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DanielPFlorian

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Transformers-Github-Issues

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3511?src=pr&el=h1) Report\n> Merging [#3511](https://codecov.io/gh/huggingface/transformers/pull/3511?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/601ac5b1dc1438f00d09696588f2deb0f045ae3b&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3511?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3511 +/- ##\n==========================================\n+ Coverage 77.79% 77.80% +0.01% \n==========================================\n Files 100 100 \n Lines 17051 17051 \n==========================================\n+ Hits 13265 13267 +2 \n+ Misses 3786 3784 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3511?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/3511/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `79.27% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3511/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.94% <0.00%> (+0.13%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3511/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.32% <0.00%> (+0.17%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3511?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3511?src=pr&el=footer). Last update [601ac5b...572e04d](https://codecov.io/gh/huggingface/transformers/pull/3511?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Credit to people at Microsoft Asia: Ning Wu and Nan Dua\r\nTo achieve the best performance on the \"zh\" test sets, you just need to add \r\n\"final_text = tok_text\" after line 497 in squad_metrics.py (only for zh). Because there isn't space and subword in Chinese, so we don't need to execute the get_final_test() function.", "This is very interesting! Thanks for letting us know @nooralahzadeh!", "@LysandreJik Do you think the training model \"RobertaForQuestionAnswering\" also need to be updated for 'zh' lang. Because when I try to fine-tune the xlm-r on 'zh' language and evaluate on its test set, the results became very lower than not fine-tuning.\r\n", "Just found the same problem, thanks bro!\r\n\r\n> Credit to people at Microsoft Asia: Ning Wu and Nan Dua\r\n> To achieve the best performance on the \"zh\" test sets, you just need to add\r\n> \"final_text = tok_text\" after line 497 in squad_metrics.py (only for zh). Because there isn't space and subword in Chinese, so we don't need to execute the get_final_test() function.\r\n\r\n", "I used huggingface to train & predict then use https://github.com/facebookresearch/MLQA/blob/main/mlqa_evaluation_v1.py to calculate the scores. Seem to match the paper. " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3509?src=pr&el=h1) Report\n> Merging [#3509](https://codecov.io/gh/huggingface/transformers/pull/3509?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/601ac5b1dc1438f00d09696588f2deb0f045ae3b&el=desc) will **not change** coverage by `%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3509?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3509 +/- ##\n=======================================\n Coverage 77.79% 77.79% \n=======================================\n Files 100 100 \n Lines 17051 17051 \n=======================================\n Hits 13265 13265 \n Misses 3786 3786 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3509?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3509?src=pr&el=footer). Last update [601ac5b...43fda01](https://codecov.io/gh/huggingface/transformers/pull/3509?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@sshleifer I'm seeing this as well. It doesn't happen if `num_beams=1`. Might have to do with the recent generation and bart changes. Only started happening in the last week or so. ", "Thanks for contributing!\r\n\r\nA few thoughts:\r\n\r\n1. if you pass `output_past=True` to `BartForConditionalGeneration.from_pretrained`, the code works.\r\n2. We only expect 'bart-large-xsum' and 'bart-large-cnn' to generate high quality summaries.\r\n3. The error message/traceback should be improved. Feel free to send a PR if you'd like.\r\n3. Thanks for copy pasting usable code, it made this really easy to verify :) I added \"```python\" at the beginning to prettify.\r\n\r\n\r\n### Working example\r\ncopy paste [LONG_BORING_TENNIS_ARTICLE](https://gist.github.com/sshleifer/8d9df1937fec07cf77266e222689e9a9)\r\n```python\r\nmodel_name = 'bart-large-mnli'\r\nfrom transformers import *\r\ntorch_device='cpu'\r\ntokenizer = BartTokenizer.from_pretrained(model_name)\r\nmodel = BartForConditionalGeneration.from_pretrained(model_name, output_past=True)\r\narticle_input_ids = tokenizer.batch_encode_plus([LONG_BORING_TENNIS_ARTICLE], return_tensors='pt', max_length=1024)['input_ids'].to(torch_device)\r\nsummary_ids = model.generate(article_input_ids,\r\n num_beams=4,\r\n length_penalty=2.0,\r\n max_length=100,\r\n early_stopping=True)\r\n\r\nprint([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])\r\n```", "This wasn't solved. I am using a trainer on BART and I have tried to use ```use_cache```, but it still doesn't work." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3507?src=pr&el=h1) Report\n> Merging [#3507](https://codecov.io/gh/huggingface/transformers/pull/3507?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/601ac5b1dc1438f00d09696588f2deb0f045ae3b&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3507?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3507 +/- ##\n=======================================\n Coverage 77.79% 77.80% \n=======================================\n Files 100 100 \n Lines 17051 17051 \n=======================================\n+ Hits 13265 13266 +1 \n+ Misses 3786 3785 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3507?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3507/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `97.58% <ø> (ø)` | |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3507/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `81.29% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3507/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90NS5weQ==) | `94.98% <ø> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3507/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.94% <0.00%> (+0.13%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3507?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3507?src=pr&el=footer). Last update [601ac5b...ea7e6a8](https://codecov.io/gh/huggingface/transformers/pull/3507?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This is more of a general PyTorch question than a Transformers-question. Have you tried asking on StackOverflow or the PyTorch forums?" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3504?src=pr&el=h1) Report\n> Merging [#3504](https://codecov.io/gh/huggingface/transformers/pull/3504?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/601ac5b1dc1438f00d09696588f2deb0f045ae3b&el=desc) will **decrease** coverage by `0.02%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3504?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3504 +/- ##\n==========================================\n- Coverage 77.79% 77.76% -0.03% \n==========================================\n Files 100 100 \n Lines 17051 17051 \n==========================================\n- Hits 13265 13260 -5 \n- Misses 3786 3791 +5 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3504?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3504/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `90.65% <0.00%> (-0.84%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3504/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.32% <0.00%> (+0.17%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3504?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3504?src=pr&el=footer). Last update [601ac5b...5779256](https://codecov.io/gh/huggingface/transformers/pull/3504?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Interesting idea! What do you think @thomwolf ?", "Hi, any update on this, even partial code?", "I'm gunna take a crack this weekend, hopefully, By starting from the [distilbert example](https://github.com/huggingface/transformers/tree/master/examples/distillation) and modifying. I'll post a branch if I make meaningful progress.", "Hi, just checking in to see if there's a branch already (couldn't find it). Thanks!", "Yes, it is will be great! Any updates?\r\n", "I'm gunna wait until the code is stable/reusable to release it, sorry for the change of plans.", "As per https://twitter.com/sam_shleifer/status/1276160367853547522, it looks like distilBART has been released :)", "https://huggingface.co/sshleifer/distilbart-cnn-12-6# the tokenizer by name sshleifer/distilbart-cnn-12-6 leads to an error, works with facebook/bart-cnn-large-tokenizer", "I've faced the same issue with sshleifer/distilbart-cnn-12-6\n\n_________\n\nBest regards,\nVladislav Kozlenko\n[image: phone:] +380 685954166\n\n[image: skype:]\[email protected] <[email protected]>\n[image: position:] Software Engineer\n\n[image: SP Group]\nSoftware Planet Group\nCompany No: 9428594\n[image: phone:] +44 1483 80 24 23\n\n[image: location:] Ukraine, Cherkasy 18000\n[image: site:] softwareplanetgroup.com\n\n\nThis email and any files transmitted with it are confidential and intended\nsolely for the use of the individual or entity to whom they are addressed.\nIf you have received this email in error please notify the sender\nimmediately.\n\n\n\nOn Thu, 25 Jun 2020 at 20:42, Amanpreet Singh <[email protected]>\nwrote:\n\n> https://huggingface.co/sshleifer/distilbart-cnn-12-6# the tokenizer by\n> name sshleifer/distilbart-cnn-12-6 leads to an error, works with\n> facebook/bart-cnn-large-tokenizer\n>\n> —\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/3503#issuecomment-649724552>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AJWNBQIUYRMVUXUT6B42V4LRYOD67ANCNFSM4LVX67LQ>\n> .\n>\n", "I can't reproduce the error on master. If somebody can, it would be great if they could make a separate issue and I will try to resolve.\r\n\r\nAll the distilbart- tokenizers are identical to the is identical to the `facebook/bart-large-cnn` tokenizer, which is identical to the facebook/bart-cnn-xsum` tokenizer. @julien-c is there a fancy AWS way to synchronize/symlink them? ", "I've tried several models and I'm getting the same error each time it\ncreates a tokenizer.\n[image: image.png]\n\n_________\n\nBest regards,\nVladislav Kozlenko\n[image: phone:] +380 685954166\n\n[image: skype:]\[email protected] <[email protected]>\n[image: position:] Software Engineer\n\n[image: SP Group]\nSoftware Planet Group\nCompany No: 9428594\n[image: phone:] +44 1483 80 24 23\n\n[image: location:] Ukraine, Cherkasy 18000\n[image: site:] softwareplanetgroup.com\n\n\nThis email and any files transmitted with it are confidential and intended\nsolely for the use of the individual or entity to whom they are addressed.\nIf you have received this email in error please notify the sender\nimmediately.\n\n\n\nOn Thu, 25 Jun 2020 at 21:34, Sam Shleifer <[email protected]> wrote:\n\n> I can't reproduce the error on master. If somebody can, it would be great\n> if they could make a separate issue and I will try to resolve.\n>\n> All the distilbart- tokenizers are identical to the is identical to the\n> facebook/bart-large-cnn tokenizer, which is identical to the\n> facebook/bart-cnn-xsum` tokenizer. @julien-c <https://github.com/julien-c>\n> is there a fancy AWS way to synchronize/symlink them?\n>\n> —\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/3503#issuecomment-649748636>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AJWNBQKQZXUC4YAY7WWJQ7TRYOKCVANCNFSM4LVX67LQ>\n> .\n>\n", "@vladislavkoz Please make a new issue with instructions to reproduce, following the issue template. Feel free to assign me.", "Here is an issue https://github.com/huggingface/transformers/issues/5286\n\n_________\n\nBest regards,\nVladislav Kozlenko\n[image: phone:] +380 685954166\n\n[image: skype:]\[email protected] <[email protected]>\n[image: position:] Software Engineer\n\n[image: SP Group]\nSoftware Planet Group\nCompany No: 9428594\n[image: phone:] +44 1483 80 24 23\n\n[image: location:] Ukraine, Cherkasy 18000\n[image: site:] softwareplanetgroup.com\n\n\nThis email and any files transmitted with it are confidential and intended\nsolely for the use of the individual or entity to whom they are addressed.\nIf you have received this email in error please notify the sender\nimmediately.\n\n\n\nOn Thu, 25 Jun 2020 at 21:38, Vladislav Kozlenko <\[email protected]> wrote:\n\n> I've tried several models and I'm getting the same error each time it\n> creates a tokenizer.\n> [image: image.png]\n>\n> _________\n>\n> Best regards,\n> Vladislav Kozlenko\n> [image: phone:] +380 685954166\n>\n> [image: skype:]\n> [email protected] <[email protected]>\n> [image: position:] Software Engineer\n>\n> [image: SP Group]\n> Software Planet Group\n> Company No: 9428594\n> [image: phone:] +44 1483 80 24 23\n>\n> [image: location:] Ukraine, Cherkasy 18000\n> [image: site:] softwareplanetgroup.com\n>\n>\n> This email and any files transmitted with it are confidential and\n> intended solely for the use of the individual or entity to whom they are\n> addressed. If you have received this email in error please notify the\n> sender immediately.\n>\n>\n>\n> On Thu, 25 Jun 2020 at 21:34, Sam Shleifer <[email protected]>\n> wrote:\n>\n>> I can't reproduce the error on master. If somebody can, it would be great\n>> if they could make a separate issue and I will try to resolve.\n>>\n>> All the distilbart- tokenizers are identical to the is identical to the\n>> facebook/bart-large-cnn tokenizer, which is identical to the\n>> facebook/bart-cnn-xsum` tokenizer. @julien-c\n>> <https://github.com/julien-c> is there a fancy AWS way to\n>> synchronize/symlink them?\n>>\n>> —\n>> You are receiving this because you commented.\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/transformers/issues/3503#issuecomment-649748636>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/AJWNBQKQZXUC4YAY7WWJQ7TRYOKCVANCNFSM4LVX67LQ>\n>> .\n>>\n>\n", "I didn't assign you. I Just read the message to late.\n\n_________\n\nBest regards,\nVladislav Kozlenko\n[image: phone:] +380 685954166\n\n[image: skype:]\[email protected] <[email protected]>\n[image: position:] Software Engineer\n\n[image: SP Group]\nSoftware Planet Group\nCompany No: 9428594\n[image: phone:] +44 1483 80 24 23\n\n[image: location:] Ukraine, Cherkasy 18000\n[image: site:] softwareplanetgroup.com\n\n\nThis email and any files transmitted with it are confidential and intended\nsolely for the use of the individual or entity to whom they are addressed.\nIf you have received this email in error please notify the sender\nimmediately.\n\n\n\nOn Thu, 25 Jun 2020 at 21:45, Vladislav Kozlenko <\[email protected]> wrote:\n\n> Here is an issue https://github.com/huggingface/transformers/issues/5286\n>\n> _________\n>\n> Best regards,\n> Vladislav Kozlenko\n> [image: phone:] +380 685954166\n>\n> [image: skype:]\n> [email protected] <[email protected]>\n> [image: position:] Software Engineer\n>\n> [image: SP Group]\n> Software Planet Group\n> Company No: 9428594\n> [image: phone:] +44 1483 80 24 23\n>\n> [image: location:] Ukraine, Cherkasy 18000\n> [image: site:] softwareplanetgroup.com\n>\n>\n> This email and any files transmitted with it are confidential and\n> intended solely for the use of the individual or entity to whom they are\n> addressed. If you have received this email in error please notify the\n> sender immediately.\n>\n>\n>\n> On Thu, 25 Jun 2020 at 21:38, Vladislav Kozlenko <\n> [email protected]> wrote:\n>\n>> I've tried several models and I'm getting the same error each time it\n>> creates a tokenizer.\n>> [image: image.png]\n>>\n>> _________\n>>\n>> Best regards,\n>> Vladislav Kozlenko\n>> [image: phone:] +380 685954166\n>>\n>> [image: skype:]\n>> [email protected] <[email protected]>\n>> [image: position:] Software Engineer\n>>\n>> [image: SP Group]\n>> Software Planet Group\n>> Company No: 9428594\n>> [image: phone:] +44 1483 80 24 23\n>>\n>> [image: location:] Ukraine, Cherkasy 18000\n>> [image: site:] softwareplanetgroup.com\n>>\n>>\n>> This email and any files transmitted with it are confidential and\n>> intended solely for the use of the individual or entity to whom they are\n>> addressed. If you have received this email in error please notify the\n>> sender immediately.\n>>\n>>\n>>\n>> On Thu, 25 Jun 2020 at 21:34, Sam Shleifer <[email protected]>\n>> wrote:\n>>\n>>> I can't reproduce the error on master. If somebody can, it would be\n>>> great if they could make a separate issue and I will try to resolve.\n>>>\n>>> All the distilbart- tokenizers are identical to the is identical to the\n>>> facebook/bart-large-cnn tokenizer, which is identical to the\n>>> facebook/bart-cnn-xsum` tokenizer. @julien-c\n>>> <https://github.com/julien-c> is there a fancy AWS way to\n>>> synchronize/symlink them?\n>>>\n>>> —\n>>> You are receiving this because you commented.\n>>> Reply to this email directly, view it on GitHub\n>>> <https://github.com/huggingface/transformers/issues/3503#issuecomment-649748636>,\n>>> or unsubscribe\n>>> <https://github.com/notifications/unsubscribe-auth/AJWNBQKQZXUC4YAY7WWJQ7TRYOKCVANCNFSM4LVX67LQ>\n>>> .\n>>>\n>>\n", "@sshleifer ATM you need to duplicate the tokenizer files in each model if you want them to be loadable by the model hub, the inference API, etc.", "I was able to create tokenizer only with 'distilbart-xsum-12-1' and\n'distilbart-xsum-9-6'. Then on the summarization step, I'm getting another\nerror. I've added a comment here\n<https://github.com/huggingface/transformers/issues/5286>\n\n_________\n\nBest regards,\nVladislav Kozlenko\n[image: phone:] +380 685954166\n\n[image: skype:]\[email protected] <[email protected]>\n[image: position:] Software Engineer\n\n[image: SP Group]\nSoftware Planet Group\nCompany No: 9428594\n[image: phone:] +44 1483 80 24 23\n\n[image: location:] Ukraine, Cherkasy 18000\n[image: site:] softwareplanetgroup.com\n\n\nThis email and any files transmitted with it are confidential and intended\nsolely for the use of the individual or entity to whom they are addressed.\nIf you have received this email in error please notify the sender\nimmediately.\n\n\n\nOn Fri, 26 Jun 2020 at 02:11, Julien Chaumond <[email protected]>\nwrote:\n\n> @sshleifer <https://github.com/sshleifer> ATM you need to duplicate the\n> tokenizer files in each model if you want them to be loadable by the model\n> hub, the inference API, etc.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/3503#issuecomment-649862320>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AJWNBQMZYWYTBBAM5PXRRHDRYPKTPANCNFSM4LVX67LQ>\n> .\n>\n", "Hey @sshleifer , thanks for the distilled BART version I was able to fine tune it with the same script on BillSum dataset as T5 but the numbers are way different between the two. I just wanted to understand if I might be doing something wrong with regards to fine tuning distilBART, does it require student training everytime?\r\nReference numbers on BillSum Dataset:\r\n\r\nT5-base:\r\navg_train_loss = tensor(1.5333, device='cuda:0')\r\navg_val_loss = tensor(1.4528, device='cuda:0')\r\nepoch = 1\r\nloss = tensor(1.6734, device='cuda:0')\r\nrouge1 = 0.49188267841912325\r\nrouge2 = 0.26436589848185027\r\nrougeL = 0.3591894400892483\r\ntrain_loss = tensor(1.6734, device='cuda:0')\r\nval_loss = tensor(1.4528, device='cuda:0')\r\n\r\ndBART-cnn-12-6:\r\navg_train_loss = tensor(1.3013, device='cuda:0')\r\navg_val_loss = tensor(1.4013, device='cuda:0')\r\nepoch = 1\r\nloss = tensor(1.4901, device='cuda:0')\r\nrouge1 = 0.3681518923769047\r\nrouge2 = 0.15683286277623087\r\nrougeL = 0.23453727441540043\r\ntrain_loss = tensor(1.4901, device='cuda:0')\r\nval_loss = tensor(1.4013, device='cuda:0')\r\n\r\nPS. I am using a modified version of the older finetune.py so it doesn't have Rouge for validation epochs.\r\n\r\nThanks", "@amanpreet692 I moved your issue [here](https://github.com/huggingface/transformers/issues/5336) and will reply there.\r\nOthers, I am closing this since the model is released and I don't want to spam everyone. This shouldn't discourage people making new issues! \r\n" ]

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[ "Hi @creat89, \r\n\r\nThanks for posting this issue! \r\n\r\nYou are correct there is some inconsistent behavior here. \r\n\r\n1. We should probably in general not allow using `batch_encode_plus()` of a simple string. For this the `encode_plus()` function should be used. \r\n2. It seems like there is an inconsistency between `encode_plus([string])` and `encode_plus(string)`. This should probably be fixed.", "Well, the issue not only happens with a simple string. In my actual code I was using a batch of size 2. However, I just used a simple example to demonstrate the issue.\r\n\r\nI didn't find any inconsistency between `encode_plus([string])` and `encode_plus(string)` but `batch_encode_plus([strings])` and `batch_encode_plus([[tokens]])` ", "Sorry, I was skimming through your problem too quickly - I see what you mean now. \r\nI will take a closer look at this.", "Created a PR this fixes this behavior. Thanks for pointing this out @creat89 :-) ", "There has been a big change in tokenizers recently :-) which adds a `is_pretokenized` flag to the input which makes everything much easier. This should then be used as follows: \r\n``\r\nbert_tokenizer.batch_encode_plus([tokens], is_pretokenized=True))\r\n``", "Cool, that's awesome and yes, I'm sure that makes everything easier. Cheers!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3501?src=pr&el=h1) Report\n> Merging [#3501](https://codecov.io/gh/huggingface/transformers/pull/3501?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/601ac5b1dc1438f00d09696588f2deb0f045ae3b&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3501?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3501 +/- ##\n=======================================\n Coverage 77.79% 77.80% \n=======================================\n Files 100 100 \n Lines 17051 17053 +2 \n=======================================\n+ Hits 13265 13268 +3 \n+ Misses 3786 3785 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3501?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3501/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `97.59% <100.00%> (+0.01%)` | :arrow_up: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3501/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.94% <0.00%> (+0.13%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3501?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3501?src=pr&el=footer). Last update [601ac5b...5e11181](https://codecov.io/gh/huggingface/transformers/pull/3501?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Could you make sure the kwarg in `examples/summarization/bart/run_bart_sum.py` is correct? Thanks!", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3500?src=pr&el=h1) Report\n> Merging [#3500](https://codecov.io/gh/huggingface/transformers/pull/3500?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/601ac5b1dc1438f00d09696588f2deb0f045ae3b&el=desc) will **increase** coverage by `0.50%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3500?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3500 +/- ##\n==========================================\n+ Coverage 77.79% 78.30% +0.50% \n==========================================\n Files 100 100 \n Lines 17051 17051 \n==========================================\n+ Hits 13265 13351 +86 \n+ Misses 3786 3700 -86 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3500?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `97.58% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.94% <0.00%> (+0.13%)` | :arrow_up: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/3500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `56.49% <0.00%> (+27.59%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3500?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3500?src=pr&el=footer). Last update [601ac5b...70f9258](https://codecov.io/gh/huggingface/transformers/pull/3500?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "> Could you make sure the kwarg in `examples/summarization/bart/run_bart_sum.py` is correct? Thanks!\r\n\r\nSure, updated all occurrences on that file", "@dougian : we are going to merge/adopt this after a few other PRs are merged in order to coordinate the signature with T5's signature. Thanks for your contribution!\r\n", "Fixed on master by other PRs, closing. Thanks!" ]

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[ "Have a look at the `mask_tokens` method in `run_language_modeling.py`. This takes in the `input_ids`, performs masking on them and returns the masked `input_ids` and corresponding `masked_lm_labels`.", "@Drpulti I am also getting the same error as you, and I believe it is because `-100` exists in the `masked_lm_labels` returned by `mask_tokens`. \r\n\r\nThese are fed to the `forward` hook of `BertForMaskedLM` (or whatever pre-trained model you are using), and ultimately to `CrossEntropyLoss`, which throws an error for `labels < 0`.\r\n\r\nhttps://github.com/huggingface/transformers/blob/601ac5b1dc1438f00d09696588f2deb0f045ae3b/src/transformers/modeling_bert.py#L1001-L1004\r\n\r\nThe docstring says:\r\n\r\nhttps://github.com/huggingface/transformers/blob/601ac5b1dc1438f00d09696588f2deb0f045ae3b/src/transformers/modeling_bert.py#L933-L937\r\n\r\nbut I don't see the logic where `masked_lm_labels == -100` are ignored. You can even see a comment that says `-100` is masked, \r\n\r\nhttps://github.com/huggingface/transformers/blob/601ac5b1dc1438f00d09696588f2deb0f045ae3b/src/transformers/modeling_bert.py#L1002\r\n\r\nbut again, where is the code that does this? I figure that both of us might be missing the step that properly handles these `-100` values.", "I believe that the `-100` part is handled by `CrossEntropyLoss` (https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#nll_loss)\r\n\r\nI think that in your case, you might be having some mismatch between pytorch and transformers versions. Try upgrading to the latest of both and check if the error is still there.", "When my label contains -100, I get this error when running “IndexError: Target -100 is out of bounds.”", "Could you be a bit more specific as to where the error is coming from? Maybe a stack trace would be nice. Also, please upgrade your pytorch and transformers packages. I'm running transformers 2.5.0 and pytorch 1.4.0 and don't get any such issue.", "@Genius1237 in fact,i think i don't relly know what is the meaning of masked_lm_labels, I want to know what he expresses and how can we get him\r\n\r\n", "@tom1125 I'm not understanding you. Are you saying that you want to know how `masked_lm_labels` are computed and how it's used in computing the loss?", "@Genius1237 yes ,and i want to know how to get it,thanks", "An input sentence is a sequence of sub-word tokens, represented by their IDs. This is what `input_ids` would represent (before masking). The `mask_tokens` methods takes in this, and chooses 15% of the tokens for a \"corruption\" process. In this \"corruption\" process, 80% of the chosen tokens become [MASK], 10% get replaced with a random word and 10% are untouched.\r\n\r\nThe goal of the bert model will be to take in the \"corrupted\" `input_ids` and predict the correct token for each token. The correct tokens, `masked_lm_labels` are also produced by the `mask_token` methods. The values of this tensor would ideally be a clone of the \"uncorrupted\" `input_ids`, but since the loss is computed over only the \"corrupted\" tokens, the value of `masked_lm_labels` for the 85% of tokens that aren't chosen for \"corruption\" is set to `-100` so that it gets ignored by `CrossEntropyLoss`.", "@Genius1237 thank you very much,it really helps me.", "> I believe that the `-100` part is handled by `CrossEntropyLoss` (https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#nll_loss)\r\n> \r\n> I think that in your case, you might be having some mismatch between pytorch and transformers versions. Try upgrading to the latest of both and check if the error is still there.\r\n\r\nYou are right! Thanks. I will try updating both packages", "> I believe that the `-100` part is handled by `CrossEntropyLoss` (https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#nll_loss)\r\n> \r\n> I think that in your case, you might be having some mismatch between pytorch and transformers versions. Try upgrading to the latest of both and check if the error is still there.\r\n\r\nYou are right, upgrade helped to resolve the issue. I'm closing the thread." ]

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[ "That's a good point @nooralahzadeh! We should probably add the `model_type` field to the XLM-ROBERTA config. \r\n\r\n@julien-c - If it's fine for you I can add the `model_type` `xlm-roberta` to all `xlm-roberta` configs of the following models: \r\nhttps://huggingface.co/models?search=xlm-rob \r\n\r\nAs @nooralahzadeh pointed out, the fine-tuned XLM-ROBERTA models would otherwise default to the XLM model (because of its name and the implemented Fallback pattern in https://github.com/huggingface/transformers/blob/f6a23d19116a62bd3c662d0aa381130b49abcff7/src/transformers/configuration_auto.py#L190) \r\n\r\nShould be easy with a S3 AWS script. ", "Hmm, there is a `xlm-roberta` before `xlm` and `roberta` in [CONFIG_MAPPING](https://github.com/huggingface/transformers/blob/f6a23d19116a62bd3c662d0aa381130b49abcff7/src/transformers/configuration_auto.py#L65) so it should be correctly picked up, no?", "True, @nooralahzadeh I guess in order to avoid falling to 'xlm' you have to name your files/folders 'xlm-roberta-something' (so you need to include the full xlm-roberta name) or you can just manually change the config of your xlm-roberta and add the `model_type`", "Thanks, this is what I said in the first comment: need to have \"xlm-roberta\" in its name!", "Hello thanks, this command not work! can you help me? please\r\npython run_squad.py \\\r\n--model_type xlm-roberta\r\n\r\n", "Hey @Forutanrad,\r\n\r\ncould you please open a new issue for this?", "Hello, yes thanks.\n\nOn Thu, 3 Feb 2022, 13:28 Patrick von Platen, ***@***.***>\nwrote:\n\n> Hey @Forutanrad <https://github.com/Forutanrad>,\n>\n> could you please open a new issue for this?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/3498#issuecomment-1028806963>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ASUDOKXALOAGOJF5NSHE4MDUZJGSRANCNFSM4LVTH64A>\n> .\n> Triage notifications on the go with GitHub Mobile for iOS\n> <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675>\n> or Android\n> <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.\n>\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n" ]

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[ "Code is released for the preceding paper: Latent Retrieval for Weakly Supervised Open Domain Question Answering\" https://www.aclweb.org/anthology/P19-1612/\r\nhttps://github.com/google-research/language/tree/master/language/orqa", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I am also interested on it. Any news? Thanks", "I'm also interested on this, are you planning to include this model in the library??", "Finally the REALM code has been released here https://github.com/google-research/language/tree/master/language/realm.\r\nI think that this issue should be re-opened. @aced125 are you able to re-open it?", "I hope somebody can reopen this and make it happen", "Any updates on this ? ", "Any update? \r\n", "I think this could be implemented easily with RAG by making sure that we\nboth finetune doc encoder and question encoder of the retriever model. This\nmight be very useful for us.\n\nOn Wed, Feb 10, 2021, 23:42 Lysandre Debut <[email protected]> wrote:\n\n> Reopened #3497 <https://github.com/huggingface/transformers/issues/3497>.\n>\n> —\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/3497#event-4314060510>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AEA4FGULJ5VEDTMJD6F4ZXLS6JPJPANCNFSM4LVQ3UVQ>\n> .\n>\n", "Any Update on this?", "@OctoberChang I did extend the RAG in a way that can be used to experiment REALM stuff. \r\n\r\nhttps://paperswithcode.com/paper/fine-tune-the-entire-rag-architecture", "@shamanez , thanks for the pointer to your RAG project [(link)](https://github.com/huggingface/transformers/tree/master/examples/research_projects/rag-end2end-retriever)\r\nDo you have any pre-trained (not fine-tuned on downstream tasks) models with ICT or Salient span masking [[link]](https://arxiv.org/pdf/2002.08909.pdf) that can be load from Huggingface Transformers?", "I haven't extended into the ICT. Sorry.\r\n@OctoberChang I think to get REALM models loaded with the current HF retrieval framework, we might need some workarounds. This is mainly because RAG uses a generative model and REALM consists of extractive LM. ", "@qqaatw's WIP PR (https://github.com/huggingface/transformers/pull/13292) adds REALM." ]

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[ "Have you tried loading your checkpoint in the model using `from_pretrained`:\r\n\r\n```py\r\nmodel = BertForTokenClassification.from_pretrained(\"checkpoint)\r\n```\r\n\r\n? It should work out of the box. Please be aware that the NER head will be randomly initialized as it is not in the sequence classification checkpoint.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "I have encountered the same error running the LM finetuning script on Google Colab, also with the Bert model type but using the pretrained weights from 'allenai/scibert_scivocab_uncased'\r\n\r\n```\r\n!python run_language_modeling.py \\\r\n --output_dir=models \\\r\n --model_type=bert \\\r\n --model_name_or_path='allenai/scibert_scivocab_uncased' \\\r\n --do_train \\\r\n --train_data_file=train_sample.txt \\\r\n --do_eval \\\r\n --eval_data_file=val_sample.txt \\\r\n --mlm \\\r\n --line_by_line\r\n```\r\n\r\nEnvironment\r\n- transformers : 2.7.0\r\n- Python: 3.6.9\r\n- PyTorch: 1.4.0\r\n\r\n\r\nAbbreviated stack trace : \r\n```\r\nIteration: 0% 0/2971 [00:00<?, ?it/s]/pytorch/aten/src/THC/THCTensorIndex.cu:361: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2, IndexIsMajor = true]: block: [116,0,0], thread: [96,0,0] Assertion `srcIndex < srcSelectDimSize` failed. \r\n/pytorch/aten/src/THC/THCTensorIndex.cu:361: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2, IndexIsMajor = true]: block: [116,0,0], thread: [97,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\r\n/pytorch/aten/src/THC/THCTensorIndex.cu:361: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2, IndexIsMajor = true]: block: [116,0,0], thread: [98,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\r\n...\r\n/pytorch/aten/src/THC/THCTensorIndex.cu:361: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2, IndexIsMajor = true]: block: [116,0,0], thread: [61,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\r\n/pytorch/aten/src/THC/THCTensorIndex.cu:361: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2, IndexIsMajor = true]: block: [116,0,0], thread: [62,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\r\n/pytorch/aten/src/THC/THCTensorIndex.cu:361: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2, IndexIsMajor = true]: block: [116,0,0], thread: [63,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\r\nTraceback (most recent call last):\r\n File \"run_language_modeling.py\", line 799, in <module>\r\n main()\r\n File \"run_language_modeling.py\", line 749, in main\r\n global_step, tr_loss = train(args, train_dataset, model, tokenizer)\r\n File \"run_language_modeling.py\", line 353, in train\r\n outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\", line 987, in forward\r\n encoder_attention_mask=encoder_attention_mask,\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\", line 790, in forward\r\n encoder_attention_mask=encoder_extended_attention_mask,\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\", line 407, in forward\r\n hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\", line 368, in forward\r\n self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\", line 314, in forward\r\n hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\", line 216, in forward\r\n mixed_query_layer = self.query(hidden_states)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py\", line 87, in forward\r\n return F.linear(input, self.weight, self.bias)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\", line 1372, in linear\r\n output = input.matmul(weight.t())\r\nRuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`\r\n`", "I believe this is because the `allenai/scibert_scivocab_uncased` checkpoint does not have a maximum length for the tokenizer.\r\n\r\nWould you mind trying again but this time specifying `--block_size=512` as an additional argument, to limit the size of sequences to 512 tokens?g", "@Rshcaroline do you mind trying the same argument `--block_size=512` and see if that fixes your issue?", "@LysandreJik Thanks very much for the tip, that solved the problem.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I think this is OOM from CUDA side.", "In case somebody is having the same in issue in the latest version, `--max_seq_length 512` fixes this issue for me." ]

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[ "You can use `run_language_modeling.py` for this purpose. Just set `--model_type` to `gpt2`, set `--model_name_or_path` to the gpt2 model checkpoint you want (`gpt2`) and set `--train_data_file` to your dataset and you should be ready to go.", "Thanks! The issue is that I want to use the pre-trained version of GPT-2. I remember `run_lm_finetuning` had a few lines of code where it would download and load that pre-trained model.", "You can specify `gpt2` in `--model_name_or_path`. That corresponds to one of the pre-trained checkpoints that it'll download and use. The other possible pre-trained models that you can specify there are `gpt2-medium`,`gpt2-large`,`gpt2-xl` and `distilgpt2`.", "If specifying `gpt2` there downloads the checkpoints then how do you train from scratch? I've been specifying that parameter and it seems like it is training from scratch (starting perplexity ~1000).", "I believe that if you want to train from scratch, you'll have to point that to a folder with a config file (with the parameters of the model) and no `pytorch_model.bin` checkpoint file in that folder.", "@Genius1237 Hi there, \r\nI am finetuning the 124M model based on my dataset (almost 2mb) and I am using Colab by Max Wolf. I am wondering if there is a way that I can generate texts based on my trained model + internet context (not only metadata). I wanna generate some notes regarding current issues (such as COVID-19) based on my trained model. \r\nCould you help me with that, please? thanks.\r\n ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3492?src=pr&el=h1) Report\n> Merging [#3492](https://codecov.io/gh/huggingface/transformers/pull/3492?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/17dceae7a1de5577cd0c07a97dcd5821a08af07c&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3492?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3492 +/- ##\n==========================================\n- Coverage 77.80% 77.79% -0.01% \n==========================================\n Files 100 100 \n Lines 17051 17051 \n==========================================\n- Hits 13266 13265 -1 \n- Misses 3785 3786 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3492?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.81% <0.00%> (-0.14%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3492?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3492?src=pr&el=footer). Last update [17dceae...70cab16](https://codecov.io/gh/huggingface/transformers/pull/3492?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Awesome! One more data point for #3357 ", "Hey @stefan-it, thanks for creating some great models. It looks like a few [dbmdz models](https://huggingface.co/dbmdz) are missing model cards, including the default model for NER (`dbmdz/bert-large-cased-finetuned-conll03-english`). Are these licensed as MIT as well?" ]

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[ "In the same file, there is also `--do_lower_case` wrongly apply to `roberta-base` and `xlnet-large-cased` model. It will be great if this PR also includes that fix.", "> In the same file, there is also `--do_lower_case` wrongly apply to `roberta-base` and `xlnet-large-cased` model. It will be great if this PR also includes that fix.\r\n\r\nThe intention is to have consistency across all the experiments, where all input is lowercase for all examples. Just because roberta and xlnet don't have lowercased models, doesn't mean their input has to be the cased version.", "Thanks for raising this. Should be fixed by #3738\r\n\r\nif one saves their tokenizers using `save_pretrained()` there shouldn't be a need for passing the `do_lower_case` manually anymore." ]

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[ "Do I need to build my own iterator for this?\r\nHow should the tokens be arranged for multiple choice question solving?\r\n\r\ne.g. (question) (token1) (token2) (MCoption)(answer1)\r\n. ----> is this the right format for all of the GPT-2, BERT and XLNet? \r\n\r\nThank you", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Please complete the issue template, I don't understand what the issue is.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3488?src=pr&el=h1) Report\n> Merging [#3488](https://codecov.io/gh/huggingface/transformers/pull/3488?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/17dceae7a1de5577cd0c07a97dcd5821a08af07c&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3488?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3488 +/- ##\n==========================================\n- Coverage 77.80% 77.79% -0.01% \n==========================================\n Files 100 100 \n Lines 17051 17051 \n==========================================\n- Hits 13266 13265 -1 \n- Misses 3785 3786 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3488?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3488/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.81% <0.00%> (-0.14%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3488?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3488?src=pr&el=footer). Last update [17dceae...3e0a394](https://codecov.io/gh/huggingface/transformers/pull/3488?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "LGTM", "Merging to unblock other testing efforts!" ]

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[ "@julien-c Do you know why does it fail?" ]

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[ "Do you use the `zsh` shell? cause in this case I think you have to do `pip install -e \".\"`\r\n\r\nI think this has something to do with the shells. Can you compare `zsh` to pure `bash` shell?", "Fails in bash identically.\r\nOne clue, this is also happening for `pip install tokenizers` followed by import `tokenizers`, but not for `import numpy`.", "And with `pip install -e \".\"` it works ?", "I fixed it by **restarting** my zsh shell. Now I can't reproduce the bug in a new zsh shell. Closing.\r\n" ]

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[ "This is from this right? https://circleci.com/gh/huggingface/transformers/26455?utm_campaign=workflow-failed&utm_medium=email&utm_source=notification", "Yeah exactly - it should be fixed now. \r\nI will push a slightly cleaner version in a second :-) ", "In general, every example test that creates folder or files should deleted them afterwards to avoid same file naming collisions. Will open a PR about this. @julien-c @sshleifer @LysandreJik " ]

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[ "This seems to be similar to [Issue #3466](https://github.com/huggingface/transformers/issues/3466)", "Hi, this issue was solved with https://github.com/huggingface/transformers/commit/e2c05f06ef58ea77103d2c64492dd8d9a0b21c3f\r\n\r\nCould you try to pull the repository once again and try then?", "Yes, that seems to have fixed it! I still get a bunch of warnings which are also related to indentation I believe? I've posted the full log of my build below with all the other warnings, but I'll close this issue for now:\r\n\r\n```\r\nRunning Sphinx v2.4.4\r\nmaking output directory... done\r\nbuilding [mo]: targets for 0 po files that are out of date\r\nbuilding [html]: targets for 38 source files that are out of date\r\nupdating environment: [new config] 38 added, 0 changed, 0 removed\r\n/Users/venkat/opt/miniconda3/envs/tf-pt/lib/python3.6/site-packages/recommonmark/parser.py:75: UserWarning: Container node skipped: type=document\r\n warn(\"Container node skipped: type={0}\".format(mdnode.t))\r\n/Users/venkat/opt/miniconda3/envs/tf-pt/lib/python3.6/site-packages/recommonmark/parser.py:75: UserWarning: Container node skipped: type=document\r\n warn(\"Container node skipped: type={0}\".format(mdnode.t))\r\n/Users/venkat/opt/miniconda3/envs/tf-pt/lib/python3.6/site-packages/recommonmark/parser.py:75: UserWarning: Container node skipped: type=document\r\n warn(\"Container node skipped: type={0}\".format(mdnode.t))\r\nreading sources... [100%] usage\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_utils.py:docstring of transformers.PreTrainedModel.from_pretrained:23: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_tf_utils.py:docstring of transformers.TFPreTrainedModel.from_pretrained:20: WARNING: Definition list ends without a blank line; unexpected unindent.\r\nWARNING: error while formatting arguments for transformers.pipeline: 'function' object has no attribute '__mro__'\r\n/Users/venkat/Downloads/transformers/src/transformers/pipelines.py:docstring of transformers.Pipeline:6: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/data/processors/utils.py:docstring of transformers.data.processors.utils.DataProcessor.get_dev_examples:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.\r\n/Users/venkat/Downloads/transformers/src/transformers/data/processors/utils.py:docstring of transformers.data.processors.utils.DataProcessor.get_example_from_tensor_dict:3: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/data/processors/utils.py:docstring of transformers.data.processors.utils.DataProcessor.get_train_examples:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.batch_encode_plus:32: WARNING: Bullet list ends without a blank line; unexpected unindent.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.build_inputs_with_special_tokens:4: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.encode:37: WARNING: Bullet list ends without a blank line; unexpected unindent.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.encode_plus:36: WARNING: Bullet list ends without a blank line; unexpected unindent.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.prepare_for_model:17: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.prepare_for_model:18: WARNING: Block quote ends without a blank line; unexpected unindent.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.tokenize:9: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.tokenize:10: WARNING: Block quote ends without a blank line; unexpected unindent.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.tokenize:10: WARNING: Inline strong start-string without end-string.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_utils.py:docstring of transformers.PreTrainedTokenizer.truncate_sequences:3: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_tf_albert.py:docstring of transformers.TFAlbertModel.call:47: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_tf_albert.py:docstring of transformers.TFAlbertModel.call:48: WARNING: Block quote ends without a blank line; unexpected unindent.\r\n/Users/venkat/Downloads/transformers/src/transformers/configuration_auto.py:docstring of transformers.AutoConfig.from_pretrained:7: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_auto.py:docstring of transformers.AutoTokenizer:12: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_auto.py:docstring of transformers.AutoTokenizer.from_pretrained:6: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_auto.py:docstring of transformers.AutoModel.from_pretrained:10: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:29: WARNING: Title underline too short.\r\n\r\n``AutoModelForPreTraining``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:29: WARNING: Title underline too short.\r\n\r\n``AutoModelForPreTraining``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_auto.py:docstring of transformers.AutoModelForPreTraining.from_pretrained:9: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:35: WARNING: Title underline too short.\r\n\r\n``AutoModelWithLMHead``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:35: WARNING: Title underline too short.\r\n\r\n``AutoModelWithLMHead``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_auto.py:docstring of transformers.AutoModelWithLMHead.from_pretrained:10: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:41: WARNING: Title underline too short.\r\n\r\n``AutoModelForSequenceClassification``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:41: WARNING: Title underline too short.\r\n\r\n``AutoModelForSequenceClassification``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_auto.py:docstring of transformers.AutoModelForSequenceClassification.from_pretrained:10: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:47: WARNING: Title underline too short.\r\n\r\n``AutoModelForQuestionAnswering``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:47: WARNING: Title underline too short.\r\n\r\n``AutoModelForQuestionAnswering``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_auto.py:docstring of transformers.AutoModelForQuestionAnswering.from_pretrained:10: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:53: WARNING: Title underline too short.\r\n\r\n``AutoModelForTokenClassification``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/auto.rst:53: WARNING: Title underline too short.\r\n\r\n``AutoModelForTokenClassification``\r\n~~~~~~~~~~~~~~~~~~~~~\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_auto.py:docstring of transformers.AutoModelForTokenClassification.from_pretrained:10: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/docs/source/model_doc/t5.rst:7: WARNING: Title underline too short.\r\n\r\nOverview\r\n~~~~~\r\n/Users/venkat/Downloads/transformers/src/transformers/tokenization_t5.py:docstring of transformers.T5Tokenizer.build_inputs_with_special_tokens:4: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_t5.py:docstring of transformers.T5Model:9: WARNING: Duplicate explicit target name: \"exploring the limits of transfer learning with a unified text-to-text transformer\".\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_t5.py:docstring of transformers.T5Model.forward:54: WARNING: Unexpected indentation.\r\n/Users/venkat/Downloads/transformers/src/transformers/modeling_t5.py:docstring of transformers.T5Model.forward:55: WARNING: Block quote ends without a blank line; unexpected 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of transformers.XLNetForMultipleChoice.forward:65: WARNING: Inline literal start-string without end-string.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:25: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:29: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:36: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:40: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:44: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:48: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:52: WARNING: Line block ends without a blank 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block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:264: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:268: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:272: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:276: WARNING: Line block ends without a blank line.\r\n/Users/venkat/Downloads/transformers/docs/source/pretrained_models.rst:280: WARNING: Line block ends without a blank line.\r\nlooking for now-outdated files... none found\r\npickling environment... done\r\nchecking consistency... done\r\npreparing documents... done\r\nwriting output... [100%] usage\r\n/Users/venkat/Downloads/transformers/docs/source/examples.md:440: WARNING: None:any reference target not found:\r\ngenerating indices... genindexdone\r\nhighlighting module code... [100%] transformers.tokenization_xlnet\r\nwriting additional pages... search/Users/venkat/opt/miniconda3/envs/tf-pt/lib/python3.6/site-packages/sphinx_rtd_theme/search.html:21: RemovedInSphinx30Warning: To modify script_files in the theme is deprecated. Please insert a <script> tag directly in your theme instead.\r\n {% endblock %}\r\ndone\r\ncopying images... [100%] imgs/warmup_linear_schedule.png\r\ncopying static files... ... done\r\ncopying extra files... done\r\ndumping search index in English (code: en)... done\r\ndumping object inventory... done\r\nbuild succeeded, 107 warnings.\r\n```", "Yes there are a few warnings, but they're inoffensive. We're working towards removing most of them :)" ]

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[ "I agree. Tried for an hour last night to add coverage for `run_bart_sum.py` and got stuck on two things.\r\n\r\n1) A `circleci` job that installs `pytorch_lightning` (and potentially other dependencies)\r\n2) the ability to import `examples/transformer_base.py`\r\n\r\n### Suggested Approach:\r\n- add the aforementioned circleci job\r\n- a flag like `@require_lightning` to decorate some tests\r\n- some code changes to get the imports working sanely. \r\n- Checklist/Instructions for how new examples/ contributors can add test coverage.\r\n\r\nHappy to help with this!\r\nCC: @LysandreJik , @julien-c, @thomwolf @patrickvonplaten ", "Cool. Most of these seem easy. Sharing code between examples is a bit harder. I am not sure if we should have and examples package, or symlink that shared file somehow. Currently we add it to the path before running the code. ", "With the Pytorch-Lightning examples, I've been running into an [issue](https://github.com/huggingface/transformers/pull/3437) loading trained models with `--do_predict` (when not also using `--do_train`), so it would be helpful to add some model-loading test as well :) ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "I believe that the `collate` function should take care of it. However, you will need to create the `attention_mask` variable when you have inputs of variable length, so that the model does not attend to the padded indices.", "@Genius1237 I'm a real noob (I come from a programming background and no ML), but I'm using the GPT2 model, not a masked-language modeling like BERT. Do I still need to do such a thing? I didn't see any `attention_mask` reference in TextDataset loaders in the example file.\r\n\r\nAlso, how does the `collate` function knows to pad the examples to `bucket_size` if no such variable is passed to it?", "The `TextDataset` class takes text and converts it into blocks of size `block_size` (512), concatenating consecutive blocks if needed. My guess is that `LineByLineTextDataset` exists to cater to those who would like to have examples being limited to one sentence each, and thus the max size in one batch would be determined by the longest sentence in that batch.\r\n\r\n`attention_mask` is definitely needed when you have sequences of different length. Have a look at Have a look at https://github.com/huggingface/transformers/issues/1408 . Something like this should do on top of the existing version of `LineByLineTextDataset`.\r\n```\r\ndef collate(examples: List[torch.Tensor]):\r\n padding_value = 0 if tokenizer._pad_token is None else tokenizer.pad_token_id\r\n input_ids = pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\r\n\r\n max_length = input_ids.shape[1]\r\n attention_mask = torch.stack([torch.cat([torch.ones(len(t), dtype=torch.long), torch.zeros(max_length - len(t), dtype=torch.long)]) for t in examples])\r\n\r\n return input_ids, attention_mask\r\n```", "Gotcha, thank you so much @Genius1237 ! I hope it would work.\r\nDo I need to unpack `attention_mask` somehow or the collate function in the DataLoader will take care of that?", "And in your code you probably meant to wrote this instead, right?\r\n\r\n```python\r\ninput_ids = pad_sequence(examples, batch_first=True, padding_value=padding_value)\r\n```\r\n\r\nAlso, the same code should be pasted in ```def evaluate():``` right?", "Same code in evaluate. The collate function is called by the dataloader. The dataloader calls the __getitem__ on the dataset `batch_size` times and sends that output to the collate function. The output of the collate function is what you will get when you do `for batch in train_dataloader`. The batch in this case will be a 2 tuple, with `batch[0]` having the input_ids and `batch[1]` having the attention_mask.", "I really appreciate your help, thanks a lot. I'll ping the maintainers to change the example code so others can benefit. @thomwolf @LysandreJik @patrickvonplaten Thanks!", "Hey, @Genius1237, I'm getting this error using your code:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"run_language_modeling.py\", line 974, in <module>\r\n main()\r\n File \"run_language_modeling.py\", line 924, in main\r\n global_step, tr_loss = train(args, train_dataset, model, tokenizer)\r\n File \"run_language_modeling.py\", line 508, in train\r\n inputs = inputs.to(args.device)\r\nAttributeError: 'tuple' object has no attribute 'to'\r\n```\r\nUsing the previous code I'm not getting any error.", "When you do `for batch in train_dataloader`, batch is basically whatever is returned by the `collate` function, which is this cause becomes a 2-tuple. In your case `input_ids` is a 2-tuple containing 2 tensors. You'll have to split that into 2, i.e `input_ids, attention_mask = inputs`, and move forward with that, pushing both those tensors to the required device (`input_ids = input_ids.to(args.device); attention_mask = attention_mask.to(args.device)`).", "Can you please help me? I'm really clueless...\r\n\r\nI tried:\r\n\r\n```python\r\nfor _ in train_iterator:\r\n epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\r\n for step, batch in enumerate(epoch_iterator):\r\n\r\n # Skip past any already trained steps if resuming training\r\n if steps_trained_in_current_epoch > 0:\r\n steps_trained_in_current_epoch -= 1\r\n continue\r\n\r\n inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)\r\n input_ids, attention_mask = inputs\r\n inputs = input_ids.to(args.device)\r\n labels = labels.to(args.device)\r\n attention_mask = attention_mask.to(args.device)\r\n model.train()\r\n outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)\r\n loss = outputs[0] # model outputs are always tuple in transformers (see doc)\r\n```\r\n\r\nBut I get this error:\r\n```\r\nTraceback (most recent call last): \r\n File \"run_language_modeling.py\", line 976, in <module>\r\n main()\r\n File \"run_language_modeling.py\", line 926, in main\r\n global_step, tr_loss = train(args, train_dataset, model, tokenizer)\r\n File \"run_language_modeling.py\", line 510, in train\r\n labels = labels.to(args.device)\r\nAttributeError: 'tuple' object has no attribute 'to'\r\n```\r\n\r\nAlso, the rest of the code doesn't seem to use `attention_mask` variable, wouldn't it be redundant?", "```\r\nfor _ in train_iterator:\r\n epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\r\n for step, batch in enumerate(epoch_iterator):\r\n\r\n # Skip past any already trained steps if resuming training\r\n if steps_trained_in_current_epoch > 0:\r\n steps_trained_in_current_epoch -= 1\r\n continue\r\n\r\n input_ids, attention_mask = batch\r\n inputs, labels = mask_tokens(input_ids, tokenizer, args) if args.mlm else (input_ids, input_ids)\r\n inputs = inputs.to(args.device)\r\n labels = labels.to(args.device)\r\n attention_mask = attention_mask.to(args.device)\r\n model.train()\r\n outputs = model(inputs, masked_lm_labels=labels, attention_mask=attention_mask) if args.mlm else model(inputs, labels=labels, attention_mask=attention_mask)\r\n loss = outputs[0] # model outputs are always tuple in transformers (see doc)\r\n```", "Thank you, it works!\r\n\r\nBtw, I don't see a difference in output between training using the attention mask and the original code. Does it mean something?", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", " In training a GPT-2 i have the same question too. I don't see any significant change between training using the attention mask or without. Did u have any answer to this @timsoraro ?\r\nAlso my perplexity score i can say is too low, any idea for this behaviour?\r\n\r\n", "@niklaras I didn't see much difference either after many experiments with or without, I got the same quality of generation." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "You're probably on an old transformers version." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3474?src=pr&el=h1) Report\n> Merging [#3474](https://codecov.io/gh/huggingface/transformers/pull/3474?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e392ba6938f50655a195ea7ec8a260b1e9fc6058&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3474?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3474 +/- ##\n=======================================\n Coverage 77.56% 77.56% \n=======================================\n Files 100 100 \n Lines 16970 16970 \n=======================================\n+ Hits 13162 13163 +1 \n+ Misses 3808 3807 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3474?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3474/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.85% <0.00%> (+0.13%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3474?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3474?src=pr&el=footer). Last update [e392ba6...d9e6d4e](https://codecov.io/gh/huggingface/transformers/pull/3474?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Do you mind giving a reproducible example so that we may debug easily?", "I was following this issue because I ran into the same problem. I won't use the code I ran into the problem with here but here is the gist:\r\n\r\n```python\r\nfrom transformers import XLNetForSequenceClassification\r\n\r\nmodel = XLNetForSequenceClassification.from_pretrained(\"xlnet-base-cased\")\r\ninputs = torch.randint(0, 100, (32, 100)) # 100 words in vocab, batch size 32, seq_len = 100\r\nmasks = torch.ones(inputs.size(), dtype=torch.bool) # none of the tokens are padding\r\nlabels = torch.randint(0, 2, (32,)) # binary classification\r\n\r\nresult = model(inputs, attention_mask=masks, labels=labels)\r\n```\r\n\r\nError:\r\n\r\n> Traceback (most recent call last):\r\n File \"<input>\", line 1, in <module>\r\n File \"/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/transformers/modeling_xlnet.py\", line 1150, in forward\r\n inputs_embeds=inputs_embeds,\r\n File \"/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 532, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/transformers/modeling_xlnet.py\", line 778, in forward\r\n input_mask = 1.0 - attention_mask\r\n File \"/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/tensor.py\", line 394, in __rsub__\r\n return _C._VariableFunctions.rsub(self, other)\r\nRuntimeError: Subtraction, the `-` operator, with a bool tensor is not supported. If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.\r\n\r\nI use OSX Mojave, torch==1.4.0, if it helps.\r\n\r\nEdit: by converting the masks to dtype `torch.uint32`, I was able to get it to work, but I'm not sure if masking using an integer mask is the correct way of handling this.", "The `torch.bool` was introduced in `torch==1.2.0` but we're looking to accommodate `torch>=1.0.0`, so we have to handle such cases with a `uint` syntax. All model inputs should be `uint`, and not `bool` for that specific reason.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "When you encode with `tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')` a [CLS] token is added in the beginning and a [SEP] token is added at the end. You can verify this by: \r\n\r\n```python \r\nfrom transformers import AlbertTokenizer, TFAlbertForMaskedLM\r\n\r\ntokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')\r\ntokenizer.decode(tokenizer.encode(\"This is a test!\")) # gives '[CLS] this is a test![SEP]'\r\n```\r\n\r\nNow the encoded string has two added tokens, one in the begging, one in the end. This means that the two new tokens also produce two logits whose argmax token that in your case happened to be `time` and `your`. If you don`t want to add [CLS] and [SEP] when encoding, use:\r\n\r\n```python \r\nfrom transformers import AlbertTokenizer, TFAlbertForMaskedLM\r\n\r\ntokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')\r\ntokenizer.decode(tokenizer.encode(\"This is a test!\", add_special_tokens=False)) # gives 'this is a test!'\r\n```\r\n\r\n", "So they can't output special tokens properly.", "I mean they can, but the model does not have to output \"[CLS]\" when you feed \"[CLS]\" in the model. " ]

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[ "\r\n\r\nHappy to help on this @LysandreJik @patrickvonplaten", "The test runs fine locally on my computer. A couple of wild thoughts:\r\n\r\n- It happened after merging #3411 and #3428 which adds quite some long `t5-large` and `t5-base` tests to the test examples. Could this test fail because of some kind of time-out error?\r\n- The test consumes 10GB RAM when running locally - this doesn't seem too much to fail though.", "Did you try connecting to a failing circle-ci box, to investigate? ", "unsubscribe\r\n\r\n\r\n\r\n\r\n------------------&nbsp;原始邮件&nbsp;------------------\r\n发件人:&nbsp;\"Julien Chaumond\"<[email protected]&gt;;\r\n发送时间:&nbsp;2020年3月27日(星期五) 晚上10:15\r\n收件人:&nbsp;\"huggingface/transformers\"<[email protected]&gt;;\r\n抄送:&nbsp;\"Subscribed\"<[email protected]&gt;;\r\n主题:&nbsp;Re: [huggingface/transformers] CircleCI ExamplesTests::test_run_squad failing (#3469)\r\n\r\n\r\n\r\n Did you try connecting to a failing circle-ci box, to investigate?\r\n—\r\nYou are receiving this because you are subscribed to this thread.\r\nReply to this email directly, view it on GitHub, or unsubscribe.", "I think this is solved with https://github.com/huggingface/transformers/pull/3485 ." ]

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[ "The file `run_language_modeling.py` does indeed not have a variable called `MODEL_CLASSES`. Can you explain what you are trying to do exactly?" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3467?src=pr&el=h1) Report\n> Merging [#3467](https://codecov.io/gh/huggingface/transformers/pull/3467?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/63f4d8cad010f1972254007ad56b22fe5ed203fe&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3467?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3467 +/- ##\n=======================================\n Coverage 77.84% 77.84% \n=======================================\n Files 100 100 \n Lines 17060 17060 \n=======================================\n+ Hits 13280 13281 +1 \n+ Misses 3780 3779 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3467?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3467/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.32% <0.00%> (+0.17%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3467?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3467?src=pr&el=footer). Last update [63f4d8c...1681ac8](https://codecov.io/gh/huggingface/transformers/pull/3467?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Was fixed with https://github.com/huggingface/transformers/commit/e2c05f06ef58ea77103d2c64492dd8d9a0b21c3f" ]

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[ "Had the same Issue, I think it was already fixed in an older commit, but the pip package doesnt seem to be updated. Try installing transformers from this repo:\r\n`git clone https://github.com/huggingface/transformers`\r\n`cd transformers`\r\n`pip install .`", "I also had the same issue as @paras55, and the solution provided by @mowoe fixed it! Thanks!", "this is still an issue in 2.7.0.", "Are you sure? I think it was fixed in this commit: [c76c3ce](https://github.com/huggingface/transformers/commit/c76c3cebed3c707178d9f721349c5abd5206a57f). ", "I cloned the repo, checked out the 2.7.0 tag and built and installed the wheel and was running into the same issue.\r\n\r\nResult of `pip list`\r\n\r\n```\r\nPackage Version \r\n---------------------- ------------\r\nabsl-py 0.9.0 \r\nastor 0.8.1 \r\nastroid 2.3.3 \r\nasttokens 2.0.3 \r\nattrs 19.3.0 \r\nautopep8 1.5 \r\nboto3 1.12.27 \r\nbotocore 1.15.27 \r\ncachetools 4.0.0 \r\ncertifi 2019.11.28 \r\nchardet 3.0.4 \r\nclick 7.1.1 \r\ndataclasses 0.7 \r\ndecorator 4.4.2 \r\ndocutils 0.15.2 \r\nentrypoints 0.3 \r\nfilelock 3.0.12 \r\nflake8 3.7.9 \r\nflake8-aaa 0.7.1 \r\ngast 0.2.2 \r\ngoogle-auth 1.11.3 \r\ngoogle-auth-oauthlib 0.4.1 \r\ngoogle-pasta 0.2.0 \r\ngrpcio 1.27.2 \r\nh5py 2.10.0 \r\nidna 2.9 \r\nimportlab 0.5.1 \r\nimportlib-metadata 1.5.0 \r\nisort 4.3.21 \r\njmespath 0.9.5 \r\njoblib 0.14.1 \r\nKeras-Applications 1.0.8 \r\nKeras-Preprocessing 1.1.0 \r\nlazy-object-proxy 1.4.3 \r\nMarkdown 3.2.1 \r\nmccabe 0.6.1 \r\nmore-itertools 8.2.0 \r\nmypy 0.761 \r\nmypy-extensions 0.4.3 \r\nnetworkx 2.4 \r\nninja 1.9.0.post1 \r\nnumpy 1.18.2 \r\noauthlib 3.1.0 \r\nopt-einsum 3.2.0 \r\npackaging 20.1 \r\npandas 1.0.3 \r\npip 20.0.2 \r\npluggy 0.13.1 \r\nprotobuf 3.11.3 \r\npy 1.8.1 \r\npyasn1 0.4.8 \r\npyasn1-modules 0.2.8 \r\npycodestyle 2.5.0 \r\npyflakes 2.1.1 \r\npylint 2.4.4 \r\npyparsing 2.4.6 \r\npytest 5.3.5 \r\npython-dateutil 2.8.1 \r\npytype 2020.2.20 \r\npytz 2019.3 \r\nPyYAML 5.3.1 \r\nregex 2020.2.20 \r\nrequests 2.23.0 \r\nrequests-oauthlib 1.3.0 \r\nrsa 4.0 \r\ns3transfer 0.3.3 \r\nsacremoses 0.0.38 \r\nscikit-learn 0.22.2.post1\r\nscipy 1.4.1 \r\nsentencepiece 0.1.85 \r\nsetuptools 45.2.0 \r\nsix 1.14.0 \r\ntensorboard 2.0.2 \r\ntensorflow 2.0.0 \r\ntensorflow-determinism 0.3.0 \r\ntensorflow-estimator 2.0.1 \r\ntermcolor 1.1.0 \r\ntokenizers 0.5.2 \r\ntqdm 4.43.0 \r\ntransformers 2.7.0 \r\ntyped-ast 1.4.1 \r\ntyping-extensions 3.7.4.1 \r\nurllib3 1.25.8 \r\nwcwidth 0.1.8 \r\nWerkzeug 1.0.0 \r\nwheel 0.34.2 \r\nwrapt 1.11.2 \r\nzipp 3.0.0 \r\n```\r\n\r\n\r\nthe code I ran (unnecessary parts excluded):\r\n```\r\nfor a, b in zip(train_text_a, train_text_b):\r\n tokens_dict = tokenizer.encode_plus(a, b, max_length=10,\r\n pad_to_max_length=True)\r\n train_input_ids.append(np.asarray([tokens_dict[\"input_ids\"]]))\r\n train_input_masks.append(np.asarray([tokens_dict[\"attention_mask\"]]))\r\n train_input_segment_ids.append(np.asarray([tokens_dict[\"token_type_ids\"]]))\r\n```\r\n\r\nand the traceback I received:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<input>\", line 104, in <module>\r\nKeyError: 'token_type_ids'\r\n```", "Yeah I think we are talking about a completely different problem here. @amoux and @paras55 had this exception in the squad.py of this repo (Which shouldnt raise one, when used this way). Your Exception however is a KeyError in your code (line 104). Maybe try adding `return_token_type_ids=True` as argument to your tokenizer. This is still an issue, as this should be default. Maybe open a new one, as this isnt the same problem.", "You're definitely correct. `return_token_type_ids` solved the problem. Much appreciated @mowoe!", "\r\nAn example for question answering with DistilBERT\r\n\r\n```\r\nfrom transformers import DistilBertTokenizer, DistilBertForQuestionAnswering\r\nimport torch\r\n\r\ntokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased',return_token_type_ids = True)\r\nmodel = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad')\r\n\r\ncontext = \"The US has passed the peak on new coronavirus cases, President Donald Trump said and predicted that some states would reopen this month.The US has over 637,000 confirmed Covid-19 cases and over 30,826 deaths, the highest for any country in the world.\"\r\nquestion = \"What was President Donald Trump's prediction?\"\r\n\r\n# question = \"How many deaths have been reported from the virus?\"\r\n\r\nencoding = tokenizer.encode_plus(question, context)\r\n\r\n\r\ninput_ids, attention_mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\r\n\r\nstart_scores, end_scores = model(torch.tensor([input_ids]), attention_mask=torch.tensor([attention_mask]))\r\n\r\nans_tokens = input_ids[torch.argmax(start_scores) : torch.argmax(end_scores)+1]\r\nanswer_tokens = tokenizer.convert_ids_to_tokens(ans_tokens , skip_special_tokens=True)\r\n\r\nall_tokens = tokenizer.convert_ids_to_tokens(input_ids)\r\n\r\nprint (\"\\nAnswer Tokens: \")\r\nprint (answer_tokens)\r\n\r\nanswer_tokens_to_string = tokenizer.convert_tokens_to_string(answer_tokens)\r\n\r\nprint (\"\\nFinal Answer : \")\r\nprint (answer_tokens_to_string)\r\n\r\n```\r\n\r\nOutput is - \r\nAnswer Tokens:\r\n['some', 'states', 'would', 're', '##open', 'this', 'month']\r\n\r\nFinal Answer :\r\nsome states would reopen this month\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "`transformers` 2.6.0 has dropped support for Python 3.5: https://github.com/huggingface/transformers/releases/tag/v2.6.0", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3461?src=pr&el=h1) Report\n> Merging [#3461](https://codecov.io/gh/huggingface/transformers/pull/3461?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3ee431dd4c720e67e35a449b453d3dc2b15ccfff&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3461?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3461 +/- ##\n=======================================\n Coverage 77.79% 77.80% \n=======================================\n Files 100 100 \n Lines 17049 17051 +2 \n=======================================\n+ Hits 13264 13267 +3 \n+ Misses 3785 3784 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3461?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3461/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `97.58% <ø> (ø)` | |\n| [src/transformers/tokenization\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3461/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdDUucHk=) | `95.83% <ø> (ø)` | |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3461/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `81.29% <100.00%> (+0.08%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3461/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90NS5weQ==) | `94.98% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3461/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.32% <0.00%> (+0.17%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3461?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3461?src=pr&el=footer). Last update [3ee431d...d57c03b](https://codecov.io/gh/huggingface/transformers/pull/3461?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "I can run the following code succesfully: \r\n\r\n```\r\nfrom transformers import GPT2Model, GPT2Tokenizer\r\nimport torch\r\n\r\nmodel = GPT2Model.from_pretrained('gpt2')\r\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2', pad_token='<PAD>')\r\n\r\ninput_ids = tokenizer.encode(\"Hello, how are you?\", return_tensors='pt')\r\ninputs_embeds = model.wte(input_ids)\r\n\r\nmodel(inputs_embeds=inputs_embeds) # runs without error\r\n```\r\n\r\nCan you update `transformers` to the most current version and verify that you can run the code snippet I posted? \r\n\r\n", "Running ```conda update transformers``` returned that I already have the latest version.\r\nAs for you snippet, I still get the same error : \r\n```Python\r\nTraceback (most recent call last):\r\n\r\n File \"<ipython-input-136-d0df910b9d57>\", line 10, in <module>\r\n model(inputs_embeds=inputs_embeds) # runs without error\r\n\r\n File \"C:\\Users\\cnelias\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 547, in __call__\r\n result = self.forward(*input, **kwargs)\r\n\r\nTypeError: forward() got an unexpected keyword argument 'inputs_embeds'\r\n```\r\nIs this because I installed ```transformers``` with ```conda``` instead of ```pip``` ?\r\n\r\nEdit : this is indeed probably a conda issue. When I run the snippet in ```atom``` (with the python depedency and not anaconda) instead of ```spyder```, then it works.", "I get the same error . upgrading transformers vai pip doesn't solve the problem. any solution?" ]

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[ "？how to solve it" ]

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[ "Importing `TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING` from `transformers` works for me. Can you update your transformers library and see whether you still get the import error.", "Hi,\r\n\r\nI have the same issue here. I have installed transformers from source with the latest update.\r\n But still this **ImportError: cannot import name 'TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING'**", "Somehow, on a second try `pip install transformers` worked for me. But cant tell why it didnt worked at the beginning.\r\n", "Thanks for the quick response. I will try again by installing it not from source.\r\n...\r\nNope, still not working.\r\n", "Make sure you really update and install from source. If in doubt, recreate your virtual env.", "`pip uninstall transformers ` and `pip install transformers ` make sure your transformers version is update.", "Thanks, I got around the problem by converting it to pytorch model. But now I am facing other issues ! Anyway thank you again for your helps" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "@royeis did you find solution for this? I am facing the same issue. ", "@SarikGhazarian \r\nEnded up solving this with a workaround. Constructed a Pytorch model that had huggingface's BertModel as a module and another linear layer that received Bert's outputs and acted as a classifier. Bert module parameters were frozen and training worked properly from there." ]

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[ "Merging this as discussed (preliminarily) with @LysandreJik last week", "This is great." ]

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[ "The German and English CoNLL datasets use the IOB1 tagging scheme. `B-` is only used to separate two adjacent entities of the same type." ]

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[ "We had to turn that model off because it was really expensive/hard to operationalize.\r\n\r\nWe'll add a warning to that particular webpage. (cc @LysandreJik)" ]

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[ "@acarrera94 does this look OK?", "This looks great! Something else we might want to figure out is the best configuration of max_seq_length and max_target_length. For example, the tesla V100 in google colab can for sure handle about max_seq_length=768 and max_target_length=56 with a batch size of 4. It can't handle max_seq_length=1028 with the same configuration, since it will run out of memory. ", "Going to address by trimming batches so that they don't add extra padding. (EDIT: this is not enough, still need to truncate on both sides.)", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3451?src=pr&el=h1) Report\n> Merging [#3451](https://codecov.io/gh/huggingface/transformers/pull/3451?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b0ad06951708b782e45b02a4d092f6fcde68a9b9&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3451?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3451 +/- ##\n==========================================\n+ Coverage 78.02% 78.03% +0.01% \n==========================================\n Files 104 104 \n Lines 17709 17709 \n==========================================\n+ Hits 13817 13819 +2 \n+ Misses 3892 3890 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3451?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3451/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `92.10% <0.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3451/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.96% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3451?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3451?src=pr&el=footer). Last update [b0ad069...94a0baa](https://codecov.io/gh/huggingface/transformers/pull/3451?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hard to unittest cause of the PL dependency but I verified that the script runs locally.", "@sshleifer, in MT code it is common to group the batches together to minimize padding. Do you think this is worth implementing? One downside of this method is that is doesn't really work on TPU or architectures that expect fixed sizes. ", "Yes, is something like [SortishSampler](https://github.com/fastai/fastai/blob/master/fastai/text/data.py#L99) the right idea?\r\n\r\nIt seems easy to implement if you only consider padding on the `source` side. Do you know of an intelligent `key_func` to sort examples that considers both sides?\r\n\r\n\r\n\r\n\r\n", "lgtm" ]

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[ "This syntax is not support by PyPi unfortunately, so we had to revert this.", "> This syntax is not support by PyPi unfortunately, so we had to revert this.\r\n\r\nI see...is there another way of getting the pinned version? \r\nIn general, do we need isort? Doesn't black also sort the import statements? ", "Other ways are:\r\n- bug the isort maintainer to release a version containing this commit\r\n- push a new forked package to PyPI, like `isort-pvp` or `isort-black-compat` or whatever.\r\n\r\nYes we need isort.", "I have also observed this issue with #3402" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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