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DanielPFlorian

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Transformers-Github-Issues

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3812?src=pr&el=h1) Report\n> Merging [#3812](https://codecov.io/gh/huggingface/transformers/pull/3812?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/301bf8d1b43d99efe1fdb5ba15871e975b3cb6cf&el=desc) will **increase** coverage by `0.04%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3812?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3812 +/- ##\n==========================================\n+ Coverage 78.27% 78.31% +0.04% \n==========================================\n Files 106 106 \n Lines 17964 17996 +32 \n==========================================\n+ Hits 14061 14094 +33 \n+ Misses 3903 3902 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3812?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/3812/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `98.98% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/3812/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `68.62% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/3812/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `86.25% <100.00%> (+0.67%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/3812/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3812/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `93.00% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3812?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/3812?src=pr&el=footer). Last update [301bf8d...44c92f3](https://codecov.io/gh/huggingface/transformers/pull/3812?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "I'm having the [exact same issue](https://github.com/pytorch/fairseq/issues/1971), with the official BART code on fairseq.\r\n\r\nThe author is currently looking into it.", "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 downloaded data from [here](https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz) and was able to get 45.37 / 22.30 / 37.19 using facebook/bart-large-xsum model\r\n", "> I downloaded data from [here](https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz) and was able to get 45.37 / 22.30 / 37.19 using facebook/bart-large-xsum model\r\n\r\nHi @swethmandava , this dataset seems to have different train/valid/test split from the original dataset. Can you reproduce the scores with the original dataset?" ]

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[ "I think I might have broken this on `master` when merging #3688 🤔\r\n\r\nHi @ereday could you please try from the `trainer` branch described in PR #3800?\r\n\r\nOtherwise, hotfixing this in your code should be easy (just remove the `all_token_type_ids` line)" ]

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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", "Fixed by #4209" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3808?src=pr&el=h1) Report\n> Merging [#3808](https://codecov.io/gh/huggingface/transformers/pull/3808?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/301bf8d1b43d99efe1fdb5ba15871e975b3cb6cf&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3808?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3808 +/- ##\n=======================================\n Coverage 78.27% 78.27% \n=======================================\n Files 106 106 \n Lines 17964 17964 \n=======================================\n Hits 14061 14061 \n Misses 3903 3903 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3808?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/3808?src=pr&el=footer). Last update [301bf8d...7a12e87](https://codecov.io/gh/huggingface/transformers/pull/3808?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This looks nice", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3806?src=pr&el=h1) Report\n> Merging [#3806](https://codecov.io/gh/huggingface/transformers/pull/3806?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/01c37dcdb529bff38aadd51001cb5812e5fe9b21&el=desc) will **increase** coverage by `0.15%`.\n> The diff coverage is `90.14%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3806?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3806 +/- ##\n==========================================\n+ Coverage 78.26% 78.42% +0.15% \n==========================================\n Files 106 106 \n Lines 17964 17864 -100 \n==========================================\n- Hits 14060 14009 -51 \n+ Misses 3904 3855 -49 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3806?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_mmbt.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tbWJ0LnB5) | `22.11% <40.00%> (+4.51%)` | :arrow_up: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.94% <92.00%> (-0.03%)` | :arrow_down: |\n| [src/transformers/modeling\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hbGJlcnQucHk=) | `75.31% <100.00%> (+0.06%)` | :arrow_up: |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.40% <100.00%> (+0.24%)` | :arrow_up: |\n| [src/transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jdHJsLnB5) | `97.81% <100.00%> (+0.78%)` | :arrow_up: |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `98.15% <100.00%> (+0.56%)` | :arrow_up: |\n| [src/transformers/modeling\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19lbGVjdHJhLnB5) | `73.20% <100.00%> (+0.55%)` | :arrow_up: |\n| [src/transformers/modeling\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19mbGF1YmVydC5weQ==) | `84.49% <100.00%> (+0.67%)` | :arrow_up: |\n| [src/transformers/modeling\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19ncHQyLnB5) | `86.25% <100.00%> (+0.31%)` | :arrow_up: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `81.78% <100.00%> (+0.24%)` | :arrow_up: |\n| ... and [3 more](https://codecov.io/gh/huggingface/transformers/pull/3806/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3806?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/3806?src=pr&el=footer). Last update [01c37dc...2e7f6f4](https://codecov.io/gh/huggingface/transformers/pull/3806?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\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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[ "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/3803?src=pr&el=h1) Report\n> Merging [#3803](https://codecov.io/gh/huggingface/transformers/pull/3803?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/01c37dcdb529bff38aadd51001cb5812e5fe9b21&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3803?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3803 +/- ##\n=======================================\n Coverage 78.26% 78.27% \n=======================================\n Files 106 106 \n Lines 17964 17964 \n=======================================\n+ Hits 14060 14061 +1 \n+ Misses 3904 3903 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3803?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3803/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.84% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3803?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/3803?src=pr&el=footer). Last update [01c37dc...8f77ccd](https://codecov.io/gh/huggingface/transformers/pull/3803?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "You might want to open this PR on https://github.com/stefan-it/fine-tuned-berts-seq instead, as the script is hosted there right now (cc @stefan-it)", "> You might want to open this PR on https://github.com/stefan-it/fine-tuned-berts-seq instead, as the script is hosted there right now (cc @stefan-it)\r\n\r\nI thought it would be easier just to have the script in the example, but can close this pr and open it there if you prefer?", "What do you think @stefan-it? Are you ok with us including the script here?", "Hi, sorry for the late reply! I've fixed some errors in the script last week. Would be great if @r-tinn could check the latest Version! If it's working then you can of course integrate it into Transformers :)", "Looks good to me, the problem seems to be fixed", "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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[ "Hey @tholiao, \r\n\r\nThanks a lot for the PR :-) This looks good so far. Don't worry about running the script against the evaluation set - we can do this once this is merged! \r\n\r\nCan you make sure that the `run_examples_torch` pass? Don't worry too much about the `check_code_quality` test - there have been some issues with `isort` and I can manually fix that later. ", "Should be fine now. ", "Hi @tholiao, I went into your PR branch and checked the `make style` issues. It seems like you have other params set up for `black` than this lib. Also `isort` seems to have some problems with the imports here. \r\n\r\nI added the tiny changes, I suggested above and correctly formatted everything (black and isort) in this PR https://github.com/huggingface/transformers/pull/3817. The PR uses your commits, so you are an author of the commit :-) " ]

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[ "Hi @douwekiela, thanks for the PR. This should be fixed soon in a more stable way in the soon-to-be-merged #3800 \r\nLet us know if it works.", "Should be fixed on master by #3800, please open a new issue if you encounter other problems." ]

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[ "So, thinking about it more, I've re-run a MNLI training with the previous default max_seq_length of 128 (instead of the \"new\" default of tokenizer.max_len – _soon to be renamed_), and training is naturally way faster with the smaller sequence length (light blue line below in relative time):\r\n\r\n<img width=\"1473\" alt=\"Screenshot 2020-04-15 at 18 47 23\" src=\"https://user-images.githubusercontent.com/326577/79398369-8ee88080-7f4e-11ea-843f-84b59c8f3ad5.png\">\r\n\r\nSo I'm thinking of reverting the default to 128. Does it make sense? Are people familiar with GLUE mostly training models on shorter sequence length? (@VictorSanh @srush @thomwolf @LysandreJik)\r\n\r\nOr do they debug their trainings with short seq lengths, and then train with the model's max length?", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3800?src=pr&el=h1) Report\n> Merging [#3800](https://codecov.io/gh/huggingface/transformers/pull/3800?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b7cf9f43d259fbad45d899c1769110aafc9f410a&el=desc) will **decrease** coverage by `0.16%`.\n> The diff coverage is `63.48%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3800?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3800 +/- ##\n==========================================\n- Coverage 78.26% 78.10% -0.17% \n==========================================\n Files 106 111 +5 \n Lines 17928 18459 +531 \n==========================================\n+ Hits 14032 14417 +385 \n- Misses 3896 4042 +146 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3800?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `43.59% <43.59%> (ø)` | |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `71.90% <52.63%> (-3.81%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `68.86% <78.26%> (+0.86%)` | :arrow_up: |\n| [src/transformers/data/processors/utils.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvdXRpbHMucHk=) | `27.15% <80.00%> (+2.46%)` | :arrow_up: |\n| [src/transformers/training\\_args.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmluZ19hcmdzLnB5) | `87.69% <80.00%> (-12.31%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `89.23% <89.23%> (ø)` | |\n| [src/transformers/data/datasets/glue.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFzZXRzL2dsdWUucHk=) | `90.19% <90.19%> (ø)` | |\n| [...rc/transformers/data/datasets/language\\_modeling.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFzZXRzL2xhbmd1YWdlX21vZGVsaW5nLnB5) | `91.83% <91.83%> (ø)` | |\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.01% <100.00%> (+0.02%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `97.01% <100.00%> (ø)` | |\n| ... and [11 more](https://codecov.io/gh/huggingface/transformers/pull/3800/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3800?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/3800?src=pr&el=footer). Last update [b7cf9f4...d1db901](https://codecov.io/gh/huggingface/transformers/pull/3800?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "> So, thinking about it more, I've re-run a MNLI training with the previous default max_seq_length of 128 (instead of the \"new\" default of tokenizer.max_len – _soon to be renamed_), and training is naturally way faster with the smaller sequence length (light blue line below in relative time):\r\n> \r\n> So I'm thinking of reverting the default to 128. Does it make sense? Are people familiar with GLUE mostly training models on shorter sequence length? (@VictorSanh @srush @thomwolf @LysandreJik)\r\n> \r\n> Or do they debug their trainings with short seq lengths, and then train with the model's max length?\r\n\r\nI can answer about MNLI dataset. The sequence pairs in the dataset are quite short in MNLI both in training, dev and test with similar length distributions. The vast majority of sequences are under 128 tokens. So 128 is fine for MNLI. \r\nFor QNLI, 256 is more suitable.", "The revamps look awesome! Really looking forward to the merge and can't wait to try out the Trainer modules (nothing against argparse :joy:)", "Ok, updated the PR summary above with regression tests on `run_ner.py` and `run_language_modeling.py` that show that we reproduce the documented results.\r\n\r\nSo this should be ready to merge! 🎉 " ]

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[ "yes exactly - this should not be a problem :-) " ]

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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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[ "I would prefer **v2**) because the parameters saved in each of the model configuration files are important for the models behavior and would be nice to see in the config (also to compare to other models' configs)", "Need to look into it more but on principle this is very nice. (If Configs were `dataclass`-backed it would be even cleaner to implement – might be too big of a change though)\r\n\r\nI agree that v2 is probably better, but will think about it some more.\r\n\r\nFor config hosted on our S3, what should we do? Update the official ones, but not the user-uploaded ones? Or just do all of them? :)", "I would update all of them by downloading, save_pretrained() and upload. I already a very similar script I would only need to adapt a tiny bit", "Does this impact the process for changing the default config?", "Sounds good!", "Ok, **v2** is now implemented. I agree with @julien-c that the `save_pretrained() `method should be kept as clean as possible and I think we can keep backward compatibility (for very special edge cases) by allowing a boolean argument to the `to_json_file()` method. ", "Awesome, merging this" ]

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[ "It's probably normal since Roberta's tokenizers is a byte-level tokenizer which split words at byte-level (ie. possibly smaller than the character unit).\r\n\r\nCc @n1t0 ", "There is a bug indeed, the offsets shouldn't be shifted after the `<mask>` token. I should be able to fix this.\r\nI'm not sure I'll be able to have the right offsets for the `<mask>` token though as this one is tricky.", "This is now fixed on the latest `master`, with the output being\r\n```\r\n['<s>', 'ĠA', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']\r\n['', 'A', ',', '<mask>', 'Allen', 'N', 'LP', 'sentence', '.', ''] \r\n```\r\nThe spaces are not part of the offsets because the `trim_offsets` option is `True` by default." ]

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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", "I'm having the same issue. Training is fine but get this error when evaluate." ]

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[ "LGTM" ]

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[ "Hi @seyonechithrananda, do you have a `torch.data.Dataset` for your classification dataset?\r\n\r\nIf you do, this will be pretty easy following #3800 (i.e. the amount of code required should be pretty minimal)", "Thanks for the response @julien-c! I originally grab a list of SMILES sequences and their corresponding labels into two separate lists, before tokenizing the sequences and converting them into a tensor (following the `RobertaForSequenceClassification` docs). Will look into `torch.data.Dataset`. \r\n\r\nLink to code I was using previously (I originally tried to fine-tune for classification without `run_glue.py`): https://t.co/lqVqh3L1oA?amp=1\r\n\r\n> Hi @seyonechithrananda, do you have a `torch.data.Dataset` for your classification dataset?\r\n> \r\n> If you do, this will be pretty easy following #3800 (i.e. the amount of code required should be pretty minimal)\r\n\r\n", "Hi @julien-c! Followed your tips and created a Dataset class following a similar tutorial. However I ran into an issue with CUDA in the training pipeline:\r\n\r\n```\r\nAttributeError Traceback (most recent call last)\r\n<ipython-input-15-ea2a288fbd03> in <module>()\r\n 8 if torch.cuda.is_available():\r\n 9 sent = sent.cuda()\r\n---> 10 label = labels.cuda()\r\n 11 output = model.forward(sent)[0]\r\n 12 _, predicted = torch.max(output, 1)\r\n\r\nAttributeError: 'list' object has no attribute 'cuda'\r\n\r\n```\r\n\r\nDo you know why this issue is occurring? I use a pre-trained RoBERTA model (trained on MLM for a diff. dataset). [Here](https://colab.research.google.com/drive/1Q9pvFQoEe_4NIO853-tDy0ERZ3pyNzwT) is the notebook. Also, can we utilize the config from a MLM RoBERTa model for sequence classification or should it be the `roberta-base` config?\r\n\r\nThanks for the help! \r\n\r\n", "Managed to create a variant of `run_glue.py` and get it working. Thanks for the help1" ]

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[ "You might find this useful http://nlp.seas.harvard.edu/2018/04/03/attention.html\r\nI think PyTorch already have it implemented in their library\r\nhttps://pytorch.org/docs/stable/nn.html?highlight=transformer#torch.nn.Transformer", "thank you，It is a answer for me." ]

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[ "Hi, and thanks for your report! Indeed some of these seem to be bugs.\r\n\r\n## XLM\r\n\r\nThis seems to be a bug. It doesn't behave as the documentation says, I'm looking into it. Encoding sequences with the bos token instead of the cls token should do the trick.\r\n\r\n## XLNet\r\n\r\nThe way XLNet encode sequences is with the format `A <sep> B <sep> <cls>`, as it can be seen in the [original repository](https://github.com/zihangdai/xlnet/blob/0b642d14dd8aec7f1e1ecbf7d6942d5faa6be1f0/data_utils.py#L481-L487). I'm not finding any usage of the `<s>` and `</s>` tokens in the repo, even though they're declared.\r\nIt's interesting that you obtained better results when using `<s>` and `</s>`!\r\n\r\n## RoBERTa and BART\r\n\r\nThis is a slightly more complicated issue. The #1196 issue describes it well, and the https://github.com/huggingface/transformers/pull/2778 PR addresses this as well. Here the correct tokenization is the first one:\r\n\r\n```\r\n['<s>', 'ĠCR', 'ICK', 'ET', '1', 'ĠM', 'ATCH', '</s>']\r\n```\r\n\r\nIt is interesting that you're getting better results with the second one. I believe the original implementation outputs the same results as the result pasted above. I'm guessing you obtained the second result with the following:\r\n\r\n```py\r\ntokenizer.build_inputs_with_special_tokens(\r\n tokenizer.encode('She likes <mask> cats.', add_special_tokens=False)\r\n)\r\n```\r\n\r\nwhich indeed yields the tokenization you mentioned. This happens because when encoding without special tokens, no space is added between the initial special token and the first sequence token (seeing as there's not special tokens). When using this method, you would need to specify you want that prefix space so that it adds it. You can do so with the `add_prefix_space` option for the `encode` method:\r\n\r\n```py\r\ntokenizer.build_inputs_with_special_tokens(\r\n tokenizer.encode('She likes <mask> cats.', add_special_tokens=False, add_prefix_space=True)\r\n)\r\n```\r\n\r\nThis yields the same results as the first method. Let me know if I can be of further help.\r\n", "Thanks, that clarifies it. \r\n\r\nYou're right that the XLNet implementation declares the ` <s>` and `</s>` but then does not seem to use them, which is strange. Also strange that we are seeing better results with these tags but this could also be a problem in our code. Perhaps you could then set the `tokenizer.bos_token` and `tokenizer.eos_token` fields to `None` for the `XLNetTokenizer` if they are not used?" ]

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[ "it seems to be that it can be solved by without installing it from pip", "> it seems to be that it can be solved by without installing it from pip\r\n\r\ncould you please explain how you solved the issue ? did not understand.\r\nthanks,", "@uunal, do you have this issue too?", "@JonathanSum yes, can not find a solution. Updated all packages, check dependency but still same error persists.", "@uunal see the link above? If you don't want to waste your time, please feel free to use it, and it is the 01 training notebook. It has the solution. \r\n\r\nThe solution applied: \"it seems to be that it can be solved by without installing it from pip\"", "Ooh I get it now, pip version has a problem:) thanks", "Anyways I found why it is not working with pip version, check out this commit:https://github.com/huggingface/transformers/commit/b7cf9f43d259fbad45d899c1769110aafc9f410a" ]

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[ "see https://github.com/huggingface/transformers/issues/3668", "> see #3668\r\n\r\nthanks a lot!" ]

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[ "We are currently working on implementing the encoder decoder framework. See PR: https://github.com/huggingface/transformers/pull/3383", "I think in a week it should be ready :-) ", "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", "See https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16#bert2bert-summarization-with-%F0%9F%A4%97-encoderdecoder-framework", "Thank u Patrick\n\nOn Mon, Aug 3, 2020 at 10:19 PM Patrick von Platen <[email protected]>\nwrote:\n\n> See\n> https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16#bert2bert-summarization-with-%F0%9F%A4%97-encoderdecoder-framework\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/3785#issuecomment-668127257>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABUHUMC742YUNXLDJDGVCGTR63TBPANCNFSM4MHODIYA>\n> .\n>\n" ]

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[ "Have you tried using the exact same methods `tokenize` and `convert_tokens_to_ids`?", "```py\r\nfrom transformers import AlbertTokenizer, AlbertForMaskedLM\r\ntokenizer = AlbertTokenizer.from_pretrained('albert-base-v2)\r\nmodel = AlbertForMaskedLM.from_pretrained('albert-base-v2) \r\n\r\ntext = \"[CLS] Who was Jim Henson ? [SEP]\"\r\ntokenized_text = tokenizer.tokenize(text)\r\nindexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)\r\n\r\nsegments_ids = [0 for x in range(0,len(tokenized_text))]\r\ntokens_tensor = torch.tensor([indexed_tokens])\r\ntokens_tensor = tokens_tensor.to('cuda')\r\n\r\nwith torch.no_grad():\r\n predictions_0 = model(tokens_tensor)\r\n\r\nprint(tokenized_text) \r\nprint(predictions_0) \r\ndel predictions_0 \r\n```", "I cannot copy but predictions_0 does not contain 2 elements, but just one.\r\nso: \r\n\r\nloss, prediction_scores = outputs[:2] gives me an error (not in range)\r\n\r\nprediction_scores = outputs[0] works, but i do not know what is the output, i hope logits.", "I had to format your first comment as it was unreadable, please [see how to use code blocks](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks).\r\n\r\nIn your first snipped you're using:\r\n\r\n```py\r\noutputs = model(input_ids, masked_lm_labels=input_ids)\r\n```\r\n\r\nwhile in the second snippet:\r\n\r\n```py\r\npredictions_0 = model(tokens_tensor)\r\n```\r\n\r\nYou're not sending the `masked_lm_labels`, which is what is used to compute the loss. If you were to use these labels, the loss would be computed, resulting in a tuple with 2 elements as an output.\r\n\r\nHere's the [documentation](https://huggingface.co/transformers/model_doc/albert.html#transformers.AlbertForMaskedLM) for the `AlbertForMaskedLM` model.\r\n", "Thanks," ]

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[ "Any updates on this? Just curious.", "Reformer will be added next week and then work will start on Longformer :-) ", "Look forward to it!", "Longformer is added now - closing!", "@patrickvonplaten I have been using `Longformer` self attention with `LongBart` for summarisation recently and have done some side-by-side comparison to hf `BartForConditionalGeneration`. I noticed that `LongBart` is actually using more memory than hf `BartForConditionalGeneration` (when they're set up the equivalently). I looked into this and have found that this is coming from the self attention layer, i.e. `Longformer` self attention is using more memory than the normal multi-head self attention in `BartForConditionalGeneration`.\r\n\r\nWondering if this is expected or a bug? If it's expected, could you please explain? I thought the point of `Longformer` self attention was to reduce memory consumption...", "It depends very much on the sequence length of your input. Did you benchmark your results using the benchmarking utils? ", "@alexgaskell10, what is the sequence length? If the sequence length is shorter than the window size (for LongBart, it is probably 1024), you will see a bit of an increase in memory. For sequences longer than the window size (say, 2048), `LongformerSelfAttention` should be much more memory efficient compared to regular selfattention.\r\n", "Thanks to both for the quick responses. I have only tried with input lengths <= 1024 but nothing beyond that. Makes sense that the benefits of `Longformer` self attention are more evident as sequences get longer, thanks.\r\n\r\n@patrickvonplaten no I didn't know there was a script for this already, I just used something I wrote. I'll have a look at this.\r\n\r\n@ibeltagy the sequence length I have set equal to window size (and tried for several different values, all <= 1024). I thought that if I used a sequence length of 1024 and window size of 1024 then `Longformer` and multi-head self attention layers would be equivalent (thereby making `LongBart` and `BartForConditionalGeneration` equivalent). Is there some overhead to using `Longformer` self attention which means it is more costly for sequences <= 1024?", "> equivalent\r\n\r\nthey are not perfectly equivalent but close\r\n\r\n> which means it is more costly for sequences <= 1024?\r\n\r\nyes, the current implementation has a bit of overhead with sequences shorter than the window length. We are planning to address that in the future. One way to do so is to switch to regular selfattention if the sequence is short, but this probably requires additional pertaining to teach the model to work with both types of selfattention. ", "Great, all makes sense. I'll run benchmarking for longer sequences and flag if anything unusual shows all. Thanks!" ]

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[ "I think I remember someone mentioning this before. @LysandreJik does it ring any bell?", "The error at the time was due to https://github.com/scipy/scipy/issues/11237. I'll look into it and try to reproduce @jarednielsen.", "The `AlbertTokenizer` is using `SentencePiece` which is based on protobuffs. This seems to be the error, which would point to an error with `SentencePiece` rather than with `AlbertTokenizer`. Would you mind trying to import `XLNetTokenizer`, which is also based on `SentencePiece` and show us the results?", "Same issue occurs with `XLNetTokenizer`. Would resolving https://github.com/huggingface/tokenizers/issues/53 enable us to move away from protobuf and fix this?", "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", "Don't think this is stale; still waiting on a fix in the tokenizers repo.", "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", "Not stale", "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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[ "Same issue here. i still have an error. could you write answer here?", "our hero LysandreJik assigned this problem to himself. Let's have confidence in him to solve it (:", "You fine-tuned your TF checkpoint using the original implementation, is that correct?", "Thanks you, but i find way resolved my problem. i fine-tuning albert with pre-train load from checkpoint tf so i just convert to bin model and using hugging face abtract class to load . Done!", "Yes, I fine-tuned it using python3 albert/run_squad_v2.py with adam optimizer. Then I tried to convert squad checkpoint using the hugging face transformer model. I will appreciate your help because I am waiting for two weeks for this problem to be solved.", "> Thanks you, but i find way resolved my problem. i fine-tuning albert with pre-train load from checkpoint tf so i just convert to bin model and using hugging face abtract class to load . Done!\r\n\r\nis this checkpoint find-tuned on SQUAD ? because I have no problem converting ALBERT checkpoint that was not fine-tuned on downstream tasks.", "you can try function in repo to convert checkpoint tf to pytorch bin model: https://github.com/lonePatient/albert_pytorch.git", "This feature is not currently supported by our conversion scripts. I can take a look later this week, or you can try modifying the code yourself:\r\n\r\n- Change from `AlbertForPreTraining` to `AlbertForQuestionAnswering` in the [conversion file](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py).\r\n- Rename the weights in your model to those of our `AlbertForQuestionAnswering` by replacing the layers like it is done in [this method](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_albert.py#L79-L106). Something like `name = name.replace(\"classifier\", \"qa_outputs\")` would probably work.\r\n\r\nPlease note that this would work in the case where the ALBERT official implementation has the same Question Answering model as we do (that is, a single linear layer on top of the transformer). If there isn't, you would need to create a model similar to `AlbertForQuestionAnswering` but with the correct head.", "> This feature is not currently supported by our conversion scripts. I can take a look later this week, or you can try modifying the code yourself:\r\n> \r\n> * Change from `AlbertForPreTraining` to `AlbertForQuestionAnswering` in the [conversion file](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py).\r\n> * Rename the weights in your model to those of our `AlbertForQuestionAnswering` by replacing the layers like it is done in [this method](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_albert.py#L79-L106). Something like `name = name.replace(\"classifier\", \"qa_outputs\")` would probably work.\r\n> \r\n> Please note that this would work in the case where the ALBERT official implementation has the same Question Answering model as we do (that is, a single linear layer on top of the transformer). If there isn't, you would need to create a model similar to `AlbertForQuestionAnswering` but with the correct head.\r\n\r\nProblem still exists . A message saying \"AttributeError: 'AlbertForQuestionAnswering' object has no attribute 'shape'\" appears even though I did all what you said. I think it's worth fixing it by you later this week. Google Colab offers TPUv3 which has 128GB and Hugging face transformers only support GPU where google collab offers P100 that has 16GB. That is an 8x performance boost for TPU so it will take me days to fine-tune it using transformers library only with GPU.", "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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[ "Tested on `RUN SLOW=1 pytest tests/test_modeling_tf_t5.py` and all tests pass." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3776?src=pr&el=h1) Report\n> Merging [#3776](https://codecov.io/gh/huggingface/transformers/pull/3776?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6e603cb7892b49a2cbbc10ba859759f92c3fb7a6&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `90.90%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3776?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3776 +/- ##\n=======================================\n Coverage 77.00% 77.00% \n=======================================\n Files 128 128 \n Lines 21602 21624 +22 \n=======================================\n+ Hits 16634 16652 +18 \n- Misses 4968 4972 +4 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3776?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3776/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `94.73% <90.90%> (-5.27%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3776/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.26% <0.00%> (-0.16%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3776/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.49% <0.00%> (-0.12%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3776?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/3776?src=pr&el=footer). Last update [6e603cb...acbdaf3](https://codecov.io/gh/huggingface/transformers/pull/3776?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "So no way to get output logits?", "The `forward` method returns logits, like other models with language modeling heads. Is that what you meant?" ]

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[ "I am also looking for a solution to this problem.", "I want the tokenizer to do something like this one\r\nhttps://github.com/huggingface/transformers/issues/1036#issuecomment-522201118", "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", "It has been three years since this was marked stale, has hugging face or others implemented something that I could use for this use case?" ]

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[ "This is a property we added to be able to know what kind of final layers the model has. It is used for instance to enable tagging and filtering on our model hub: https://huggingface.co/models" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3772?src=pr&el=h1) Report\n> Merging [#3772](https://codecov.io/gh/huggingface/transformers/pull/3772?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7972a4019f4bc9f85fd358f42249b90f9cd27c68&el=desc) will **increase** coverage by `0.07%`.\n> The diff coverage is `90.73%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3772?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3772 +/- ##\n==========================================\n+ Coverage 78.26% 78.33% +0.07% \n==========================================\n Files 106 106 \n Lines 17928 18027 +99 \n==========================================\n+ Hits 14031 14122 +91 \n- Misses 3897 3905 +8 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3772?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jdHJsLnB5) | `97.03% <80.00%> (-1.22%)` | :arrow_down: |\n| [src/transformers/modeling\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19ncHQyLnB5) | `85.93% <86.66%> (-0.28%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `95.43% <86.66%> (-0.75%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9jdHJsLnB5) | `98.40% <87.50%> (-1.18%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.96% <90.90%> (-0.13%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90NS5weQ==) | `95.16% <91.59%> (+0.17%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.88% <92.30%> (+0.08%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `97.01% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `96.48% <100.00%> (-0.02%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `83.80% <100.00%> (+0.59%)` | :arrow_up: |\n| ... and [3 more](https://codecov.io/gh/huggingface/transformers/pull/3772/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3772?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/3772?src=pr&el=footer). Last update [7972a40...d9c7a86](https://codecov.io/gh/huggingface/transformers/pull/3772?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "[it's in `./src/transformers`.](https://github.com/huggingface/transformers/tree/master/src/transformers)" ]

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[ "To load TF models you should use the TF class `TFBertForMaskedLM` and not `BertForMaskedLM` which is the PyTorch class.", "Thanks for the response\r\nI found that BertForPreTraining also allows to loads when the TF model", "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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[ "The `EOS` token stand for `End Of Sentence`, and is used as a STOP token.\r\n\r\nI.E. when the model generate this token, it literally means the generation is done and should be stop. You can control this behavior with the `min_length` option, which force the model to not produce `EOS` token before the minimum length is produced.\r\n\r\nIn your first try, after the model generated enough token, `EOS` token can be generated at any moment, even before reaching `max_length`. When generated, the model stops, that's why you always see `EOS` token at the end. That's normal behavior.\r\n\r\nAs for your second try, it's weird indeed. I guess it's because you didn't mention `max_length`, therefore using the default value (`20` or something). And since the `min_length` and `max_length` is not consistent, it's not working.\r\n\r\n---\r\n\r\nCan you try to specify both `min_length` and `max_length` with consistent value and try again ? \r\n\r\nFor example :\r\n```\r\nsample_output = model.generate(\r\n input_ids, \r\n do_sample=True, \r\n min_length=1000,\r\n max_length=1050, \r\n top_p=0.92, \r\n top_k=0\r\n)\r\n```", "Thanks for the suggestion, I tried it and it worked!\r\n\r\nHowever, I'm wondering if <<e>eos> is the only token used as a stop token because when I tried generating text with XLNet, it would generate <<e>eop> and <<e>eod> tokens until the max length was reached.", "At the moment we only allow to have a single EOS token. It would be great if you could open a feature request for multiple EOS tokens for 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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[ "Maybe your dataset is too small to make much sense even if you get a smaller perplexity.", "Thanks for your response.\r\n\r\nHere are the sizes of my corpus:\r\n\r\n1. Training set ~ 77 MB\r\n2. Validation set ~ 10 MB\r\n3. Testing set ~ 10 MB\r\n\r\nI build this corpus as under:\r\n\r\n1. Distribute all files into training, testing and validation sets such as 80%, 10% and 10% ratio.\r\n2. Merge the contents of the files into one file for each set, which eventually generates files such as train.txt, valid.txt, and test.txt\r\n3. Remove extra spaces, tab spaces, and end line character. \r\n4. Perform tokenization of textual data in each file with custom utility, in a way that each word and punctuation is separated with just one space character.\r\n5. Then, pass these files to GPT-2 language modeling script.\r\n\r\nI did not use any kind of special tokens such as padding, masking, “|< endoftext >|” tokens etc in building my corpus.\r\n\r\nIs this a right strategy? Or will there any kind of problem in this strategy?\r\n\r\n", "> Thanks for your response.\r\n> \r\n> Here are the sizes of my corpus:\r\n> \r\n> 1. Training set ~ 77 MB\r\n> 2. Validation set ~ 10 MB\r\n> 3. Testing set ~ 10 MB\r\n> \r\n> I build this corpus as under:\r\n> \r\n> 1. Distribute all files into training, testing and validation sets such as 80%, 10% and 10% ratio.\r\n> 2. Merge the contents of the files into one file for each set, which eventually generates files such as train.txt, valid.txt, and test.txt\r\n> 3. Remove extra spaces, tab spaces, and end line character.\r\n> 4. Perform tokenization of textual data in each file with custom utility, in a way that each word and punctuation is separated with just one space character.\r\n> 5. Then, pass these files to GPT-2 language modeling script.\r\n> \r\n> I did not use any kind of special tokens such as padding, masking, “|< endoftext >|” tokens etc in building my corpus.\r\n> \r\n> Is this a right strategy? Or will there any kind of problem in this strategy?\r\n\r\nyou'd better set special tokens like “<|start|>” ,'<|end|>' at the start and end of text like this \"<|start|>a sentence<|end|>\"。", "I do not need any kind of sentence modeling for my purpose. Do I still require to specify special tokens in order to perform language modeling via GPT-2? \r\n\r\nDoes this effect on calculating false perplexity?\r\n", "> I do not need any kind of sentence modeling for my purpose. Do I still require to specify special tokens in order to perform language modeling via GPT-2?\r\n> \r\n> Does this effect on calculating false perplexity?\r\n\r\nU can read this paper:\r\n\"Semantics of the Unwritten\"\r\nHe Bai,1 Peng Shi,1 Jimmy Lin,1,2 Luchen Tan,2 Kun Xiong,2 Wen Gao,4 Jie Liu,3 Ming Li,1,2 1 David R. Cheriton School of Computer Science, University of Waterloo\r\n2 RSVP.ai 3 Capital Normal University\r\n4 School of Electronics Engineering and Computer Science, Peking University\r\n\r\nAnd then you get it.", "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", "@mhd-git-test I have a similar problem too with GPT-2. I get a perplexity score of 7 . Did you find an answer to your problem?\r\n\r\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3766?src=pr&el=h1) Report\n> Merging [#3766](https://codecov.io/gh/huggingface/transformers/pull/3766?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7972a4019f4bc9f85fd358f42249b90f9cd27c68&el=desc) will **decrease** coverage by `0.98%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3766?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3766 +/- ##\n==========================================\n- Coverage 78.26% 77.28% -0.99% \n==========================================\n Files 106 106 \n Lines 17928 17928 \n==========================================\n- Hits 14031 13855 -176 \n- Misses 3897 4073 +176 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3766?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3766/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9weXRvcmNoX3V0aWxzLnB5) | `8.72% <0.00%> (-81.21%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/3766/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `85.71% <0.00%> (-10.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/3766/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jdHJsLnB5) | `95.61% <0.00%> (-2.64%)` | :arrow_down: |\n| [src/transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/3766/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `73.48% <0.00%> (-2.30%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3766/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `91.16% <0.00%> (-1.64%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/3766/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `80.20% <0.00%> (-1.35%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3766?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/3766?src=pr&el=footer). Last update [7972a40...04737ae](https://codecov.io/gh/huggingface/transformers/pull/3766?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This looks good to me." ]

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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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[ "Following!", "> Following!\r\n\r\nI don't know what it means.\r\n", "Use transformer-XL", "Longformer is exactly designed for your use case (https://github.com/allenai/longformer)", "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", "Reformer and longformer are being worked on to be included in this library.", "Looks like they've got longformer now. \r\n\r\nhttps://huggingface.co/transformers/model_doc/longformer.html", "Is longformer a multilingual model? or are there options to work with longer texts that are not in English? ", "You can also use Text Guide, a clever text truncation method and use a transformer model with a standard 512 limit.\r\nAnd if you have extremely long text instances (longer than 4096 == Longformer model limit) you can also use this approach to further improve your results.\r\n\r\nPaper: https://arxiv.org/abs/2104.07225\r\nCode: https://github.com/krzysztoffiok/TextGuide\r\nA brief description: https://www.quora.com/If-I-have-a-long-text-say-10-paragraphs-how-can-I-use-BERT-or-other-newer-and-better-models-such-as-RoBERTa-for-feature-extraction-that-represents-the-entire-document-Seems-like-BERT-has-limits-Are-there-packages\r\n" ]

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[ "The built documentation:\r\n\r\nhttps://30100-155220641-gh.circle-artifacts.com/0/docs/_build/html/index.html", "How to view the built documentation:\r\n\r\n", "@LysandreJik this might interest you!", "@sshleifer Thanks for the comment!\r\n\r\n[The CircleCI doc](https://circleci.com/docs/2.0/artifacts/) says:\r\n\r\n> Artifacts are stored on Amazon S3 and are protected with your CircleCI account for private projects. There is a 3GB curl file size limit. **Artifacts will be accessible for thirty days after creation**." ]

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[ "cc @julien-c ", "See https://github.com/huggingface/transformers/issues/4661#issuecomment-636911923" ]

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[ "You're using `transformers` version **2.6.0**, but T5 model was released in version **2.7.0**.\r\n\r\nUpdate your library with :\r\n\r\n`pip install --upgrade transformers`", "Thanks, error is not reproduced in 2.8.0" ]

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[ "For questions like this it's often best to dive into the source code. It helps understanding it a lot.\r\n\r\nBut your example seems wrong, you may have made some copy-pasting errors. If `output[1][0].shape = [768]` then also, because tensors across an axis must have same dimensions (like matrices) `output[2][0].shape = [768]`.", "@BramVanroy I double checked but it is not a copy-pasting error as far as I can tell. I will dive into the source code to try to understand why the length of electra's output is different from the bert models.", "Can you post a reproducible full, but minimal, example?", "@BramVanroy I figured it out. Turns out even though in the config the output_hidden_states is set to false somewhere hidden in the code I am using it get sets to true.\r\n\r\nIn case of the bert models output[2] are the hidden layers and for electra [1] are the hidden layers. I'm still not very sure what output[1] is in case of the output of the bert models but for my particular use case it is not important right now.\r\n\r\nThank you for taking your time to help me. I will close the issue." ]

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[ "Hey @tholiao,\r\n\r\nThanks for the catch! You're right it should be the newstest2014! Do you want to open a PR to change it? Or I can do it as well", "No worries, I'll submit a PR.", "@patrickvonplaten, would you mind running evaluate_wmt.py on this branch and computing sacreBLEU via command line? (`cat newstest2014_de_translations.txt | sacrebleu -t wmt14 -l en-de --tokenize intl`)", "Thanks a lot for the PR! I will running the script once the PR is merged :-) " ]

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[ "Hi @enzoampil, \r\n\r\nThanks again for the PR - I reviewed it. I think we can start by deleting a lot of the code and keeping it simple. It can be quite hard to get used to all the \"under-the-hood\" behavior that happens in pipelines. I think we should stick to the format that was used for the `summarization` pipeline e.g. and we shouldn't need a `__init__` fn in the beginning. \r\n\r\nWe should also add tests for generation in `tests/test_pipelines.py` .\r\n\r\nLet me know if the comments are clear! If the PR seems too much, just let me know - I can help then a bit as well :-) ", "Hi @patrickvonplaten , thank you for the very clear comments and concrete changes requested. I will work on this by this weekend :)", "That sounds great :-) \r\nDon't worry yet about adding the `task_specific_params` to each of the models configs - I will do this at a later stage! Regarding the tests, you can use the same test logic that was used for `summarization` :-) ", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3758?src=pr&el=h1) Report\n> Merging [#3758](https://codecov.io/gh/huggingface/transformers/pull/3758?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9180e3fa4a396fc5a066ab88b85445e26d69bc4c&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3758?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3758 +/- ##\n=======================================\n Coverage 78.58% 78.58% \n=======================================\n Files 106 106 \n Lines 18003 18003 \n=======================================\n Hits 14148 14148 \n Misses 3855 3855 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3758?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/3758?src=pr&el=footer). Last update [9180e3f...9180e3f](https://codecov.io/gh/huggingface/transformers/pull/3758?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "@patrickvonplaten I've applied the requested changes (all tests passing), including the pipeline tests, and changing the class name to `TextGenerationPipeline`, as recommended by @julien-c and @thomwolf .\r\n\r\nKeen to get feedback on this! Thanks :smile:", "Great work @enzoampil! We will add the generation pipeline in the next release and I think it's gonna be a very useful and widely used feature! \r\n\r\n@enzoampil, @thomwolf, @LysandreJik, @julien-c - After the changes requested above, I'd be happy to merge from my side. \r\nA couple of things that were added in this PR and would need discussion are:\r\n1. `XLM` is not supported in this `TextGeneration` pipeline. I played around with multiple models of `XLM` and never had any reasonable results for generation, so I think it's best to remove it here. The other models `GPT1, GPT2, CTRL, XLNet, Transfo-XL` work very well. \r\n2. `XLNet` and `Transfo-XL` need a padding text to work well. This is added and removed afterward, so the user doesn't notice it at all. In a follow-up PR we could maybe add a warning about it.\r\n3. We can now also introduce `text_generation` task_specific_params to the dicts (Transfo-XL and XLNet need a new min and max length) - I can do this after the PR is merged. \r\n4. I think we can remove the `run_generation` script then completely no? \r\n5. Tensorflow tests should also be added in a future PR (or feel free to add them here @enzoampil - forgot to mention that in the review actually)", "Thank you so much @patrickvonplaten ! Will apply the rest of the changes within the next day or two :) ", "Awesome, thanks @enzoampil! LGTM.", "Wanted to thank you guys again for guiding me through my first (relatively) big PR for `transformers` @patrickvonplaten @julien-c @thomwolf 😄 \r\n\r\nThe work of HuggingFace with both the implementation and democratisation of state of the art NLP is something I deeply resonate with. I've been an industry practitioner of NLP for the passed few years and `transformers` has really helped me a lot.\r\n\r\nWith this, I've recently decided to dedicate a large chunk of my open source time contributing to this package! Looking forward to helping out more and more.\r\n\r\nI will keep an eye out for specific issues that I can help out with, and am very open to advice on how I can help in a way that's most useful 🙂 ", "@LysandreJik \r\n\r\n1. I think we can fix the XLNet generation issue by setting `max_length` as the max length of the *generated* text, rather than the full text. This can be implemented by ensuring that we add the number of tokens in `prompt_text` to the `max_length` argument. Something like below:\r\n```\r\nmax_length = max_length + len(input_ids.squeeze())\r\n```\r\n\r\nHowever, this may require that we set `max_length` as an explicit argument for `__call__`, rather than as part of `generate_kwargs`. @patrickvonplaten Do you think this makes sense to do?\r\n\r\n2. Sure thing, will work on adding `TextGenerationPipeline` to `./docs/source/main_classes/pipelines.rst`", "Sorry, I forgot to add the `max_length` as generation task specific params to the XLNet and TransfoXL configs. I will do this now.", "@enzoampil - Sorry for fiddling in your code so much :D \r\nIt's actually not as easy as I thought to have the final output correct for XLNet and Transfo-XL. My commits suggestions now should work. You should run `make style` once they are integrated :-) ", "Maybe we should also add an optional `padding` argument to the `__call__` function that overwrites `self.PADDING` for XLNet and Transfo-XL @LysandreJik. But we can do this in a separate PR @enzoampil - let's try to merge this one first.", "> Sorry, I forgot to add the `max_length` as generation task specific params to the XLNet and TransfoXL configs. I will do this now.\r\n\r\nOk added it to the config of Transfo-XL and XLNet \r\n\r\n@LysandreJik @thomwolf, we also might want to discuss the default generation params for each model. I think it might e.g. be better to set `do_sample=True` for all models that can generate.", "I don't have any strong opinions on whether we should sample or not; However, I think whatever the choice we should make sure that it is explicit in the pipeline documentation that we may control it from the pipeline directly. \r\n\r\nMaybe a link linking to the `generate` method would do the trick, alongside a small explanation that all kwargs will be passed to this underlying method.", "@patrickvonplaten Ran `make_style` and just fixed a minor bug from the `generation` line I think being accidentally taken out from one of your prior [commits](https://github.com/huggingface/transformers/pull/3758/commits/29ce6d82e835e1225c26b5cc4c4ce9f6fe1451ff). The pipeline seems to work fine now :smile:\r\n\r\nAlso, not sure if this is specific to this PR, but there are tests that are suddenly returning an error for the lines that contain `self._create_and_check_torchscript(config, inputs_dict)`.\r\n\r\nSample error:\r\n```\r\n_____________ AlbertModelTest.test_torchscript_output_hidden_state _____________\r\n[gw7] linux -- Python 3.7.7 /usr/local/bin/python\r\n\r\nself = <tests.test_modeling_albert.AlbertModelTest testMethod=test_torchscript_output_hidden_state>\r\n\r\n def test_torchscript_output_hidden_state(self):\r\n config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()\r\n \r\n config.output_hidden_states = True\r\n> self._create_and_check_torchscript(config, inputs_dict)\r\n\r\ntests/test_modeling_common.py:197: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\ntests/test_modeling_common.py:206: in _create_and_check_torchscript\r\n model = model_class(config=configs_no_init)\r\n/usr/local/lib/python3.7/site-packages/transformers/modeling_albert.py:455: in __init__\r\n self.init_weights()\r\n/usr/local/lib/python3.7/site-packages/transformers/modeling_utils.py:392: in init_weights\r\n self.apply(self._init_weights)\r\n/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py:289: in apply\r\n module.apply(fn)\r\n/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py:289: in apply\r\n module.apply(fn)\r\n/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py:290: in apply\r\n fn(self)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\nself = AlbertModel(\r\n (embeddings): AlbertEmbeddings(\r\n (word_embeddings): Embedding(99, 128, padding_idx=0)\r\n (position_... )\r\n )\r\n )\r\n )\r\n (pooler): Linear(in_features=36, out_features=36, bias=True)\r\n (pooler_activation): Tanh()\r\n)\r\nmodule = Embedding(99, 128, padding_idx=0)\r\n\r\n def _init_weights(self, module):\r\n \"\"\" Initialize the weights.\r\n \"\"\"\r\n if isinstance(module, (nn.Linear, nn.Embedding)):\r\n # Slightly different from the TF version which uses truncated_normal for initialization\r\n # cf https://github.com/pytorch/pytorch/pull/5617\r\n> module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\r\nE RuntimeError: normal_ expects std > 0.0, but found std=0\r\n\r\n/usr/local/lib/python3.7/site-packages/transformers/modeling_albert.py:377: RuntimeError\r\n________________________ BertModelTest.test_headmasking ________________________\r\n[gw1] linux -- Python 3.7.7 /usr/local/bin/python\r\n\r\nself = <tests.test_modeling_bert.BertModelTest testMethod=test_headmasking>\r\n\r\n def test_headmasking(self):\r\n if not self.test_head_masking:\r\n return\r\n \r\n global_rng.seed(42)\r\n config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()\r\n global_rng.seed()\r\n \r\n config.output_attentions = True\r\n config.output_hidden_states = True\r\n configs_no_init = _config_zero_init(config) # To be sure we have no Nan\r\n for model_class in self.all_model_classes:\r\n> model = model_class(config=configs_no_init)\r\n\r\ntests/test_modeling_common.py:260: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n/usr/local/lib/python3.7/site-packages/transformers/modeling_bert.py:619: in __init__\r\n self.init_weights()\r\n/usr/local/lib/python3.7/site-packages/transformers/modeling_utils.py:392: in init_weights\r\n self.apply(self._init_weights)\r\n/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py:289: in apply\r\n module.apply(fn)\r\n/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py:289: in apply\r\n module.apply(fn)\r\n/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py:290: in apply\r\n fn(self)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\nself = BertModel(\r\n (embeddings): BertEmbeddings(\r\n (word_embeddings): Embedding(99, 32, padding_idx=0)\r\n (position_embed...\r\n (pooler): BertPooler(\r\n (dense): Linear(in_features=32, out_features=32, bias=True)\r\n (activation): Tanh()\r\n )\r\n)\r\nmodule = Embedding(99, 32, padding_idx=0)\r\n\r\n def _init_weights(self, module):\r\n \"\"\" Initialize the weights \"\"\"\r\n if isinstance(module, (nn.Linear, nn.Embedding)):\r\n # Slightly different from the TF version which uses truncated_normal for initialization\r\n # cf https://github.com/pytorch/pytorch/pull/5617\r\n> module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\r\nE RuntimeError: normal_ expects std > 0.0, but found std=0\r\n\r\n/usr/local/lib/python3.7/site-packages/transformers/modeling_bert.py:525: RuntimeError\r\n```", "Those test are probably falling because the new Pytorch version was released. Can you just tense your branch in master?: \r\n```\r\n$ git fetch upstream\r\n$ git rebase upstream/master\r\n```\r\n(Assuming that you added the master branch as a remote branch \"upstream\").\r\n\r\nThe test should then pass :-)", "@patrickvonplaten Apologies, I'm having issues with the rebase suggested above.\r\n\r\nI initially tried it but ended up showing up as a co-committer with the rebased commits, which explains why I performed a `force-push` above to revert the rebase. It *might* be related to an issue I'm having where I'm forced to do a `rebase --skip` with each of the conflicts (same situation as [here](https://stackoverflow.com/questions/14410421/git-rebase-merge-conflict-cannot-continue)).\r\n\r\nMay I please ask for some assistance / advice with this?", "Once again, thanks so much! Looking forward to contributing more in the future 😄@patrickvonplaten @julien-c " ]

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[ "Did you find anything on this? Been digging around for the same, seems like `from_pretrained` used to allow `weight` as an arg?", "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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[ "Thanks for the PR @aced125 - could you run the slow tests as well and see whether they pass? \r\nI think checking these three tests should be good enough:\r\n\r\n`RUN_SLOW=1 pytest tests/test_modeling_gpt2.py`\r\n`RUN_SLOW=1 pytest tests/test_modeling_t5.py`\r\n`RUN_SLOW=1 pytest tests/test_modeling_bart.py`", "Yep @patrickvonplaten , done the above, all passed in a Google Colab notebook: https://colab.research.google.com/drive/12-WUburVlYHsrgKhMMt5MRXOPKbfaS3l", "Hi @patrickvonplaten, wondering if you managed to take a look at this?", "I'm wondering if there is any experimental results demonstrating that `trace_log_probs` is a helpful thing to have in the repo? ", "Hi @sshleifer I'll be honest I haven't done any RL in the NLP domain (using transformers in the drug discovery domain) but I know people have tried to optimize ROUGE score for summarization and stuff like that in the past. I can try and maybe put something together for this though? \r\n\r\nI do think it is quite a useful feature to have in general though, will need it at some point IMO.", "Cool, thanks for the transparency. From my seat, it would be preferable for you to experiment a bit on a branch to see what works before we merge this into master, as you suggest.", "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", "> Hi @sshleifer I'll be honest I haven't done any RL in the NLP domain (using transformers in the drug discovery domain) but I know people have tried to optimize ROUGE score for summarization and stuff like that in the past. I can try and maybe put something together for this though?\r\n> \r\n> I do think it is quite a useful feature to have in general though, will need it at some point IMO.\r\n\r\nHi, I have a similar use case where I require the log_probs from the `generate()` method. Did you find any solution for it? Was your PR merged?", "+1 - I'd also find this feature useful", "This feature would be useful for incorporating a popular technique called \"unlikelihood_training\".\r\n\r\nSee (https://github.com/facebookresearch/unlikelihood_training/blob/944465589c0fab534fe6d14a5db2850ddeee43ce/custom/gpt2/run_gpt2.py#L85) \r\n\r\nYou have to sample from the model to produce negative candidates.\r\n\r\nOnce this feature is added; adding the unlikelihood loss becomes extremely easy and efficient.", "I'd also want to get the gradients from the `generate` method! " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3755?src=pr&el=h1) Report\n> Merging [#3755](https://codecov.io/gh/huggingface/transformers/pull/3755?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7972a4019f4bc9f85fd358f42249b90f9cd27c68&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3755?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3755 +/- ##\n=======================================\n Coverage 78.26% 78.26% \n=======================================\n Files 106 106 \n Lines 17928 17928 \n=======================================\n+ Hits 14031 14032 +1 \n+ Misses 3897 3896 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3755?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3755/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/3755?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/3755?src=pr&el=footer). Last update [7972a40...c40ade1](https://codecov.io/gh/huggingface/transformers/pull/3755?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\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", "The bug is still valid and I have raised https://github.com/huggingface/transformers/pull/4924" ]

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[ "Hi @hahadashi, \r\n\r\nCan you add a code snippet so that we know which model you are using and so that we can reproduce the behavior? ", "> Hi @hahadashi,\r\n> \r\n> Can you add a code snippet so that we know which model you are using and so that we can reproduce the behavior?\r\n\r\nthx your response, i use this tutor https://www.tensorflow.org/tutorials/text/transformer#encoder_layer train the the model", "Sorry maybe I was not precise enough:\r\nWhich model of the `transformers` library (e.g. Bert, GPT2) did you use? And can you copy / paste the exact code which has a `transformers` model in it that was slow for inference.", "> Sorry maybe I was not precise enough:\r\n> Which model of the `transformers` library (e.g. Bert, GPT2) did you use? And can you copy / paste the exact code which has a `transformers` model in it that was slow for inference.\r\n\r\nthx your response, \r\n`class MultiHeadAttention(tf.keras.layers.Layer):\r\n def __init__(self, d_model, num_heads):\r\n super(MultiHeadAttention, self).__init__()\r\n self.num_heads = num_heads\r\n self.d_model = d_model\r\n \r\n assert d_model % self.num_heads == 0\r\n \r\n self.depth = d_model // self.num_heads\r\n \r\n self.wq = tf.keras.layers.Dense(d_model)\r\n self.wk = tf.keras.layers.Dense(d_model)\r\n self.wv = tf.keras.layers.Dense(d_model)\r\n \r\n self.dense = tf.keras.layers.Dense(d_model)\r\n \r\n def split_heads(self, x, batch_size):\r\n \"\"\"分拆最后一个维度到 (num_heads, depth).\r\n 转置结果使得形状为 (batch_size, num_heads, seq_len, depth)\r\n \"\"\"\r\n x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\r\n return tf.transpose(x, perm=[0, 2, 1, 3])\r\n \r\n def call(self, v, k, q, mask):\r\n batch_size = tf.shape(q)[0]\r\n \r\n q = self.wq(q) # (batch_size, seq_len, d_model)\r\n k = self.wk(k) # (batch_size, seq_len, d_model)\r\n v = self.wv(v) # (batch_size, seq_len, d_model)\r\n \r\n q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)\r\n k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)\r\n v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)\r\n \r\n # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\r\n # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\r\n scaled_attention, attention_weights = scaled_dot_product_attention(\r\n q, k, v, mask)\r\n \r\n scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, num_heads, depth)\r\n\r\n concat_attention = tf.reshape(scaled_attention, \r\n (batch_size, -1, self.d_model)) # (batch_size, seq_len_q, d_model)\r\n\r\n output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)\r\n \r\n return output, attention_weights`\r\n\r\n\r\nduring i use the decoder predict, eg, \r\n1、first i input \"sos\", get a argmax result \"A\"\r\n2、 i input \"sos A\" get a argmax \"B\"\r\n3、 i input \"sos A B\" get a argmax \"C\" \r\n4......, \r\n If we don't save the intermediate state，there are a lot of repetitive operations.\r\nlike \r\nthe step 2, sos has compute in step 1, \r\nstep 3, \"A\" has compute in step 2,\r\nthx\r\n\r\nIf my idea is wrong, hope you give me some other speed up advices,", "It's seems like you're not using the `transformers` repository.\r\n\r\nI suggest you use Stack Overflow, where you will more likely receive answers to your question. ", "> I suggest you use Stack Overflow, where you will more likely receive answers to your question.\r\n\r\nOK, thx" ]

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[ "Was this opened by mistake? Can we close it? " ]

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[ "Modify the script :)", "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 need to install from source, as explained [here](https://github.com/huggingface/transformers#run-the-examples).", "Thanks!" ]

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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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[ "I solve this problem with distributed training.\r\n\r\nThe DataParallel is really slow with a lot of defects, just don't use it.\r\n\r\n", "Hi @ntubertchen, how did you make distributed training to work with the `run_language_modeling.py` script? Or did you do everything from scratch?", "run_language_modeling.py has written the distributed part, just use the command." ]

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[ "For conditional output generation, you might want to take a look at `CTRL` or you would have to fine-tuned gpt2. To just produce \"any\" fake text, you could just use `GPT2` out of the box as in this demo: \r\nhttps://transformer.huggingface.co/doc/gpt2-large", "Thanks for your reply :-)\r\n\r\nWhat is cTRL ? do you have any references ?", "Sorry I should have linked that: \r\nhttps://huggingface.co/transformers/model_doc/ctrl.html\r\n\r\nCTRL is a very big model so quite difficult to run on a local machine - it might be easier to fine-tuned gpt2. I think @mariamabarham knows well how to fine-tune gpt2 - maybe you can add a couple lines? :-) ", "I think You can use gt2 with [run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py). You can consider the [TextDataset](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py#L66) class or [LineByLineDataset](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py#L107) or define your own dataset class that suits better with your data structure.", "Do you have a code example ? I am a little bit a newbie in NLP ^^ ", "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", "Found this https://colab.research.google.com/drive/1VI3oBIOQYsym2x5oOux7DTNhpdR0r4uw", "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" ]