denotes the split, where the book id is the name of the text file. https://console.cloud.google.com/storage/browser/deepmind-gutenberg/train/ I don't believe there's anywhere else (even in the supplied metadata), where the mapping of id -> split can be found.\r\n\r\nTherefore I end up making a network call `tf.io.gfile.listdir` to get all the files within each of the split directories. https://github.com/lucidrains/nlp/commit/adbacbd85decc80db2347d0882e7dab4faa6fd03#diff-cece8f166a85dd927caf574ba303d39bR78\r\n\r\nDoes this network call need to be eventually stubbed out for testing?", "Ohh nevermind, I think I can use `download_custom` here with `listdir` as the custom function. Ok, I'll keep trying to make the dummy data work!" ]

"2020-06-15T21:02:26Z"

"2020-07-06T15:35:02Z"

"2020-07-06T15:35:02Z"

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"2020-06-15T16:03:22Z"

"2020-06-16T08:25:54Z"

"2020-06-16T08:25:52Z"

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"2020-06-14T08:20:38Z"

"2020-06-14T09:16:41Z"

"2020-06-14T09:16:41Z"

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[ "Actually when there is only one configuration, then you don't need to specify the configuration in `load_dataset`. You can run:\r\n```python\r\ndataset = load_dataset('allocine')\r\n```\r\nand it works.\r\n\r\nMaybe we should take that into account in the nlp viewer @srush ?", "@lhoestq Just to understand the exact semantics. Are you suggesting that if there is exactly 1 configuration I should not show the configuration menu and just treat it as if there were 0 configurations? ", "The configuration menu is fine imo.\r\nIt was more about the code snippet presented in the viewer.\r\nFor example for Allociné it currently shows this snippet to load the dataset:\r\n```python\r\n!pip install nlp\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('allocine', 'allocine')\r\n```\r\nHowever for datasets with one or zero configurations, the second argument in `load_dataset` is optional. For Allociné, that has one configuration, we can expect to show instead:\r\n```python\r\n!pip install nlp\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('allocine')\r\n```", "> Actually when there is only one configuration, then you don't need to specify the configuration in `load_dataset`. You can run:\r\n> \r\n> ```python\r\n> dataset = load_dataset('allocine')\r\n> ```\r\n> \r\n> and it works.\r\n> \r\n> Maybe we should take that into account in the nlp viewer @srush ?\r\n\r\nOh ok, I didn't expect it would work! \r\n\r\nAnyway, I think it's intrinsically better to simply remove the optional parameter. \r\nThe dummy data folder architecture seems also more logical this way.\r\n", "Fixed in the viewer. Checked that allocine works.", "Awesome thanks :)\r\n\r\nClosing this." ]

"2020-06-13T10:12:10Z"

"2020-06-18T07:41:21Z"

"2020-06-18T07:41:20Z"

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[ "C4 is too large to be shown in the viewer" ]

"2020-06-13T08:26:16Z"

"2020-10-27T15:35:29Z"

"2020-10-27T15:35:13Z"

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"2020-06-13T06:26:54Z"

"2020-06-18T07:41:44Z"

"2020-06-18T07:41:44Z"

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[ "@jplu @thomwolf @patrickvonplaten @lhoestq -- How do I request reviewers? Thanks." ]

"2020-06-12T15:53:59Z"

"2020-06-18T07:46:24Z"

"2020-06-18T07:46:23Z"

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[ "I will take a look :-) " ]

"2020-06-12T01:09:37Z"

"2020-06-19T08:24:19Z"

"2020-06-19T08:24:19Z"

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[ "Nice !\r\n\r\nAlso it looks like we can have a train_test_split method for free:\r\n```python\r\ntrain_indices, test_indices = train_test_split(range(len(dataset)))\r\ntrain = dataset.sort(indices=train_indices)\r\ntest = dataset.sort(indices=test_indices)\r\n```\r\n\r\nand a shuffling method for free:\r\n```python\r\nshuffled_indices = shuffle(range(len(dataset)))\r\nshuffled_dataset = dataset.sort(indices=shuffled_indices)\r\n```\r\n\r\nMaybe we can have a specific API for train_test_split and shuffle. They are two features asked quite often (see #147, #166)", "Ok, I think this one is ready to merge.\r\n\r\n@patrickvonplaten @jplu @mariamabarham @joeddav @n1t0 @julien-c you may want to give it a look, it adds a bunch of methods to reorder/split/select rows in a dataset:\r\n- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)\r\n- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)\r\n- `dataset.shuffle(seed)`: shuffle a dataset rows\r\n- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)\r\n\r\nAll these methods are **not** in-place which means they return new ``Dataset``, which is the default behavior in the library.", "> Might be a solution to put 0.25 and 0.75 as default values for respectively `test_size` and `train_size`. WDYT?\r\n\r\nAccording to sklearn documentation, it is indeed set to 0.25 and 0.75 if both `test_size` and `train_size` are None.\r\nLet me add it.", "I think we're good to go now :) @joeddav @thomwolf @jplu " ]

"2020-06-11T16:22:20Z"

"2020-06-18T16:23:25Z"

"2020-06-18T16:23:24Z"

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"2020-06-11T15:57:51Z"

"2020-08-02T18:14:36Z"

"2020-06-12T08:14:16Z"

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"2020-06-11T15:20:16Z"

"2020-06-12T08:15:57Z"

"2020-06-12T08:15:56Z"

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[ "Thanks a lot, @aleSuglia for the very detailed and introductive feature request.\r\nIt seems like we could build something pretty useful here indeed.\r\n\r\nOne of the questions here is that Arrow doesn't have built-in support for generic \"tensors\" in records but there might be ways to do that in a clean way. We'll probably try to tackle this during the summer.", "I was looking into Facebook MMF and apparently they decided to use LMDB to store additional features associated with every example: https://github.com/facebookresearch/mmf/blob/master/mmf/datasets/databases/features_database.py\r\n\r\n", "I saw the Mozilla common_voice dataset in model hub, which has mp3 audio recordings as part it. It's use predominantly maybe in ASR and TTS, but dataset is a Language + Voice Dataset similar to @aleSuglia's point about Language + Vision. \r\n\r\nhttps://huggingface.co/datasets/common_voice", "Hey @thomwolf, are there any updates on this? I would love to contribute if possible!\r\n\r\nThanks, \r\nAlessandro ", "Hi @aleSuglia :) In today's new release 1.17 of `datasets` we introduce a new feature type `Image` that allows to store images directly in a dataset, next to text features and labels for example. There is also an `Audio` feature type, for datasets containing audio data. For tensors there are `Array2D`, `Array3D`, etc. feature types\r\n\r\nNote that both Image and Audio feature types take care of decoding the images/audio data if needed. The returned images are PIL images, and the audio signals are decoded as numpy arrays.\r\n\r\nAnd `datasets` also leverage end-to-end zero copy from the arrow data for all of them, for maximum speed :)" ]

"2020-06-11T13:42:18Z"

"2022-02-10T13:26:35Z"

"2022-02-10T13:26:35Z"

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[ "Hello ! Thanks for adding this one :)\r\n\r\nThis looks great, you just have to do the last steps to make the CI pass.\r\nI can see that two things are missing:\r\n1. the dummy data that is used to test that the script is working as expected\r\n2. the json file with all the infos about the dataset\r\n\r\nYou can see the steps to help you create the dummy data and generate the dataset_infos.json file right [here](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset)" ]

"2020-06-11T04:14:57Z"

"2020-06-12T22:03:03Z"

"2020-06-12T22:03:03Z"

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[ "When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your message.", "Yeah, that worked! Thanks :) " ]

"2020-06-10T16:04:19Z"

"2020-06-11T14:35:12Z"

"2020-06-11T14:35:12Z"

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"2020-06-10T13:44:42Z"

"2020-06-11T10:34:37Z"

"2020-06-11T10:34:36Z"

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[ "this seems to work for my specific problem:\r\n\r\n`self.train_ds, self.test_ds, self.val_ds = map(_prepare_ds, ('train', 'test[:25%]+test[50%:75%]', 'test[75%:]'))`", "Currently you can indeed split a dataset using `ds_test = nlp.load_dataset('imdb, split='test[:5000]')` (works also with percentages).\r\n\r\nHowever right now we don't have a way to shuffle a dataset but we are thinking about it in the discussion in #166. Feel free to share your thoughts about it.\r\n\r\nOne trick that you can do until we have a better solution is to shuffle and split the indices of your dataset:\r\n```python\r\nimport nlp\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nimdb = nlp.load_dataset('imbd', split='test')\r\ntest_indices, val_indices = train_test_split(range(len(imdb)))\r\n```\r\n\r\nand then to iterate each split:\r\n```python\r\nfor i in test_indices:\r\n example = imdb[i]\r\n ...\r\n```\r\n", "I added a small guide [here](https://github.com/huggingface/nlp/tree/master/docs/splits.md) that explains how to split a dataset. It is very similar to the tensorflow datasets guide, as we kept the same logic.", "Thanks a lot, the new explanation is very helpful!\r\n\r\nAbout using train_test_split from sklearn: I stumbled across the [same error message as this user ](https://github.com/huggingface/nlp/issues/147 )and thought it can't be used at the moment in this context. Will check it out again.\r\n\r\nOne of the problems is how to shuffle very large datasets, which don't fit into the memory. Well, one strategy could be shuffling data in sections. But in a case where the data is sorted by the labels you have to swap larger sections first. \r\n", "We added a way to shuffle datasets (shuffle the indices and then reorder to make a new dataset).\r\nYou can do `shuffled_dset = dataset.shuffle(seed=my_seed)`. It shuffles the whole dataset.\r\nThere is also `dataset.train_test_split()` which if very handy (with the same signature as sklearn).\r\n\r\nClosing this issue as we added the docs for splits and tools to split datasets. Thanks again for your feedback !", "https://huggingface.co/docs/datasets/v1.0.1/package_reference/builder_classes.html#datasets.Split still links to https://github.com/huggingface/datasets/tree/main/docs/splits.md which is a 404\r\n", "The updated documentation doesn't link to this anymore: https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/builder_classes#datasets.Split" ]

"2020-06-10T13:18:13Z"

"2023-03-14T13:56:07Z"

"2020-06-18T22:20:24Z"

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[ "Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.\r\n If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=[\"title\", \"text\"]` in the arguments of `.map`", "Hi ! Thanks for your reply.\r\n\r\nBut since size of `input_ids` < size of `text`, I am wondering why\r\nsize of `input_ids` + `text` > 2x the size of `text` 🤔", "Hard to tell... This is probably related to the way apache arrow compresses lists of integers, that may be different from the compression of strings.", "Thanks for your point. 😀, It might be answer.\r\nSince this is hard to know, I'll close this issue.\r\nBut if somebody knows more details, please comment below ~ 😁" ]

"2020-06-10T01:27:07Z"

"2020-06-10T12:46:34Z"

"2020-06-10T12:46:34Z"

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[ "Yes, the new release of tokenizers solves this and should be out soon.\r\nIn the meantime, you can install it with `pip install tokenizers==0.8.0-dev2`", "If others run into this issue, a quick fix is to use python 3.6 instead of 3.7+. Serialization differences between the 3rd party `dataclasses` package for 3.6 and the built in `dataclasses` in 3.7+ cause the issue.\r\n\r\nProbably a dumb fix, but it works for me." ]

"2020-06-09T17:12:34Z"

"2020-06-10T21:45:32Z"

"2020-06-09T17:26:53Z"

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[ "Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table)", "Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prior to performing map.\r\n\r\nE.g. \r\n```\r\nnew_info = list of length dataset['train']\r\n\r\ndataset['train'] = dataset['train'].map(lambda x: some_function(x, new_info[index of x]))\r\n\r\ndef some_function(x, new_info_x):\r\n # adds new_info[index of x] as a field to x\r\n x['new_info'] = new_info_x\r\n return x\r\n```\r\nI was thinking to instead create a new field in the arrow dataset so that instance x contains all the necessary information when map function is applied (since I don't have index information to pass to map function).", "This is what I have so far: \r\n\r\n```\r\nimport pyarrow as pa\r\nfrom nlp.arrow_dataset import Dataset\r\n\r\naug_dataset = dataset['train'][:]\r\naug_dataset['new_info'] = new_info\r\n\r\n#reformat as arrow-table\r\nschema = dataset['train'].schema\r\n\r\n# this line doesn't work:\r\nschema.append(pa.field('new_info', pa.int32()))\r\n\r\ntable = pa.Table.from_pydict(\r\n aug_dataset,\r\n schema=schema\r\n)\r\ndataset['train'] = Dataset(table) \r\n```", "Maybe you can use `with_indices`?\r\n\r\n```python\r\nnew_info = list of length dataset['train']\r\n\r\ndef some_function(indice, x):\r\n # adds new_info[index of x] as a field to x\r\n x['new_info'] = new_info_x[indice]\r\n return x\r\n\r\ndataset['train'] = dataset['train'].map(some_function, with_indices=True)\r\n```", "Oh great. That should work. I missed that in the documentation- thanks :) " ]

"2020-06-09T16:38:12Z"

"2020-06-09T16:51:42Z"

"2020-06-09T16:51:42Z"

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