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DanielPFlorian

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Transformers-Github-Issues

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[ "I also have this issue when i run run_multiple_choice.py in RACE data with RoBERTA.", "I get the same error when I try to fine-tune Squad", "Tagging @LysandreJik ", "> \r\n> \r\n> I also have this issue when i run run_multiple_choice.py in RACE data with RoBERTA.\r\n\r\nSame here. Any solution?", "@nielingyun @orena1 @Onur90 maybe try pulling again from the latest version of the repo and see if it works? The error went away after I pulled recently, not sure if that fixed it or something else I did - let me know if that worked", "@ethanjperez by latest version you mean **latest commit** or the **latest release** (v2.6.0)? It is still not working with the **latest commit**." ]

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[ "Accidental double post -- closing this in favour of #3311 " ]

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[ "Hi @moinnadeem, \r\n\r\nThanks for posting this! \r\nAs it is implemented in the moment, you are right, GPT2 Tokenizer does not add the BOS in the beginning nor the EOS token in the end. \r\nYou can see e.g. that the XLNet tokenizer has a method that adds special tokens to the encoded input string (see https://github.com/huggingface/transformers/blob/4e4403c9b44324671cb795df2ef30e70fe3b606e/src/transformers/tokenization_xlnet.py#L241), whereas the GPT2 tokenizer does not have such a function and thus uses the default one which does not add any special tokens. \r\n\r\nAs far as I can see this could be a feature request, where a `build_inputs_with_special_tokens()` would be added to `tokenization_gpt2.py`. \r\n\r\nThe expected behavior could be:\r\ninput_string -> BOS + encoded(input_string) + EOS in the case of GPT2. \r\n\r\nFeel free to open a PR to include this feature :-) In the meantime you can obviously just manually add the BOS and EOS token before encoding. \r\n\r\n@mfuntowicz do you think such a PR would make sense? ", "I don't think this has been fixed, right?", "It's not really a bug because the default behavior of GPT2 is to just not add bos or eos tokens. GPT2 is mainly used to generate text so it would not make a lot of sense to add a EOS of a input prompt. If one wants he could just manually add `gpt2_tokenizer.eos_token` to the input and the eos_token_id will be added", "> It's not really a bug because the default behavior of GPT2 is to just not add bos or eos tokens. GPT2 is mainly used to generate text so it would not make a lot of sense to add a EOS of a input prompt. If one wants he could just manually add `gpt2_tokenizer.eos_token` to the input and the eos_token_id will be added\r\n\r\nI think in the original GPT2 model, there *are* special tokens for bos and eos, both of which are `<|endoftext|>`, right? So if I want to finetune it, we should do the same thing -- add both bos and eos to the corpus for finetune, right?", "@zhujl1991 - yes this is correct. \r\nWe also set bos and eos token to `<|endoftet|>` for GPT2 as you can verify as follows:\r\n\r\n```python\r\nfrom transformers import GPT2Tokenizer\r\ntok = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\nprint(tok.eos_token)\r\nprint(tok.bos_token)\r\n```\r\n\r\nHowever, I don't think we plan on adding these tokens automatically when tokenizing an input string because the main use case for GPT2 is open-domain text generation where these tokens should not be added.\r\nI agree that they could /should be added for fine-tuning. \r\n\r\nSo I'm not sure if we want to add any special \"fine-tune\" behavior to the GPT2Tokenizer. @LysandreJik - what do you think?", "\r\n\r\n> @zhujl1991 - yes this is correct.\r\n> We also set bos and eos token to `<|endoftet|>` for GPT2 as you can verify as follows:\r\n> \r\n> ```python\r\n> from transformers import GPT2Tokenizer\r\n> tok = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n> print(tok.eos_token)\r\n> print(tok.bos_token)\r\n> ```\r\n> \r\n> However, I don't think we plan on adding these tokens automatically when tokenizing an input string because the main use case for GPT2 is open-domain text generation where these tokens should not be added.\r\n> I agree that they could /should be added for fine-tuning.\r\n> \r\n> So I'm not sure if we want to add any special \"fine-tune\" behavior to the GPT2Tokenizer. @LysandreJik - what do you think?\r\n\r\nThe behavior of \"set add_special_tokens to True but no special tokens are added while there are special tokens in the tokenizer\" looks like a bug to me anyway. If the user doesn't want to add special tokens when tokenizing, e.g., as you said, when generating text, the user should set add_special_tokens to False.", "I see what you mean @zhujl1991 -> Thinking about backwards compatibility and that by default `add_special_tokens` is set to `True`, I still do not think that we should add this feature to the `__call__` or `encode_plus` functions for GPT2. On the other hand such a functionality would be very useful for training/fine-tuning.\r\n\r\nI see three options:\r\n\r\n1) overwrite the __call__ method in GPT2 to have add_special_tokens=`False` by default and append BOS and EOS if set to `True` => I don't like this option as it's quite hacky and would still not be 100% backward compatible\r\n\r\n2) Add a new method `prepare_for_training` where the input is prepared for fine-tuning / training as you said.\r\n\r\n3) Don't do anything about it and let the user overwrite such a method himself. \r\n\r\nI would be fine with option 2), but also don't think it's that important of a feature (option 3))....let's see what @LysandreJik @sgugger, @thomwolf and @sshleifer think", "IMO this is something that should be written by the user for their specific needs (option 3). We can document more that the tokenizers are pre-set for the most common tasks the corresponding models are used for, to avoid any user being too surprised.\r\n\r\nI feel that if we add a method, it will cover some use cases but not all and it will either be overly too complex or only used by a small percentage of the users.", "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", "Ran into this too – this seems like a bug to me, or at the least not intuitive behaviour.\r\n\r\nIf there's a tokeniser that has an EOS token, and I encode with `add_special_tokens=True`, I'd expect it to include the eos token at the end of sentence. ", "+1 on this.\r\n\r\nThe main issue here is really how opaque and unintuitive this has been for me. Here's my thought process:\r\n\r\n```\r\nfrom transformers import GPT2TokenizerFast\r\ngpt2_tok = GPT2TokenizerFast.from_pretrained(\"gpt2\")\r\ngpt2_tok(\"Mary had a little lamb\")[\"input_ids\"]\r\n# prints [24119, 550, 257, 1310, 19343]\r\n```\r\nMh, weird, no special tokens? I've used HF before and I thought the default was to add them?\r\n\r\nThen I went and looked up the function, and indeed the default is to have them on. Bah, whatevs. Let's explicitly pass it as on:\r\n\r\n```\r\ngpt2_tok(\"Mary had a little lamb\", add_special_tokens=True)[\"input_ids\"]\r\n# No difference :D\r\n```\r\nThis part is what got me massively confused. I think it's entirely fine to change the default behavior for GPT-2 if the majority of the users don't care/want those tokens, but it would be more intuitive to change the default to add_special_tokens=False, and actually add the special tokens when the option is passed explicitly! :)\r\n", "I had some thoughts over this question too.\r\nIn the end, I realized that the model has been trained using \"full paragraphs/articles of text\", which means that spaces and new line symbols were part of the training. The <|endoftext|> token was added between paragraphs/articles.\r\nSo the <|endoftext|> should only be added at the beginning and end of text paragraphs/articles for fine-tuning, but it seems to be a detail since in fact, it is just a kind of text formatting.\r\nFor text generation, usually you think of the \"end of text\" as a punctuation mark or newline character not as the <|endoftext|> token which denotes the end of a paragraph/article.\r\nSo I think that the code is perfectly right.\r\n", "@patrickvonplaten \r\n\r\nHi, I also believe that BOS should be prepended before an input sentence (w1, w2, ...) for two reasons:\r\n\r\n1. Without BOS, the model cannot calculate the probability of generating the first token, i.e. P(w1|BOS).\r\n2. BOS also affects the probability of generating the following words, e.g. P(w2|w1) != P(w2|w1, BOS).\r\n\r\nFor the second point, see the following example:\r\n\r\n```\r\nimport torch\r\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel\r\n\r\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\r\nmodel = GPT2LMHeadModel.from_pretrained('gpt2')\r\ninputs = tokenizer(\"<|endoftext|>This\", return_tensors=\"pt\")\r\n# inputs: {'input_ids': tensor([[50256, 1212]]), 'attention_mask': tensor([[1, 1]])}\r\noutputs = model(**inputs, labels=inputs[\"input_ids\"])\r\ntokenizer.convert_ids_to_tokens(outputs.logits[0][1].topk(20)[1])\r\n# ['Ġis', 'Ġarticle', 'Ġpost', 'Ġweek', 'Ġpage', 'Ġstory', 'Ġyear', 'Ġwas', 'Ġmonth', 'Ġsite', 'Ġbook', 'Ġpast', 'Ġitem', 'Ġproject', 'Ġblog', 'Ġstudy', 'Ġsection', 'Ġmorning', 'Ġvideo', 'Ġgame']\r\n\r\ninputs = tokenizer(\"This\", return_tensors=\"pt\")\r\n# {'input_ids': tensor([[1212]]), 'attention_mask': tensor([[1]])}\r\noutputs = model(**inputs, labels=inputs[\"input_ids\"])\r\ntokenizer.convert_ids_to_tokens(outputs.logits[0][0].topk(20)[1])\r\n# ['Ġis', ',', '.', 'Ċ', \"'s\", 'Ġwas', 'Ġto', 'Ġand', 'Ġthe', 'Ġin', 'Ġhas', 'Ġof', 'Ġwill', 'Ġa', ':', 'Ġare', 'Ġcan', 'Ġ(', '-', 'Ġfor']\r\n```\r\nComparing these two generations, the prediction with \"<|endoftext|>\" seems more accurate (e.g. Without BOS, some punctuations are predicted as the next word of \"This\").\r\n\r\nDue to the lack of documentation, I am not entirely sure if the \"<|endoftext|>\" token is actually used as a BOS token during training, but the following example suggests it may be the case.\r\n\r\n```\r\ninputs = tokenizer(\"<|endoftext|>\", return_tensors=\"pt\")\r\noutputs = model(**inputs, labels=inputs[\"input_ids\"])\r\ntokenizer.convert_ids_to_tokens(outputs.logits[0][0].topk(20)[1])\r\n\r\n# ['Ċ', 'The', '\"', 'A', 'I', 'In', '.', 'It', 'S', 'This', 'B', '-', 'C', 'We', '1', 'T', \"'\", 'P', '(', 'G']\r\n```\r\n\r\nEven if you opt not to prepend BOS, I believe these things should be clarified more in the documentation.", "To add confirmation that `<|endoftext|>` is also a BOS token, the official repo uses it for inference as well: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/generate_unconditional_samples.py#L60", "Would be nice to add some documentation on this in the GPT2Tokenizer [docs](https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.GPT2Tokenizer).\r\n\r\n(Self-note for future PR) ", "@patrickvonplaten \r\n> However, I don't think we plan on adding these tokens automatically when tokenizing an input string because the main use case for GPT2 is open-domain text generation where these tokens should not be added.\r\n\r\nHi.\r\nI'd like to ask you a question.\r\nCould you explain how the model want to stop generation if there's no EOS token?", "> @patrickvonplaten\r\n> \r\n> > However, I don't think we plan on adding these tokens automatically when tokenizing an input string because the main use case for GPT2 is open-domain text generation where these tokens should not be added.\r\n> \r\n> Hi. I'd like to ask you a question. Could you explain how the model want to stop generation if there's no EOS token?\r\n\r\nI'm trying to train the model with EOS tokens at the end. Let's see if that works...\r\n\r\nShouldn't the EOS tokens be set by default when we use `DataCollatorForLanguageModeling(..., mlm=False)`? It makes sense to me that they should. If not, **at least** [this documentation](https://huggingface.co/docs/transformers/tasks/language_modeling) should be changed and the EOS token should be added at the end of each raw text.\r\n\r\n", "> > @patrickvonplaten\r\n> > > However, I don't think we plan on adding these tokens automatically when tokenizing an input string because the main use case for GPT2 is open-domain text generation where these tokens should not be added.\r\n> > \r\n> > \r\n> > Hi. I'd like to ask you a question. Could you explain how the model want to stop generation if there's no EOS token?\r\n> \r\n> I'm trying to train the model with EOS tokens at the end. Let's see if that works...\r\n> \r\n> Shouldn't the EOS tokens be set by default when we use `DataCollatorForLanguageModeling(..., mlm=False)`? It makes sense to me that they should. If not, **at least** [this documentation](https://huggingface.co/docs/transformers/tasks/language_modeling) should be changed and the EOS token should be added at the end of each raw text.\r\n\r\nI agree with you. I really don't understand how CLM train without EOS token.", "> I'm trying to train the model with EOS tokens at the end. Let's see if that works...\r\n\r\nthis worked for me, but I really had to make sure that the EOS token was always at the end of each sequence", "> this worked for me, but I really had to make sure that the EOS token was always at the end of each sequence\r\n\r\nDo you mean the inference is working? How the model decide to stop generate if there's no EOS token or there're multiple EOS tokens when they concat sequences as mentioned in [this documentation](https://huggingface.co/learn/nlp-course/chapter7/6#preparing-the-dataset)?\r\n", "> > this worked for me, but I really had to make sure that the EOS token was always at the end of each sequence\r\n> \r\n> Do you mean the inference is working? How the model decide to stop generate if there's no EOS token or there're multiple EOS tokens when they concat sequences as mentioned in [this documentation](https://huggingface.co/learn/nlp-course/chapter7/6#preparing-the-dataset)?\r\n\r\nTokenizers used in causal models don't append the EOS token by default, while the ones in encoder-decoder (like T5) do.\r\n```\r\nt5_tokenizer(\"My name is Sarah and I live in London\")\r\nOut[7]: {'input_ids': [499, 564, 19, 8077, 11, 27, 619, 16, 1524, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\r\nt5_tokenizer.eos_token_id\r\nOut[8]: 1\r\ngpt2_tokenizer(\"My name is Sarah and I live in London\")\r\nOut[9]: {'input_ids': [3666, 1438, 318, 10490, 290, 314, 2107, 287, 3576], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}\r\ngpt2_tokenizer.eos_token_id\r\nOut[10]: 50256\r\n```\r\nTo make the model generate EOS tokens at inference time, I had to tokenize my texts and then add the EOS token at the end, like: `tokenized_texts = tokenizer([t + tokenizer.eos_token for t in texts])`\r\n\r\nIf the tokenizer doesn't even have an EOS token, then you may have to create a new one, or rely on some heuristics to stop the generation.", "Expect to see more GPT2-related fine-tuning cases in transformers doc.\r\n" ]

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

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3309?src=pr&el=h1) Report\n> Merging [#3309](https://codecov.io/gh/huggingface/transformers/pull/3309?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2187c49f5cde57306c3fd1eb67dbc68fab9c6403&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3309?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3309 +/- ##\n==========================================\n+ Coverage 76.92% 76.93% +0.01% \n==========================================\n Files 100 100 \n Lines 16953 16953 \n==========================================\n+ Hits 13041 13043 +2 \n+ Misses 3912 3910 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3309?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3309/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.09% <0.00%> (+0.26%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3309?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/3309?src=pr&el=footer). Last update [2187c49...003d51b](https://codecov.io/gh/huggingface/transformers/pull/3309?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3307?src=pr&el=h1) Report\n> Merging [#3307](https://codecov.io/gh/huggingface/transformers/pull/3307?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/fa5423b1695cd24856bcff47214172e0f540d924&el=desc) will **decrease** coverage by `0.94%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3307?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3307 +/- ##\n==========================================\n- Coverage 77.79% 76.85% -0.95% \n==========================================\n Files 145 145 \n Lines 25355 25356 +1 \n==========================================\n- Hits 19726 19488 -238 \n- Misses 5629 5868 +239 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3307?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/3307/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25feGxtLnB5) | `83.00% <100.00%> (+0.06%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/3307/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `23.38% <0.00%> (-73.39%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/3307/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/3307/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `84.21% <0.00%> (-1.76%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3307/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `79.59% <0.00%> (+0.33%)` | :arrow_up: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/3307/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `81.09% <0.00%> (+1.37%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/3307/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.36% <0.00%> (+49.37%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/3307/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/3307?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/3307?src=pr&el=footer). Last update [fa5423b...e371b9a](https://codecov.io/gh/huggingface/transformers/pull/3307?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I agree with this, but will let others chime in.\r\n\r\nHowever as discussed in https://github.com/huggingface/transformers/issues/2453#issuecomment-656103152 I think `sacremoses` is MIT-licensed", "Now that `sacremoses` has changed the license (link from @julien-c and https://github.com/alvations/sacremoses/commit/90376dfaf0f41399a090e7620feb3c2494f865a6) the original reason for this pull request is gone.\r\n\r\nFeel free to simply close this if you currently don't want to use this to reduce the default dependencies. For this use case it would probably make sense to also make the `xlnet` and `gpt` dependencies optional.", "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/3306?src=pr&el=h1) Report\n> Merging [#3306](https://codecov.io/gh/huggingface/transformers/pull/3306?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/68ef0a111f8740f06ca4e5a00374ec4e2adb0a6d&el=desc) will **decrease** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3306?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3306 +/- ##\n==========================================\n- Coverage 77.48% 77.47% -0.02% \n==========================================\n Files 99 99 \n Lines 16799 16799 \n==========================================\n- Hits 13017 13015 -2 \n- Misses 3782 3784 +2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3306?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3306/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `85.68% <0.00%> (-0.54%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3306/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.84% <0.00%> (+0.13%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3306?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/3306?src=pr&el=footer). Last update [68ef0a1...6053b4e](https://codecov.io/gh/huggingface/transformers/pull/3306?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks for contributing @jannesgg – if this is a Swedish LM, could you add the language tag to the top of the model card:\r\n\r\n```\r\n---\r\nlanguage: swedish\r\n---\r\n```", "Thanks! [`Model page`](https://huggingface.co/jannesg/bertsson)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3305?src=pr&el=h1) Report\n> Merging [#3305](https://codecov.io/gh/huggingface/transformers/pull/3305?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b2028cc26b61a9dad960274d427e261af7c9bdc8&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3305?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3305 +/- ##\n==========================================\n- Coverage 77.47% 77.46% -0.01% \n==========================================\n Files 99 99 \n Lines 16799 16799 \n==========================================\n- Hits 13015 13014 -1 \n- Misses 3784 3785 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3305?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3305/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `85.68% <0.00%> (-0.36%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3305/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.70% <0.00%> (+0.13%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3305?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/3305?src=pr&el=footer). Last update [b2028cc...85e70d9](https://codecov.io/gh/huggingface/transformers/pull/3305?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@anjubaoGDUT If you can provide the code in text (instead of image) that can copy and paste, it is easy to test. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3301?src=pr&el=h1) Report\n> Merging [#3301](https://codecov.io/gh/huggingface/transformers/pull/3301?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/47591763137f17021928e686ef171f25c240f076&el=desc) will **decrease** coverage by `0.35%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3301?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3301 +/- ##\n==========================================\n- Coverage 77.68% 77.33% -0.36% \n==========================================\n Files 99 99 \n Lines 16799 16799 \n==========================================\n- Hits 13051 12991 -60 \n- Misses 3748 3808 +60 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3301?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/3301/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `28.89% <0.00%> (-6.50%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3301/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `82.46% <0.00%> (-5.91%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3301/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `91.01% <0.00%> (-0.99%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3301?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/3301?src=pr&el=footer). Last update [4759176...46b9c45](https://codecov.io/gh/huggingface/transformers/pull/3301?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I just symlinked all 24 under Google's namespace to this one in 68ef0a111f8740f06ca4e5a00374ec4e2adb0a6d.\r\n\r\nThanks for uploading the models @iuliaturc-google and @srush!\r\n\r\nExample model page: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2", "Thanks Sasha and Julien!" ]

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[ "I saw that to use the examples, it has to be installed from source." ]

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[ "Wow that was quick :D " ]

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[ "Not 100% sure how this results in a \"Prettier API,\" but agree this isn't a big deal to fix downstream. (my current code explicitly sets `do_sample=True` just in case something like this happened.)\r\n\r\nIf you are creating any demo generation notebooks/tooling like Write With Transformer, I recommend explicitly noting this behavior." ]

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[ "Yes, this will be quite hard and is not a feature that is implemented at the moment nor a feature that we plan on implementing soon. \r\n\r\nAn easy way to get what you want though, will be to clone the repo and adapt the code. You can easily add the layer outputs (e.g. `ffn_output`) you want to the `return` functions of the different Albert layers (you will probalby have to return and retrieve it multiple times until you have it in the `AlbertForSequenceClassification.formard() `function", "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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[ "Can you please open an issue on https://github.com/huggingface/tokenizers?\r\n\r\nThanks!\r\ncc @n1t0 @mfuntowicz " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3295?src=pr&el=h1) Report\n> Merging [#3295](https://codecov.io/gh/huggingface/transformers/pull/3295?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/af471ce5e8ca7c19183e70bb998561170addc276?src=pr&el=desc) will **increase** coverage by `0.19%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3295?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3295 +/- ##\n==========================================\n+ Coverage 77.82% 78.02% +0.19% \n==========================================\n Files 98 98 \n Lines 16666 16666 \n==========================================\n+ Hits 12970 13003 +33 \n+ Misses 3696 3663 -33\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3295?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3295/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.58% <0%> (-0.14%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3295/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `68.4% <0%> (+0.4%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3295/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.37% <0%> (+5.9%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3295?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/3295?src=pr&el=footer). Last update [af471ce...62cb24d](https://codecov.io/gh/huggingface/transformers/pull/3295?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "I had little modification on the source code and solve the problem. (But I may changed the behavior and the return format of class BertForPretraining)\r\n\r\n```python\r\ndef gather_indexes_auto(sequence_tensor, masked_lm_labels):\r\n \"\"\"Gathers the vectors according to masked_lm_labels over a minibatch.\r\n Input \r\n sequence_tensor: (batch_size, sequence_length, hidden_size)\r\n masked_lm_labels: (batch_size, sequence_length)\r\n Output\r\n output_tensor: (-1, hidden_size)\r\n output_lm_labels: (-1, )\r\n \"\"\"\r\n batch_size = sequence_tensor.size(0)\r\n sequence_length = sequence_tensor.size(1)\r\n hidden_size = sequence_tensor.size(2)\r\n # Flatten sequence_tensor into (-1, hidden_size)\r\n # Flatten masked_lm_labels into (-1, )\r\n sequence_tensor_flat = sequence_tensor.view(batch_size*sequence_length, hidden_size)\r\n masked_lm_labels_flat = masked_lm_labels.view(-1)\r\n # Get non -100 index \r\n # Note: the input index of torch.index_select is 1-D tensor\r\n masked_lm_location = masked_lm_labels_flat.ge(0).nonzero().view(-1)\r\n # Select corresponding values \r\n output_tensor = torch.index_select(sequence_tensor_flat, dim=0, index=masked_lm_location)\r\n output_lm_labels = torch.index_select(masked_lm_labels_flat, dim=0, index=masked_lm_location)\r\n return output_tensor, output_lm_labels\r\n```\r\nAnd in class `BertForPreTraining`\r\n```\r\nclass BertForPreTraining(BertPreTrainedModel):\r\n ...\r\n def forward(...):\r\n outputs = self.bert(\r\n input_ids,\r\n attention_mask=attention_mask,\r\n token_type_ids=token_type_ids,\r\n position_ids=position_ids,\r\n head_mask=head_mask,\r\n inputs_embeds=inputs_embeds,\r\n )\r\n\r\n sequence_output, pooled_output = outputs[:2]\r\n sequence_output, output_lm_labels = gather_indexes_auto(sequence_output, masked_lm_labels)\r\n\r\n prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)\r\n\r\n outputs = (prediction_scores, seq_relationship_score,) + outputs[\r\n 2:\r\n ] # add hidden states and attention if they are here\r\n\r\n if masked_lm_labels is not None and next_sentence_label is not None:\r\n loss_fct = CrossEntropyLoss()\r\n masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), output_lm_labels.view(-1))\r\n next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))\r\n total_loss = masked_lm_loss + next_sentence_loss\r\n outputs = (total_loss,) \r\n\r\n return outputs # (loss), \r\n```\r\nNote that this code only compute and return the loss on <MASKED> labels and thus save a lot computations and GPU memories.\r\nBefore this change, I can only run BERT-MEDIUM (L8 H512 A8) on batch_size = 64 using P100 (16G RAM), after this change, I can run the pretraining using batch_size = 128.", "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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[ "By \"it returns null\" you mean it returns an empty array? That's because it didn't identify any named entity in your sequence." ]

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[ "@patrickvonplaten my OS is mac OS 10.14.6, Python 3.6.10, tensorflow 2.0, transformers version is the resoruce code in github", "Hi @policeme, which example did you use? ", "\r\nlike this", "@patrickvonplaten ", "your example like this\r\n\r\n", "Please don't paste screenshots of your code on issues. Copy and Paste the code in code format. We can't copy the code this way. Typing your code from a screenshot is very time-consuming. \r\n\r\nConsidering the problem you have. How did you train the model that was saved in `...ckpt.index`? Did you use this library? From your error message, it seems like your Bert TF model saved in `.ckpt.index` does not have the correct form. \r\n\r\nThe example you mention should be used if you trained your model with this library.", "sorry about it, this is my code\r\n`config = AutoConfig.from_pretrained(r'/Users/maxiong/Workpace/Code/transformers/pre_model/bert_config.json')\r\ntokenizer = AutoTokenizer.from_pretrained(r'/Users/maxiong/Workpace/Code/transformers/pre_model/bert_config.json')\r\nmodel = AutoModel.from_pretrained(r'/Users/maxiong/Workpace/Code/transformers/pre_model/bert_model.ckpt.index', from_tf=True, config=config)\r\n`\r\nthis bert model is from google official chinese bert base model, in your answer, the model can be used with this libary, which was trained by this library, if that, if I want use own pretraning bert model, how to use it with this library" ]

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[ "@srush Can you please take a look?", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3290?src=pr&el=h1) Report\n> Merging [#3290](https://codecov.io/gh/huggingface/transformers/pull/3290?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8320feec09309a94f673e1e7ce2a93da81eb3366&el=desc) will **increase** coverage by `0.18%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3290?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3290 +/- ##\n==========================================\n+ Coverage 77.81% 77.99% +0.18% \n==========================================\n Files 98 98 \n Lines 16666 16666 \n==========================================\n+ Hits 12969 12999 +30 \n+ Misses 3697 3667 -30 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3290?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.84% <0.00%> (+0.27%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3290/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.47% <0.00%> (+5.00%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3290?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/3290?src=pr&el=footer). Last update [8320fee...dd1b783](https://codecov.io/gh/huggingface/transformers/pull/3290?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Looks excellent. I will let @LysandreJik merge tomorrow, and confirm multi-gpu / TPU work. \r\n\r\nWant to try SQuAD next?", "> Want to try SQuAD next?\r\n\r\nSure, I'll give it a go." ]

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[ "Hey @lazarevskiVsg, thanks a lot for pointing this out!\r\nIt should be fixed now :-) " ]

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[ "Should be fixed, I'll push updated images for all the others Dockerfile in the next hours. \r\n\r\nThanks for reporting @edwardcqian ", "I'm closing, feel free to reopen if I missed something 👍 " ]

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[ "The tokenizer adds special tokens (here, specific to BERT) at the beginning and end of the sentence. \r\n\r\nYou can check that with:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\nlen(tokenizer.encode(TEXT)) == 18\r\n```", "@julien-c is there any way to avoid the special tokens when extracting the features? I expected \"add_special_tokens=False\" would prevent this from happening?", "I would suggest not using the Pipeline and just doing `tokenizer.encode()` then `outputs = model(input_ids)`" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3286?src=pr&el=h1) Report\n> Merging [#3286](https://codecov.io/gh/huggingface/transformers/pull/3286?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/68ef0a111f8740f06ca4e5a00374ec4e2adb0a6d&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `68.29%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3286?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3286 +/- ##\n==========================================\n+ Coverage 77.48% 77.50% +0.01% \n==========================================\n Files 99 99 \n Lines 16799 16768 -31 \n==========================================\n- Hits 13017 12996 -21 \n+ Misses 3782 3772 -10 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3286?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/3286/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `98.91% <ø> (ø)` | |\n| [src/transformers/modeling\\_transfo\\_xl\\_utilities.py](https://codecov.io/gh/huggingface/transformers/pull/3286/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190cmFuc2ZvX3hsX3V0aWxpdGllcy5weQ==) | `64.61% <ø> (+11.28%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/3286/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90cmFuc2ZvX3hsLnB5) | `89.15% <55.00%> (-2.04%)` | :arrow_down: |\n| [src/transformers/modeling\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/3286/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190cmFuc2ZvX3hsLnB5) | `77.00% <80.95%> (+1.37%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3286/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `82.46% <0.00%> (-3.76%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3286?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/3286?src=pr&el=footer). Last update [68ef0a1...f2cc11a](https://codecov.io/gh/huggingface/transformers/pull/3286?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "> Ok good job.\r\n> \r\n> I feel like we could remove all the dead code related to sampling softmax (see my comments)\r\n\r\nSound good, will do that then!", "Dead code is now removed. This removed a lot of code. To not re-invent the wheel the code is kept in the branch `add_sampling_and_training_to_transfo_xl_models` and documented by the feature request: #3310 , if someone wants to pick up implementing sample softmax again. \r\n\r\nThis PR still adds language modeling capabilities to TF transfoXL." ]

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[ "A common way of evaluating LMs is to measure their Perplexity. \r\nSay you want to finetune GPT2 on your dataset D.\r\nDefine train, val and test datasets (maybe something around 75%, 10%, 15%.\r\nMeasure the [perplexity](https://towardsdatascience.com/perplexity-intuition-and-derivation-105dd481c8f3) on train and val after each epoch. Compare train and eval curves for overfitting. \r\n\r\nThere are a ton of other evaluation measures that might be better for your task - Google will be your best friend :-) ", "@patrickvonplaten can you provide example/code implementation? " ]

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[ "Oh, I guess a workaround is to pass in `ignore_labels=[]` when creating the pipeline. This makes the nlp call return all tokens, including the ones that are not part of NER. Then I can just chunk two tokens together if their label is the same and they are nearby. Does this make sense, or am I missing something fundamental?", "Hi again, would you be open to a PR fixing this?", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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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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[ "seems to be a sentencepiece issue, please open an issue at https://github.com/google/sentencepiece" ]

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[ "and how to use AutoTokenizer to load a local vocab file, instead of download a vocab file from server" ]

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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/3279?src=pr&el=h1) Report\n> Merging [#3279](https://codecov.io/gh/huggingface/transformers/pull/3279?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3814e167d99c4b2e135b250d73deaa3f63ebef0c&el=desc) will **decrease** coverage by `0.07%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3279?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3279 +/- ##\n==========================================\n- Coverage 78.02% 77.94% -0.08% \n==========================================\n Files 98 98 \n Lines 16670 16666 -4 \n==========================================\n- Hits 13007 12991 -16 \n- Misses 3663 3675 +12 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3279?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3279/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `96.26% <100.00%> (-0.04%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3279/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `86.22% <0.00%> (-1.97%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3279/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.72% <0.00%> (-0.14%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3279?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/3279?src=pr&el=footer). Last update [3814e16...1b8aa30](https://codecov.io/gh/huggingface/transformers/pull/3279?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I found one more `forward` in bertabs, these others are not obviously wrong.\r\n\r\n`git grep \"\\.forward(\"`\r\n\r\n```\r\nexamples/ner/run_pl_ner.py: outputs = self.forward(**inputs)\r\nexamples/ner/run_pl_ner.py: outputs = self.forward(**inputs)\r\n```\r\non a lightning module so OK\r\n\r\n```\r\nexamples/summarization/bertabs/modeling_bertabs.py: See :obj:`onmt.modules.RNNDecoderBase.forward()` \r\n``` \r\nIn documentation so OK\r\n\r\n```\r\nsrc/transformers/modeling_bart.py: return super().forward(positions)\r\nsrc/transformers/modeling_roberta.py: return super().forward(\r\n```\r\ntried to change and got \"super() is not callable\".\r\n\r\nMerging!\r\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3278?src=pr&el=h1) Report\n> Merging [#3278](https://codecov.io/gh/huggingface/transformers/pull/3278?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3814e167d99c4b2e135b250d73deaa3f63ebef0c?src=pr&el=desc) will **decrease** coverage by `<.01%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3278?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3278 +/- ##\n==========================================\n- Coverage 78.02% 78.02% -0.01% \n==========================================\n Files 98 98 \n Lines 16670 16667 -3 \n==========================================\n- Hits 13007 13004 -3 \n Misses 3663 3663\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3278?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3278/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.7% <ø> (-0.16%)` | :arrow_down: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3278/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `96.28% <100%> (-0.01%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3278/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.37% <0%> (+0.17%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3278?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/3278?src=pr&el=footer). Last update [3814e16...473dab8](https://codecov.io/gh/huggingface/transformers/pull/3278?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "### Summary:\r\n`generation_mode` is a flag that\r\n - tells the decoder NOT to make decoder_attn_mask (ignored pad tokens and causal tokens)\r\n - keeps position_embeds correct even though we only decode one new token at a time.\r\n - tells the decoder \r\n\r\n\r\nThe easiest way to get rid of it in `modeling_utils.py`: \r\n\tpass a kwarg from `BartModel.prepare_inputs_from_generation` (then it never needs to be unset, and modeling_utils.py doesn't need to know about it)\r\n\tI don't know how to get rid of the logic entirely. It's tough to know whether you're in generation mode at step 0 because the cache is empty.", "I updated this PR to implement the solution I proposed.", "Merging, but feel free to ask further questions!" ]

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[ "I don't follow your workflow. Why do you want to run forward after you generate using the generated_ids as decoder_input_ids?\r\n\r\nIt would be easier to follow if you:\r\n- checked whether the behavior you expect is achieved by the authors' implementation in fairseq\r\n- made the example smaller\r\n\r\nThanks for contributing!\r\n", "@sshleifer Thanks for the reply! \r\nYour latest PR already fixed the generation_mode, thanks! The rest of the problem (that you probably failed to follow) is like a sanity check, but the result failed to meet the expectation for me.\r\n\r\nBasically, if I feed sequence s1 to the encoder to let the model to generate, suppose sequence s2 is generated, then if I directly feed <encoder_input_ids=s1, decoder_input_ids=s2> to the model, the output of the decoder would be something that resembles s2. However, as shown in the code below, the actual output of the decoder given input <s1,s2> is a sequence \"and and ... and\", which is largly different from s2, and that's where I'm confused.\r\n\r\n```\r\nwith torch.no_grad():\r\n result = model.generate(input_ids=input_ids, eos_token_ids=tokenizer.eos_token_id, num_beams=4, max_length=20)\r\n\r\nmodel.model.decoder.generation_mode=False\r\nnew_result = model(input_ids, decoder_input_ids=result)\r\n\r\nprint(tokenizer.decode(torch.argmax(new_result[0][0], dim=1)))\r\n# ' and and and and and and and and and and and and and and and and and and and'\r\n```\r\n\r\nOf course we won't use this code in practice (since it's just a sanity check), but I post this because I'm wondering if it's my incorrect way of using `forward` function when training that caused this confusion. As for me, if I want to train the summarization model using <paragraph=s1, summary=s2> pairs, I formerly feed <encoder_input=s1, decoder_input=< bos >+s2> and train the model with decoder_output=s2+< eos >. May I ask if it is the correct way?\r\n\r\nThanks again for the kind help!", "For summarization finetuning, I'd recommend:\r\n- prepend a space to s1 and s2,\r\n- then use `tokenizer.batch_encode_plus(s1, max_length=1024)` for `input_ids` and `attention_mask`\r\n- then `tokenizer.batch_encode_plus(s2, max_length=1024)['input_ids']` to get `decoder_input_ids`\r\n", "@sshleifer Thanks for the update! I'll try out you method and report the result I get for the problem above tomorrow.", "The underlying issue here is fixed, closing!" ]

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[ "I have the same issue and I get (sometimes wildly) different results from run to run. Here's my code:\r\n\r\nhttps://github.com/dmitriydligach/Thyme/blob/master/RelKeras/et.py\r\n\r\nDoes anybody have a solution yet?", "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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[ "Did you find an answer to what the cause is?", "Well, I guess that you need infiniband between the nodes to train a model as large as bert. The ethernet interface seems to be a bottleneck during the gradient synchronization process.\r\n\r\nTo test this out, I tried out a much smaller bert model on the existing setup without infiniband. Less gradient information to exchange, so I was able to observe a speedup. I tried out a normal sized model on a setup with infiniband and I was observing some speedup (albeit not perfect scaling, only like a 50-60% improvement).\r\n\r\nI concluded that not having infiniband was the issue. Maybe this could be updated in the readme where the instructions for `run_language_modeling.py` are given.", "I also encounter this. And I found the `fairseq` code base scales linearly on the same ethernet hardware I have than `run_language_modeling.py`", "Could you share some details on what model you trained on `fairseq` and that model's size?", "Since `run_language_modeling.py` uses only 1 GPU per node in the code, could you share what changes need to be made to the file in order to work with Multi-GPU, Multi-Node settings like `Azure NC24s_v3 nodes`?\r\n\r\nIn the code in `examples/run_language_modeling.py`, `1` GPU per node is hard-coded (in the last line)\r\n\r\n\r\n \r\n if args.local_rank == -1 or args.no_cuda:\r\n device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\r\n args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()\r\n else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs\r\n torch.cuda.set_device(args.local_rank)\r\n device = torch.device(\"cuda\", args.local_rank)\r\n torch.distributed.init_process_group(backend=\"nccl\")\r\n args.n_gpu = 1\r\n", "You do not need any modifications. You will have to launch the script via `torch.distributed.launch`. It'll be something like this\r\n```\r\npython -m torch.distributed.launch --nproc_per_node 4 --nnodes $NODE_COUNT --node_rank $RANK --master_addr $MASTER_ADDR run_lm_finetuning.py ....\r\n```\r\n`$NODE_COUNT` will be your number of nodes. You'll have to find a way to obtain `$RANK` and `$MASTER_ADDR` depending on your cluster configuration.\r\n\r\nSince when you run via DistributedDataParallel, one process has only one gpu associated with it, that line does something to ensure that.", "Is there any update on this? I suppose that not having infiniband interconnect was the only limiting factor? ", "Yes. Ran faster on a system with infiniband.\n\nOn Thu, Nov 19, 2020 at 6:08 PM gvijqb <[email protected]> wrote:\n\n> Is there any update on this? I suppose that not having infiniband\n> interconnect was the only limiting factor?\n>\n> —\n> You are receiving this because you modified the open/close state.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/3274#issuecomment-730347349>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ADZB3Y3PKZBAOFOP5SSA3BLSQUGUXANCNFSM4LJHN2QA>\n> .\n>\n\n\n-- \nRegards\nAnirudh Srinivasan\nResearch Fellow\nMicrosoft Research, India\n" ]

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[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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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", "Any answer on this? " ]

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[ "The script has been renamed [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py) to better reflect the fact that it can also be used to train a new model from scratch.\r\n\r\nLet us know if it works.", "Hi @julien-c thanks for the quick reply!\r\n\r\nThat makes sense, I must have missed the fact it got renamed. I have one more question about the \"new\" script:\r\n\r\nHow difficult is it make to script compatible for finetuning a new model like BART? Is it as simple as adding the model to the list inside the script or will it need a lot of workarounds to get it working?" ]

[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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

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[ "You should add more information about your environment like OS, Python Version etc?\r\nI just installed it on Windows 10, WSL etc Python 3.6.9 :: Anaconda, Inc.; It worked..", "I installed it on Mac, Python 3.6.10 Anaconda" ]

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[ "Good to merge for me. Tested whether model can generate text and everything seems fine.\r\n@julien-c ", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3268?src=pr&el=h1) Report\n> Merging [#3268](https://codecov.io/gh/huggingface/transformers/pull/3268?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cc4c37952a961f2d13e83f3d5ba6dab811d0bbfd&el=desc) will **increase** coverage by `0.19%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3268?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3268 +/- ##\n==========================================\n+ Coverage 77.82% 78.01% +0.19% \n==========================================\n Files 98 98 \n Lines 16666 16666 \n==========================================\n+ Hits 12970 13002 +32 \n+ Misses 3696 3664 -32 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3268?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/3268/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `96.16% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3268/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.19% <0.00%> (+5.72%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3268?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/3268?src=pr&el=footer). Last update [cc4c379...501291a](https://codecov.io/gh/huggingface/transformers/pull/3268?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/3267?src=pr&el=h1) Report\n> Merging [#3267](https://codecov.io/gh/huggingface/transformers/pull/3267?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cc4c37952a961f2d13e83f3d5ba6dab811d0bbfd&el=desc) will **not change** coverage by `%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3267?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3267 +/- ##\n=======================================\n Coverage 77.82% 77.82% \n=======================================\n Files 98 98 \n Lines 16666 16666 \n=======================================\n Hits 12970 12970 \n Misses 3696 3696 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3267?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/3267?src=pr&el=footer). Last update [cc4c379...4bb2bc3](https://codecov.io/gh/huggingface/transformers/pull/3267?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Indeed, thanks!" ]

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[ "I have a suspicion that this will also fix the GPU test runner. " ]

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[ "Interesting - will investigate. Probably something with the `device` of `unfinished_sents` then!\r\n\r\n@julien-c ", "Caused by previously unexposed kwargs changing behavior:\r\npassing\r\n```\r\ndo_sample=False, early_stopping=True\r\n```\r\nin the unit test fixes them.", "I'm getting this error @sshleifer ", "https://github.com/huggingface/transformers/issues/5221", "I have early stopping = False, but do_sample = True" ]

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[ "Is good to merge for me if you guys are fine with it. Can open a new PR for a test proposal for this one and then also adapt the dutch bert model config on AWS. ", "Isn't this breaking other hosted configs than the ones you're listing?\r\n\r\nLike https://huggingface.co/microsoft/DialoGPT-large for instance? (more generally, we don't control which configs users use the lib with, so adding keys is fine, but renaming keys – or worse, changing their types – is costly)", "PS: I do agree that having a `eos_token_id` is cleaner than having `eos_token_ids`", "> Isn't this breaking other hosted configs than the ones you're listing?\r\n> \r\n> Like https://huggingface.co/microsoft/DialoGPT-large for instance? (more generally, we don't control which configs users use the lib with, so adding keys is fine, but renaming keys – or worse, changing their types – is costly)\r\n\r\nThat's a very good point! There is one scenario, where it could break other hosted configs:\r\n\r\n- The user defined an `eos_token_ids` that is different from the default `eos_token_id` the model has. \r\n\r\nBut most of the time (just from browsing through some `config.json` files) the model was trained with HF and then saved and uploaded which means that the `eos_token_ids` was saved and is included in the `config.json`. In this case the values are the same as is the case for `https://huggingface.co/microsoft/DialoGPT-large`. In this case we have still have a dead parameter in the config which should be removed. \r\n\r\nI propose the following: \r\nI can write a script that checks the following for each configs:\r\n\r\n1) does eos_token_ids exist ?\r\n2) is eos_token_ids == default config.eos_token_id ?\r\n\r\nIf there are a lot of 1) and 2) then would write a bash script that simply replaces the line \"`eos_token_ids` = [ ... ]\" with `eos_token_ids`=...\r\n\r\nWill report the results here", "If you need an exhaustive list of all hosted models (with their config + files), you can do\r\n```python\r\napi = HfApi()\r\nmodels = api.model_list()\r\n```\r\n\r\n", "Okey here is my analysis of the 308 (not bad actually! ) added community models:\r\n\r\n1. **66** can't load either their config (n)or their tokenizer (including 3 facebook bart models because we call them directly by `bart-large-cnn` and not by `facebook/bart-large-cnn` -> should maybe add a new link or change model name online)\r\n 2. **79** currently have wrong `pad_token_id`, `eos_token_id`, `bos_token_id` in their configs. IMPORTANT: The reason for this is that we used to have the wrong defaults saved in `PretrainedConfig()` - see e.g. [here](https://github.com/huggingface/transformers/pull/2885/commits/77d958ac7f0b008df17656e3652246f602aef095)\r\nthe default value for **any** model for `pad_token_id` was 0. People trained a model with the lib, saved it and the resulting config.json now had a `pad_token_id = 0` saved. This was then uploaded. But it's wrong and should be corrected regardless of this PR. \r\n3. For **68** after changing `eos_token_ids` to `eos_token_id` we will have to remove the `eos_token_ids` parameter and possibly adapt the `eos_token_id` parameter - almost all of which we have to change anyway (1 exception) \r\n4. For **162** models everything is fine! \r\n\r\nHere the full analysis log [here](https://github.com/patrickvonplaten/files_to_link_to/blob/master/results.txt)\r\nHere the code that created this log (simple comparison of loaded tokenizer and config with default config): [here](https://github.com/patrickvonplaten/files_to_link_to/blob/master/test_all_community_models.py)\r\nHere the 308 models I checked: [here](https://github.com/patrickvonplaten/files_to_link_to/blob/master/all_community_models.txt)\r\n\r\n**First conclusion:** \r\n- I think we can merge this PR as all models for which this PR would change to a \"wrong\" behavior already have a \"wrong\" behavior that should be fixed. The sooner we merge the sooner we have the correct API. \r\n\r\n**Second conclusion:** \r\nI think besides the FB models 1) is not really our job to fix. \r\nBut 2) and 3) I think should be fixed on AWS. I'm happy to do this using some automated bash/python scripting. I would try out that it work on 1,2 community models and then apply it to all other cases (to not screw something up on AWS). \r\n\r\nWould that be good for you @julien-c @thomwolf @LysandreJik ?\r\n\r\nIn a future PR we could think about some automated testing that tokenizer configs are equal to model configs. ", "> If you need an exhaustive list of all hosted models (with their config + files), you can do\r\n> \r\n> ```python\r\n> api = HfApi()\r\n> models = api.model_list()\r\n> ```\r\n\r\nGreat, this will be very helpful when writing an automated script to corret the config.json of the community models! ", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3264?src=pr&el=h1) Report\n> Merging [#3264](https://codecov.io/gh/huggingface/transformers/pull/3264?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8becb732931bbab5dd75cca5f5e7c75b2516d10b&el=desc) will **decrease** coverage by `0.09%`.\n> The diff coverage is `97.91%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3264?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3264 +/- ##\n==========================================\n- Coverage 77.64% 77.55% -0.10% \n==========================================\n Files 100 100 \n Lines 16979 16970 -9 \n==========================================\n- Hits 13184 13161 -23 \n- Misses 3795 3809 +14 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3264?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `98.92% <ø> (ø)` | |\n| [src/transformers/configuration\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JhcnQucHk=) | `100.00% <ø> (ø)` | |\n| [src/transformers/configuration\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3Q1LnB5) | `96.55% <ø> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.58% <94.11%> (+0.21%)` | :arrow_up: |\n| [src/transformers/configuration\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2FsYmVydC5weQ==) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/configuration\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JlcnQucHk=) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/configuration\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2Rpc3RpbGJlcnQucHk=) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/configuration\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2ZsYXViZXJ0LnB5) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/configuration\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2dwdDIucHk=) | `97.29% <100.00%> (ø)` | |\n| [src/transformers/configuration\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3JvYmVydGEucHk=) | `100.00% <100.00%> (ø)` | |\n| ... and [7 more](https://codecov.io/gh/huggingface/transformers/pull/3264/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3264?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/3264?src=pr&el=footer). Last update [8becb73...8296647](https://codecov.io/gh/huggingface/transformers/pull/3264?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "More in-detail analysis why **67** (actually one more now) models can't be loaded - log files are updated and use `api = HfApi(); models = api.model_list()` now. \r\n\r\nA) **34** models can't even load their config file. The reasons for this are either: \r\n\r\n1. **11/34**: Model identifier is wrong, e.g. `albert-large` does not exist anymore, it seems like it was renamed to `albert-large-v1`. These models have saved the wrong name online that how it is saved on AWS. Can easily be corrected. *e.g.*\r\nThe model_identifier: b`ertabs-finetuned-xsum-extractive-abstractive-summarization` does not exist, but `remi/bertabs-finetuned-xsum-extractive-abstractive-summarization` does exist -> wrong model identifier. Just 11 cases, so easy to correct.\r\n\r\n2. **23/34**: There is an unrecognized `model_type` in the config.json, `e.g.` \r\n\r\n> \"Error: Message: Unrecognized model in hfl/rbtl3. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: t5, distilbert, albert, camembert, xlm-roberta, bart, roberta, flaubert, bert, openai-gpt, gpt2, transfo-xl, xlnet, xlm, ctrl\r\n> \"\r\n\r\nHere I think we should add a `model_type` to the config (probably `bert` most of the time)\r\n\r\nB) **33** models can load their config, but cannot load their tokenizers. The error message is almost always the same **32/33**: \r\n\r\n> TOK ERROR: clue/roberta_chinese_base tokenizer can not be loaded\r\n> Message: Model name 'clue/roberta_chinese_base' was not found in tokenizers model name list (roberta-base, roberta-large, roberta-large-mnli, distilroberta-base, roberta-base-openai-detector, roberta-larg\r\n> e-openai-detector). We assumed 'clue/roberta_chinese_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 vocab\r\n> ulary files at this path or url.\r\n\r\nHere: the model has neither of: \r\n\r\n- `vocab_file`\r\n- `added_tokens_file`\r\n- `special_tokens_map_file`\r\n- `tokenizer_config_file`\r\n\r\n-> So we would have to upload one of those files to make it work. Not sure how time-consuming this is!\r\n\r\nand we got one tokenizer which does not even have a path: `Message: stat: path should be string, bytes, os.PathLike or integer, not NoneType`\r\n\r\nSo I think it's mostly just renaming the model identifiers to their correct names and adding some tokenizer names.\r\n\r\n@julien-c " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3263?src=pr&el=h1) Report\n> Merging [#3263](https://codecov.io/gh/huggingface/transformers/pull/3263?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/afea70c01c7d2a844662a4d66b9f9d933cc6449c?src=pr&el=desc) will **increase** coverage by `0.11%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3263?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3263 +/- ##\n==========================================\n+ Coverage 77.82% 77.93% +0.11% \n==========================================\n Files 98 98 \n Lines 16666 16666 \n==========================================\n+ Hits 12970 12989 +19 \n+ Misses 3696 3677 -19\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3263?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3263/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.84% <0%> (+0.13%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3263/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `85.68% <0%> (+3.22%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3263?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/3263?src=pr&el=footer). Last update [afea70c...14537eb](https://codecov.io/gh/huggingface/transformers/pull/3263?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Merged it (not sure it was ready yet, but feel free to update in another PR).\r\n\r\n**[Model page](https://huggingface.co/camembert-base)**\r\n\r\nLet us know if we can help in any way @benjamin-mlr @louismartin @pjox\r\n\r\nYou can also add \r\n```\r\n---\r\nlanguage: french\r\n---\r\n```\r\n\r\non top of the README for the model to pop up when looking for FR models" ]

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[ "This seems like a valid point, what do you think @LysandreJik?" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3261?src=pr&el=h1) Report\n> Merging [#3261](https://codecov.io/gh/huggingface/transformers/pull/3261?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/afea70c01c7d2a844662a4d66b9f9d933cc6449c&el=desc) will **increase** coverage by `0.12%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3261?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3261 +/- ##\n==========================================\n+ Coverage 77.82% 77.94% +0.12% \n==========================================\n Files 98 98 \n Lines 16666 16666 \n==========================================\n+ Hits 12970 12990 +20 \n+ Misses 3696 3676 -20 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3261?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3261/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.56% <0.00%> (-0.14%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3261/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `86.22% <0.00%> (+3.75%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3261?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/3261?src=pr&el=footer). Last update [afea70c...547efb9](https://codecov.io/gh/huggingface/transformers/pull/3261?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Well, I don't think the `SequenceClassification\" head is not the right one, as it is supposed for sequence classification/regression tasks (see https://huggingface.co/transformers/model_doc/albert.html#transformers.AlbertForSequenceClassification).\r\n\r\nNER requires a per token classification implementation: https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_albert.py#L791 ", "Will this be fixed by switching to AutoModel? I think we are doing that here for `run_pl_ner.py` https://github.com/huggingface/transformers/pull/3290", " I knew what the problem was. In version 2.5.1, there is no definition for `AlbertForTokenClassification` in `run_ner.py`\r\nHowever, it is included in the master branch. I'll close this request.", "@srush I think using AutoModelForTokenClassification is better than calling each model class. How about making a change to `run_ner.py`?", "Yes, if you can send that PR and add me as a reviewers" ]

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[ "Hi @woiza,\r\n\r\ntry to use: `DistilBertForSequenceClassification` instead of `BertForSequenceClassification` :)\r\n\r\n", "@stefan-it \r\n\r\nthat worked, thank you!" ]

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[ "@DenceChen thanks for sharing :-) Feel free trying to add a model if you need it!" ]

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[ "I met the same issue when running the run_squad.py using the 2.5.0 version. ", "Which PyTorch version are you using for your benchmarks? Is it the same for both, or is it different?", "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/3257?src=pr&el=h1) Report\n> Merging [#3257](https://codecov.io/gh/huggingface/transformers/pull/3257?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/012d775b14d1ab673aab7eae151823a74a8525a6&el=desc) will **not change** coverage by `%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3257?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3257 +/- ##\n=======================================\n Coverage 77.54% 77.54% \n=======================================\n Files 103 103 \n Lines 17268 17268 \n=======================================\n Hits 13390 13390 \n Misses 3878 3878 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3257?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/3257?src=pr&el=footer). Last update [012d775...012d775](https://codecov.io/gh/huggingface/transformers/pull/3257?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Does this add the ability to train a language model using method in ELECTRA? (Thanks!)", "It doesn't, we're currently working on a pre-training script incorporating the ELECTRA method but it's still a few weeks out.", "> It doesn't, we're currently working on a pre-training script incorporating the ELECTRA method but it's still a few weeks out.\r\n\r\nIs this an active branch/issue? I'm interested in contributing if so, but I can't find it", "Not public yet, will let you know when it is @shoarora!" ]

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[ "We are currently working on it :-) \r\nCheck out PR: #3257" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3254?src=pr&el=h1) Report\n> Merging [#3254](https://codecov.io/gh/huggingface/transformers/pull/3254?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2e81b9d8d76a4d41a13f74eb5e0f4a65d8143cab?src=pr&el=desc) will **decrease** coverage by `1.1%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/3254?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #3254 +/- ##\n==========================================\n- Coverage 77.93% 76.82% -1.11% \n==========================================\n Files 98 98 \n Lines 16666 16666 \n==========================================\n- Hits 12988 12804 -184 \n- Misses 3678 3862 +184\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3254?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3254/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9weXRvcmNoX3V0aWxzLnB5) | `8.72% <0%> (-81.21%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/3254/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `85.71% <0%> (-10%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3254/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `82.46% <0%> (-3.23%)` | :arrow_down: |\n| [src/transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/3254/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `73.48% <0%> (-2.3%)` | :arrow_down: |\n| [src/transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/3254/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jdHJsLnB5) | `96% <0%> (-2.23%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/3254/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `80.2% <0%> (-1.35%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3254/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `93.56% <0%> (-0.14%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3254?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/3254?src=pr&el=footer). Last update [2e81b9d...28fce2c](https://codecov.io/gh/huggingface/transformers/pull/3254?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "If you could include your environment and steps to replicate the issue, that would help. Its working for me on version 2.5.1 (from master branch).", "Hi. Sorry then I must have used the old version of transformer, I will close the issue and reopen if needed. thanks. ", "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 is quite a weird way to encode your data with `ds[0][0:3]`. There are two cases:\r\n\r\n1) You have a single string you want to encode:\r\n```\r\ninput_str = 'hello, what time is it?'\r\ninput_ids_dict = tokenizer.encode_plus(input_str)\r\n```\r\n\r\n2) You have a batch of input string data that you want to encode:\r\n\r\n```\r\ninput_str_batch = ['hello what time is it' , 'hello, how are you?', 'Hey, I'm Peter']\r\ninput_ids_dict = tokenizer.batch_encode_plus(input_str_batch, pad_to_max_length=True)\r\n```\r\n\r\nAlso see #3237" ]

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[ "My wild guess is maybe there is no specific reason for this. It's just an implementation issue. If I really want do feed a tensor of different number of dimensions I can just do reshape twice (i.e., one before forward one after)." ]

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[ "Unfortunately, I don't think the conversion you are describing is currently supported. \r\nIf I were to attempt this on my own, I would try to modify https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bart_original_pytorch_checkpoint_to_pytorch.py to take in a path to a checkpoint, rather than a `torch.hub` alias.", "This function converts from saved fairseq checkpoints: https://github.com/huggingface/transformers/blob/7a7fdf71f80452fcae064bd016f06e9a0f0f19ed/src/transformers/convert_bart_original_pytorch_checkpoint_to_pytorch.py#L81\r\n\r\nLet me know if that helps!" ]

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[ "@sshleifer May I ask could you reproduce the error in your machine? I ran the same code on a Linux machine with master-branch of transformers, but still got the same error. I'm planning to use BartModel these days so please notify me at your earliest convenience if there're any updates. Many thanks!", "Yes, will try to fix it today! Thanks for reporting!", "> Yes, will try to fix it today! Thanks for reporting!\r\n\r\nThanks Sam,\r\n\r\nThe code works well this time! Thanks again for the contribution." ]

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