Datasets:

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DanielPFlorian

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Transformers-Github-Issues

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[ "It seems you're not actually putting anything on GPU. You need to call `bertModel.to(device)` and `input_ids.to(device)`.\r\n\r\nThe BERT model is much larger than GloVe, however, so you'll never get the same speed.", "Thank you @LysandreJik. It actually increases speed but not that much.\r\nIs there any model which is better than Glove and also fast in embedding like Glove.", "The fatest model we have in the library that I know of would be `DistilBERT`, which would still be slower than GloVe, but faster than BERT.\r\n\r\nGloVe and transformer models are very different concepts, and solve different problems, hence why transformer models are way bigger/slower.", "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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[ "Pull request welcome!" ]

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[ "Will close this to track this at more general #4185", "Please the \" checkpoint\" file to reset the path to \"mobilebert_variables.ckpt\" \r\nThis can solved my issue.\r\n" ]

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[ "I think we're missing some info here, can you add your complete code snippet to the issue's title? \r\n\r\n### 🤣🤣 \r\n\r\nYou can even add some binary data in there for good measure.\r\n\r\nHehe I'm just kidding :trollface: ", "Managed: I just didn’t account for the May 7 transformers release! \n\nSent from my iPhone\n\n> On 12 May 2020, at 2:08 PM, Julien Chaumond <[email protected]> wrote:\n> \n> \n> I think we're missing some info here, can you add your complete code snippet to the issue's title?\n> \n> 🤣🤣\n> \n> You can even add some binary data in there for good measure.\n> \n> Hehe I'm just kidding \n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "The same issue in Colab. I use DialoGPT repo but issue is the same", "Help, please . Anyone. I can't load on other devices. I have copied the model from the device where it was trained on GPU, now I cannot load on other device with CPU.", "It's hard to help with this much information. Can you open a new issue, respecting the template, letting us know what's your issue?", "https://github.com/huggingface/transformers/commit/05deb52dc170226fcad899c240ee9568986d8d5a#diff-8530ad4c95b70580d10d594b7411a8be seemed to break this pretrained model, which was last modified on February 18. There masked_bias was introduced. Try reverting to a version before that, like cec3cdd." ]

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[ "Hi huggingface/transformers,\r\n\r\nI am Xu Tan from Microsoft Research. Thanks for that you mentioned MPNet in huggingface/transformers repo.\r\n\r\nMPNet is an advanced pre-trained models for language understanding, which inherits the advantages of BERT and XLNet, while well address their problems. We have MPNet pre-trained models, codes (the version for huggingface/transformers) and downstream task results ready.\r\n\r\nHow can we proceed to incorporate MPNet into huggingface/transformers repo? Thanks very much!\r\n\r\n\r\nBest,\r\nXu\r\nFrom: RyanHuangNLP <[email protected]>\r\nSent: Tuesday, May 12, 2020 5:48 PM\r\nTo: huggingface/transformers <[email protected]>\r\nCc: Xu Tan <[email protected]>; Mention <[email protected]>\r\nSubject: [huggingface/transformers] MPNet: Masked and Permuted Pre-training for Language Understanding (#4308)\r\n\r\n🌟 New model addition\r\nModel description\r\n\r\nMPNet: Masked and Permuted Pre-training for Language Understanding<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Farxiv.org%2Fpdf%2F2004.09297.pdf&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639246486&sdata=%2BE5kPD%2F1nyY9bgRtqr%2FCFf2YzsI8E%2BcTzWX0gcUSnWs%3D&reserved=0>\r\nuse both Masked Language Model and Permuted Language Model\r\n\r\nOpen source status\r\n\r\n * [MPNet ] the model implementation is available: (https://github.com/microsoft/MPNet<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fmicrosoft%2FMPNet&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639246486&sdata=%2Fp94TR%2BQVvRNdoio0kjL%2FjmGgtQb8SlcQouAnas8gmY%3D&reserved=0>)\r\n * [MPNet pretrain weight ] the model weights are available: (https://modelrelease.blob.core.windows.net/pre-training/MPNet/mpnet.example.pt<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmodelrelease.blob.core.windows.net%2Fpre-training%2FMPNet%2Fmpnet.example.pt&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639256488&sdata=F8O2da%2FYPXS8AjmIVt4D%2FOaNLOYmSVgfyICRQuHf0BI%3D&reserved=0>)\r\n * [@tobyoup<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Ftobyoup&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639266479&sdata=hJn0ZIEEzgukAII%2FtaXEl6ZmIt2jQqRwtSP2Hu1lujs%3D&reserved=0>@StillKeepTry ] who are the authors: (mention them, if possible by @gh-username<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fgh-username&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639266479&sdata=3ntmS0WjsV8DxOyrDyt7Qduc6jGMzU%2BfdcniizaYuLQ%3D&reserved=0>)\r\n\r\n—\r\nYou are receiving this because you were mentioned.\r\nReply to this email directly, view it on GitHub<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers%2Fissues%2F4308&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639276475&sdata=t3iSTAMR54mEsK%2Blgc9pjxuYZmnYQcwJjtje%2BQ05yeU%3D&reserved=0>, or unsubscribe<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FALQV7L4JWDDOBVHDVZYRCD3RRELMPANCNFSM4M6VMZPQ&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639276475&sdata=4496eTEWR5xHALKY9MGu6Aal%2Fx%2BU1dn87FkpASm72ME%3D&reserved=0>.\r\n", "+ RyanHuangNLP\r\n\r\nFrom: Xu Tan\r\nSent: Sunday, July 5, 2020 6:58 PM\r\nTo: huggingface/transformers <[email protected]>; huggingface/transformers <[email protected]>\r\nSubject: RE: [huggingface/transformers] MPNet: Masked and Permuted Pre-training for Language Understanding (#4308)\r\n\r\nHi huggingface/transformers,\r\n\r\nI am Xu Tan from Microsoft Research. Thanks for that you mentioned MPNet in huggingface/transformers repo.\r\n\r\nMPNet is an advanced pre-trained models for language understanding, which inherits the advantages of BERT and XLNet, while well address their problems. We have MPNet pre-trained models, codes (the version for huggingface/transformers) and downstream task results ready.\r\n\r\nHow can we proceed to incorporate MPNet into huggingface/transformers repo? Thanks very much!\r\n\r\n\r\nBest,\r\nXu\r\nFrom: RyanHuangNLP <[email protected]<mailto:[email protected]>>\r\nSent: Tuesday, May 12, 2020 5:48 PM\r\nTo: huggingface/transformers <[email protected]<mailto:[email protected]>>\r\nCc: Xu Tan <[email protected]<mailto:[email protected]>>; Mention <[email protected]<mailto:[email protected]>>\r\nSubject: [huggingface/transformers] MPNet: Masked and Permuted Pre-training for Language Understanding (#4308)\r\n\r\n🌟 New model addition\r\nModel description\r\n\r\nMPNet: Masked and Permuted Pre-training for Language Understanding<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Farxiv.org%2Fpdf%2F2004.09297.pdf&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639246486&sdata=%2BE5kPD%2F1nyY9bgRtqr%2FCFf2YzsI8E%2BcTzWX0gcUSnWs%3D&reserved=0>\r\nuse both Masked Language Model and Permuted Language Model\r\n\r\nOpen source status\r\n\r\n * [MPNet ] the model implementation is available: (https://github.com/microsoft/MPNet<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fmicrosoft%2FMPNet&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639246486&sdata=%2Fp94TR%2BQVvRNdoio0kjL%2FjmGgtQb8SlcQouAnas8gmY%3D&reserved=0>)\r\n * [MPNet pretrain weight ] the model weights are available: (https://modelrelease.blob.core.windows.net/pre-training/MPNet/mpnet.example.pt<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmodelrelease.blob.core.windows.net%2Fpre-training%2FMPNet%2Fmpnet.example.pt&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639256488&sdata=F8O2da%2FYPXS8AjmIVt4D%2FOaNLOYmSVgfyICRQuHf0BI%3D&reserved=0>)\r\n * [@tobyoup<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Ftobyoup&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639266479&sdata=hJn0ZIEEzgukAII%2FtaXEl6ZmIt2jQqRwtSP2Hu1lujs%3D&reserved=0>@StillKeepTry ] who are the authors: (mention them, if possible by @gh-username<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fgh-username&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639266479&sdata=3ntmS0WjsV8DxOyrDyt7Qduc6jGMzU%2BfdcniizaYuLQ%3D&reserved=0>)\r\n\r\n—\r\nYou are receiving this because you were mentioned.\r\nReply to this email directly, view it on GitHub<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers%2Fissues%2F4308&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639276475&sdata=t3iSTAMR54mEsK%2Blgc9pjxuYZmnYQcwJjtje%2BQ05yeU%3D&reserved=0>, or unsubscribe<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FALQV7L4JWDDOBVHDVZYRCD3RRELMPANCNFSM4M6VMZPQ&data=02%7C01%7CXu.Tan%40microsoft.com%7C36a49d494f604784087708d7f6598986%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637248737639276475&sdata=4496eTEWR5xHALKY9MGu6Aal%2Fx%2BU1dn87FkpASm72ME%3D&reserved=0>.\r\n", "Hi @tobyoup, it's very cool that you want to contribute your architecture!\r\n\r\nYou can follow this [checklist](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model) to add your model. Once you open a PR, we'll be very happy to help you finish the integration!", "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", "@tobyoup @LysandreJik Is there any update about MPNET in transformers?", "We are actively preparing the integration codes, and will be ready these weeks.", "@tobyoup Is MPNET. ready in transformers? Hello, any updates?", "@tobyoup @LysandreJik Is there any update about MPNET in transformers?", "~The PR for the MPNET integration has not seen any progress yet.~ @tobyoup, don't hesitate to ping us if you need help or pointers towards the integration.\r\n\r\nThere are ongoing efforts to implement MPNet on https://github.com/huggingface/transformers/pull/5804 by @StillKeepTry", "Thanks for the reminder! We are adapting the codes these days and we expect to give the first PR by this weekend.\r\n\r\nThanks\r\nXu\r\nFrom: Lysandre Debut <[email protected]>\r\nSent: Friday, November 13, 2020 1:27 AM\r\nTo: huggingface/transformers <[email protected]>\r\nCc: Xu Tan <[email protected]>; Mention <[email protected]>\r\nSubject: Re: [huggingface/transformers] MPNet: Masked and Permuted Pre-training for Language Understanding (#4308)\r\n\r\n\r\nThe PR for the MPNET integration has not seen any progress yet. @tobyoup<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Ftobyoup&data=04%7C01%7CXu.Tan%40microsoft.com%7C9c1b4ed7fdb847535a2908d887302e38%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637407988352073356%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=Q2NWAAxvh5LXn6d24FW7Uy4jBfs2%2BsodWvfPY3F%2BZVc%3D&reserved=0>, don't hesitate to ping us if you need help or pointers towards the integration.\r\n\r\n—\r\nYou are receiving this because you were mentioned.\r\nReply to this email directly, view it on GitHub<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers%2Fissues%2F4308%23issuecomment-726222432&data=04%7C01%7CXu.Tan%40microsoft.com%7C9c1b4ed7fdb847535a2908d887302e38%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637407988352073356%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fnG9MzRgWBxG4tGKsoRJd0GCSHEH8AMvpNZFYNhOcJs%3D&reserved=0>, or unsubscribe<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FALQV7L6OVO3TCAHLX3UDVVLSPQLGXANCNFSM4M6VMZPQ&data=04%7C01%7CXu.Tan%40microsoft.com%7C9c1b4ed7fdb847535a2908d887302e38%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637407988352083352%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=kFPFzyRHfpImvd55Kr33XodHoQ8HtMlT8LhC5WDf4qA%3D&reserved=0>.\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "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 have this problem too , how did you resolve it?can you share your ideas?" ]

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[ "An error message would be great, want to submit a PR?\r\n\r\nFor multiple masked token support, I'm not entirely sure. The sampling might be very use case-specific.", "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 facing a situation where I've to fetch probabilities from BERT MLM for multiple words in a single sentence.\r\n\r\n Original : \"Mountain Dew is an energetic drink\"\r\n Masked : \"[MASK] is an energetic drink\"\r\n\r\nBut BERT MLM task doesn't consider two tokens at a time for the MASK. ", "@stephen-pilli \r\n\r\nHere's how to mask multiple tokens.\r\n\r\n```\r\nimport torch\r\n\r\nsentence = \"The capital of France <mask> contains the Eiffel <mask>.\"\r\n\r\ntoken_ids = tokenizer.encode(sentence, return_tensors='pt')\r\n\r\n# print(token_ids)\r\n\r\ntoken_ids_tk = tokenizer.tokenize(sentence, return_tensors='pt')\r\n\r\nprint(token_ids_tk)\r\n\r\nmasked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()\r\n\r\nmasked_pos = [mask.item() for mask in masked_position ]\r\n\r\nprint (masked_pos)\r\n\r\nwith torch.no_grad():\r\n\r\n output = model(token_ids)\r\n\r\nlast_hidden_state = output[0].squeeze()\r\n\r\nprint (\"\\n\\n\")\r\n\r\nprint (\"sentence : \",sentence)\r\n\r\nprint (\"\\n\")\r\n\r\nlist_of_list =[]\r\n\r\nfor mask_index in masked_pos:\r\n\r\n mask_hidden_state = last_hidden_state[mask_index]\r\n\r\n idx = torch.topk(mask_hidden_state, k=100, dim=0)[1]\r\n\r\n words = [tokenizer.decode(i.item()).strip() for i in idx]\r\n\r\n list_of_list.append(words)\r\n\r\n print (words)\r\n\r\n\r\nbest_guess = \"\"\r\n\r\nfor j in list_of_list:\r\n\r\n best_guess = best_guess+\" \"+j[0]\r\n```" ]

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[ "If you don't tell us what code you use, we cannot help you. Be detailed and use code block. Don't post screenshots. ", "I agree. I apologize, was kinda annoyed that version diff can cause this. Will share a minimal example. (In the mean time, re-verifying via setting up different env's)", "Did you manage to solve the error?", "Hey I am sorry for the delay, I am having fever since yesterday, I am trying to replicate it. ", "Okay, so i am not able to replicate the same on a dummy test i had run just now; seems all good ; Will ping back if the same thing happens for my code; My apologies to great devs ;", "@AdityaSoni19031997 I am also facing the same issue. New transformers version is not using GPU and it's quite slow.", "I have solved the issue.\r\nYou have to manually set the device and load the tensors to GPU:\r\n\r\n```\r\nfrom transformers import BertTokenizer, BertModel, BertForMaskedLM\r\n\r\n\r\ndef assign_GPU(Tokenizer_output):\r\n \r\n tokens_tensor = Tokenizer_output['input_ids'].to('cuda:0')\r\n token_type_ids = Tokenizer_output['token_type_ids'].to('cuda:0')\r\n attention_mask = Tokenizer_output['attention_mask'].to('cuda:0')\r\n \r\n output = {'input_ids' : tokens_tensor, \r\n 'token_type_ids' : token_type_ids, \r\n 'attention_mask' : attention_mask}\r\n \r\n return output\r\n\r\n\r\n\r\nsentence = 'Hello World!'\r\ntokenizer = BertTokenizer.from_pretrained('bert-large-uncased')\r\nmodel = BertModel.from_pretrained('bert-large-uncased')\r\n\r\ninputs = assign_GPU(tokenizer(sentence, return_tensors=\"pt\"))\r\nmodel = model.to('cuda:0')\r\noutputs = model(**inputs)\r\noutputs\r\n```", "In my case, it was a conflict of driver's IIRC." ]

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

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

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[ "I have just solved this problem.\r\nYou have to set an additional flag: --do_lower_case.\r\nI wonder why the run_squad.py behaves differently than run_glue.py, etc. Is there is a code improve on the way?", "You shouldn't have to set `--do_lower_case` as it should be lowercased by default for that model.", "> You shouldn't have to set `--do_lower_case` as it should be lowercased by default for that model.\r\n\r\nI thought it is and it should be, but it isn't", "Closing this b/c #4245 was merged\r\n\r\n(we still need to investigate why the lowercasing is not properly populated by the model's config)\r\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4300?src=pr&el=h1) Report\n> Merging [#4300](https://codecov.io/gh/huggingface/transformers/pull/4300?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7cb203fae4e7964e9e99400b375d660ebce765ee&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `91.66%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4300?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4300 +/- ##\n==========================================\n- Coverage 78.21% 78.21% -0.01% \n==========================================\n Files 120 120 \n Lines 20038 20040 +2 \n==========================================\n+ Hits 15673 15674 +1 \n- Misses 4365 4366 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4300?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/4300/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `75.73% <75.00%> (ø)` | |\n| [src/transformers/modeling\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/4300/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hbGJlcnQucHk=) | `77.20% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4300/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.82% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/4300/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `83.66% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4300/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.98% <100.00%> (+0.02%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4300/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4300?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/4300?src=pr&el=footer). Last update [7cb203f...7eef4f5](https://codecov.io/gh/huggingface/transformers/pull/4300?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Update: Also asked about this on shared [PyTorch Slack channel](https://app.slack.com/client/T2077MDKQ/CN963QVJB)", "I don't see anything obviously wrong, but I'm not very familiar with your codebase. You are looking for tensor attributes - are you sure that you don't have any other tensor attributes that don't correspond to former parameters? \r\nFinally, we also have _former_parameters in pytorch, introduced here https://github.com/pytorch/pytorch/pull/36523. You may find it useful, but we can't guarantee that it will remain as a stable API. ", "I think you forgot GPT-2:\r\n`transformers/modeling_gpt2.py\", line 464, in forward\r\n attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility\r\nStopIteration\r\n`", "Yes, if I remember correctly I didn't try to remove all the calls to `next(self.parameters())` in this PR – do you want to open a PR to fix this?" ]

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[ "I also have the same problem @alexandrenriq ", "I think there's a mistake in the code which sets the `AutoModelForQuestionAnswering` as `XLNetForQuestionAnsweringSimple`. You can run the code by substituting `AutoModelForQuestionAnswering` into `XLNetForQuestionAnswering` (or by removing `cls_index` and `p_mask` in the batches to make `XLNetForQuestionAnsweringSimple` work). \r\n\r\nNevertheless, after the training is done, I can't reproduce the scores right (`Results: {'exact': 0.03784295175023652, 'f1': 0.6317807409886281,...`).", "I also found the same issue, and I believe this issue appears for a very very long time. ", "see also #3535", "> see also #3535\n\n@brettkoonce I saw it, it didn't work. ", "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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[ "Which library are you using? Could you maybe post a complete code example (with all relevant imports) to reproduce the error? ", "https://github.com/jameschartouni/arabic_translation/blob/google-t5/Model_1.ipynb\r\n\r\nLet me know if you have difficulty viewing the code! We are using colab and use a fresh install. ", "I see you use the official T5 code that connects to HF code. The error seems to be happening in the `t5` library though (in `t5/models/hf_model.py`). Could you open an issue there? We haven't written the code so it's very hard for us to debug this." ]

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[ "A simple fix could be:\r\n\r\n1. Adding in file [modeling_encoder_decoder.py](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_encoder_decoder.py) @ line 140:\r\n\r\n```\r\nkwargs_decoder[\"is_decoder\"]=True\r\n```\r\n\r\n2. Adding in file [modeling_auto.py](https://github.com/huggingface/transformers/blob/61d22f9cc7fbb0556d1e14447957a7ac6441421b/src/transformers/modeling_auto.py#L570) @ line 570\r\n\r\n```\r\nif not isinstance(config, PretrainedConfig):\r\n config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)\r\n if kwargs.pop(\"is_decoder\", None):\r\n config.is_decoder=True\r\n```\r\n\r\nThat's is it! It should be good to go. The crossattention layers will be initialized; although it's weights will not be initialized. That will have to be trained.\r\n \r\n\r\n", "Hi @hammad001 - thanks a lot for finding this bug. It has helped me a lot. This PR: https://github.com/huggingface/transformers/pull/4680 should fix it :-) " ]

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[ "The slow bart summarization tests all pass with this? ", "yes!", "LGTM", "Correct, @LysandreJik not a breaking change.\r\n", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4290?src=pr&el=h1) Report\n> Merging [#4290](https://codecov.io/gh/huggingface/transformers/pull/4290?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7d7fe4997f83d6d858849a659302b9fdc32c3337&el=desc) will **increase** coverage by `0.06%`.\n> The diff coverage is `95.65%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4290?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4290 +/- ##\n==========================================\n+ Coverage 78.16% 78.22% +0.06% \n==========================================\n Files 120 120 \n Lines 20005 20013 +8 \n==========================================\n+ Hits 15636 15655 +19 \n+ Misses 4369 4358 -11 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4290?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/4290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `88.88% <75.00%> (+26.38%)` | :arrow_up: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/4290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `96.79% <100.00%> (+0.56%)` | :arrow_up: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.08% <100.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/4290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `87.14% <100.00%> (+6.19%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.77% <0.00%> (+0.16%)` | :arrow_up: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/4290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `90.00% <0.00%> (+5.00%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4290?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/4290?src=pr&el=footer). Last update [7d7fe49...54d704c](https://codecov.io/gh/huggingface/transformers/pull/4290?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Merging in advance of release tomorrow. Feel free to leave comments and I will fix them!" ]

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[ "Duplicated of [https://github.com/huggingface/transformers/issues/4166](url)" ]

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[ "Thanks for the detailed error description @binshengliu! I linked a PR that should fix it :-) ", "Hi, there's still some chance we get `nan` values during training. It does not always happen but is still pretty easy to reproduce. My current version is `2.11.0`.\r\n\r\n```\r\nimport torch\r\nfrom transformers import T5Model, T5Tokenizer\r\n\r\ntorch.manual_seed(0)\r\ntokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\r\nmodel = T5Model.from_pretrained(\"t5-base\").cuda().half()\r\ntext = \"hello world!\"\r\nmodel.train()\r\ninputs = tokenizer.encode(text, return_tensors=\"pt\").cuda()\r\nfor idx in range(1000):\r\n out = model(input_ids=inputs, decoder_input_ids=inputs)\r\n if torch.isnan(out[0]).any():\r\n print(idx)\r\n print(out[0][:, :, :10])\r\n exit()\r\n```\r\n\r\nOutput:\r\n```\r\n143\r\ntensor([[[nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.],\r\n [nan, nan, nan, 0., nan, nan, nan, nan, nan, 0.],\r\n [nan, nan, 0., nan, nan, 0., 0., 0., nan, nan]]], device='cuda:0',\r\n dtype=torch.float16, grad_fn=<SliceBackward>)\r\n```", "Sorry I just noticed you commented https://github.com/huggingface/transformers/issues/4586#issuecomment-639704855. I can confirm the issue I encountered is caused by the same reason.", "Hi, I have the same problem. I get NAN when using fp16. And when I set fp16=False, the NAN problem is gone. ", "Yeah that's still an open problem....not sure how easy it will be to solve it, see: https://github.com/huggingface/transformers/issues/4586#issuecomment-639704855", "Same when fine-tuning GPT Neo." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I have the exact same issue. When I share GPT2 model trained with AlbertTokenizer on huggingface.co the default code snippet doesn't work. The reason is that AutoTokenizer detects tokenizer type by model type. Is there any workaround?", "Hi @obsh and @khashei – yes you need to add a `tokenizer_class` attribute to your model's config.json:\r\n\r\n> tokenizer_class (str, optional) – The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the model by default).\r\n\r\nhttps://huggingface.co/transformers/main_classes/configuration.html\r\n\r\ncc @patrickvonplaten " ]

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[ "Good to be merged for me :) We should update the `setup.py` in same time to do not limit anymore to TF 2.1. Should it be done in this PR? /cc @julien-c @LysandreJik ", "I think you can cc @LysandreJik @julien-c ", "Yes can you do it in the same PR? and it'll be ready to merge.", "Nice! Unfortunately there are other issues with TF2.2, hence why the tests fail and why the version was pinned. I'm patching it now.", "Yep I think @mfuntowicz is working on it as it is related to the Tokenizer. At least we have identified why it is not working :)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4282?src=pr&el=h1) Report\n> Merging [#4282](https://codecov.io/gh/huggingface/transformers/pull/4282?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b290c32e1617fa74f44ccd8b83365fe764437be9&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4282?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4282 +/- ##\n==========================================\n+ Coverage 78.39% 78.40% +0.01% \n==========================================\n Files 120 120 \n Lines 19932 19932 \n==========================================\n+ Hits 15626 15628 +2 \n+ Misses 4306 4304 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4282?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4282/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `93.10% <0.00%> (+0.16%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4282/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `74.38% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4282?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/4282?src=pr&el=footer). Last update [b290c32...5babcfc](https://codecov.io/gh/huggingface/transformers/pull/4282?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/4281?src=pr&el=h1) Report\n> Merging [#4281](https://codecov.io/gh/huggingface/transformers/pull/4281?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b290c32e1617fa74f44ccd8b83365fe764437be9&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4281?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4281 +/- ##\n==========================================\n- Coverage 78.39% 78.39% -0.01% \n==========================================\n Files 120 120 \n Lines 19932 19932 \n==========================================\n- Hits 15626 15625 -1 \n- Misses 4306 4307 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4281?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4281/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.77% <0.00%> (-0.17%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4281?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/4281?src=pr&el=footer). Last update [b290c32...2222fd5](https://codecov.io/gh/huggingface/transformers/pull/4281?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Awesome, thanks for sharing! Did you fine-tune https://huggingface.co/dbmdz/bert-base-turkish-cased?\r\n\r\n cc @stefan-it ", "[Model page](https://huggingface.co/lserinol/bert-turkish-question-answering)", "> Awesome, thanks for sharing! Did you fine-tune https://huggingface.co/dbmdz/bert-base-turkish-cased?\r\n> \r\n> cc @stefan-it\r\n\r\nYes" ]

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[ "👍 \r\n" ]

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[ "Hi, you can find the model identifiers in this JSON: https://huggingface.co/api/models\r\n\r\nThen you can get all files URLs by removing `bert/` from the objects' keys and prefixing with URL endpoint \"https://cdn.huggingface.co/\"", "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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[ "@thomwolf @sshleifer ", "Yeah, here is working code for the copy approach, starting with bart-large-cnn weights.\r\n\r\n```python\r\nfrom copy import deepcopy\r\nfrom transformers import BartForConditionalGeneration\r\n\r\n# Get original model\r\nmodel = BartForConditionalGeneration.from_pretrained('bart-large-cnn')\r\n\r\nsd = model.state_dict()\r\n\r\nshorter_pos_embeds = sd['model.encoder.embed_positions.weight']\r\nnew_config = model.config\r\nnew_config.max_position_embeddings = 2048\r\nnew_model = BartForConditionalGeneration(new_config)\r\n# If you want to learn everything from scratch, you can stop here.\r\ncorrectly_shaped_pos_weight = new_model.model.encoder.embed_positions.weight\r\n\r\ncorrectly_shaped_pos_weight[:shorter_pos_embeds.shape[0]] = shorter_pos_embeds\r\n\r\nsd['model.decoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\nsd['model.encoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\n\r\nnew_model.load_state_dict(sd, strict=True)\r\n```", "@sshleifer \r\nCan we use this same approach for T5 as well ?", "Does it make sense to initialize the weights by copying the first 512 weights multiple times ? \r\n\r\nLike : `[[0:512], [0:512], [0:512], [0:512]]`\r\n\r\nSo the model just have to learn the \"relativeness\" between positions ? ", "I am editing finetune.py \r\n```\r\n model = SummarizationTrainer(args)\r\n sd = model.model.state_dict()\r\n shorter_pos_embeds = sd['model.encoder.embed_positions.weight']\r\n new_config = model.config\r\n new_config.max_position_embeddings = 4096\r\n new_model = BartForConditionalGeneration(new_config)\r\n correctly_shaped_pos_weight = new_model.model.encoder.embed_positions.weight\r\n correctly_shaped_pos_weight[:shorter_pos_embeds.shape[0]] = shorter_pos_embeds\r\n sd['model.decoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\n sd['model.encoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\n new_model.load_state_dict(sd, strict=True)\r\n model.model = new_model.cuda()\r\n trainer = generic_train(model, args)\r\n```\r\n\r\n\r\n```\r\nINFO:transformers.tokenization_utils:loading file https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json from cache at /u/tuhin1/.cache/torch/transformers/1ae1f5b6e2b22b25ccc04c000bb79ca847aa226d0761536b011cf7e5868f0655.ef00af9e673c7160b4d41cfda1f48c5f4cba57d5142754525572a846a1ab1b9b\r\nINFO:transformers.tokenization_utils:loading file https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt from cache at /u/tuhin1/.cache/torch/transformers/f8f83199a6270d582d6245dc100e99c4155de81c9745c6248077018fe01abcfb.70bec105b4158ed9a1747fea67a43f5dee97855c64d62b6ec3742f4cfdb5feda\r\nINFO:transformers.modeling_utils:loading weights file https://cdn.huggingface.co/facebook/bart-large/pytorch_model.bin from cache at /u/tuhin1/.cache/torch/transformers/2e7cae41bb1dd1f18e498ff4ff0ea85f7e9bc2b637439e2d95c485c5d5bdd579.5be2a88ec29f5969270f98902db392beab8be8a6a7ecc588d410ada3e32c4263\r\nINFO:transformers.modeling_utils:Weights of BartForConditionalGeneration not initialized from pretrained model: ['final_logits_bias']\r\nINFO:transformers.modeling_utils:Weights from pretrained model not used in BartForConditionalGeneration: ['encoder.version', 'decoder.version']\r\nINFO:lightning:GPU available: True, used: True\r\nINFO:lightning:CUDA_VISIBLE_DEVICES: [0]\r\nINFO:lightning:Using 16bit precision.\r\n/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:23: RuntimeWarning: You have defined a `val_dataloader()` and have defined a `validation_step()`, you may also want to define `validation_epoch_end()` for accumulating stats.\r\n warnings.warn(*args, **kwargs)\r\nTraceback (most recent call last):\r\n File \"/dccstor/tuhinstor/transformers/examples/finetune1.py\", line 196, in <module>\r\n main(args)\r\n File \"/dccstor/tuhinstor/transformers/examples/finetune1.py\", line 177, in main\r\n trainer = generic_train(model, args)\r\n File \"/dccstor/tuhinstor/transformers/examples/lightning_base.py\", line 278, in generic_train\r\n trainer.fit(model)\r\n File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py\", line 765, in fit\r\n self.single_gpu_train(model)\r\n File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py\", line 484, in single_gpu_train\r\n self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)\r\n File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/trainer/optimizers.py\", line 18, in init_optimizers\r\n optim_conf = model.configure_optimizers()\r\n File \"/dccstor/tuhinstor/transformers/examples/lightning_base.py\", line 87, in configure_optimizers\r\n optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\r\n File \"/dccstor/tuhinstor/transformers/src/transformers/optimization.py\", line 117, in __init__\r\n super().__init__(params, defaults)\r\n File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/torch/optim/optimizer.py\", line 51, in __init__\r\n self.add_param_group(param_group)\r\n File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/torch/optim/optimizer.py\", line 202, in add_param_group\r\n raise ValueError(\"can't optimize a non-leaf Tensor\")\r\nValueError: can't optimize a non-leaf Tensor\r\n```\r\n\r\nAny idea to resolve this @sshleifer", "Update I fixed it\r\n ```\r\n model = SummarizationTrainer(args)\r\n sd = model.model.state_dict()\r\n shorter_pos_embeds = sd['model.encoder.embed_positions.weight']\r\n new_config = model.config\r\n new_config.max_position_embeddings = 3000\r\n new_model = BartForConditionalGeneration(new_config)\r\n correctly_shaped_pos_weight = new_model.model.encoder.embed_positions.weight.cuda()\r\n correctly_shaped_pos_weight[:shorter_pos_embeds.shape[0]] = shorter_pos_embeds.cuda()\r\n sd['model.decoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\n sd['model.encoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\n new_model.load_state_dict(sd, strict=True)\r\n model.model = new_model.cuda()\r\n trainer = generic_train(model, args)\r\n```\r\n\r\nHowever i get OOM , I have a 32 GB NVIDIA V100 , i feel it caches a bit too much any work around ?\r\nRuntimeError: CUDA out of memory. Tried to allocate 550.00 MiB (GPU 0; 31.72 GiB total capacity; 26.46 GiB already allocated; 81.88 MiB free; **4.04 GiB cached**)", "My batch size is 1", "@tuhinjubcse could you solve the OOM error? is it possible for you to share your working script for finetuning with longer sequence? appreciate it.", "> I am editing finetune.py\r\n> \r\n> ```\r\n> model = SummarizationTrainer(args)\r\n> sd = model.model.state_dict()\r\n> shorter_pos_embeds = sd['model.encoder.embed_positions.weight']\r\n> new_config = model.config\r\n> new_config.max_position_embeddings = 4096\r\n> new_model = BartForConditionalGeneration(new_config)\r\n> correctly_shaped_pos_weight = new_model.model.encoder.embed_positions.weight\r\n> correctly_shaped_pos_weight[:shorter_pos_embeds.shape[0]] = shorter_pos_embeds\r\n> sd['model.decoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\n> sd['model.encoder.embed_positions.weight'] = correctly_shaped_pos_weight\r\n> new_model.load_state_dict(sd, strict=True)\r\n> model.model = new_model.cuda()\r\n> trainer = generic_train(model, args)\r\n> ```\r\n> \r\n> ```\r\n> INFO:transformers.tokenization_utils:loading file https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json from cache at /u/tuhin1/.cache/torch/transformers/1ae1f5b6e2b22b25ccc04c000bb79ca847aa226d0761536b011cf7e5868f0655.ef00af9e673c7160b4d41cfda1f48c5f4cba57d5142754525572a846a1ab1b9b\r\n> INFO:transformers.tokenization_utils:loading file https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt from cache at /u/tuhin1/.cache/torch/transformers/f8f83199a6270d582d6245dc100e99c4155de81c9745c6248077018fe01abcfb.70bec105b4158ed9a1747fea67a43f5dee97855c64d62b6ec3742f4cfdb5feda\r\n> INFO:transformers.modeling_utils:loading weights file https://cdn.huggingface.co/facebook/bart-large/pytorch_model.bin from cache at /u/tuhin1/.cache/torch/transformers/2e7cae41bb1dd1f18e498ff4ff0ea85f7e9bc2b637439e2d95c485c5d5bdd579.5be2a88ec29f5969270f98902db392beab8be8a6a7ecc588d410ada3e32c4263\r\n> INFO:transformers.modeling_utils:Weights of BartForConditionalGeneration not initialized from pretrained model: ['final_logits_bias']\r\n> INFO:transformers.modeling_utils:Weights from pretrained model not used in BartForConditionalGeneration: ['encoder.version', 'decoder.version']\r\n> INFO:lightning:GPU available: True, used: True\r\n> INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\r\n> INFO:lightning:Using 16bit precision.\r\n> /dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:23: RuntimeWarning: You have defined a `val_dataloader()` and have defined a `validation_step()`, you may also want to define `validation_epoch_end()` for accumulating stats.\r\n> warnings.warn(*args, **kwargs)\r\n> Traceback (most recent call last):\r\n> File \"/dccstor/tuhinstor/transformers/examples/finetune1.py\", line 196, in <module>\r\n> main(args)\r\n> File \"/dccstor/tuhinstor/transformers/examples/finetune1.py\", line 177, in main\r\n> trainer = generic_train(model, args)\r\n> File \"/dccstor/tuhinstor/transformers/examples/lightning_base.py\", line 278, in generic_train\r\n> trainer.fit(model)\r\n> File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py\", line 765, in fit\r\n> self.single_gpu_train(model)\r\n> File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py\", line 484, in single_gpu_train\r\n> self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)\r\n> File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/pytorch_lightning/trainer/optimizers.py\", line 18, in init_optimizers\r\n> optim_conf = model.configure_optimizers()\r\n> File \"/dccstor/tuhinstor/transformers/examples/lightning_base.py\", line 87, in configure_optimizers\r\n> optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\r\n> File \"/dccstor/tuhinstor/transformers/src/transformers/optimization.py\", line 117, in __init__\r\n> super().__init__(params, defaults)\r\n> File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/torch/optim/optimizer.py\", line 51, in __init__\r\n> self.add_param_group(param_group)\r\n> File \"/dccstor/tuhinstor/condatuhin/envs/BARTQA/lib/python3.7/site-packages/torch/optim/optimizer.py\", line 202, in add_param_group\r\n> raise ValueError(\"can't optimize a non-leaf Tensor\")\r\n> ValueError: can't optimize a non-leaf Tensor\r\n> ```\r\n> \r\n> Any idea to resolve this @sshleifer\r\n\r\nWhich file are you editing?" ]

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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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[ "Yes, this is https://github.com/huggingface/transformers/issues/1784 come again, the issue is in the doc, not in the code", "Indeed, thanks for raising this issue." ]

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[ "LGTM" ]

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[ "Yes, I think you're right. Relaunching training from a checkpoint would however require the user to copy the weights to other machines, right?", "> Yes, I think you're right. Relaunching training from a checkpoint would however require the user to copy the weights to other machines, right?\r\n\r\nYes, loading from checkpoint can be done on all nodes.\r\n", "So I think we should save checkpoints and final models on each **local master**.\r\n\r\nDo you agree?", "Would there be a difference in checkpoints between each local master ?\r\nIf they're identical I'd think it be best to reduce write overhead and save only once (i.e world master)", "No difference, but the issue is that you won't be able to restart training from a checkpoint (unless you copy it around) if we only save to world master", "Ah, I see, thanks for the clarification. \r\nI'm assuming a shared file system so it's all saving and loading from the same file. \r\nI guess for other cluster configurations that makes sense, but care would have to be taken to avoid write/mkdir conflicts from multiple processes.", "I see. Thinking about it more, yes, I think it like it if we only save from world_master. (even if it adds one step for some users). Thanks for your insight." ]

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[ "Yes: you need to use the path to the saved model as the `--model_name_or_path` argument.\r\n\r\n`--model_name_or_path` is the argument that conditions which model is loaded.\r\n\r\nWill close this for now." ]

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[ "Hey! \r\n\r\nThanks a lot for your contribution on the trainer :+1:\r\n\r\nYour PR looks great! Just have a small comment, see above. Don't hesitate if you have questions!", "#4252 \r\n\r\n```\r\n05/11/2020 19:59:05 - INFO - oauth2client.transport - Attempting refresh to obtain initial access_token\r\nINFO:tensorflow:Found TPU system:\r\n05/11/2020 19:59:05 - INFO - tensorflow - Found TPU system:\r\nINFO:tensorflow:*** Num TPU Cores: 8\r\n05/11/2020 19:59:05 - INFO - tensorflow - *** Num TPU Cores: 8\r\nINFO:tensorflow:*** Num TPU Workers: 1\r\n05/11/2020 19:59:05 - INFO - tensorflow - *** Num TPU Workers: 1\r\nINFO:tensorflow:*** Num TPU Cores Per Worker: 8\r\n05/11/2020 19:59:05 - INFO - tensorflow - *** Num TPU Cores Per Worker: 8\r\n```\r\nSo its grabbing by name now by specifying --tpu_name", "How do I see what fails with the code quality in __init__.py? I do run make style hehe\r\n\r\nGreat work with the trainer @jplu I saw your other plans for it. Would be very neat.\r\n\r\nIll mess around with the TFdataset and tpus on another branch, i need to read up on some stuff. Is there anywhere i can see the plans for a framework agnostic approach for the datasets?", "Thanks :)\r\n\r\nFor the style I always do it manually in this following order:\r\n\r\n```\r\nisort --recursive examples templates tests src utils\r\nblack --line-length 119 --target-version py35 examples templates tests src utils\r\nflake8 examples templates tests src utils\r\n```\r\nThen git add/commit/push.\r\n\r\nWhat is currently working in your MPC example? Single GPU and CPU?", "> Thanks :)\r\n> \r\n> For the style I always do it manually in this following order:\r\n> \r\n> ```\r\n> isort --recursive examples templates tests src utils\r\n> black --line-length 119 --target-version py35 examples templates tests src utils\r\n> flake8 examples templates tests src utils\r\n> ```\r\n> \r\n> Then git add/commit/push.\r\n> \r\n> What is currently working in your MPC example? Single GPU and CPU?\r\n\r\nYep only tested on my trusty rtx2080 and in colab on gpu.", "Very Good!\r\n\r\nYou should also update the examples [README](https://github.com/huggingface/transformers/blob/master/examples/README.md) to add the check to the multiple_choice TFTrainer support :wink: ", "Can you also add a TF section in the multiple_choice [README](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) plz", "> Can you also add a TF section in the multiple_choice [README](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) plz\r\n\r\nI should sleep on this. Thanks alot for the help!", "Looks very good! Let me know once you think it can be merged.", "@ViktorAlm I think it's great if you link to your Colab! Maybe just save it to a repo so that one can take a look at the history (and open issues if it ever breaks:)", "None of the failing flake8 code quality seems to be related to me. If this works I think its done for now.", "Looks good to me!\r\n\r\n@julien-c and @LysandreJik should double check and merge if it seems good for them.", "Maybe add a link to the syn dataset you added. It's [that one](https://aclweb.org/aclwiki/TOEFL_Synonym_Questions_(State_of_the_art)) right?", "In the Colab ideally we would expose/expand a little bit more what happens in the example scripts (see what we did in the Colab for GLUE for instance), i.e. the calls to Trainer/TFTrainer.\r\n\r\nWe can improve/expand this in the future though @ViktorAlm @jplu ", "> Maybe add a link to the syn dataset you added. It's [that one](https://aclweb.org/aclwiki/TOEFL_Synonym_Questions_(State_of_the_art)) right?\r\n\r\nIts my own dataset which ill release once I'm done" ]

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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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[ "(fixed git history locally for future PRs)", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4266?src=pr&el=h1) Report\n> Merging [#4266](https://codecov.io/gh/huggingface/transformers/pull/4266?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3487be75ef8f0d51c51208f266644fc04a947085&el=desc) will **decrease** coverage by `0.84%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4266?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4266 +/- ##\n==========================================\n- Coverage 78.39% 77.55% -0.85% \n==========================================\n Files 120 120 \n Lines 19932 19932 \n==========================================\n- Hits 15626 15458 -168 \n- Misses 4306 4474 +168 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4266?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4266/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9weXRvcmNoX3V0aWxzLnB5) | `8.72% <0.00%> (-81.21%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/4266/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `85.71% <0.00%> (-10.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/4266/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jdHJsLnB5) | `95.19% <0.00%> (-2.63%)` | :arrow_down: |\n| [src/transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/4266/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `73.44% <0.00%> (-2.30%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/4266/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `80.41% <0.00%> (-1.38%)` | :arrow_down: |\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4266/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `40.51% <0.00%> (-0.58%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4266/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.93% <0.00%> (ø)` | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4266?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/4266?src=pr&el=footer). Last update [3487be7...4aab0f5](https://codecov.io/gh/huggingface/transformers/pull/4266?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I found two common tests that require both packages:\r\n```\r\ntest_batch_encode_plus_tensors\r\ntest_pt_tf_model_equivalence\r\n```\r\nSo I run test_pt_tf_model_equivalence for all test_modeling_tf* files, and\r\ntest_batch_encode_plus_tensors for all files. \r\nStill saves quite a bit of time.\r\n", "Kinda reluctant to do this change because then we need to manually maintain over time a whitelist of tests, which complicates the (already convoluted) CI setup. I'm also worried we might miss some side-effects of having both frameworks installed.\r\n\r\nWhat time do those tests take?", "6 mins / 13 mins, at least once per PR.\r\nI think it's a good tradeoff.\r\n- we are losing 0 coverage.\r\n- people rarely add new tests that check equivalence of torch and tf. We would rarely need to update the whitelist.\r\n- For most bugs, we would all be able to reproduce it locally where we have both frameworks installed.\r\n\r\nDon't feel too strongly, of course.\r\n- Also happy to move the old job to less often then every push.", "What does 6 mins / 13 mins mean?", "Before this change `run_test_torch_and_tf` takes 6 mins, and the whole CircleCI suite takes 13mins.", "How do you measure the whole CircleCI suite, given that the tests are (AFAIK) parallelized?", "By looking at circleci output like [this](https://app.circleci.com/pipelines/github/huggingface/transformers/5174/workflows/3aecb0e9-14f0-4762-ad6a-73b60db2d626) and guessing\r\n", "[This](https://app.circleci.com/pipelines/github/huggingface/transformers/6581/workflows/b114b306-2553-42cf-bb67-ba507e051929) is after pipelines improvements, so I may be overestimating the benefit of the change. Although it seems ridiculous that `check_code_quality` took 3 mins.", "@julien-c seems like we don't want to do this? Happy to abandon." ]

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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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[ "LGTM, or even better we could duplicate those tokenizer files into `distilbert-base-uncased-finetuned-sst-2-english` as now we kind of assume that a model == model + tokenizer.", "Merging this for now and can update to @julien-c's proposal in a future PR", "Yeah, just wanted to ping @mfuntowicz on this model/tokenizer subject ", "Hi guys, I pulled from master after the merge of the PR but the behaviour is still the same. It doesn't look like aligning the tokenizer resolved this issue." ]

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[ "This PR should fix the problem:\r\nhttps://github.com/huggingface/transformers/pull/4264\r\n\r\nA mismatched tokenizer is hooked up with the pipeline.", "yes, you are right. \r\n\r\nI also tested all sentences both positive and negative. But it is showing NEGATIVE all the time.", "> This PR should fix the problem:\r\n> #4264\r\n> \r\n> A mismatched tokenizer is hooked up with the pipeline.\r\n\r\nThis PR didn't resolve the issue. I pulled from master but behaviour still the same. Have you tested?", "Yes, I'm always getting NEGATIVE class regardless of the text", "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", "Same issue...incorrect sentiment for \"nlp(\"i hate you\")\" shows [{'label': 'POSITIVE', 'score': 0.536677360534668}]", "in 3.1, I always get label=\"POSITIVE\". Must be a bug somewhere", "I get Positive label with high score(0.99) for neutral statements like \"This is just a statement\" or \"My name is this\".\r\nI don't know whats wrong. I used the base example.\r\nfrom transformers import pipeline\r\nsentiment_model = pipeline('sentiment-analysis')\r\nsentiment_model('This is just a statement.')" ]

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[ "Hi @guillaume-be , only the CoNLL-2003 models are using this IOB1 tagging scheme, because it is used in the original dataset. You could convert it (+ needs proper testing) but this requires a modification of the original dataset.\r\n\r\nI could train models e.g. on the WikiANN corpus (see [here](https://www.aclweb.org/anthology/P17-1178/) and WikiANN with balanced train/dev/test splits [here](https://www.aclweb.org/anthology/P19-1015/)) for English, but this corpus is then just a \"silver standard\" :)", "Hi @stefan-it ,\r\n\r\nThank you for your response. Assuming the CoNLL-03 model is in IOB1 tagging scheme, it would still use `B-*` labels in case of multi-token entities. For example:\r\n```\r\nAlex I-PER\r\nis O\r\ngoing O\r\nto O\r\nLos B-LOC\r\nAngeles I-LOC\r\n```\r\nHowever, the model currently outputs the following for this example:\r\n```\r\nAlex I-PER\r\nis O\r\ngoing O\r\nto O\r\nLos I-LOC\r\nAngeles I-LOC\r\n```\r\n\r\nThis means that it is impossible to differentiate multi-token entities (such as Los Angeles) from multiple consecutive entities, e.g. `Alex Bob and Amy are going to Los Angeles`. So far I have never seen the model uploaded generate a `B-*` token. ", "`B-*` is only used when an entity follows right after another entity, e.g. different political parties, like this example from the training dataset:\r\n\r\n```bash\r\nMAY NNP I-NP O\r\n1996 CD I-NP O\r\nCDU NNP I-NP I-ORG\r\n/ SYM O I-ORG\r\nCSU NNP I-NP I-ORG\r\nSPD NNP I-NP B-ORG\r\nFDP NNP I-NP B-ORG\r\nGreens NNP I-NP B-ORG\r\nPDS NNP I-NP B-ORG\r\n```\r\n\r\nJust have a look at the last column. `CDU/CSU` can be considered as one entity, but right after it another political party `SPD` is followed, so it is tagged with a beginning `B-` :)", "@stefan-it thank you for the clarification!", "@stefan-it are there plans to [merge this pr](https://github.com/huggingface/transformers/pull/3957)? it could help with this issue. i have it myself as i need to pool entity embeddings. https://github.com/huggingface/transformers/issues/3548", "@petulla Should be merged soon 👍 " ]

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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", "use torch.unsqueeze(0) on b_input_ids,b_input_masks so that they become of shape (batch_size(=1),dim)", "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 @nimahm2020, \r\n\r\nThanks for reporting. \r\nIt seems the tokenizer used for the default Sentiment Analysis pipeline was not aligned with the actual model. More precisely, the model is using an **uncased** vocab while the tokenizer is **cased**.\r\n\r\nI've opened a PR(#4264) to solve the issue, if you can/want to give it a try and let us know if it solves the issue :).", "> Hi @nimahm2020,\r\n> \r\n> Thanks for reporting.\r\n> It seems the tokenizer used for the default Sentiment Analysis pipeline was not aligned with the actual model. More precisely, the model is using an **uncased** vocab while the tokenizer is **cased**.\r\n> \r\n> I've opened a PR(#4264) to solve the issue, if you can/want to give it a try and let us know if it solves the issue :).\r\n\r\nHi @mfuntowicz \r\nIn term of accuracy , accidentally, I found that Transformer 2.8 provide the same accuracy performance as it was before . It seems that nlp=pipline (sentiment- analysis ) in Transformer 2.9 is a bit under-estimated . \r\nAs a brief ; Transformer connects to Flask with following modifications that i have no clue why it should be : \r\n1- run Flask app with debug=False \r\n2- install Transfomer 2.8 ( NOT last version is 2.9) \r\n\r\nmore deep explanation and comments are always appreciated . \r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "It seems this example was not updated after #3734. This is not the correct way to do it, as it now requires the user to specify the entire context all the time. Will open a PR 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" ]

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[ "I was incorrectly treating the output of the first layer as the actual embedding lookup when it is in fact the embedding lookup *plus* positional and token type embeddings. See [here](https://stackoverflow.com/a/61704348/424306) for more details." ]

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[ "This is great, thanks @liuzzi!\r\n\r\nWe would need to add the corresponding tests. Can you do it, or would you like me to do it? If so, may I push directly on your fork?", "@LysandreJik sure! i just invited you to collaborate. I haven't been inside the transformers testing code so it'd probably be much faster for you to do. Let me know if you have any questions/if i can help.", "I just pushed the tests on it, thanks for your contribution!", "Pushed a style change, rebased and merged into master. Thanks @liuzzi !" ]

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[ "Hello @manzar96 \r\nThe `.generate` method can be used with any HF model with a LM head on top.", "Ok thank you.\r\nSo it can be used only with those models https://pytorch.org/hub/huggingface_pytorch-transformers/ (having a LM head on top) ", "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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[ "Those models should contain config that initializes their tokenizer with the correct `do_lower_case=True` option. Is this not the case?\r\n\r\ni.e. we are moving the tokenizer configuration out of those example scripts (see updated scripts for `Trainer` at https://github.com/huggingface/transformers/tree/master/examples).", "I haven't gone through the recent update sorry. \r\nIt looks like the [examples page](https://github.com/huggingface/transformers/tree/master/examples/) has completely changed now. \r\nBut in the folder of [question-answering](https://github.com/huggingface/transformers/tree/master/examples/question-answering), the configuration to replicate results still remains the same. \r\n\r\nShall I modify this there and create a request ? \r\nBecause the `do_lower_case` argument is a `store_true` argument and not a `boolean` with default as `true`. \r\n\r\nI spent 3 runs to realise the error because it worked correctly with cased models. \r\n\r\nReference https://github.com/huggingface/transformers/blob/4658896ee105c504eba23db2efefc5fe14191b81/examples/question-answering/run_squad.py#L579\r\n\r\n\r\n", "Closing this b/c #4245 was merged\r\n\r\n(we still need to investigate why the lowercasing is not properly populated by the model's config)\r\n\r\n" ]

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[ "I don't think you can load .tars directly. Simply use the name as described in [the model page](https://huggingface.co/allenai/scibert_scivocab_uncased), and it should work.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(\"allenai/scibert_scivocab_uncased\")\r\n```", "Thank You @BramVanroy .\r\nIts working." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4253?src=pr&el=h1) Report\n> Merging [#4253](https://codecov.io/gh/huggingface/transformers/pull/4253?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/c558761ad11beba5e70dd505e1c97bdc4eb6ee9c&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4253?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4253 +/- ##\n=======================================\n Coverage 78.16% 78.16% \n=======================================\n Files 120 120 \n Lines 20005 20005 \n=======================================\n Hits 15637 15637 \n Misses 4368 4368 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4253?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/4253?src=pr&el=footer). Last update [c558761...c558761](https://codecov.io/gh/huggingface/transformers/pull/4253?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "For the notebook:\r\n```\r\n \"environ[\\\"OMP_NUM_THREADS\\\"] = str(cpu_count(logical=True))\\n\",\r\n \"environ[\\\"OMP_WAIT_POLICY\\\"] = 'ACTIVE'\r\n```\r\n It is better to place the environ setting before importing onnxruntime. I used to find that placing these settings after importing onnxruntime does not work.\r\n\r\n\r\n```\r\n options.execution_mode = ExecutionMode.ORT_SEQUENTIAL \r\n```\r\nORT_SEQUENTIAL is the default value so the line is optional.", "@tianleiwu I agree with your regarding optional argument, especially GPT2 and the past context which might provide a significant boost in performance.\r\n\r\nFor the scope of this release, I decided to not provide support for this, as it would have required much more thinking / changes in the library. \r\n\r\nWe might have a look at possible solutions for this in short/mid term 👍 " ]

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[ "TPUs can be autodiscovered if you use them through AI-platform by running the trainer with `gcloud ai-platform jobs submit training ...` This is the advised usage of TPUs I put in the trainer.\r\n\r\nNevertheless if your want to use it through its name, this is done in the following PR #4270 thanks to @ViktorAlm", "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 @gyuwankim the problem with English CoNLL dataset is, that it is not freely available (because it requires access to Reuters corpora in order to create it from scratch with the provided scripts).\r\n\r\nYou can find the dataset somehow on GitHub, but legally this is very dubious (and you have no guarantee that it is the original dataset).\r\n\r\nThat's the reason why I did use the GermEval 2014 dataset :)", "But what you think about adding a detailed description of training a NER model on the WNUT17 dataset?\r\n\r\nData can be accessed here: https://github.com/juand-r/entity-recognition-datasets/tree/master/data/WNUT17\r\n\r\nI could add a short example with some resuls (for other Transformer-based models as well). So we have both German and an English example of how to use the NER script :)\r\n\r\n/cc @julien-c ", "Additionally, the BERT evaluation strategy cannot be simply reproduced, see this [thread](https://github.com/allenai/allennlp/pull/2067#issuecomment-443961816) on the problem.\r\n\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "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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