Datasets:

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DanielPFlorian

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Transformers-Github-Issues

like 0

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[ "👍 \r\n\r\nWould you like to add example inputs for all models for `pt`? If you do, please open a PR against this file: https://github.com/huggingface/widgets-server/blob/master/DefaultWidget.ts\r\n\r\nThanks:)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6019?src=pr&el=h1) Report\n> Merging [#6019](https://codecov.io/gh/huggingface/transformers/pull/6019?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/778e635fc900e5afc90e5f8f4ca74b5d5fc2976a&el=desc) will **increase** coverage by `1.37%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6019?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6019 +/- ##\n==========================================\n+ Coverage 77.31% 78.68% +1.37% \n==========================================\n Files 146 146 \n Lines 26249 26249 \n==========================================\n+ Hits 20295 20655 +360 \n+ Misses 5954 5594 -360 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6019?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6019/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `43.98% <0.00%> (-49.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6019/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.90% <0.00%> (-33.90%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6019/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `95.53% <0.00%> (+69.51%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6019/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `96.77% <0.00%> (+73.38%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6019?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/6019?src=pr&el=footer). Last update [778e635...b79103c](https://codecov.io/gh/huggingface/transformers/pull/6019?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@stas00 this might be up your alley.", "Added to my todo queue - thank you, @sshleifer", "https://github.com/huggingface/transformers/pull/6149" ]

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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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[ "Feel free to apply the change", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6016?src=pr&el=h1) Report\n> Merging [#6016](https://codecov.io/gh/huggingface/transformers/pull/6016?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3996041d0ae23ce23dfb8a343e6344f2f8d54c16&el=desc) will **increase** coverage by `0.22%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6016?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6016 +/- ##\n==========================================\n+ Coverage 78.29% 78.51% +0.22% \n==========================================\n Files 146 146 \n Lines 26249 26249 \n==========================================\n+ Hits 20552 20610 +58 \n+ Misses 5697 5639 -58 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6016?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6016/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `26.02% <0.00%> (-69.52%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6016/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `43.98% <0.00%> (-49.38%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6016/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6016/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `82.25% <0.00%> (+1.29%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6016/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `98.79% <0.00%> (+33.89%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6016/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `95.18% <0.00%> (+74.91%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6016?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/6016?src=pr&el=footer). Last update [3996041...a80f84b](https://codecov.io/gh/huggingface/transformers/pull/6016?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6015?src=pr&el=h1) Report\n> Merging [#6015](https://codecov.io/gh/huggingface/transformers/pull/6015?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3b44aa935a4d8f1b0e93a23070d97be6b9c9506b&el=desc) will **increase** coverage by `1.29%`.\n> The diff coverage is `22.22%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6015?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6015 +/- ##\n==========================================\n+ Coverage 77.32% 78.61% +1.29% \n==========================================\n Files 146 146 \n Lines 26253 26270 +17 \n==========================================\n+ Hits 20299 20652 +353 \n+ Misses 5954 5618 -336 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6015?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/6015/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `40.87% <ø> (ø)` | |\n| [src/transformers/trainer\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6015/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyX3RmLnB5) | `16.24% <22.22%> (-0.30%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6015/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `43.98% <0.00%> (-49.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6015/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.90% <0.00%> (-33.90%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6015/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `83.95% <0.00%> (-2.26%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6015/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `95.53% <0.00%> (+69.51%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6015/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `96.77% <0.00%> (+73.38%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6015?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/6015?src=pr&el=footer). Last update [3b44aa9...5ff70d6](https://codecov.io/gh/huggingface/transformers/pull/6015?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "As long as you don't create too many merge conflicts, I can wait ;-)", "PR available #6038 ", "Merge conflicts were a mess so opened a new version." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6014?src=pr&el=h1) Report\n> Merging [#6014](https://codecov.io/gh/huggingface/transformers/pull/6014?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a5404052135a455f9b3ada6154dcfbe7c6ac3968&el=desc) will **increase** coverage by `1.32%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6014?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6014 +/- ##\n==========================================\n+ Coverage 77.18% 78.51% +1.32% \n==========================================\n Files 146 146 \n Lines 26252 26252 \n==========================================\n+ Hits 20263 20612 +349 \n+ Misses 5989 5640 -349 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6014?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6014/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `26.02% <0.00%> (-69.52%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6014/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `43.98% <0.00%> (-49.38%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6014/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6014/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `82.31% <0.00%> (+1.28%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6014/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `96.77% <0.00%> (+73.38%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6014/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `95.18% <0.00%> (+74.91%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6014?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/6014?src=pr&el=footer). Last update [a540405...78af589](https://codecov.io/gh/huggingface/transformers/pull/6014?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Thanks for flagging, the stable doc was building on a wrong commit hash, hence the DPR models being present.\r\nThis is fixed now." ]

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[ "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "I am also facing same issue while trying to install transformers version 3.0.2...Did you find any solution?", "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", "\r\nhow to fix this....pls help\r\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6009?src=pr&el=h1) Report\n> Merging [#6009](https://codecov.io/gh/huggingface/transformers/pull/6009?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f5b5c5bd7e213dea1645f07902b681f88e3cf954&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6009?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6009 +/- ##\n==========================================\n- Coverage 78.52% 78.51% -0.01% \n==========================================\n Files 146 146 \n Lines 26252 26252 \n==========================================\n- Hits 20614 20612 -2 \n- Misses 5638 5640 +2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6009?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6009/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `81.19% <0.00%> (-0.30%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6009/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (-0.26%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6009?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/6009?src=pr&el=footer). Last update [f5b5c5b...fed314c](https://codecov.io/gh/huggingface/transformers/pull/6009?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "looks good now 👍\r\n\r\n(we'll have a better system for model cards soon-ish)" ]

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[ "Phew, won't be able answer all the questions in single comment, will try my best.\r\n\r\n2. IMO for generating paraphrases you probably won't need negative examples.\r\n\r\n3. You assumption is correct, knowledge learned from one task can be useful for other similar tasks. You can approach this as a multitask problem.\r\n 1. identify if two sentences are paraphrases of each other\r\n 2. given one sentence generate it's paraphrase\r\n\r\n With T5 you can use task prefixes for multitask learning, so for identification your example could look something like\r\n```\r\ninput_text: \"sent1: sentence 1 text sent2: sentence two text\"\r\ntarget_text: \"equivalent\" if sent2 is paraphrase of sent1 else \"not equivalent\"\r\n```\r\nand for generation\r\n```\r\ninput_text: \"paraphrase: sentence\"\r\ntaregt_tetx: \"paraphrase of sentence\"\r\n```\r\n\r\n4. Task prefixes are not required for T5 (required when doing multitask training), but if your task is similar to one of the tasks used in T5's pre-training mixture, then use the prefixes so you can exploit the knowledge already learned by the model. \r\nIn my notebook I didn't use task prefixes (even though I was doing sentiment classification) because I wanted to see if it makes any difference if we don't use prefixes. Again use task prefixes when doing multitask learning or if your task similar to one of the tasks used in T5's pre-training mixture.\r\n\r\n5. Check which dataset is closer to your own task and make decide on that.\r\n\r\n10) I used `model.model` because here the first `model` is an instance of lightening model and the HF model is initialized in the first model so `model.model`, but once you save using `.save_pretrained` then you can load using `.from_pretrained` and you can do `model.generate`.\r\n\r\nAnd for evaluation you can use BLUE, ROUGE and METEOR metrics. I usually use [nlg-eval ](https://github.com/Maluuba/nlg-eval)for calculating theses metrics. Generate predictions on your test data, then give your original reference file and generated file to nlg eval and it'll calculate the metrics.\r\n\r\nAnd yes `.from_pretrained` sets model in eval model by default.\r\n\r\nHope this helps.\r\n\r\n", "Thank you, @patil-suraj ! I learn a lot from your answers!\r\n\r\n**4.** I thought T5 is already pretrained with several with several different tasks, which means T5 is multitasking model. We can use the prefix in the appendix of the paper to perform the corresponding task. Though we finetuned it for a new task (like in your notebook), the ability for pretrained task is not lost, right? If so, it is surprising for me T5 didn't mess thing up and knows what to do when you didn't give it any prefix.\r\n\r\n**5. & 3.** If I want to finetune on QQP, should I also use MRPC's data set (i.e. my own data+ MRPC)? On the other hand, if I train a new prefix, should I use QQP + MRPC + my own data? Will the finetuned T5 overfit a little for QQP and MRPC, as the model see them several times (though for the training of different prefix)? Similarly, if I use the QQP+MRPC+my dataset to finetune T5 paraphrase detection AND then use the positive examples in the QQP+MRPC+my data set to finetune T5 paraphrase generation, will this be information leakage? Should I avoid using same positive examples in two tasks?\r\n\r\n**10.** none of the example uses 'model.train()' to set the mode for training. Is this redundant? \r\n\r\n**11**. Thanks for suggestion nlg-eval ! However, metrics like BLUE can't really evaluate the quality of paraphrase generated. ( Like really good ones should have diverse phrases and structures but still same meaning).\r\n\r\nHope someone can address question 1. 6. 7. 8. 9. too.\r\n\r\n", "**4.** Yes T5 is a multitask model and you can use the the prefixes to perform the corresponding task. But note that, the results reported on individual tasks in the paper are reported after again fine-tuning the model on that task specifically. \r\nAnd after fine-tuning the model can forget about other tasks\r\n\r\n**5.** To answer the first question, you probably won't need to use those datasets, by using task prefix the model can exploit already available knowledge. \r\n\r\n**10**. pytorch lightning automatically puts model in train mode for train loop and in eval mode for eval loop.\r\n\r\n**11**. Yes, BLUE won't make much sense, ROUGE seems like a good metric for this task. ", "Thanks, @patil-suraj . \r\n\r\n**4.** Could you explain what do you mean by forget? Here is my understanding: model is first fine-tuned on task **A** with data set **a** using prefix **Aa**, so now the model has the set of parameters **aa** and we call it model **AA**; Then I use the resulting model to further fine tune on task **B** with data set **b** using **Bb**, so the model's parameters change to **bb** and we call it model **BB**. Thus, if we use the final model **BB** to perform task on task **A**, the model may/may not 'recoganize' prefix **Aa**, but the performance will be worse than model **AA**.\r\n\r\nIf what I say above is correct, then my original understanding that transfer learning 'using the same model (structure and set of parameters are the same) for different tasks' is wrong. If so, the transfer learning or learning from multiple tasks are just give better initialization using the current task result for the next task.\r\n\r\n **5.** If the understanding in 4 is correct, I think I may need to reuse the data set when training a new prefix.\r\n", "**4.**, forget here is the context of multitask learning, if you take a multitask model and then only fine-tune it for one task then there's a chance that it can forget about other tasks.", "Thanks,@patil-suraj!\r\n How about just one prefix/task? Will the model forget? \r\n\r\nFor example, I have paraphrase data set A and paraphrase data set B.\r\n**Fine tune 1:** I first fine tune t5-large on data set A using prefix 'para:' with 2 epoch. The resulting model is T5-A.\r\n I then fine tune t5-A on data set B using prefix 'para:' with 2 epoch. The resulting model is T5-B.\r\n**Fine tune 2:** I first combine data set A and data set B into one file. Then I fine tune t5-large on the combined data set using prefix 'para:' with 2 epoch. The resulting model is T5-2.\r\n\r\nWill T5-B forget about data set A? I tried two fine tunning methods and T5-2 seems worse than T5-B (T5-2 with more epoches seems worse than T5-B too). \r\n\r\nMy thought: If it is gradient descent and both methods have converged and only one optimal solution, they should have no difference. However, in real life, there are maybe a lot of local optimum, numerically, there is no guarantee which of the method is better andThe T5-2 should have a higher chance to be better as it has larger data set and prevent overfitting.", "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!", "> Thanks!\r\n\r\nLove this amazing library! Happy to contribute! 😊" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6005?src=pr&el=h1) Report\n> Merging [#6005](https://codecov.io/gh/huggingface/transformers/pull/6005?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7e251ae0395bd3e558c633fe8664fbdf612cb4f4&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `95.83%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6005?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6005 +/- ##\n==========================================\n- Coverage 78.66% 78.66% -0.01% \n==========================================\n Files 146 146 \n Lines 26230 26231 +1 \n==========================================\n Hits 20634 20634 \n- Misses 5596 5597 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6005?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6005/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.52% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6005/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.96% <90.90%> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6005/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `87.20% <100.00%> (+0.02%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6005/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (-0.26%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6005?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/6005?src=pr&el=footer). Last update [7e251ae...ead6b62](https://codecov.io/gh/huggingface/transformers/pull/6005?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Very weird. Only the seq2seq examples have a dependency on nltk, which in turn has a dependency on sqlite3?\r\n\r\nAre we pinning a recent nltk version?", "We are requiring rouge_scorer, which uses nltk for stemming. There is no pinning.", "@mfuntowicz fixed, thanks!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6003?src=pr&el=h1) Report\n> Merging [#6003](https://codecov.io/gh/huggingface/transformers/pull/6003?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d1d15d6f2de9e2cde48ff3ea2072add3311ce2ac&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6003?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6003 +/- ##\n=======================================\n Coverage 78.52% 78.53% \n=======================================\n Files 146 146 \n Lines 26314 26316 +2 \n=======================================\n+ Hits 20664 20667 +3 \n+ Misses 5650 5649 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6003?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6003/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `95.71% <100.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6003/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `97.11% <0.00%> (+0.28%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6003?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/6003?src=pr&el=footer). Last update [d1d15d6...6c95793](https://codecov.io/gh/huggingface/transformers/pull/6003?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "we have a good test for `MbartDataset` I'll add a test for just the tokenizer to `tokenization_mbart.py`" ]

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[ "@Ji-Xin would you mind taking a stab at this?\r\nCommand:\r\n```bash\r\nRUN_SLOW=1 pytest examples/deebert/ \r\n```" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6000?src=pr&el=h1) Report\n> Merging [#6000](https://codecov.io/gh/huggingface/transformers/pull/6000?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6e161955105f7e012dba5d51842923fc25fc5cdf&el=desc) will **increase** coverage by `1.18%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6000?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6000 +/- ##\n==========================================\n+ Coverage 77.32% 78.51% +1.18% \n==========================================\n Files 146 146 \n Lines 26242 26242 \n==========================================\n+ Hits 20291 20603 +312 \n+ Misses 5951 5639 -312 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6000?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6000/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `43.98% <0.00%> (-49.38%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6000/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `81.19% <0.00%> (-0.30%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6000/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.82% <0.00%> (-0.29%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6000/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6000/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `96.77% <0.00%> (+73.38%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6000?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/6000?src=pr&el=footer). Last update [6e16195...e043625](https://codecov.io/gh/huggingface/transformers/pull/6000?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Great work!\r\n\r\n@stefan-it maybe we need to implement a \"Fine-tune\" button similar to GitHub's \"Fork\" button at some point 😉\r\n\r\n@PhilipMay What's your feedback on Optuna?", "> @PhilipMay What's your feedback on Optuna?\r\n\r\n@julien-c Optuna is awsome. I love it. Very good documentation, clean code, nice integrations and I like the pruning integration." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5999?src=pr&el=h1) Report\n> Merging [#5999](https://codecov.io/gh/huggingface/transformers/pull/5999?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/76f52324b1e2d2bb631c80895a5f16ddc303a099&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5999?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5999 +/- ##\n=======================================\n Coverage 78.66% 78.66% \n=======================================\n Files 146 146 \n Lines 26230 26229 -1 \n=======================================\n Hits 20633 20633 \n+ Misses 5597 5596 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5999?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/5999/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190cmFuc2ZvX3hsLnB5) | `79.12% <0.00%> (-0.04%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5999/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `97.11% <0.00%> (+0.28%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5999?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/5999?src=pr&el=footer). Last update [76f5232...c6a5a2a](https://codecov.io/gh/huggingface/transformers/pull/5999?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5998?src=pr&el=h1) Report\n> Merging [#5998](https://codecov.io/gh/huggingface/transformers/pull/5998?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/33d7506ea10ca92886fd1bb3b5306a1a720c58fe&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5998?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5998 +/- ##\n=======================================\n Coverage 78.48% 78.49% \n=======================================\n Files 146 146 \n Lines 26230 26232 +2 \n=======================================\n+ Hits 20587 20591 +4 \n+ Misses 5643 5641 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5998?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/5998/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `95.58% <100.00%> (+0.13%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5998/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.71% <0.00%> (+0.50%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5998?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/5998?src=pr&el=footer). Last update [33d7506...39fdead](https://codecov.io/gh/huggingface/transformers/pull/5998?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Will be fixed by #5982", "Fixed by #5982" ]

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[ "Hi @ashispapu, T5 pre-training is not yet available in transformers, I'm working on it but might take some time. Feel free to take a stab.", "Hi @patil-suraj Thanks for the response. Let me try it.", "@patil-suraj, thanks for working on the different T5 ropes. Can you point to the branch you 're working for pre-training? I'd be interested to contribute.", "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 interested in T5 pre-training from scratch. Any update on this project?" ]

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[ "> Note that there is some moving around of the code in Trainer coming in #5982 (I'll probably merge it today) so you may need to adapt a bit the code.\r\n\r\nI'll wait for the merge of #5982 and introduce the fix for #5990 after.\r\n\r\n> Note that there is no test_steps field since the evaluation is supposed to be complete on the test set (users can always pass along a shorter dataset if they want).\r\n\r\nI see. \r\nAt the moment, the functionality is that it will refuse a dataset that does not implement `__len__`, whether it is the EVAL dataset or the TEST dataset.\r\n\r\n", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5995?src=pr&el=h1) Report\n> Merging [#5995](https://codecov.io/gh/huggingface/transformers/pull/5995?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/c69ea5efc4eac65b183e8d07b1bf91d20bbe0c8c&el=desc) will **increase** coverage by `0.40%`.\n> The diff coverage is `78.57%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5995?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5995 +/- ##\n==========================================\n+ Coverage 78.50% 78.90% +0.40% \n==========================================\n Files 146 146 \n Lines 26249 26264 +15 \n==========================================\n+ Hits 20606 20723 +117 \n+ Misses 5643 5541 -102 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5995?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/5995/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `60.63% <78.57%> (+19.75%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5995/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (+0.75%)` | :arrow_up: |\n| [src/transformers/tokenization\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/5995/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydC5weQ==) | `92.23% <0.00%> (+0.91%)` | :arrow_up: |\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/5995/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `97.36% <0.00%> (+1.31%)` | :arrow_up: |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/5995/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `77.90% <0.00%> (+3.48%)` | :arrow_up: |\n| [src/transformers/training\\_args.py](https://codecov.io/gh/huggingface/transformers/pull/5995/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmluZ19hcmdzLnB5) | `86.73% <0.00%> (+6.12%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5995?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/5995?src=pr&el=footer). Last update [c69ea5e...4066671](https://codecov.io/gh/huggingface/transformers/pull/5995?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi,\r\n\r\n- Merged to include all changes from #5982\r\n- Now accepts `IterableDataset` for any dataset (TRAIN, EVAL, TEST)\r\n- Displays information (how many steps, etc...)\r\n- `test_trainer.py` includes an end-to-end test of `train()` and `predict()`\r\n\r\nIt fixes the issue #5990 with my code.\r\n\r\nChecklist:\r\n- `make test` is positive (all passed, no failed)\r\n- `make style` and `make quality`\r\n\r\nLooking forward to review.\r\n\r\nI'm still unsure about how to test what the dataset is:\r\n- the type hinting says `train_dataset: Dataset`\r\n- `pytorch` indicates it is good practice to implement `__len__` on Map-Style dataset, but in the code there is no way this could be enforced\r\n- `pytorch` relies only on the logic: user will inherit `Dataset` and implement `__len__` or user will inherit `IterableDataset` and not implement `__len__`. In `Dataloader` every time there is a doubt it is checking if the object is an instance of `Dataset` or `IterableDataset`\r\n- in my code, I followed my first rule: either the object has `__len__` or it does not. \r\n\r\nStill wondering if I should code it `pytorch`-style and trust the given answer blindly, or keep it the way it is done, which is a bit more paranoid ?\r\nAny comment appreciated.", "- the test is whether a `Dataset` object has `__len__` or not\r\n- iterable dataset is OK for training, __only__ if `max_steps` has a strictly positive value\r\n- iterable dataset is not acceptable for evaluation or prediction\r\n\r\nThe confusion about `eval_steps` has been purged.\r\n\r\nThe test `test_trainer_iterable_dataset` in `test_trainer.py` has been extended to check for corner cases and associated exceptions. Only the exception type is checked, not exception message.\r\n\r\nChecklist:\r\n- `make test` passed (no fail)\r\n- `make style`\r\n- `make quality`\r\n\r\nLooking forward to review.", "Nitpicking is fine in my book.\r\n\r\n- removed redundant test on dataset (eval / test)\r\n\r\nChecklist:\r\n- `make test` passed (no fail)\r\n- `make style`\r\n- `make quality`\r\n\r\nUp for review again.", "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, is this PR still being reviewed? I would like to use `Trainer` with an `IterableDataset` and this looks like exactly what's needed to make that happen. If you have time, I would greatly appreciate this PR to get into the next version :) thank you!", "I realize a part has been merged, but not everything.", "And I can't seem to find a way to re-open this PR. So I guess, I should open a new one, and link to this one...", "@carson-sestili The PR #7858 has been merged to master and fixes the bug.\r\nYou can already use it by installing from source.\r\n", "@j-rossi-nl Thank you very much!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5994?src=pr&el=h1) Report\n> Merging [#5994](https://codecov.io/gh/huggingface/transformers/pull/5994?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/33d7506ea10ca92886fd1bb3b5306a1a720c58fe&el=desc) will **decrease** coverage by `0.23%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5994?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5994 +/- ##\n==========================================\n- Coverage 78.48% 78.25% -0.24% \n==========================================\n Files 146 146 \n Lines 26230 26230 \n==========================================\n- Hits 20587 20525 -62 \n- Misses 5643 5705 +62 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5994?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/5994/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `20.27% <0.00%> (-74.92%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/5994/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.90% <0.00%> (-33.90%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5994/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `84.71% <0.00%> (-1.51%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/5994/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `81.02% <0.00%> (-1.29%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5994/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `81.49% <0.00%> (+0.29%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/5994/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.36% <0.00%> (+49.37%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/5994/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `95.53% <0.00%> (+69.51%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5994?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/5994?src=pr&el=footer). Last update [33d7506...fbf4b03](https://codecov.io/gh/huggingface/transformers/pull/5994?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I will let you know ASAP!", "As far as I know, @mrm8488 hasn't used finetune.py for T5. He has linked his training colabs in model cards and they just pass `labels` directly and `decoder_input_ids` are created by the `T5ForConditionalGeneration`", " I usually follow this one https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb", "Also I think #5866 should be merged before this, because if `</s>` is not added then it might miss the last token ", "> can we unittest this behavior somehow?\r\n\r\nif we move this logic to dataset or collate function then we can unit test `__getitem__` or `collat_fn`. Would this be a good idea ?", "Yes, great idea. Whichever seems more natural.", "Hi @sshleifer , does this PR needs any other changes before merging ?", "sorry for being slow!" ]

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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/5992?src=pr&el=h1) Report\n> Merging [#5992](https://codecov.io/gh/huggingface/transformers/pull/5992?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d32279438a73e71961f53baa4fb47d0f08c2984d&el=desc) will **increase** coverage by `0.25%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5992?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5992 +/- ##\n==========================================\n+ Coverage 78.25% 78.51% +0.25% \n==========================================\n Files 146 146 \n Lines 26214 26214 \n==========================================\n+ Hits 20515 20581 +66 \n+ Misses 5699 5633 -66 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5992?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/5992/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `26.02% <0.00%> (-69.52%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/5992/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `43.98% <0.00%> (-49.38%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5992/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/5992/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.60% <0.00%> (+1.16%)` | :arrow_up: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/5992/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `82.31% <0.00%> (+1.28%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/5992/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `98.79% <0.00%> (+33.89%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/5992/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `95.18% <0.00%> (+74.91%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5992?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/5992?src=pr&el=footer). Last update [d322794...59c00a2](https://codecov.io/gh/huggingface/transformers/pull/5992?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Merging as the failures are not related to the changeset introduced in this PR" ]

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[ "Hi, thanks for raising this issue! Indeed, this looks like an issue. Do you want to open a PR with the fix you propose?", "Hey @maurice-g, \r\n\r\nThanks a lot for the fix!" ]

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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/5989?src=pr&el=h1) Report\n> Merging [#5989](https://codecov.io/gh/huggingface/transformers/pull/5989?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/33d7506ea10ca92886fd1bb3b5306a1a720c58fe&el=desc) will **increase** coverage by `0.17%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5989?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5989 +/- ##\n==========================================\n+ Coverage 78.48% 78.66% +0.17% \n==========================================\n Files 146 146 \n Lines 26230 26230 \n==========================================\n+ Hits 20587 20634 +47 \n+ Misses 5643 5596 -47 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5989?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/5989/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.90% <0.00%> (-33.90%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5989/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `81.49% <0.00%> (+0.29%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/5989/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `95.53% <0.00%> (+69.51%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5989?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/5989?src=pr&el=footer). Last update [33d7506...ec916e3](https://codecov.io/gh/huggingface/transformers/pull/5989?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Hi @nghuyong , you won't need to fix this warning, the reason for this warning is that cross-attention layer is added newly in the model as both of these models are encoder models and cross-attention is not available for encoder only models.\r\n\r\nThis warning will go away when you train the model, after training EncoderDecoder model you can load it using just `EncoderDecoderModel.from_pretrained`\r\n\r\nHope this helps.", "@patil-suraj Thanks for your reply\r\nI still have a question, should I follow the instruction in the model card of [bert2bert-cnn_dailymail-fp16](https://github.com/huggingface/transformers/blob/master/model_cards/patrickvonplaten/bert2bert-cnn_dailymail-fp16/README.md#training-script):\r\n**make sure you checkout to the branch more_general_trainer_metric**\r\nto train a seq2seq model", "yes, that branch has a change in `Trainer` class to make it work with `EncoderDecoder` models.", "I will open a cleaner PR soon to integrate this branch into master.", "@patrickvonplaten I'm also modifying `Trainer` to support generative metrics and other seq2seq functionalities like label smoothing loss etc in this PR #6769, it's for `examples/seq2seq` right now, but if you think it's useful then can try to move it into `Trainer`", "I think it's fine to leave it separated for now! Eventually it would be nice to move everything to Trainer", "That will be really COOL ! Thanks for your work, it will be very convenient to use~ @patrickvonplaten @patil-suraj ", "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 could be one of the reasons for strange T5 behaviour.", "Yes this is an excellent catch @patil-suraj !" ]

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[ "I get an error when trying to use the hosted inference API too.\r\n\r\n\r\n> ⚠️ Error loading model Can't load weights for 't5-11b'. Make sure that: - 't5-11b' is a correct model identifier listed on 'https://huggingface.co/models' - or 't5-11b' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt. OSError(\"Can't load weights for 't5-11b'. Make sure that:\\n\\n- 't5-11b' is a correct model identifier listed on 'https://huggingface.co/models'\\n\\n- or 't5-11b' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.\\n\\n\")\r\n", "This is a known issue, and is due to the fact that the [checkpoint size is 42GB](https://huggingface.co/t5-11b#list-files) while the max supported file size for Cloudfront is [20GB](https://aws.amazon.com/blogs/aws/amazon-cloudfront-support-for-20-gb-objects/).\r\n\r\nYou should use the `use_cdn=False` flag in `AutoModel.from_pretrained(use_cdn=False)` to work around this for now.", "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", "Forgot to update this issue at the time, but note that this now works transparently", "Hi. I am having trouble when downloading t5-11b model too but with bit different issue. I am keep getting 'connection rest by peer' error and failing to download the pretrained weights. Is there other way to download the model parameters?", "Hi @nobellant215 you could try downloading the weights file directly: `https://huggingface.co/t5-11b/resolve/main/pytorch_model.bin`" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5985?src=pr&el=h1) Report\n> Merging [#5985](https://codecov.io/gh/huggingface/transformers/pull/5985?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/c3206eef44e9fbfca9ed4527f528107fcba31888&el=desc) will **decrease** coverage by `0.39%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5985?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5985 +/- ##\n==========================================\n- Coverage 78.65% 78.26% -0.40% \n==========================================\n Files 146 146 \n Lines 26227 26230 +3 \n==========================================\n- Hits 20630 20530 -100 \n- Misses 5597 5700 +103 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5985?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5985/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.96% <100.00%> (+0.07%)` | :arrow_up: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5985/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `87.17% <100.00%> (+0.02%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/5985/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `20.27% <0.00%> (-74.92%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/5985/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `81.02% <0.00%> (-1.29%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5985/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `85.96% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5985/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `81.49% <0.00%> (+0.29%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/5985/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.36% <0.00%> (+49.37%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5985?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/5985?src=pr&el=footer). Last update [c3206ee...bfd8d3a](https://codecov.io/gh/huggingface/transformers/pull/5985?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@patil-suraj @julien-c @sgugger @sshleifer ", "I tried different learning rate like 1.76e-3, 5e-5.(https://github.com/huggingface/transformers/issues/4727)\r\ndifferent dataset type: TextDataset, line_by_lineDataset.\r\nNone of them works.", "I don't think your script will work like this, since the model requires two sets of labels (labels and sentence_order_label). The `DataCollatorForLanguageModeling` will generate the labels, but you will need to add something that generates the pair of sentences and adds the sentence_order_label.", "> I don't think your script will work like this, since the model requires two sets of labels (labels and sentence_order_label). The `DataCollatorForLanguageModeling` will generate the labels, but you will need to add something that generates the pair of sentences and adds the sentence_order_label.\r\n\r\n@sgugger Thanks for the reply! The `AlbertForPreTraining` model did requires 2 different labels. I am planning to create a PR for that, before that,\r\n**Could I have any hints about why the `AlbertForMaskedLM` model didn't converge to 0?** Actually this is my major concern, I am planing to train the Albert using larger datasets like the whole wikipedia, if the MLM task even not converge on small dataset(say, wikiText-2), I think it impede my way to scale up the dataset.", "@sgugger I used larger batch batch size the lower the learning rate did help the model converged to 0 in a very tiny text data(not wikiText-2), I repeated several articles several time to create that text dataset.", "> I don't think your script will work like this, since the model requires two sets of labels (labels and sentence_order_label). The `DataCollatorForLanguageModeling` will generate the labels, but you will need to add something that generates the pair of sentences and adds the sentence_order_label.\r\n\r\n@sgugger hi，I want to train a albert tiny model. Could you tell me is there any methods or class that can be used to generate the pair of sentences and adds the sentence_order_label ? Thanks a lot.\r\n\r\nI have gotten it after read your commit. I think the class LineByLineWithSOPTextDataset in \"transformers/data/datasets/language_modeling.py\" can solve my problem. Thank yuo again,hh.", "I also resolved with larger batch size (96) and lower learning_rate (5e-5). Thank you. " ]

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[ "Duplicate of #4224" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5982?src=pr&el=h1) Report\n> Merging [#5982](https://codecov.io/gh/huggingface/transformers/pull/5982?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2c0da7803a75f0fc6e6d484e23ca283faa32d785&el=desc) will **decrease** coverage by `0.15%`.\n> The diff coverage is `60.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5982?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5982 +/- ##\n==========================================\n- Coverage 78.66% 78.51% -0.16% \n==========================================\n Files 146 146 \n Lines 26227 26240 +13 \n==========================================\n- Hits 20632 20602 -30 \n- Misses 5595 5638 +43 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5982?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/5982/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `40.87% <60.00%> (+2.41%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/5982/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `26.02% <0.00%> (-69.52%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5982/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5982/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `81.49% <0.00%> (+0.29%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/5982/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `98.79% <0.00%> (+33.89%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5982?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/5982?src=pr&el=footer). Last update [2c0da78...e583bf0](https://codecov.io/gh/huggingface/transformers/pull/5982?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Sadly changing the `__iter__` function does not work as it's used inside TensorFlow (specifically in `tensorflow/python/util/nest.py`).", "Ok closing this prototype now that we have a way forward (see [the forum](https://discuss.huggingface.co/t/new-model-output-types/195/8))." ]

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[ "Hi @mingyang3github \r\nmasking is implemented in ` DataCollatorForLanguageModeling`'s `mask_tokens` method, [here](https://github.com/huggingface/transformers/blob/master/src/transformers/data/data_collator.py#L103)\r\n\r\nFor pre-training you can use the language modeling script, it takes of the dataset and masking. ", "> Hi @mingyang3github\r\n> masking is implemented in ` DataCollatorForLanguageModeling`'s `mask_tokens` method, [here](https://github.com/huggingface/transformers/blob/master/src/transformers/data/data_collator.py#L103)\r\n> \r\n> For pre-training you can use the language modeling script, it takes of the dataset and masking.\r\n\r\nHi @patil-suraj ,\r\nThank you for your reply. If I understand this correctly, DataCollatorForLanguageModeling's method is static masking, right?\r\n\r\nBased on RoBERTa model's paper, the dynamic masking refers to \"training data was duplicated 10 times so that each sequence is masked in 10 different ways over the 40 epochs of training.\"\r\nHas this method implemented been implemented in DataCollatorForLanguageModeling ?", "It is dynamic masking, as the masking is handled in the collate function, the same examples gets masked differently. Because every batch first goes through collate function before the batch is returned from the loader, so each time the examples goes through collate it gets masked differently.", "Hi @patil-suraj , is that mean we **always use dynamic masking**, when use `DataCollatorForLanguageModeling`, no matter pre-training for Bert, RoBERTa or else.", "@buaapengbo\r\nYes, the `DataCollatorForLanguageModeling` always does dynamic masking no matter the model", "I thought I responded, but it turns out I didn't. Sorry.\r\n@patil-suraj Thank you very much! That makes sense! \r\n", "Thanks for the answer. For the original roberta, they seem to use the same mask per sentence 4 times during training and 10 different masks for the same sentence during the whole training. But this function always returns different masks for the same sentence during training. This means the model always receives different masks for the same sentence during the whole training process right?" ]

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[ "```\r\nfrom transformers import AutoModelWithLMHead, AutoTokenizer\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(\"language-modeling/output/\")\r\nmodel = AutoModelWithLMHead.from_pretrained(\"language-modeling/output/\")\r\n\r\n\r\ninput_text=\"organic che\"\r\nfeatures = tokenizer([input_text], return_tensors='pt')\r\n\r\noutput = model.generate(input_ids=features['input_ids'], \r\n attention_mask=features['attention_mask'])\r\n\r\ntokenizer.decode(output[0])\r\n\r\n```", "I want to make a query auto complete these are the user queries separated by new line\r\n\r\n", "@patil-suraj ", "should I add some special token at the end and start of every search query\r\n", "as far as I can see, your dataset is format is correct, also you don't need to add any special tokens, tokenizer adds that by default.", "--line_by_line I added then the error is coming \r\nYou are attempting to pad samples but the tokenizer you are using (GPT2Tokenizer) does not have one.", "I want to make a text auto complete am I using correct model ? do I have sufficient training sentences? should I add --line_by_line while training? Please help!!\r\n@patil-suraj ", "Hi @vyaslkv you can use GPT-2 for auto complete, as for training examples you will need to experiment.\r\n\r\npinging @sgugger for the error.", "LineByLineDataset is not really suitable for GPT2: you should concatenate your texts with the separation token and feed chunks of the the model size (can't remember if it's 512 or 1024 at the top of my mind but it should be in the config of the model). Like the error message says, GPT2 does not know padding.", "@sgugger can you explain me a bit which token to use and how the code will look like in that case so sorry if I am asking too much or can you give me some reference which I could use\r\n\r\nThanks for responding ", "The separation token will automatically be added by the tokenizer. The rest is just standard python: concatenate all your lists of tokens in a big numpy array, then reshape it to `something x model_len`, something being the number of \"sequences\" (they'll actually span over several lines of your dataset) you can build with your dataset. You can then iterate through the rows of that array as a dataset.", "In this what changes I need to do\r\n```\r\n[python run_language_modeling.py \\\r\n --output_dir=output \\\r\n --model_type=gpt2 \\\r\n --model_name_or_path=gpt2 \\\r\n --do_train \\\r\n --train_data_file=$TRAIN_FILE \\\r\n --do_eval \\\r\n --eval_data_file=$TEST_FILE](url)\r\n```", "The script will do this automatically for you if you don't add the line by line flag. (Except the sentences are separated by new lines and not the special token.) You can try to replace the new lines by \"<|endoftext|>\"", "cool Thanks @sgugger Just to clarify If I add \"<|endoftext|>\" in place of new line I don't need to make any changes right?", "Normally, no.", "Thanks @sgugger Thanks a ton really for help so quick ", "@sgugger @patil-suraj I trained with the format you shared but it is generating some irrelevant text not from the training data I gave. What I am missing in this case \r\n```\r\nfrom transformers import AutoModelWithLMHead, AutoTokenizer\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(\"output1/\")\r\nmodel = AutoModelWithLMHead.from_pretrained(\"output1/\")\r\ninput_ids = tokenizer.encode('Vegetative reproduction of Agave', return_tensors='pt')\r\n# set return_num_sequences > 1\r\nbeam_outputs = model.generate(\r\n input_ids, \r\n max_length=50, \r\n num_beams=10, \r\n no_repeat_ngram_size=2, \r\n num_return_sequences=10, \r\n early_stopping=True\r\n)\r\n\r\n# now we have 3 output sequences\r\nprint(\"Output:\\n\" + 100 * '-')\r\nfor i, beam_output in enumerate(beam_outputs):\r\n print(\"{}: {}\".format(i, tokenizer.decode(beam_output, skip_special_tokens=False)))\r\n```", "I want to generate text autocomplete from the training data text\r\n", "@sgugger can you please help", "Hi @vyaslkv , I think the best place to ask this question is [HF forums](https://discuss.huggingface.co/) someone who has already worked on similar task can answer it better. Although @sgugger might have some answers :)", "@patil-suraj Thanks I will put my question there as well\r\n", "https://discuss.huggingface.co/t/search-query-autocomplete-from-the-queries-i-have-in-my-data/546", "@sgugger @patil-suraj no one has responded on the forum 😔", "@patil-suraj I didn't get any response can you please help\r\n", "Hi @vyaslkv , I'll see if anyone I know has worked on similar problem and get back to you.", "@patil-suraj Thanks", "@patil-suraj ?", "Hello, @patil-suraj we found anything related to that?\r\n\r\nThanks!!", "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/5976?src=pr&el=h1) Report\n> Merging [#5976](https://codecov.io/gh/huggingface/transformers/pull/5976?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2c0da7803a75f0fc6e6d484e23ca283faa32d785&el=desc) will **decrease** coverage by `1.38%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5976?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5976 +/- ##\n==========================================\n- Coverage 78.66% 77.28% -1.39% \n==========================================\n Files 146 146 \n Lines 26227 26227 \n==========================================\n- Hits 20632 20270 -362 \n- Misses 5595 5957 +362 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5976?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/5976/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `23.38% <0.00%> (-73.39%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/5976/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `26.02% <0.00%> (-69.52%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5976/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (-0.51%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/5976/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `98.79% <0.00%> (+33.89%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/5976/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.36% <0.00%> (+49.37%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5976?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/5976?src=pr&el=footer). Last update [2c0da78...f83fdd3](https://codecov.io/gh/huggingface/transformers/pull/5976?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5975?src=pr&el=h1) Report\n> Merging [#5975](https://codecov.io/gh/huggingface/transformers/pull/5975?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ae67b2439fb15954bfd8f0fdf521cf1a650bafb9&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5975?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5975 +/- ##\n=======================================\n Coverage 78.51% 78.51% \n=======================================\n Files 146 146 \n Lines 26214 26214 \n=======================================\n+ Hits 20581 20582 +1 \n+ Misses 5633 5632 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5975?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/5975/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190cmFuc2ZvX3hsLnB5) | `79.16% <0.00%> (ø)` | |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5975/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+0.25%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5975?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/5975?src=pr&el=footer). Last update [ae67b24...ed8722d](https://codecov.io/gh/huggingface/transformers/pull/5975?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Was fixed in different PR: #5999 " ]

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[ "Thanks for flagging the issue, the fix is on its way to review.", "Oh, I opened a PR for this, but it seems you fixed it yourself. I will close my PR then," ]

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[ "@stas00, this might be up your alley! ", "will do, thank you!", "a large part is done: https://github.com/huggingface/transformers/pull/6196\r\n", "problem: not all `results` in tests are objects, some are plain dict and can't be called with `.key_name`. So there is a large chunk of tests that haven't been re-written because of that.\r\n\r\nSo we could add a wrapper to the test utils that will make the code consistent with those tests where `results` is an object.\r\n\r\n```\r\nclass DictAttr:\r\n def __init__(self, args):\r\n for k in args:\r\n setattr(self, k, args[k])\r\n\r\n def __getitem__(self, item):\r\n return getattr(self, item)\r\n```\r\nand then in the tests:\r\n```\r\n# import DictAttr and then\r\ndata = {\r\n \"loss_1\": 1,\r\n \"mems_1\": 2,\r\n} \r\nresult = DictAttr(data)\r\n```\r\nnow it works either way:\r\n```\r\nprint(result[\"loss_1\"]) # 1\r\nprint(result.loss_1) # 1\r\n```\r\n\r\nNot sure about the best class name for this, suggestions?\r\n\r\nSo practically, with this change the test code will look\r\n\r\n```\r\n--- a/tests/test_modeling_tf_albert.py\r\n+++ b/tests/test_modeling_tf_albert.py\r\n@@ -136,14 +136,12 @@ class TFAlbertModelTester:\r\n\r\n sequence_output, pooled_output = model(input_ids)\r\n\r\n- result = {\r\n+ result = DictAttr({\r\n \"sequence_output\": sequence_output.numpy(),\r\n \"pooled_output\": pooled_output.numpy(),\r\n- }\r\n- self.parent.assertListEqual(\r\n- list(result[\"sequence_output\"].shape), [self.batch_size, self.seq_length, self.hidden_size]\r\n- )\r\n- self.parent.assertListEqual(list(result[\"pooled_output\"].shape), [self.batch_size, self.hidden_size])\r\n+ })\r\n+ self.parent.assertEqual(result.sequence_output.shape, (self.batch_size, self.seq_length, self.hidden_size))\r\n+ self.parent.assertEqual(result.pooled_output.shape, (self.batch_size, self.hidden_size))\r\n```\r\nplus an extra import of whatever the final class name will be.", "1) can we just never create a `result` dict? It just creates unneeded indirection.\r\n2) If we need to create a results dict, cant we just do key lookup with `[key]`\r\n3) checkout `collections.UserDict`\r\n", "> * can we just never create a `result` dict? It just creates unneeded indirection.\r\n\r\nFrom looking at the existing tests, it sort of mimics the returns objects, but doesn't have the accessors for the keys.\r\n\r\nSo I'm not quite sure what you propose. A short code sample is usually most demonstrative.\r\n \r\n> * If we need to create a results dict, cant we just do key lookup with `[key]`\r\n\r\nI lost you, unless I am misunderstanding what you're suggesting, isn't the big part of this \"issue\" - replacing `[key]` with `.key`. otherwise nothing else needs to be done and this ticket can be closed - except now it's a mish-mash of results.key (most pt tests) and results[\"key\"] (most tf tests)\r\n\r\n> * checkout `collections.UserDict`\r\n\r\nI checked - it doesn't provide `[key]` and `.key` functionality.", "ignore #1, I was confused.\r\nwhen I made this issue, sylvain hadn't merged #6155 , so I guess what remains of the issue is\r\nSorry for the miscommunication!\r\n", "Your #6196 will completely close the issue.", "So I just need to complete: \"delete all mentions of check_loss_output\" then - there is one remaining test there.\r\nedit: now done", "I plan to do part 2 PR to make the rest of the tests consistent with this change, but I have to wait for this to be merged as it impacts too many files to proceed easily." ]

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[ "Do you mind doing the code quality so that we can merge? You can do `pip install -e \".[quality]` followed by `make style && make quality`.\r\n\r\nIf you don't have access to your fork, I can push on it directly to update the code quality and merge after.", "Hi @LysandreJik,\r\nI tried to do the code quality but couldn't pass `make quality` because `black` and `flake8` return with a non-zero exit code for some reason (i'm on windows). Here are their outputs:\r\n```shell\r\nblack --check --line-length 119 --target-version py35 examples templates tests src utils\r\nwould reformat C:\\Users\\u165983\\Documents\\transformers\\src\\transformers\\__init__.py\r\nwould reformat C:\\Users\\u165983\\Documents\\transformers\\templates\\adding_a_new_example_script\\run_xxx.py\r\nwould reformat C:\\Users\\u165983\\Documents\\transformers\\templates\\adding_a_new_example_script\\utils_xxx.py\r\nOh no! 💥 💔 💥\r\n3 files would be reformatted, 340 files would be left unchanged.\r\n\r\nflake8 examples templates tests src utils\r\ntests\\test_tokenization_common.py:31:5: F401 'transformers.PretrainedConfig' imported but unused\r\ntests\\test_tokenization_common.py:31:5: F401 'transformers.PreTrainedModel' imported but unused\r\ntests\\test_tokenization_common.py:31:5: F401 'transformers.TFPreTrainedModel' imported but unused\r\nsrc\\transformers\\pipelines.py:72:5: F401 '.modeling_utils.PreTrainedModel' imported but unused\r\nsrc\\transformers\\pipelines.py:73:5: F401 '.modeling_tf_utils.TFPreTrainedModel' imported but unused\r\n```\r\nI still commited and pushed on my fork. Tell me if there is anything more I can do.", "`make style` checks `black` and `isort` and updates the files, while `make quality` checks `black`, `isort` and `flake8`, but doesn't update the files and instead tells you what fails.\r\n\r\nI ran `make style` on your repository and pushed directly on it, thanks for iterating!" ]

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[ "I'm wondering should the B & I part maybe separated from the entity type part? In the sense that you average the entities (disregarding the B/I part) and vice-versa. I now have the feeling that only the first subtoken decides whether the complete word is a B or an I. ", "I want to complete this but ran into another issue while working it:\r\n\r\nAll [UNK] tokens get mapped to [UNK] in the output, instead of the actual input token (because the code is getting from ids->tokens), Also [UNK]s gets lost when using skip_special_tokens (https://github.com/huggingface/transformers/issues/6863)\r\nWhile this is a simple token alignment issue and can be solved by using `offset_mappings`. `offset_mappings` is only available with fast tokenizers, I'm wondering what would be a more general approach to solving this?", "Dear @cceyda,\r\n\r\nIn the last couple of days I started to work with Huggingface's transformers and especially NER-classification. I ran into issues that has been previously addressed in other issues you just mentioned at the beginning. Especially that subtokens that were classified with 'O' were not properly merged with the full token. \r\n\r\nFor example (Dutch):\r\nsentence = \"Als we Volkswagens OR-voorzitter **Bernd Osterloh** moeten geloven, dan moet dat binnen drie jaar het geval zijn.\"\r\n\r\nGives me as group entities: \r\n[{'entity_group': 'B-per', 'score': 0.9999980926513672, 'word': 'Bern'}, \r\n{'entity_group': 'I-per', 'score': 0.9999990463256836, 'word': 'Ost'}]\r\n\r\nI expect:\r\n[{'entity_group': 'B-per', 'score': 0.9999980926513672, 'word': 'Bernd'}, \r\n{'entity_group': 'I-per', 'score': 0.9999990463256836, 'word': 'Osterloh'}]\r\n\r\nHowever, the considered subtokens are classified as 'O':\r\n\r\n{'word': '[CLS]', 'score': 0.9999999403953552, 'entity': 'O', 'index': 0}\r\n{'word': 'Als', 'score': 0.9999999403953552, 'entity': 'O', 'index': 1}\r\n{'word': 'we', 'score': 0.9999999403953552, 'entity': 'O', 'index': 2}\r\n{'word': 'Volkswagen', 'score': 0.9999955296516418, 'entity': 'B-misc', 'index': 3}\r\n{'word': '##s', 'score': 0.9999999403953552, 'entity': 'O', 'index': 4}\r\n{'word': 'O', 'score': 0.9981945157051086, 'entity': 'I-misc', 'index': 5}\r\n{'word': '##R', 'score': 0.9999998807907104, 'entity': 'O', 'index': 6}\r\n{'word': '-', 'score': 0.9999999403953552, 'entity': 'O', 'index': 7}\r\n{'word': 'voorzitter', 'score': 0.9999998807907104, 'entity': 'O', 'index': 8}\r\n{'word': 'Bern', 'score': 0.9999980926513672, 'entity': 'B-per', 'index': 9}\r\n**{'word': '##d', 'score': 0.9999998807907104, 'entity': 'O', 'index': 10}**\r\n{'word': 'Ost', 'score': 0.9999990463256836, 'entity': 'I-per', 'index': 11}\r\n**{'word': '##er', 'score': 0.9999998807907104, 'entity': 'O', 'index': 12}\r\n{'word': '##lo', 'score': 0.9999997615814209, 'entity': 'O', 'index': 13}\r\n{'word': '##h', 'score': 0.9999998807907104, 'entity': 'O', 'index': 14}**\r\n{'word': 'moeten', 'score': 0.9999999403953552, 'entity': 'O', 'index': 15}\r\n{'word': 'geloven', 'score': 0.9999998807907104, 'entity': 'O', 'index': 16}\r\n{'word': ',', 'score': 0.9999999403953552, 'entity': 'O', 'index': 17}\r\n{'word': 'dan', 'score': 0.9999999403953552, 'entity': 'O', 'index': 18}\r\n{'word': 'moet', 'score': 0.9999999403953552, 'entity': 'O', 'index': 19}\r\n{'word': 'dat', 'score': 0.9999999403953552, 'entity': 'O', 'index': 20}\r\n{'word': 'binnen', 'score': 0.9999999403953552, 'entity': 'O', 'index': 21}\r\n{'word': 'drie', 'score': 0.9999999403953552, 'entity': 'O', 'index': 22}\r\n{'word': 'jaar', 'score': 0.9999999403953552, 'entity': 'O', 'index': 23}\r\n{'word': 'het', 'score': 0.9999999403953552, 'entity': 'O', 'index': 24}\r\n{'word': 'geval', 'score': 0.9999999403953552, 'entity': 'O', 'index': 25}\r\n{'word': 'zijn', 'score': 0.9999999403953552, 'entity': 'O', 'index': 26}\r\n{'word': '.', 'score': 0.9999999403953552, 'entity': 'O', 'index': 27}\r\n{'word': '[SEP]', 'score': 0.9999999403953552, 'entity': 'O', 'index': 28}\r\n\r\nI believe your pull request addresses these issues properly. \r\nHowever, I saw the merge did not complete since it failed on some tasks.\r\n\r\nI was wondering if there is still the intention to solve these issues.\r\n\r\nDisclaimer: I am a total newbie to git (just set up an account), so please be mild, haha.\r\nAny help is much appreciated!\r\n\r\nThank you in advance,\r\n\r\nMonique", "@cceyda I actually want this PR to move forward. Are you okay collaborating on your fork (can add me as collaborator)? I can help out with some of the issues failing so we can get this merged :smile:\r\n\r\n", "@enzoampil I have added you as a collaborator.\r\nAlso pushed some additional changes addressing the [UNK] token mapping problem I mentioned before. \r\nStill there are some things I'm not very satisfied with:\r\n\r\n1. subword prefix was fixed to '##' before. with the latest change I added a check to see if the tokenizer has an `is_subword_fn `defined (still dont like handling it this way). I know some tokenizers have `subword_prefix` but most don't and this was the most flexible solution for now. \r\n2. `offset_mappings` is needed to resolve [UNK] tokens, but is only available with fast tokenizers. Fast tokenizers don't have `convert_ids_to_tokens` so had to implement a hacky solution for those aswell.\r\n3. `skip_special_tokens` also dropped [UNK] tokens so I had to change things and rely on `special_tokens_mask`.\r\n\r\nIt is not optimal but it worked for my use cases. \r\nHaven't had a chance to look at the failing tests yet :/\r\n", "I have changed the `ignore_subwords` default to True which covers cases like\r\n```\r\n[\r\n{'word': 'Cons', 'score': 0.9994944930076599, 'entity': 'B-PER', 'index': 1},\r\n{'word': '##uelo', 'score': 0.802545428276062, 'entity': 'B-PER', 'index': 2}\r\n]\r\n```\r\nAnd honestly I don't know why subwords shouldn't be ignored for most cases. (Unless there is need for some custom logic that determines a words tag; ie by averaging the wordpieces etc etc. In which case grouped_entities shouldn't be used 🤔 )\r\nIMO Mid-word inconsistencies made by the model while `ignore_subwords = False` shouldn't effect pipelines output logic.\r\n\r\n[todo]\r\n- torch tests are passing for now but probably should add more cases? (I can't see why the tf tests are failing though, don't have dev env for that)\r\n- should add the new parameters to the doc strings.", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5970?src=pr&el=h1) Report\n> Merging [#5970](https://codecov.io/gh/huggingface/transformers/pull/5970?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7087d9b1c07781cc2eee45c97d3eadf6a1ba2b44?el=desc) will **increase** coverage by `26.30%`.\n> The diff coverage is `71.87%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5970?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5970 +/- ##\n===========================================\n+ Coverage 52.05% 78.36% +26.30% \n===========================================\n Files 236 168 -68 \n Lines 43336 32338 -10998 \n===========================================\n+ Hits 22560 25341 +2781 \n+ Misses 20776 6997 -13779 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5970?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `80.59% <71.87%> (+61.46%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl94bG0ucHk=) | `18.94% <0.00%> (-60.01%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-56.29%)` | :arrow_down: |\n| [src/transformers/tokenization\\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fY2FtZW1iZXJ0LnB5) | `37.03% <0.00%> (-29.23%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `71.84% <0.00%> (-11.00%)` | :arrow_down: |\n| [src/transformers/data/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL19faW5pdF9fLnB5) | `100.00% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_mmbt.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tbWJ0LnB5) | `23.47% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_mbart.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYmFydC5weQ==) | `100.00% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_outputs.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vdXRwdXRzLnB5) | `100.00% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_pegasus.py](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19wZWdhc3VzLnB5) | `100.00% <0.00%> (ø)` | |\n| ... and [217 more](https://codecov.io/gh/huggingface/transformers/pull/5970/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5970?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/5970?src=pr&el=footer). Last update [7087d9b...47797d1](https://codecov.io/gh/huggingface/transformers/pull/5970?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Dear @cceyda,\r\n\r\nLast two days I worked on your branch to see how it performs on my own input texts. \r\nHowever, I came accross the following issue I would like to point out to you:\r\n\r\nWhen I use the following line of code (as you suggest under 'Usage' above):\r\n\r\npipeline('ner', model=model, tokenizer=tokenizer, ignore_labels=[], grouped_entities=True, skip_special_tokens=True, ignore_subwords=True)\r\n\r\n\r\nI get the error:\r\n\r\nTypeError: __init__() got an unexpected keyword argument 'skip_special_tokens'.\r\n\r\nWhen looking in the file transformer.pipelines and looking specifically for the tokenclassificationpipeline, it seems that it is not yet implemented. Or am I missing something?\r\n\r\nBest, \r\n\r\nMonique", "@Monique497 sorry for the delay\r\nA couple of things have changed since I first wrote that example:\r\n \r\n- special tokens ([CLS][PAD][SEP]) are always skipped (per comments above) so you don't need that kwarg. This is also valid for `grouped_entities=False` \r\n\r\n``` py\r\nfrom transformers import (\r\n AutoModelForTokenClassification,\r\n AutoTokenizer,\r\n pipeline,\r\n)\r\n\r\nmodel = AutoModelForTokenClassification.from_pretrained(model_name)\r\ntokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) # note the fast tokenizer use\r\n# ignore_subwords = True by default\r\nnlp = pipeline(\"ner\",model=model,tokenizer=tokenizer, grouped_entities=True)\r\ninputs=\"test sentence\"\r\noutput=nlp(inputs)\r\n```\r\n\r\n- Another important thing is you have to **use a fast tokenizer** OR pass `offset_mapping` as a parameter because the [UNK] token resolution depends on this. (maybe I should rename this to offset_mappings). This is also valid for `grouped_entities=False` \r\n\r\n```py\r\n# you can pass it like this\r\nnlp(inputs,offset_mapping=mappings_you_calculate)\r\n\r\n```\r\n- If you are using a custom tokenizer that treats subwords differently (ie not starting with '##'), you can pass a function implementing your custom logic through `tokenizer.is_subword_fn` and `tokenizer.convert_tokens_to_string` \r\nI don't know if this is the best way to handle non standard tokenizations, but I use some custom non-standard tokenizers for Korean and this solution gave me enough flexibility.\r\n\r\nsomething like this:\r\n\r\n```py\r\ndef sub_fn(token):\r\n if token.starts_with(\"%%\"): return True\r\ntokenizer.is_subword_fn=sub_fn\r\n\r\ndef convert_tokens_to_string(self, tokens):\r\n out_string = \" \".join(tokens).replace(\" %%\", \"\").strip()\r\n return out_string\r\ntokenizer.convert_tokens_to_string=convert_tokens_to_string\r\n```\r\n\r\n@enzoampil what are your thoughts on this?\r\n", "@cceyda Sorry for taking a while, lemme do another review!", "@LysandreJik @julien-c This looks good to me. Please advise if this is good to merge or if you think there's still anything missing before merging :grin:", "Thanks for iterating! I'll check this today.", "Merging this as soon as it's green, thank you for iterating on the PR! Sorry this took so long to merge.", "Thanks @LysandreJik and congrats @cceyda !! :smile:", "fix_bart_gpu", "FYI this broke the NER pipeline:\r\n\r\n```py\r\nfrom transformers import pipeline\r\n\r\nnlp = pipeline(\"ner\")\r\n\r\nnlp(\"My name is Alex and I live in New York\")\r\n```\r\n\r\ncrashes with the following error:\r\n```\r\n raise Exception(\"To decode [UNK] tokens use a fast tokenizer or provide offset_mapping parameter\")\r\nException: To decode [UNK] tokens use a fast tokenizer or provide offset_mapping parameter\r\n```\r\n\r\nTrying to see if this can be quickly patched, otherwise we'll revert the PR while we patch this.", "oops! although returning unk tokens with slow tokenizers are not the best, I agree not forcing a fast tokenizer with a default of ignore_subword=True looks better for keeping the compatibility. I saw a bit late the _args_parser line was mis-merged during this pr merge and I see it is fixed/improved on the patch. I wasn't sure on how to test for the offset_mapping argument with the new test structure (which looks to be good at the patch). Sorry for the trouble 😅 @LysandreJik ", "No worries, thanks for taking a look at the patch!" ]

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[ "see also #3535", "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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[ "Hi! This is an interesting question, have you tried asking it over on the forums at https://discuss.huggingface.co ? You'll probably get more answers over there.", "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/5967?src=pr&el=h1) Report\n> Merging [#5967](https://codecov.io/gh/huggingface/transformers/pull/5967?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ae67b2439fb15954bfd8f0fdf521cf1a650bafb9&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5967?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5967 +/- ##\n=======================================\n Coverage 78.51% 78.51% \n=======================================\n Files 146 146 \n Lines 26214 26214 \n=======================================\n Hits 20581 20581 \n Misses 5633 5633 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5967?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/5967?src=pr&el=footer). Last update [ae67b24...e8e003f](https://codecov.io/gh/huggingface/transformers/pull/5967?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi @patrickvonplaten, can you have a look?", "Thansk @orena1 !", "pinging @patrickvonplaten for notification" ]

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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 model is defined as a `roberta` model but its tokenizer seems to be a Wordpiece tokenizer (based on the `vocab.txt` file), whereas Roberta uses a Byte-level BPE.\r\n\r\nThis is not currently supported out of the box by our `AutoTokenizer/AutoModel` features (model type ≠ tokenizer type) nor by our Pipelines but I'd like to support this in the future.", "For now, you'll have to initialize this tokenizer + model independently.\r\n\r\n```\r\nBertTokenizer.from_pretrained(\"...\")\r\nAutoModel.from_pretrained(\"...\")\r\n```\r\n\r\nAlso cc'ing model author @datquocnguyen", "I am working on it (I just have uploaded the model to huggingface yesterday). \r\nI will create pull requests soon, so that users can make use of the following scripts:\r\n\r\n tokenizer = BertweetTokenizer.from_pretrained(\"vinai/bertweet-base\")\r\n bertweet = BertweetModel.from_pretrained(\"vinai/bertweet-base\")\r\n\r\nPlease stay tuned!\r\n", "@julien-c @datquocnguyen Thanks for your answer.\r\nI just tried the AutoModel, I had some weird \"CUDA illegal memory access error\" after 2 steps. It works fine with other models such as electra or roberta. I do not know if it is related to some wrong encoding with the tokenizer (I am using the fairseq tokenizer as the tokenizer from huggingface is not working even with BertTokenizer) or something else.\r\n\r\nupdate: I may have found the issue. It may come from the max length which seems to be 130, contrary to regular Bert Base model. I was using a longer length sequence.", "> I am working on it (I just have uploaded the model to huggingface yesterday).\r\n> I will create pull requests soon, so that users can make use of the following scripts:\r\n> \r\n> ```\r\n> tokenizer = BertweetTokenizer.from_pretrained(\"vinai/bertweet-base\")\r\n> bertweet = BertweetModel.from_pretrained(\"vinai/bertweet-base\")\r\n> ```\r\n> \r\n> Please stay tuned!\r\n\r\nLooking forward to it !", "@nightlessbaron @Shiro-LK @julien-c FYI, I have just created a pull request #6129 for adding BERTweet and PhoBERT into transformers\r\n@nightlessbaron In case you want to use BERTweet right away, you might have a look at this fork https://github.com/datquocnguyen/transformers \r\nCheers,\r\nDat.", "@datquocnguyen Looks like the error still exists. From https://huggingface.co/vinai/bertweet-base, I run \r\ntokenizer = AutoTokenizer.from_pretrained(\"vinai/bertweet-base\") \r\nIt gives:\r\nOSError: Model name 'vinai/bertweet-base' was not found in tokenizers model name list (roberta-base, roberta-large, roberta-large-mnli, distilroberta-base, roberta-base-openai-detector, roberta-large-openai-detector). We assumed 'vinai/bertweet-base' was a path, a model identifier, or url to a directory containing vocabulary files named ['vocab.json', 'merges.txt'] but couldn't find such vocabulary files at this path or url.\r\n\r\nAlso, https://huggingface.co/vinai/bertweet-base?text=Paris+is+the+%3Cmask%3E+of+France gives an error", "I had the same error @steveguang had. Is there any solution?", "@steveguang @SergioBarretoJr your issue has now been solved.\r\nAlso to @Shiro-LK @nightlessbaron Please check https://github.com/datquocnguyen/transformers\r\n@julien-c Please help review this pull request #6129 BERTweet now works in Auto mode and without an additional dependency fastBPE.", "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", "This was solved by @datquocnguyen " ]

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[ "Hi! This doesn't currently work, because it's trying to instantiate a classification layer with 10 labels, while the checkpoint's classification layer has 19 labels. \r\n\r\nWhat you want is to keep the base layers, but ignore the classification layers. In order to do so, you can load your checkpoint in a base `RobertaModel`. This will only load the base model. You can then save that model to a checkpoint, and load that checkpoint from a `RobertaForSequenceClassification` model.\r\n\r\nHere's how to do this:\r\n\r\n```py\r\nfrom transformers import RobertaForSequenceClassification, RobertaConfig, RobertaModel\r\n\r\nmodel = RobertaModel.from_pretrained(pretrained_path)\r\nmodel.save_pretrained(f\"{pretrained_path}-base-model\")\r\n\r\nconfig = RobertaConfig(num_labels=10)\r\nmodel = RobertaForSequenceClassification.from_pretrained(f\"{pretrained_path}-base-model\", config=config)\r\n```\r\n\r\nYou should see the output:\r\n\r\n```\r\nSome weights of RobertaForSequenceClassification were not initialized from the model checkpoint at here-base-model and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\n```\r\n\r\nThis means that it has correctly loaded the entire base model, and has randomly initialized the classifier weights. Hope that helps!" ]

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[ "CUDA errors are always very cryptic 😕.\r\n\r\nDo you mind giving us an example that we can reproduce (e.g. with an article you're trying to encode that fails), so that we can see what's going on?", "Thank you for the comment. I split my code into 2 script files: one is tokenize; the other is transform. Somehow it works. Probably it was caused by some issue in the remote server, or my custom data class. I will close the topic. Thank you for your time. " ]

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[ "If I understand correctly and you're asking what/where are those two files:\r\n\r\n- `discriminator.json` is the configuration file for the model you want to convert\r\n- `model.bin` is the location where the converted checkpoint will be saved." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5961?src=pr&el=h1) Report\n> Merging [#5961](https://codecov.io/gh/huggingface/transformers/pull/5961?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e714412fe6b38346a1f73525b701e030857b2f21&el=desc) will **decrease** coverage by `1.22%`.\n> The diff coverage is `25.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5961?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5961 +/- ##\n==========================================\n- Coverage 78.50% 77.27% -1.23% \n==========================================\n Files 146 146 \n Lines 26214 26218 +4 \n==========================================\n- Hits 20578 20259 -319 \n- Misses 5636 5959 +323 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5961?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/testing\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5961/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90ZXN0aW5nX3V0aWxzLnB5) | `72.72% <25.00%> (-3.75%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/5961/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `23.38% <0.00%> (-73.39%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5961/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `83.95% <0.00%> (-1.26%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5961/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.82% <0.00%> (-0.29%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/5961/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.36% <0.00%> (+49.37%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5961?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/5961?src=pr&el=footer). Last update [e714412...a57f645](https://codecov.io/gh/huggingface/transformers/pull/5961?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "The test will obviously break, right?", "Any progress on merging this?", "they won't accept it as the checks have failed? \nBut it's expected for the checks to fail as I have modified `modeling_bart` and it's gonna have one more Param count.", "Is that so @sshleifer?", "Thanks for the contribution, this looks awesome!\r\n\r\nWe can't merge with failing tests, but I think the tests can pass.\r\n\r\nCould you also check\r\n```\r\nRUN_SLOW=1 pytest tests/test_modeling_bart.py\r\nRUN_SLOW=1 pytest tests/test_modeling_marian.py\r\nRUN_SLOW=1 pytest test_modeling_mbart.py\r\n```\r\nadd the `USE_CUDA=1` prefix to make them run faster on GPU.", "Actually can we add a `support_tpu` flag to BartConfig, init it to False, and only allocate `lm_head` if it's set to True. I'm concerned that we are wasting RAM when we train on GPU. (I would happily change my mind if I encountered evidence that this change doesn't change GPU RAM consumption.)", "I tried this version and it seems to work but it stucks at \"Validation sanity check\". Working colab [here](https://colab.research.google.com/drive/1NA9_EPEBNmo7feQ60iiznsLP_XbWwQmC?usp=sharing)", "Well I removed the validation check altogether by passing in the concerned\nflag to 0. Tried debugging to find out what's causing it, but I couldn't\nfigure it out.\n\nIf you will train and validate, it will work.\n\nOn Mon, 27 Jul 2020, 17:46 marton-avrios, <[email protected]> wrote:\n\n> I tried this version and it seems to work but it stucks at \"Validation\n> sanity check\".\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/pull/5960#issuecomment-664360705>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AFNPJJQYIPQDG5ZPYXDTTT3R5VV23ANCNFSM4PEHWJYA>\n> .\n>\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", "This is now supported by `Seq2SeqTrainer`. Use that if you want TPU support!" ]

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[ "I think you can try code :\r\n```\r\nfrom transformers import BertTokenizer, BertModel\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\nmodel = BertModel.from_pretrained(\"bert-base-uncased\")\r\n```", "> I think you can try code :\r\n> \r\n> ```\r\n> from transformers import BertTokenizer, BertModel\r\n> tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n> model = BertModel.from_pretrained(\"bert-base-uncased\")\r\n> ```\r\nstill succeed in running tokenizer but fail with modeling", "could due to the website limitaition of my company", "I have been banging my head on the same issue for a few days now. \r\nJust observed, it downloads these weights from AWS links ( check configuration_bert.py or configuration_distilbert.py, you'll find these files in /Anaconda3/envs/envName/Lib/site-packages/transformers/ ) and my company blocks AWS and GCP links.\r\nThis, most likely, seems to be the issue.", "> When I run below codes, I can successfully load the tokenizer but fail with loading the models.\r\n> from transformers import AutoTokenizer, AutoModelWithLMHead\r\n> tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\n> model = AutoModelWithLMHead.from_pretrained(\"bert-base-uncased\")\r\n> \r\n> Here is the error:\r\n> OSError: Can't load weights for 'bert-base-uncased'. Make sure that:\r\n> \r\n> * 'bert-base-uncased' is a correct model identifier listed on 'https://huggingface.co/models'\r\n> * or 'bert-base-uncased' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.\r\n> \r\n> I can load models after manually downloading them but I do want to directly load them via transformers.\r\n\r\nhello, I meet the same problem ,and if I download the model manually, and where I should put the file in ?", "> > When I run below codes, I can successfully load the tokenizer but fail with loading the models.\r\n> > from transformers import AutoTokenizer, AutoModelWithLMHead\r\n> > tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\n> > model = AutoModelWithLMHead.from_pretrained(\"bert-base-uncased\")\r\n> > Here is the error:\r\n> > OSError: Can't load weights for 'bert-base-uncased'. Make sure that:\r\n> > \r\n> > * 'bert-base-uncased' is a correct model identifier listed on 'https://huggingface.co/models'\r\n> > * or 'bert-base-uncased' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.\r\n> > \r\n> > I can load models after manually downloading them but I do want to directly load them via transformers.\r\n> \r\n> hello, I meet the same problem ,and if I download the model manually, and where I should put the file in ?\r\n\r\n\r\nanywhere is ok, just put your file location in BertTokenizer.from_pretrained(location)", "> > When I run below codes, I can successfully load the tokenizer but fail with loading the models.\r\n> > from transformers import AutoTokenizer, AutoModelWithLMHead\r\n> > tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\n> > model = AutoModelWithLMHead.from_pretrained(\"bert-base-uncased\")\r\n> > Here is the error:\r\n> > OSError: Can't load weights for 'bert-base-uncased'. Make sure that:\r\n> > \r\n> > * 'bert-base-uncased' is a correct model identifier listed on 'https://huggingface.co/models'\r\n> > * or 'bert-base-uncased' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.\r\n> > \r\n> > I can load models after manually downloading them but I do want to directly load them via transformers.\r\n> \r\n> hello, I meet the same problem ,and if I download the model manually, and where I should put the file in ?\r\n\r\nWhere did u download the model manually from?", "You can browse to https://huggingface.co/bert-base-uncased/tree/main for example and download pretrained models." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5958?src=pr&el=h1) Report\n> Merging [#5958](https://codecov.io/gh/huggingface/transformers/pull/5958?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/09a2f40684f77e62d0fd8485fe9d2d610390453f&el=desc) will **decrease** coverage by `0.09%`.\n> The diff coverage is `13.04%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5958?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5958 +/- ##\n==========================================\n- Coverage 78.49% 78.39% -0.10% \n==========================================\n Files 146 146 \n Lines 26210 26252 +42 \n==========================================\n+ Hits 20573 20580 +7 \n- Misses 5637 5672 +35 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5958?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/5958/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `35.50% <9.09%> (-2.34%)` | :arrow_down: |\n| [src/transformers/training\\_args.py](https://codecov.io/gh/huggingface/transformers/pull/5958/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmluZ19hcmdzLnB5) | `78.00% <100.00%> (+0.44%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5958/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+0.25%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5958?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/5958?src=pr&el=footer). Last update [09a2f40...cbc7c63](https://codecov.io/gh/huggingface/transformers/pull/5958?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", "when this will be added to library?", "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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[ "@aclifton314 just wondering if your custom dataset `__getitem__` is configured correctly? \r\ni.e I'm thinking it should be returning `return {'data': x, 'target': y}` or `return x, y`", "@danieldiamond Thank you for the response. That's a good point about `__getitem__`. The data in my csv file is structured in the following way:\r\n```\r\nTitle, Raw\r\nDistillBERT a distilled version of BERT smaller faster cheaper and lighter, As transfer learning from large-scale pretrained models becomes more prevalent in Natural Language Processing (NLP) blah blah blah.\r\n``` \r\nI'm using the `Raw` columns in the csv as training data for my model, which inherits from `GPT2LMHeadModel`. I had assumed that there wouldn't be any need for targets for this model. But maybe there's something in `Trainer` that expects this particular type of format? In which case, I presume I could always set `y` to `None`?\r\n\r\nI was reading [this post](https://discuss.huggingface.co/t/dataset-expected-by-trainer/148/2) on the HF forum and there is the following mention:\r\n```\r\nMake sure the dataloader returns the dict with same key values forward method expects.\r\nInside _training_step, you’ll pass inputs to the function, and then after the inputs are passed kept on gpu, the function does:\r\noutput = model(**inputs)\r\n```\r\n\r\nI also looked through the debugger again. `inputs` for `tr_loss += self._training_step(model, inputs, optimizer)` looks like it is created in `for step, inputs in enumerate(epoch_iterator):` (this is in `trainer.py`). Looking at `epoch_iterator`, the `iterable` attribute contains the data from `sd_dataset` (i.e. `epoch_iterator -> iterable -> dataset`).\r\n\r\nIt looks like the data is being carried through `trainer.py`, but I can't seem to figure out why `inputs` would be empty.", "Your dataset returns keys that are not known to the model. Also, it doesn't seem to tokenize your texts?\r\nIt should return a dict with the expected argument of your models, including the labels (that way the model will return the loss for the trainer). I have no idea what the code of `GPT2FinetunedWithNgrams` looks like, so that will depend on that.\r\n\r\nNote that `Trainer` is not an abstract training loop for all DL problems, it's customized to work with the transformers models, so your model should behave the same way as HF models if you want to use it.", "@sgugger incorporating your changes and upgrading to Transformers 3.0.2 solved the problem for me. I've got a long thread about it [here](https://discuss.huggingface.co/t/finetuning-gpt2-with-user-defined-loss/163/30).\r\n\r\nThanks for the help!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5956?src=pr&el=h1) Report\n> Merging [#5956](https://codecov.io/gh/huggingface/transformers/pull/5956?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e714412fe6b38346a1f73525b701e030857b2f21&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/5956?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #5956 +/- ##\n==========================================\n+ Coverage 78.50% 78.51% +0.01% \n==========================================\n Files 146 146 \n Lines 26214 26214 \n==========================================\n+ Hits 20578 20581 +3 \n+ Misses 5636 5633 -3 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5956?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5956/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.82% <0.00%> (-0.29%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5956/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (+1.00%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/5956?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/5956?src=pr&el=footer). Last update [e714412...ca424ca](https://codecov.io/gh/huggingface/transformers/pull/5956?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "merging to rerun." ]

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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" ]