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DanielPFlorian

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Transformers-Github-Issues

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[ "@Stuffooh, the following is based on my understanding and experiment.\r\n\r\nThe default params for `nlp = pipeline('feature-extraction')` uses `distilbert-base-uncased` for both the model and tokenizer.\r\n\r\nThe `nlp` object takes as input a sentence and output token-level vectors; note that token-level doesn't necessarily equal word-level since BERT uses WordPiece tokenization. Below are examples to show this.\r\n\r\n```python\r\nsent = nlp(\"This is a dog.\")\r\n\r\n# get length of output\r\nprint(len(sent[0]))\r\n> 7\r\n\r\n# it is seven because there's a [CLS] and [SEP] token added to the start and end of sentence, and the full stop `.` counts as a token.\r\n```\r\n\r\n```python\r\nsent = nlp(\"This is a untrained dog.\")\r\n\r\n# get length of output\r\nprint(len(sent[0]))\r\n> 10\r\n\r\n# similar to above example, with the addition of the word `untrained`, which in this case is broken up into three sub-pieces (tokens)\r\n```", "@leungi What I'm wondering though is how to finetune models using the pipeline feature-extraction. How to finetune 3 epochs with a certain set learning rate for example?\r\n\r\nI feel like I am misisng something here. In the run_lm_finetuning.py script for example it is easy and clear to pass all these parameters while outputting the hidden states of the model.", "@leungi How to visualise the tokens, the embeddings has assigned to?", "@gsasikiran, check out [spacyface](https://github.com/bhoov/spacyface).", "@leungi \r\n\r\n> @Stuffooh, the following is based on my understanding and experiment.\r\n> \r\n> The default params for `nlp = pipeline('feature-extraction')` uses `distilbert-base-uncased` for both the model and tokenizer.\r\n> \r\n> The `nlp` object takes as input a sentence and output token-level vectors; note that token-level doesn't necessarily equal word-level since BERT uses WordPiece tokenization. Below are examples to show this.\r\n> \r\n> ```python\r\n> sent = nlp(\"This is a dog.\")\r\n> \r\n> # get length of output\r\n> print(len(sent[0]))\r\n> > 7\r\n> \r\n> # it is seven because there's a [CLS] and [SEP] token added to the start and end of sentence, and the full stop `.` counts as a token.\r\n> ```\r\n> \r\n> ```python\r\n> sent = nlp(\"This is a untrained dog.\")\r\n> \r\n> # get length of output\r\n> print(len(sent[0]))\r\n> > 10\r\n> \r\n> # similar to above example, with the addition of the word `untrained`, which in this case is broken up into three sub-pieces (tokens)\r\n> ```\r\n\r\nIn this code, to get the [CLS] token I need to take `sent[0][0]` ?" ]

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[ "Hi, how are they truncated? When you call `tensor.shape`, is it not the shape you're expecting?", "@LysandreJik thanks for mentioning tensor.shape. I was so convinced the data was truncated and forgot to check the shape to confirm. Because of your hint I realized the output that gets printed is truncated but the actual data itself is not and is fully there.\r\n\r\nThanks ;)" ]

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[ "I have the same CUDA error with a finetuning of offical bert downloaded from s3. I'll wait for clarifications too.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hey @stefan-it, mind taking a look at this?", "Hi,\r\nI get the following error when trying to adapt the same code as BERT for Masked LM with XLM-Roberta For Masked LM, where I've replaced **'[MASK]'** with **'\\<mask>'** and '**[CLS]'** and **'[SEP]'** with **\\<s>** and **\\</s>** respectively.\r\n\r\n```\r\n/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)\r\n 1482 # remove once script supports set_grad_enabled\r\n 1483 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)\r\n-> 1484 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)\r\n 1485 \r\n 1486 \r\n\r\nRuntimeError: index out of range: Tried to access index 250004 out of table with 250001 rows. at /pytorch/aten/src/TH/generic/THTensorEvenMoreMath.cpp:418\r\n```\r\n\r\nI'm sorta new to this. Can I know whether this is related to the above discussed issue? and is much obliged to know how this error can be fixed. " ]

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[ "Hi, indeed this is an error. This will be fixed once #3198 is merged.", "Hi, I also notice from [special token's mapping in XLM repo](https://github.com/facebookresearch/XLM/blob/cd281d32612d145c6742b4d3f048f80df8669c30/xlm/data/dictionary.py#L131) that the indexing of `self.fairseq_tokens_to_ids` looks different. I am wondering if you are aware if this issue and did the corresponding remapping in the model's word embeddings." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2507?src=pr&el=h1) Report\n> Merging [#2507](https://codecov.io/gh/huggingface/transformers/pull/2507?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a3085020ed0d81d4903c50967687192e3101e770?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2507?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2507 +/- ##\n=======================================\n Coverage 73.24% 73.24% \n=======================================\n Files 87 87 \n Lines 15006 15006 \n=======================================\n Hits 10991 10991 \n Misses 4015 4015\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2507?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/2507/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JlcnQucHk=) | `100% <ø> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2507?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/2507?src=pr&el=footer). Last update [a308502...8eb4e0c](https://codecov.io/gh/huggingface/transformers/pull/2507?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi, thanks for your PR but it has been superseded by #2492 !" ]

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[ "Please edit your post and remove the images. Instead, post the code inside Python [code tags](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks). It is hard to read your post like this and impossible to copy-paste and try it out ourselves.", "@BramVanroy Thanks for your response. I have edited the post. Please reproduce the results yourself. \r\nI'll be thankful if I can find out why that's happening.", "@BramVanroy I even printed the embedding layers of the pretrained model after loading it via transformers and pytorch pretrained bert, they were fairly different.\r\n", "It seems that part of your code is missing or incorrect. You don't seem to initialize the pytorch_pretrained_bert model anywhere. This needs to be fixed, of course.\r\n\r\nIt also seems that you never called `model.eval()`.\r\n\r\nThe order of concatenation is different in both cases. (One ascending, other descending.) I'm not sure how important this is in the end. In this case, I don't think it should matter but it's worth checking.\r\n\r\nIf you can post the real, reproducible and correct code that I just need to copy-paste I can have a better look.", "> It seems that part of your code is missing or incorrect. You don't seem to initialize the pytorch_pretrained_bert model anywhere. This needs to be fixed, of course.\r\n> \r\n> It also seems that you never called `model.eval()`.\r\n> \r\n> The order of concatenation is different in both cases. (One ascending, other descending.) I'm not sure how important this is in the end. In this case, I don't think it should matter but it's worth checking.\r\n> \r\n> If you can post the real, reproducible and correct code that I just need to copy-paste I can have a better look.\r\n@BramVanroy Thanks for your quick reply.\r\nI had done model.eval() but had not added it here,\r\nSorry for the inconvenience, I have updated the snippet.\r\nGood point, I'll check again after changing the order of concatenation.\r\nBut the results were different for sum as well. And is obvious, for the raw embeddings only obtained by encoded_layers, _ = model(tokens_tensor, segments_tensors) are different in the two cases.\r\n", "@BramVanroy I tried after changing the order of concatenation as well, results remain unchanged as you suggested.", "You can also check the tokenizers: verify that the tokenisation is identical.\r\n\r\nIf you can provide a full test suite I can test it.", "@BramVanroy Tokenization is same. I got my mistake. It was because i was passing segment ids with the pytorch-pretrained-bert loaded model, while i just passing the tokenized ids to transformers loaded model.\r\nThanks for helping me figure this out.\r\nAs the input was different, encoded layers would be different.\r\n\r\nOne place where i am still stuck is that, when i don't add segment ids to the input the results are much worse.\r\nIn the documentation of transformers we just pass token ids. Why is that, what is its implications/\r\nI have added the test cases here.\r\nhttps://github.com/chikubee/Test-Suite-BERT/blob/master/test-suite.ipynb\r\nI fail to understand why that's happening. \r\nThanks in advance.", "Always go back to the source code. The order of the arguments was swapped. I had actually never noticed this before, but I think it's good practice to always provide the parameter name for optional arguments instead of treating them as positional.\r\n\r\nAs you can see, in the current implementation the second argument is actually `attention_mask`:\r\n\r\nhttps://github.com/huggingface/transformers/blob/b8f43cb273a7db25b285d78bf937590dc2ce11fc/src/transformers/modeling_bert.py#L683-L693\r\n\r\nIn `pytorch_pretrained_bert`, the second argument is `token_type_ids`.\r\n\r\nhttps://github.com/huggingface/transformers/blob/b832d5bb8a6dfc5965015b828e577677eace601e/pytorch_pretrained_bert/modeling.py#L709\r\n\r\nYou can try it again, and explicitly set the kwargs:\r\n\r\n```python\r\nmodel(tokens_tensor, token_type_ids=segments_tensors)\r\n```", "@BramVanroy yeah I saw that, works just fine, will be closing this issue. Thanks for your quick respsone.\r\nSince the use of segment tensor is just to indicate portions of the input, I wonder how its absence is affecting the results of similarity that much.\r\n", "It's because the `token_type_ids` are expected to be zero for the first segment and ones for the second, and masks are expected to be ones for unmasked tokens and zeros for masked tokens.\r\n\r\nIn your case it's not so much the absence of token_type_ids (because they are not absent; they get a default value) but they have the opposite value in the two cases. So in one case you're saying that the segment you are passing is the first one, and in the second case that you're passing in the second segment. ", "@BramVanroy Got it, Thanks Bram. Much appreciated. \r\nBut when i don't add anything explicitly (which means default 0 for first segment), the results of similarity are very bad as documented here https://github.com/chikubee/Test-Suite-BERT/blob/master/test-suite.ipynb" ]

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[ "Hi, what is the variable `bert_model` you're showing? Could you provide more information e.g. the version of transformers, your version of python, your version of torch?" ]

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[ "I'll let @n1t0 chime in if needed, but in the `Fast` versions of the tokenizers you have to define the `max_length` at initialization, not when calling `.encode()`\r\n\r\nCan you try this and let me know if it works?", "Oh You are right. When i init with max_length it works.\r\nIs this documented?\r\nThanks", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Yes , you can use a MSEloss", "thank you for replying me.\r\nCan I use cross entropy Loss, since BERT use it, and replacing the one hot vector by the weight of each class?", "SO, What are the labeles of classes?", "1 for positive and 0 for negative", "I think it may work well ,and you may use NLLloss, it will be seem as a regression problem,\r\nYou can try mse loss ,I think mse loss will have better proformence", "Why \"seen as a regression problem\" the output still 1(positive) or 0(negative). The reason of multiplying by weight is to help the model to generalized better and avoiding overconfident . ", "\r\nI am try to use like the in the figure (https://pytorch.org/docs/master/nn.html#crossentropyloss) and I am not sure what is the best way to pass the weight tensor to the model in order CrossEntropyLoss to use it.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I would suggest adding a class_weight parameter to `BertForSequenceClassification`. This should be an easy fix ", "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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[ "The idea is very good, and has been discussed previously.\r\n\r\nhttps://github.com/huggingface/transformers/issues/2520\r\nhttps://github.com/huggingface/transformers/pull/2281#issuecomment-570574944\r\n\r\nI think HuggingFace is well aware of the challenges and intricacies that are involved, so I'm sure they'll figure it out. I don't think using a separate platform is a good idea, though. There's already the rather basic webpage of (user) models (https://huggingface.co/models) so it would be better if the functionality that you are suggesting is integrated in that webpage.", "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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[ "Closing, as I was logged in under wrong account" ]

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[ "According to https://github.com/huggingface/transformers/issues/2418#issuecomment-571721526, until a fix is released you should change the `-100` in your script to `-1`. It worked for me with Albert.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "same issue for me", "Hi,\r\n\r\nCan you try with the latest 2.4.0 transformers release and let us know if you still observe the same ?", "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 met the same problem when I use `T5Model`. I think it could be some minor error in the source code.", "I think it's not a minor error, at least in my case. It seems `Albert` does not support language model fine tuning and `Albert` did not have same API with `Bert`. ", "I found this problem in `T5` model and I solve it. Please refer to #2525 if it helps.", "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/2495?src=pr&el=h1) Report\n> Merging [#2495](https://codecov.io/gh/huggingface/transformers/pull/2495?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/331065e62d11d5c26642cb92a597904eee4c159b?src=pr&el=desc) will **decrease** coverage by `0.17%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2495?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2495 +/- ##\n==========================================\n- Coverage 73.24% 73.06% -0.18% \n==========================================\n Files 87 87 \n Lines 15005 15006 +1 \n==========================================\n- Hits 10990 10964 -26 \n- Misses 4015 4042 +27\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2495?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/2495/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `81.09% <100%> (+0.04%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/2495/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `25% <0%> (-7.15%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/2495/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `66.37% <0%> (-2.3%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/2495/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `91.53% <0%> (-1.59%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/2495/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `86.91% <0%> (-0.65%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2495?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/2495?src=pr&el=footer). Last update [331065e...90d3b78](https://codecov.io/gh/huggingface/transformers/pull/2495?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks Martin!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2494?src=pr&el=h1) Report\n> Merging [#2494](https://codecov.io/gh/huggingface/transformers/pull/2494?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/331065e62d11d5c26642cb92a597904eee4c159b?src=pr&el=desc) will **increase** coverage by `1.47%`.\n> The diff coverage is `75.55%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2494?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2494 +/- ##\n==========================================\n+ Coverage 73.24% 74.71% +1.47% \n==========================================\n Files 87 87 \n Lines 15005 14792 -213 \n==========================================\n+ Hits 10990 11052 +62 \n+ Misses 4015 3740 -275\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2494?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3hsbS5weQ==) | `96.22% <100%> (+0.07%)` | :arrow_up: |\n| [src/transformers/configuration\\_xlm\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3hsbV9yb2JlcnRhLnB5) | `100% <100%> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2dwdDIucHk=) | `97.05% <100%> (+0.08%)` | :arrow_up: |\n| [src/transformers/configuration\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JlcnQucHk=) | `100% <100%> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX29wZW5haS5weQ==) | `97.22% <100%> (+0.07%)` | :arrow_up: |\n| [src/transformers/configuration\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3JvYmVydGEucHk=) | `100% <100%> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2F1dG8ucHk=) | `100% <100%> (+56.52%)` | :arrow_up: |\n| [src/transformers/configuration\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3hsbmV0LnB5) | `93.47% <100%> (+0.14%)` | :arrow_up: |\n| [src/transformers/configuration\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2Rpc3RpbGJlcnQucHk=) | `100% <100%> (ø)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `70.19% <100%> (-0.15%)` | :arrow_down: |\n| ... and [13 more](https://codecov.io/gh/huggingface/transformers/pull/2494/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2494?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/2494?src=pr&el=footer). Last update [331065e...764f836](https://codecov.io/gh/huggingface/transformers/pull/2494?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "(Sorry for spurious CI related commits I’m on mobile!)" ]

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[ "Turns lout this is a non-issue; there is a subtle nuance that I have overlooked: on the very first iteration of V2 (using `past`) we do not have any `past` and therefore the code should be modified to take argmax differently fixed code produces the correct output:\r\n\r\nV2 (fixed):\r\n\r\n```python\r\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\nmodel = GPT2LMHeadModel.from_pretrained('gpt2')\r\nmodel.eval()\r\n\r\ngenerated = tokenizer.encode(\"This apple is\")\r\nprint(generated)\r\ncontext = torch.tensor([generated])\r\npast = None\r\n\r\nfor i in tqdm(range(100)):\r\n output, past = model(context, past=past)\r\n if i == 0:\r\n token = output[0, -1, :].argmax()\r\n else:\r\n token = output[0, :].argmax()\r\n\r\n generated += [token.item()]\r\n context = token.unsqueeze(0)\r\n\r\ntokenizer.decode(generated)\r\n```\r\n\r\nproduces \r\n\r\n```python\r\n\"This apple is a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very good apple. It's a very\"\r\n```" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2492?src=pr&el=h1) Report\n> Merging [#2492](https://codecov.io/gh/huggingface/transformers/pull/2492?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2b566c182efc5330e4753b6db74c5b0518716147?src=pr&el=desc) will **decrease** coverage by `0.18%`.\n> The diff coverage is `0%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2492?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2492 +/- ##\n==========================================\n- Coverage 73.24% 73.06% -0.19% \n==========================================\n Files 87 87 \n Lines 15009 15008 -1 \n==========================================\n- Hits 10994 10966 -28 \n- Misses 4015 4042 +27\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2492?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3hsbS5weQ==) | `96.15% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2dwdDIucHk=) | `96.96% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3RyYW5zZm9feGwucHk=) | `92.3% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JlcnQucHk=) | `100% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3JvYmVydGEucHk=) | `100% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2FsYmVydC5weQ==) | `100% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2Rpc3RpbGJlcnQucHk=) | `100% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_mmbt.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX21tYnQucHk=) | `55.55% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2NhbWVtYmVydC5weQ==) | `100% <ø> (ø)` | :arrow_up: |\n| [src/transformers/configuration\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX29wZW5haS5weQ==) | `97.14% <ø> (ø)` | :arrow_up: |\n| ... and [7 more](https://codecov.io/gh/huggingface/transformers/pull/2492/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2492?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/2492?src=pr&el=footer). Last update [2b566c1...6469f90](https://codecov.io/gh/huggingface/transformers/pull/2492?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "To fast... Sorry :)\r\nhttps://github.com/huggingface/transformers/issues/2442" ]

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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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[ "IIRC BERT was trained on BookCorpus and Wikipedia", "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 am suffering from the same problem, trying to recover input texts from `examples/run_ner.py`.", "Agree. I find the use of BIO very unorthodox in this case; if B actually represented the beginning of an entity (vs. the beginning of the new entity of the same type), we could reconstruct these spans ourselves. Currently I don't think it's possible to perfectly reconstruct them, though.", "I believe this issue should be resolved by this recently merged [PR](https://github.com/huggingface/transformers/pull/3957), which allows for the extraction of **entity groups** 🙂 ", "Indeed, thanks @enzoampil!" ]

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[ "I checked the codes days before and 'label2id' and 'id2label' seemed not used and didn't influence the code execution." ]

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[ "In case @jplu has an insight on this?", "Hi @jwallat,\r\n\r\nYou might have two solutions to solve your issue:\r\n\r\n* Implement your own loss function that you can give to the compile method (see the official Tensorflow [doc](https://www.tensorflow.org/api_docs/python/tf/keras/Model?version=stable#compile))\r\n* Implement a custom training loop such as the `train` function in the [NER example](https://github.com/huggingface/transformers/blob/master/examples/run_tf_ner.py#L154)" ]

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[ "Thanks @loretoparisi that is awesome! \r\n\r\nWe _should_ be able to just load it using `RobertaModel` or `AutoModel`, though. I'll see if we need to make changes to enable this.", "Work in progress on the (remote) tokenizer config in https://github.com/huggingface/transformers/pull/2535\r\n\r\n", "@julien-c just checking if there is anything we have to do by our side for this PR. Thank you 🤗 ", "[ Umberto Tokenizer ]\r\nHi @julien-c @thomwolf,\r\nwhen we try lo load umberto tokenizer with Autotokenizer, this error occurs.\r\nI would like to remember that Umberto Tokenizer inherits from a Roberta Tokenizer\r\n\r\n```\r\n>>> tokenizer = AutoTokenizer.from_pretrained(\"Musixmatch/umberto-commoncrawl-cased-v1\")\r\n```\r\n```\r\nI0121 16:22:33.957683 139667243427648 tokenization_utils.py:327] Model name 'Musixmatch/umberto-commoncrawl-cased-v1' not found in model shortcut name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased, bert-base-finnish-cased-v1, bert-base-finnish-uncased-v1). Assuming 'Musixmatch/umberto-commoncrawl-cased-v1' is a path or url to a directory containing tokenizer files.\r\nI0121 16:22:33.957921 139667243427648 tokenization_utils.py:359] Didn't find file Musixmatch/umberto-commoncrawl-cased-v1/added_tokens.json. We won't load it.\r\nI0121 16:22:33.957994 139667243427648 tokenization_utils.py:359] Didn't find file Musixmatch/umberto-commoncrawl-cased-v1/special_tokens_map.json. We won't load it.\r\nI0121 16:22:33.958091 139667243427648 tokenization_utils.py:359] Didn't find file Musixmatch/umberto-commoncrawl-cased-v1/tokenizer_config.json. We won't load it.\r\nI0121 16:22:34.470488 139667243427648 tokenization_utils.py:398] loading file https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/vocab.txt from cache at /root/.cache/torch/transformers/d12b9cd215cbedbd1b21cbb1ab8663b6f1990a661d07b4e8ffafab79f02cfc21\r\nI0121 16:22:34.470605 139667243427648 tokenization_utils.py:395] loading file None\r\nI0121 16:22:34.470653 139667243427648 tokenization_utils.py:395] loading file None\r\nI0121 16:22:34.470714 139667243427648 tokenization_utils.py:395] loading file None\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_auto.py\", line 143, in from_pretrained\r\n return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils.py\", line 302, in from_pretrained\r\n return cls._from_pretrained(*inputs, **kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils.py\", line 438, in _from_pretrained\r\n tokenizer = cls(*init_inputs, **init_kwargs)\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_bert.py\", line 164, in __init__\r\n \"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`\".format(vocab_file))\r\nValueError: Can't find a vocabulary file at path '/root/.cache/torch/transformers/d12b9cd215cbedbd1b21cbb1ab8663b6f1990a661d07b4e8ffafab79f02cfc21'. To load the vocabulary from a Google pretrained model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`\r\n```", "[ Umberto Model ]\r\n\r\nAlso for Umberto model loading some strange things happen.\r\nSame as tokenizer, Umberto Model inherits from Roberta Model, not from BertModel.\r\n\r\n```>>> umberto = AutoModel.from_pretrained(\"Musixmatch/umberto-commoncrawl-cased-v1\")\r\nI0121 16:24:03.242502 139667243427648 file_utils.py:362] https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/config.json not found in cache or force_download set to True, downloading to /tmp/tmpehg26ac4\r\nI0121 16:24:03.746079 139667243427648 file_utils.py:377] copying /tmp/tmpehg26ac4 to cache at /root/.cache/torch/transformers/d4d9f43ce6f9f572d223e54ac6184f961c91f180333f42ba436b19060da64177.446c3bed6ceafbbbe9d3b49b0ae276ee73ef572bdfb42fd076c3ee5e6425952e\r\nI0121 16:24:03.746514 139667243427648 file_utils.py:381] creating metadata file for /root/.cache/torch/transformers/d4d9f43ce6f9f572d223e54ac6184f961c91f180333f42ba436b19060da64177.446c3bed6ceafbbbe9d3b49b0ae276ee73ef572bdfb42fd076c3ee5e6425952e\r\nI0121 16:24:03.747002 139667243427648 file_utils.py:390] removing temp file /tmp/tmpehg26ac4\r\nI0121 16:24:03.747236 139667243427648 configuration_utils.py:185] loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/config.json from cache at /root/.cache/torch/transformers/d4d9f43ce6f9f572d223e54ac6184f961c91f180333f42ba436b19060da64177.446c3bed6ceafbbbe9d3b49b0ae276ee73ef572bdfb42fd076c3ee5e6425952e\r\nI0121 16:24:03.747532 139667243427648 configuration_utils.py:199] Model config {\r\n \"attention_probs_dropout_prob\": 0.1,\r\n \"finetuning_task\": null,\r\n \"hidden_act\": \"gelu\",\r\n \"hidden_dropout_prob\": 0.1,\r\n \"hidden_size\": 768,\r\n \"id2label\": {\r\n \"0\": \"LABEL_0\",\r\n \"1\": \"LABEL_1\"\r\n },\r\n \"initializer_range\": 0.02,\r\n \"intermediate_size\": 3072,\r\n \"is_decoder\": false,\r\n \"label2id\": {\r\n \"LABEL_0\": 0,\r\n \"LABEL_1\": 1\r\n },\r\n \"layer_norm_eps\": 1e-05,\r\n \"max_position_embeddings\": 514,\r\n \"num_attention_heads\": 12,\r\n \"num_hidden_layers\": 12,\r\n \"num_labels\": 2,\r\n \"output_attentions\": false,\r\n \"output_hidden_states\": false,\r\n \"output_past\": true,\r\n \"pruned_heads\": {},\r\n \"torchscript\": false,\r\n \"type_vocab_size\": 1,\r\n \"use_bfloat16\": false,\r\n \"vocab_size\": 32005\r\n}\r\n\r\nI0121 16:24:04.271546 139667243427648 file_utils.py:362] https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/pytorch_model.bin not found in cache or force_download set to True, downloading to /tmp/tmp6e0jg2r3\r\n\r\n\r\nI0121 16:25:02.123574 139667243427648 file_utils.py:377] copying /tmp/tmp6e0jg2r3 to cache at /root/.cache/torch/transformers/faef12cb7f68b2ecafeaed7e33b6fcdbb1772a607d0b602e244d5eab2e5e6dbc.38b3b456347cfe45fa37a33fe149652d871db4c2f36947993be2c6efbdce9356\r\nI0121 16:25:02.441211 139667243427648 file_utils.py:381] creating metadata file for /root/.cache/torch/transformers/faef12cb7f68b2ecafeaed7e33b6fcdbb1772a607d0b602e244d5eab2e5e6dbc.38b3b456347cfe45fa37a33fe149652d871db4c2f36947993be2c6efbdce9356\r\nI0121 16:25:02.441466 139667243427648 file_utils.py:390] removing temp file /tmp/tmp6e0jg2r3\r\nI0121 16:25:02.484607 139667243427648 modeling_utils.py:406] loading weights file https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/pytorch_model.bin from cache at /root/.cache/torch/transformers/faef12cb7f68b2ecafeaed7e33b6fcdbb1772a607d0b602e244d5eab2e5e6dbc.38b3b456347cfe45fa37a33fe149652d871db4c2f36947993be2c6efbdce9356\r\nI0121 16:25:03.654651 139667243427648 modeling_utils.py:480] Weights of BertModel not initialized from pretrained model: ['embeddings.word_embeddings.weight', 'embeddings.position_embeddings.weight', 'embeddings.token_type_embeddings.weight', 'embeddings.LayerNorm.weight', 'embeddings.LayerNorm.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.0.attention.self.query.bias', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.0.attention.self.key.bias', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.0.attention.output.LayerNorm.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.0.output.dense.bias', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.1.output.dense.bias', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.2.attention.self.key.weight', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.2.output.dense.weight', 'encoder.layer.2.output.dense.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.3.output.dense.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.4.intermediate.dense.bias', 'encoder.layer.4.output.dense.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.8.output.LayerNorm.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.9.output.dense.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.10.attention.self.query.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.10.attention.output.dense.bias', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.10.output.dense.bias', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.11.output.dense.weight', 'encoder.layer.11.output.dense.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.11.output.LayerNorm.bias', 'pooler.dense.weight', 'pooler.dense.bias']\r\nI0121 16:25:03.654866 139667243427648 modeling_utils.py:483] Weights from pretrained model not used in BertModel: ['roberta.embeddings.word_embeddings.weight', 'roberta.embeddings.position_embeddings.weight', 'roberta.embeddings.token_type_embeddings.weight', 'roberta.embeddings.LayerNorm.weight', 'roberta.embeddings.LayerNorm.bias', 'roberta.encoder.layer.0.attention.self.query.weight', 'roberta.encoder.layer.0.attention.self.query.bias', 'roberta.encoder.layer.0.attention.self.key.weight', 'roberta.encoder.layer.0.attention.self.key.bias', 'roberta.encoder.layer.0.attention.self.value.weight', 'roberta.encoder.layer.0.attention.self.value.bias', 'roberta.encoder.layer.0.attention.output.dense.weight', 'roberta.encoder.layer.0.attention.output.dense.bias', 'roberta.encoder.layer.0.attention.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.intermediate.dense.weight', 'roberta.encoder.layer.0.intermediate.dense.bias', 'roberta.encoder.layer.0.output.dense.weight', 'roberta.encoder.layer.0.output.dense.bias', 'roberta.encoder.layer.0.output.LayerNorm.weight', 'roberta.encoder.layer.0.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.self.query.weight', 'roberta.encoder.layer.1.attention.self.query.bias', 'roberta.encoder.layer.1.attention.self.key.weight', 'roberta.encoder.layer.1.attention.self.key.bias', 'roberta.encoder.layer.1.attention.self.value.weight', 'roberta.encoder.layer.1.attention.self.value.bias', 'roberta.encoder.layer.1.attention.output.dense.weight', 'roberta.encoder.layer.1.attention.output.dense.bias', 'roberta.encoder.layer.1.attention.output.LayerNorm.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.bias', 'roberta.encoder.layer.1.intermediate.dense.weight', 'roberta.encoder.layer.1.intermediate.dense.bias', 'roberta.encoder.layer.1.output.dense.weight', 'roberta.encoder.layer.1.output.dense.bias', 'roberta.encoder.layer.1.output.LayerNorm.weight', 'roberta.encoder.layer.1.output.LayerNorm.bias', 'roberta.encoder.layer.2.attention.self.query.weight', 'roberta.encoder.layer.2.attention.self.query.bias', 'roberta.encoder.layer.2.attention.self.key.weight', 'roberta.encoder.layer.2.attention.self.key.bias', 'roberta.encoder.layer.2.attention.self.value.weight', 'roberta.encoder.layer.2.attention.self.value.bias', 'roberta.encoder.layer.2.attention.output.dense.weight', 'roberta.encoder.layer.2.attention.output.dense.bias', 'roberta.encoder.layer.2.attention.output.LayerNorm.weight', 'roberta.encoder.layer.2.attention.output.LayerNorm.bias', 'roberta.encoder.layer.2.intermediate.dense.weight', 'roberta.encoder.layer.2.intermediate.dense.bias', 'roberta.encoder.layer.2.output.dense.weight', 'roberta.encoder.layer.2.output.dense.bias', 'roberta.encoder.layer.2.output.LayerNorm.weight', 'roberta.encoder.layer.2.output.LayerNorm.bias', 'roberta.encoder.layer.3.attention.self.query.weight', 'roberta.encoder.layer.3.attention.self.query.bias', 'roberta.encoder.layer.3.attention.self.key.weight', 'roberta.encoder.layer.3.attention.self.key.bias', 'roberta.encoder.layer.3.attention.self.value.weight', 'roberta.encoder.layer.3.attention.self.value.bias', 'roberta.encoder.layer.3.attention.output.dense.weight', 'roberta.encoder.layer.3.attention.output.dense.bias', 'roberta.encoder.layer.3.attention.output.LayerNorm.weight', 'roberta.encoder.layer.3.attention.output.LayerNorm.bias', 'roberta.encoder.layer.3.intermediate.dense.weight', 'roberta.encoder.layer.3.intermediate.dense.bias', 'roberta.encoder.layer.3.output.dense.weight', 'roberta.encoder.layer.3.output.dense.bias', 'roberta.encoder.layer.3.output.LayerNorm.weight', 'roberta.encoder.layer.3.output.LayerNorm.bias', 'roberta.encoder.layer.4.attention.self.query.weight', 'roberta.encoder.layer.4.attention.self.query.bias', 'roberta.encoder.layer.4.attention.self.key.weight', 'roberta.encoder.layer.4.attention.self.key.bias', 'roberta.encoder.layer.4.attention.self.value.weight', 'roberta.encoder.layer.4.attention.self.value.bias', 'roberta.encoder.layer.4.attention.output.dense.weight', 'roberta.encoder.layer.4.attention.output.dense.bias', 'roberta.encoder.layer.4.attention.output.LayerNorm.weight', 'roberta.encoder.layer.4.attention.output.LayerNorm.bias', 'roberta.encoder.layer.4.intermediate.dense.weight', 'roberta.encoder.layer.4.intermediate.dense.bias', 'roberta.encoder.layer.4.output.dense.weight', 'roberta.encoder.layer.4.output.dense.bias', 'roberta.encoder.layer.4.output.LayerNorm.weight', 'roberta.encoder.layer.4.output.LayerNorm.bias', 'roberta.encoder.layer.5.attention.self.query.weight', 'roberta.encoder.layer.5.attention.self.query.bias', 'roberta.encoder.layer.5.attention.self.key.weight', 'roberta.encoder.layer.5.attention.self.key.bias', 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'roberta.encoder.layer.7.attention.output.LayerNorm.weight', 'roberta.encoder.layer.7.attention.output.LayerNorm.bias', 'roberta.encoder.layer.7.intermediate.dense.weight', 'roberta.encoder.layer.7.intermediate.dense.bias', 'roberta.encoder.layer.7.output.dense.weight', 'roberta.encoder.layer.7.output.dense.bias', 'roberta.encoder.layer.7.output.LayerNorm.weight', 'roberta.encoder.layer.7.output.LayerNorm.bias', 'roberta.encoder.layer.8.attention.self.query.weight', 'roberta.encoder.layer.8.attention.self.query.bias', 'roberta.encoder.layer.8.attention.self.key.weight', 'roberta.encoder.layer.8.attention.self.key.bias', 'roberta.encoder.layer.8.attention.self.value.weight', 'roberta.encoder.layer.8.attention.self.value.bias', 'roberta.encoder.layer.8.attention.output.dense.weight', 'roberta.encoder.layer.8.attention.output.dense.bias', 'roberta.encoder.layer.8.attention.output.LayerNorm.weight', 'roberta.encoder.layer.8.attention.output.LayerNorm.bias', 'roberta.encoder.layer.8.intermediate.dense.weight', 'roberta.encoder.layer.8.intermediate.dense.bias', 'roberta.encoder.layer.8.output.dense.weight', 'roberta.encoder.layer.8.output.dense.bias', 'roberta.encoder.layer.8.output.LayerNorm.weight', 'roberta.encoder.layer.8.output.LayerNorm.bias', 'roberta.encoder.layer.9.attention.self.query.weight', 'roberta.encoder.layer.9.attention.self.query.bias', 'roberta.encoder.layer.9.attention.self.key.weight', 'roberta.encoder.layer.9.attention.self.key.bias', 'roberta.encoder.layer.9.attention.self.value.weight', 'roberta.encoder.layer.9.attention.self.value.bias', 'roberta.encoder.layer.9.attention.output.dense.weight', 'roberta.encoder.layer.9.attention.output.dense.bias', 'roberta.encoder.layer.9.attention.output.LayerNorm.weight', 'roberta.encoder.layer.9.attention.output.LayerNorm.bias', 'roberta.encoder.layer.9.intermediate.dense.weight', 'roberta.encoder.layer.9.intermediate.dense.bias', 'roberta.encoder.layer.9.output.dense.weight', 'roberta.encoder.layer.9.output.dense.bias', 'roberta.encoder.layer.9.output.LayerNorm.weight', 'roberta.encoder.layer.9.output.LayerNorm.bias', 'roberta.encoder.layer.10.attention.self.query.weight', 'roberta.encoder.layer.10.attention.self.query.bias', 'roberta.encoder.layer.10.attention.self.key.weight', 'roberta.encoder.layer.10.attention.self.key.bias', 'roberta.encoder.layer.10.attention.self.value.weight', 'roberta.encoder.layer.10.attention.self.value.bias', 'roberta.encoder.layer.10.attention.output.dense.weight', 'roberta.encoder.layer.10.attention.output.dense.bias', 'roberta.encoder.layer.10.attention.output.LayerNorm.weight', 'roberta.encoder.layer.10.attention.output.LayerNorm.bias', 'roberta.encoder.layer.10.intermediate.dense.weight', 'roberta.encoder.layer.10.intermediate.dense.bias', 'roberta.encoder.layer.10.output.dense.weight', 'roberta.encoder.layer.10.output.dense.bias', 'roberta.encoder.layer.10.output.LayerNorm.weight', 'roberta.encoder.layer.10.output.LayerNorm.bias', 'roberta.encoder.layer.11.attention.self.query.weight', 'roberta.encoder.layer.11.attention.self.query.bias', 'roberta.encoder.layer.11.attention.self.key.weight', 'roberta.encoder.layer.11.attention.self.key.bias', 'roberta.encoder.layer.11.attention.self.value.weight', 'roberta.encoder.layer.11.attention.self.value.bias', 'roberta.encoder.layer.11.attention.output.dense.weight', 'roberta.encoder.layer.11.attention.output.dense.bias', 'roberta.encoder.layer.11.attention.output.LayerNorm.weight', 'roberta.encoder.layer.11.attention.output.LayerNorm.bias', 'roberta.encoder.layer.11.intermediate.dense.weight', 'roberta.encoder.layer.11.intermediate.dense.bias', 'roberta.encoder.layer.11.output.dense.weight', 'roberta.encoder.layer.11.output.dense.bias', 'roberta.encoder.layer.11.output.LayerNorm.weight', 'roberta.encoder.layer.11.output.LayerNorm.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']```", "Hi @loretoparisi and all,\r\n\r\n- I've added a `\"model_type\": \"camembert\"` to both your config.json files on our S3, so tokenizer is now properly instantiated as a CamembertTokenizer (i.e. admit a `sentencepiece.bpe.model` file): https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/config.json\r\n- I've uploaded the two `sentencepiece.bpe.model` files from your repo.\r\n\r\n**So, doing the following should now work out of the box:**\r\n```\r\ntokenizer = AutoTokenizer.from_pretrained(\"Musixmatch/umberto-commoncrawl-cased-v1\")\r\numberto = AutoModel.from_pretrained(\"Musixmatch/umberto-commoncrawl-cased-v1\")\r\n```\r\n(same thing for the wikipedia model)\r\n\r\nCan you check that it works fine now? I'll add shortcut names in a separate commit as the PR will be much shorter.\r\n\r\nFinally, can you add a README.md file to the same folders on our S3, and it will be rendered on your model's page: https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1\r\n\r\nYou can use this file to describe your model, which datasets did you train on, eval results, etc.\r\n\r\nGrazie mille!", "@julien-c 👍 great! We are applying the changes! cc @simonefrancia", "1) We tested `AutoTokenizer` and `AutoModel` with both `Musixmatch/umberto-commoncrawl-cased-v1` and `Musixmatch/umberto-wikipedia-uncased-v1` and this code worked:\r\n\r\n```python\r\nimport torch\r\nfrom transformers import AutoTokenizer, AutoModel\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(\"[name_tokenizer]\") # do_lower_case=True if uncased\r\numberto = AutoModel.from_pretrained(\"[name_model]\")\r\n\r\nencoded_input = tokenizer.encode(\"Umberto Eco è stato un grande scrittore\")\r\ninput_ids = torch.tensor(encoded_input).unsqueeze(0) # Batch size 1\r\noutputs = umberto(input_ids)\r\nlast_hidden_states = outputs[0] # The last hidden-state is the first element of the output\r\n```\r\n\r\n2) for the error [here](https://github.com/huggingface/transformers/pull/2661), we do testing in the same way and no error appears to us:\r\n\r\n<img width=\"1676\" alt=\"Schermata 2020-01-28 alle 10 48 59\" src=\"https://user-images.githubusercontent.com/7140210/73260860-27325d00-41cb-11ea-9ba8-162d820559bc.png\">\r\n So probably it's a Heisenbug\r\n\r\n\r\n3) We did two README.md, one for each model:\r\n `Umberto-commoncrawl-cased` : [link](https://mxmdownloads.s3.amazonaws.com/umberto/README_UMBERTO_COMMONCRAWL.MD)\r\n `Umberto-wikipedia-uncased` : [link](https://mxmdownloads.s3.amazonaws.com/umberto/README_UMBERTO_WIKIPEDIA.MD)\r\n\r\n\r\nThat's all, if you need other, here we are cc @loretoparisi . Thanks!", "Great, I've uploaded the READMEs to our S3 so that they'll be displayed on the model pages.\r\n\r\nI've also uploaded this tokenizer_config.json so you don't need to specify `do_lower_case: true` anymore: https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-wikipedia-uncased-v1/tokenizer_config.json\r\n\r\nFinally, I've updated your READMEs slightly to:\r\n- add more info from your repo's readme\r\n- add an example use case for our new FillMaskPipeline:\r\n\r\n```python\r\nfrom transformers import pipeline\r\n\r\nfill_mask = pipeline(\r\n\t\"fill-mask\",\r\n\tmodel=\"Musixmatch/umberto-wikipedia-uncased-v1\",\r\n\ttokenizer=\"Musixmatch/umberto-wikipedia-uncased-v1\"\r\n)\r\n\r\nresult = fill_mask(\"Umberto Eco è <mask> un grande scrittore\")\r\n```\r\n\r\nI'll close this issue and merge #2661 \r\n\r\nThanks again!", "@julien-c Hi, we saw from https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1 that we don't have the Tensorflow version of our model available for the community. How can we create and upload it? Thanks", "Hi @simonefrancia, check out this comment: https://github.com/huggingface/transformers/issues/2901#issuecomment-591710959" ]

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[ "If this is important to anyone, I have found a solution:\r\n```\r\ndef deleteEncodingLayers(model, num_layers_to_keep): # must pass in the full bert model\r\n oldModuleList = model.bert.encoder.layer\r\n newModuleList = nn.ModuleList()\r\n\r\n # Now iterate over all layers, only keepign only the relevant layers.\r\n for i in range(0, len(num_layers_to_keep)):\r\n newModuleList.append(oldModuleList[i])\r\n\r\n # create a copy of the model, modify it with the new list, and return\r\n copyOfModel = copy.deepcopy(model)\r\n copyOfModel.bert.encoder.layer = newModuleList\r\n\r\n return copyOfModel\r\n```", "Hi,\r\n\r\nThank you for your question and solution. I also want to try such kind of thing.\r\n\r\nI have a question. If I remove some layers, do I need to do pre-train from scratch again? \r\n\r\nHow does the performance look if you only do finetuning on GLUE or Squad tasks? Does the accuracy go down dramatically?\r\n\r\nThanks,\r\nZLK", "@ZLKong no, the remaining layers will remain trained. Not quite sure what you mean by only fine-tuning though.", "Thank you for your reply!\r\n\r\nI want to decrease the FLOPS by simply removing some layers from the model. I want to see if I remove some layers, how much will if effect the accuracy of SQUAD task. \r\n\r\n(If the accuracy goes down a lot, that means I might have do the pretraining again?)\r\n\r\nDo you have any experiments on this?\r\n\r\nBest,\r\nZLK\r\n", "I haven't, but I'm sure in the original paper they performed a test like that. If not, I guarantee there will be a paper out there that does given how much research has been chucked at bert :)", "OK, I will look if there are any papers about it. I will run some testings, too.\r\nThank you very much!", "If you're dealing with loading a pretrained model, there is an easier way to remove the top layer:\r\n\r\n```\r\nconfig = XLNetConfig.from_pretrained(checkpoint)\r\nconfig.n_layer = 29 #was 30 layers, in my case\r\nmodel = XLNetModel.from_pretrained(checkpoint, config = config)\r\n```\r\n\r\nThis will produce a warning that there are unused weights in the checkpoint and you'll get a model with the top layer removed.", "@ZLKong have you found any papers yet?:D\r\n\r\nEDIT: I found this paper from March 2021: [On the Effect of Dropping Layers of Pre-trained Transformer Models](https://arxiv.org/abs/2004.03844) \r\n\r\n", "> If this is important to anyone, I have found a solution:\r\n> \r\n> ```\r\n> def deleteEncodingLayers(model, num_layers_to_keep): # must pass in the full bert model\r\n> oldModuleList = model.bert.encoder.layer\r\n> newModuleList = nn.ModuleList()\r\n> \r\n> # Now iterate over all layers, only keepign only the relevant layers.\r\n> for i in range(0, len(num_layers_to_keep)):\r\n> newModuleList.append(oldModuleList[i])\r\n> \r\n> # create a copy of the model, modify it with the new list, and return\r\n> copyOfModel = copy.deepcopy(model)\r\n> copyOfModel.bert.encoder.layer = newModuleList\r\n> \r\n> return copyOfModel\r\n> ```\r\n\r\nHello there,\r\n\r\nI still don't know how to implement this. Does this just need to call the pre-trained model, for example: BERT model from TensorFlow\r\n\r\nor\r\n\r\nI need the full code BERT model?\r\n\r\nthank you", "hi @officialpatterson, thanks for providing the solution! now I'm trying to implement it with the BertModel package which doesn't have the same attributes as yours, anyway I can adapt this code to my model?\r\n\r\n```\r\nClass BERTClass(torch.nn.Module):\r\n def __init__(self):\r\n super(BERTClass, self).__init__()\r\n self.bert_model = BertModel.from_pretrained('bert-base-cased')\r\n self.dropout = torch.nn.Dropout(0.5)\r\n self.linear = torch.nn.Linear(768, 9)\r\n \r\n def forward(self, input_ids, attn_mask, token_type_ids):\r\n output = self.bert_model(\r\n input_ids, \r\n attention_mask=attn_mask, \r\n token_type_ids=token_type_ids\r\n )\r\n output_dropout = self.dropout(output.pooler_output)\r\n output = self.linear(output_dropout)\r\n return output\r\n```", "Not sure if anyone is looking for a way to remove layers for `EncoderDecoderModel` e.g. for[ some models with unbalance layers](https://aclanthology.org/2020.amta-research.10/). I've tried this, and it seems to work:\r\n\r\n```python\r\nfrom transformers import EncoderDecoderModel, BertLMHeadModel\r\nfrom transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel\r\n\r\n# Initializing a BERT bert-base-uncased style configuration\r\nconfig_encoder = BertConfig.from_pretrained(\"bert-base-multilingual-uncased\")\r\nconfig_decoder = BertConfig.from_pretrained(\"bert-base-multilingual-uncased\")\r\n\r\nconfig_encoder.num_hidden_layers = 5\r\nconfig_decoder.num_hidden_layers = 2\r\n\r\nconfig = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)\r\n\r\n# Initializing a Bert2Bert model from the bert-base-uncased style configurations\r\nmodel = EncoderDecoderModel(config=config)\r\n\r\nmodel.decoder # Shows 2 layers, if `num_hidden_layers` was unchanged, it should show 6.\r\n```\r\n\r\n[out]:\r\n\r\n```\r\nBertLMHeadModel(\r\n (bert): BertModel(\r\n (embeddings): BertEmbeddings(\r\n (word_embeddings): Embedding(105879, 768, padding_idx=0)\r\n (position_embeddings): Embedding(512, 768)\r\n (token_type_embeddings): Embedding(2, 768)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (encoder): BertEncoder(\r\n (layer): ModuleList(\r\n (0): BertLayer(\r\n (attention): BertAttention(\r\n (self): BertSelfAttention(\r\n (query): Linear(in_features=768, out_features=768, bias=True)\r\n (key): Linear(in_features=768, out_features=768, bias=True)\r\n (value): Linear(in_features=768, out_features=768, bias=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (output): BertSelfOutput(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (crossattention): BertAttention(\r\n (self): BertSelfAttention(\r\n (query): Linear(in_features=768, out_features=768, bias=True)\r\n (key): Linear(in_features=768, out_features=768, bias=True)\r\n (value): Linear(in_features=768, out_features=768, bias=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (output): BertSelfOutput(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (intermediate): BertIntermediate(\r\n (dense): Linear(in_features=768, out_features=3072, bias=True)\r\n (intermediate_act_fn): GELUActivation()\r\n )\r\n (output): BertOutput(\r\n (dense): Linear(in_features=3072, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (1): BertLayer(\r\n (attention): BertAttention(\r\n (self): BertSelfAttention(\r\n (query): Linear(in_features=768, out_features=768, bias=True)\r\n (key): Linear(in_features=768, out_features=768, bias=True)\r\n (value): Linear(in_features=768, out_features=768, bias=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (output): BertSelfOutput(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (crossattention): BertAttention(\r\n (self): BertSelfAttention(\r\n (query): Linear(in_features=768, out_features=768, bias=True)\r\n (key): Linear(in_features=768, out_features=768, bias=True)\r\n (value): Linear(in_features=768, out_features=768, bias=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (output): BertSelfOutput(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (intermediate): BertIntermediate(\r\n (dense): Linear(in_features=768, out_features=3072, bias=True)\r\n (intermediate_act_fn): GELUActivation()\r\n )\r\n (output): BertOutput(\r\n (dense): Linear(in_features=3072, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n )\r\n )\r\n )\r\n (cls): BertOnlyMLMHead(\r\n (predictions): BertLMPredictionHead(\r\n (transform): BertPredictionHeadTransform(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (transform_act_fn): GELUActivation()\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n )\r\n (decoder): Linear(in_features=768, out_features=105879, bias=True)\r\n )\r\n )\r\n)\r\n```\r\n\r\n----\r\n\r\n\r\nSimilarly, if it's just an LM encoder model, something like this should work:\r\n\r\n```python\r\nfrom transformers import BertConfig, BertLMHeadModel\r\n\r\nconfig_encoder = BertConfig.from_pretrained(\"bert-base-multilingual-uncased\")\r\nconfig_encoder.num_hidden_layers = 3\r\nmodel = BertLMHeadModel(config=config_encoder)\r\n\r\nmodel\r\n```\r\n\r\n[out]:\r\n\r\n```\r\nBertLMHeadModel(\r\n (bert): BertModel(\r\n (embeddings): BertEmbeddings(\r\n (word_embeddings): Embedding(105879, 768, padding_idx=0)\r\n (position_embeddings): Embedding(512, 768)\r\n (token_type_embeddings): Embedding(2, 768)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (encoder): BertEncoder(\r\n (layer): ModuleList(\r\n (0): BertLayer(\r\n (attention): BertAttention(\r\n (self): BertSelfAttention(\r\n (query): Linear(in_features=768, out_features=768, bias=True)\r\n (key): Linear(in_features=768, out_features=768, bias=True)\r\n (value): Linear(in_features=768, out_features=768, bias=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (output): BertSelfOutput(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (intermediate): BertIntermediate(\r\n (dense): Linear(in_features=768, out_features=3072, bias=True)\r\n (intermediate_act_fn): GELUActivation()\r\n )\r\n (output): BertOutput(\r\n (dense): Linear(in_features=3072, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (1): BertLayer(\r\n (attention): BertAttention(\r\n (self): BertSelfAttention(\r\n (query): Linear(in_features=768, out_features=768, bias=True)\r\n (key): Linear(in_features=768, out_features=768, bias=True)\r\n (value): Linear(in_features=768, out_features=768, bias=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (output): BertSelfOutput(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (intermediate): BertIntermediate(\r\n (dense): Linear(in_features=768, out_features=3072, bias=True)\r\n (intermediate_act_fn): GELUActivation()\r\n )\r\n (output): BertOutput(\r\n (dense): Linear(in_features=3072, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (2): BertLayer(\r\n (attention): BertAttention(\r\n (self): BertSelfAttention(\r\n (query): Linear(in_features=768, out_features=768, bias=True)\r\n (key): Linear(in_features=768, out_features=768, bias=True)\r\n (value): Linear(in_features=768, out_features=768, bias=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n (output): BertSelfOutput(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n (intermediate): BertIntermediate(\r\n (dense): Linear(in_features=768, out_features=3072, bias=True)\r\n (intermediate_act_fn): GELUActivation()\r\n )\r\n (output): BertOutput(\r\n (dense): Linear(in_features=3072, out_features=768, bias=True)\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n (dropout): Dropout(p=0.1, inplace=False)\r\n )\r\n )\r\n )\r\n )\r\n )\r\n (cls): BertOnlyMLMHead(\r\n (predictions): BertLMPredictionHead(\r\n (transform): BertPredictionHeadTransform(\r\n (dense): Linear(in_features=768, out_features=768, bias=True)\r\n (transform_act_fn): GELUActivation()\r\n (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\r\n )\r\n (decoder): Linear(in_features=768, out_features=105879, bias=True)\r\n )\r\n )\r\n)\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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[ "I find that the first token in the original XLM is indeed using \\</s\\> rather than \\<s\\>. " ]

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[ "Hi, I've pushed a fix that was just merged in `master`. Could you please try and install from source:\r\n```py\r\npip install git+https://github.com/huggingface/transformers\r\n```\r\nand tell me if you face the same error?", "Having follow your reply from here (https://github.com/huggingface/transformers/issues/2513#issuecomment-574406370) it now works :)\r\n\r\nNeeded to update `run_lm_finetuning.py` to latest github branch - thanks :)", "Hi @LysandreJik . Thank you for the update but the error has not been solved I'm afraid. Following are the error returned:\r\n```\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 541, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py\", line 152, in forward\r\n outputs = self.parallel_apply(replicas, inputs, kwargs)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py\", line 162, in parallel_apply\r\n return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py\", line 85, in parallel_apply\r\n output.reraise()\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/_utils.py\", line 385, in reraise\r\n raise self.exc_type(msg)\r\nRuntimeError: Caught RuntimeError in replica 0 on device 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py\", line 60, in _worker\r\n output = module(*input, **kwargs)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 541, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/transformers/modeling_bert.py\", line 889, in forward\r\n prediction_scores = self.cls(sequence_output)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 541, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/transformers/modeling_bert.py\", line 461, in forward\r\n prediction_scores = self.predictions(sequence_output)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 541, in __call__\r\n result = self.forward(*input, **kwargs)\r\n File \"/sdcc/u/hvu/.conda/envs/torch/lib/python3.6/site-packages/transformers/modeling_bert.py\", line 451, in forward\r\n hidden_states = self.decoder(hidden_states) + self.bias\r\nRuntimeError: The size of tensor a (31119) must match the size of tensor b (31116) at non-singleton dimension 2\r\n```\r\n\r\n\r\n\r\nI have solved the problem myself by implementing this piece of code in the method `def _tie_or_clone_weights(self, output_embeddings, input_embeddings)` in _modeling_utils.py_:\r\n```\r\n # Update bias size if has attribuate bias \r\n if hasattr(self, \"cls\"):\r\n self.cls.predictions.bias.data = torch.nn.functional.pad(\r\n self.cls.predictions.bias.data,\r\n (0, self.config.vocab_size - self.cls.predictions.bias.shape[0]),\r\n \"constant\",\r\n 0,\r\n )\r\n```\r\n", "@HuyVu0508 Try update this file \r\n\r\nhttps://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py\r\n\r\nIt should be somewhere \"/opt/conda/lib/python3.6/site-packages/transformers/modeling_bert.py\"", "Looks like this is probably a duplicate of #1730 \r\n\r\nAlso, there is a temp solution posted here.\r\nhttps://github.com/huggingface/transformers/issues/1730#issuecomment-550081307", "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 second this! :+1: " ]

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[ "When you enter the command \"python\" what is the output? and what environment are you using? linux/Windows/mac/etc?\r\n\r\nAlso, could you copy the exact output of \"pip install transformers\" so that we can see?", "Python 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32\r\n\r\nWarning:\r\nThis Python interpreter is in a conda environment, but the environment has\r\nnot been activated. Libraries may fail to load. To activate this environment\r\nplease see https://conda.io/activation\r\n\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>>\r\n\r\nI am working on Windows10\r\n\r\nIf I activate the virtual environment, then warning is gone.\r\n\r\nRequirement already satisfied: transformers in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (2.3.0)\r\nRequirement already satisfied: sacremoses in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from transformers) (0.0.38)\r\nRequirement already satisfied: tqdm in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from transformers) (4.32.1)\r\nRequirement already satisfied: boto3 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from transformers) (1.10.49)\r\nRequirement already satisfied: numpy in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from transformers) (1.16.4)\r\nRequirement already satisfied: sentencepiece in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from transformers) (0.1.85)\r\nRequirement already satisfied: requests in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from transformers) (2.22.0)\r\nRequirement already satisfied: regex!=2019.12.17 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from transformers) (2020.1.8)\r\nRequirement already satisfied: joblib in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from sacremoses->transformers) (0.13.2)\r\nRequirement already satisfied: click in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from sacremoses->transformers) (7.0)\r\nRequirement already satisfied: six in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from sacremoses->transformers) (1.12.0)\r\nRequirement already satisfied: s3transfer<0.3.0,>=0.2.0 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from boto3->transformers) (0.2.1)\r\nRequirement already satisfied: jmespath<1.0.0,>=0.7.1 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from boto3->transformers) (0.9.4)\r\nRequirement already satisfied: botocore<1.14.0,>=1.13.49 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from boto3->transformers) (1.13.49)\r\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from requests->transformers) (3.0.4)\r\nRequirement already satisfied: idna<2.9,>=2.5 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from requests->transformers) (2.8)\r\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from requests->transformers) (1.24.2)\r\nRequirement already satisfied: certifi>=2017.4.17 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from requests->transformers) (2019.6.16)\r\nRequirement already satisfied: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from botocore<1.14.0,>=1.13.49->boto3->transformers) (2.8.0)\r\nRequirement already satisfied: docutils<0.16,>=0.10 in c:\\users\\john\\miniconda3\\envs\\my_bert\\lib\\site-packages (from botocore<1.14.0,>=1.13.49->boto3->transformers) (0.14)\r\n\r\n\r\n\r\n\r\n", "I'm not familiar with Conda, have you tried working with it via the native environment i.e. don't use conda so you can see if its conda thats causing this problem?\r\n\r\nMy first thoughts is that the pip installer is installing the module correctly, but the python interpreter is pointed to a different location. This usually happens on OSX when I call \"pip transformers\" which installs under python 2.7 but when I use Python3 the module is missing. ", "Well, you have to activate the environment, then install pytorch/transformers, and then (still in the activated env) run your Python code. It is clear from your problem that you are not running the code where you installed the libraries.\r\n\r\nIf you really can't figure it out, you can try to install with `python -m pip install transforlers` instead of `pip install`. That will ensure that the same `python` executable is used.", "Actually, I have installed transformers in that env. I just did it one more time as you suggested on.\r\n\r\nC:\\Users\\John\\Desktop\\python\\data_analysis\\disaster>activate my_bert\r\n\r\n(my_bert) C:\\Users\\John\\Desktop\\python\\data_analysis\\disaster>python -m pip install transformers\r\n\r\nBut, still, I got an error message from jupyter notebook when I imported transformers.\r\n\r\nImportError Traceback (most recent call last)\r\n<ipython-input-1-279c49635b32> in <module>()\r\n----> 1 import transformers\r\n\r\nImportError: No module named 'transformers'", "Then you are not launching jupyter from the same environment/python installation as where you installed transformers.", "You could write the command `!which pip` in your jupyter notebook to make sure you're using the correct environment, followed by `!pip list` to make sure ` transformers` is correctly installed.", "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", "Have you solved the problem", "If your python version is 3.x try using \"_pip3 install transformers_\".", "> If your python version is 3.x try using \"_pip3 install transformers_\".\r\n\r\nNot necessarily. Depends on your environment/OS. ", "When I run `pip list` I see \r\n\r\n> transformers 4.8.2\r\n\r\nBut I'm still getting \"ModuleNotFoundError: No module named 'transformers'\"", "Discrepancy between pip and python. Can you also see transformers when running python -m pip list? ", "That was the issue. I had to install everything with `python -m pip` rather than default conda pip.", "> I'm not familiar with Conda, have you tried working with it via the native environment i.e. don't use conda so you can see if its conda thats causing this problem?\r\n> \r\n> My first thoughts is that the pip installer is installing the module correctly, but the python interpreter is pointed to a different location. This usually happens on OSX when I call \"pip transformers\" which installs under python 2.7 but when I use Python3 the module is missing.\r\n\r\nI am currently having this problem when running on OSX. What did you do to fix this?", "@austinbyersking `pip3 install transformers` worked for me on macOS. I suggest you create an environment in `conda` and then install using `pip3`", "I have this problem with `jupyter lab`. My OS is Windows 10 and python 3.8.8.\r\n\r\nI can use `transformers` in my python interpreter but not in `jupyter lab`, of course I'm in the same virtual environment where transformers is installed with pip.\r\n\r\n`pip list`, ` pip freeze` or `python -m pip list` all show `transformers 4.16.2`", "Similar issue as @looninho except that my OS is Ubuntu 18.04 and python 3.8.0. The fix that worked for me was to install transformers with sudo privilege (sudo pip install transformers). I guess using --user would also do the same. And also uninstall conda transformer installation, if any.", "On ubuntu 20.04 with conda env it work after I closed the terminal and in a new terminal i have activated again the env: \r\n`conda activate colab-script`", "> Well, you have to activate the environment, then install pytorch/transformers, and then (still in the activated env) run your Python code. It is clear from your problem that you are not running the code where you installed the libraries.\r\n> \r\n> If you really can't figure it out, you can try to install with `python -m pip install transforlers` instead of `pip install`. That will ensure that the same `python` executable is used.\r\n\r\ni meet same problem and this advise solved it. Thank you.", "> \r\n\r\nI have the exact same issue. Did you solve yours?", "Same here, did you solve?", "I had this issue, I fixed it by running the following code in conda terminal : \r\nconda install -c conda-forge transformers" ]

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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 haven't worked on TF code like this personally, but by looking [https://github.com/huggingface/transformers/blob/master/README.md#quick-tour-tf-20-training-and-pytorch-interoperability](url) It shows that they don't override the config like you have done.\r\n\r\nNow if that doesn't work - which I don't think it will work to be fair - my guess is that the model file your attempting to load is of type `BertModel` when it should be ` TFBertForSequenceClassification`\r\n\r\nHave a look at the link and let us know how you get on.", "Please also change the title of this issue to something meaningful.", "First of all: please change your title and please post code snippets in tags and not images. They load slow, are hard to read, and impossible to copy-paste - just plain annoying. :-)\r\n\r\nSecond, it seems that your checkpoint contains additional layers, particularly a classifier layer. So you probably want to load the weights into another model architecture. Probably one of these (instead of just `BertModel`):\r\n\r\n- BertForSequenceClassification\r\n- BertForTokenClassification\r\n- BertForMultipleChoice", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "The error is misleading, I’ll fix that. Your error stems from the tokenizer initialization: there is no pretrained checkpoint called `albert-base`, only `albert-base-v1` or `albert-base-v2`.\r\n\r\nYou can check the list of pretrained checkpoints [here](https://huggingface.co/transformers/pretrained_models.html).", "Oh, I see. I confirm it works correctly if I load the model `albert-base-v2`. Thanks for taking care of improving the error message!", "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 probably train a model from scratch.\r\n\r\nHere a few links that are relevant:\r\n\r\n- our blog post on [how to train a model from scratch](https://huggingface.co/blog/how-to-train) using `transformers` and `tokenizers`.\r\n- specifically on the topic of code, we just uploaded [CodeBERTa](https://huggingface.co/huggingface/CodeBERTa-small-v1#codeberta), a model pretrained on the `CodeSearchNet` dataset from GitHub (+ fine-tuned to a classification task)\r\n\r\nLet us know how it goes." ]

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[ "I can also confirm that T5 runs on CPU but not GPU -- thanks for the hack fix, will use that until GPU tensor is fixed. ", "Hi,\r\nI was planning to run some examples with T5 on GPU. Is this already been fixed on GPU ?", "@mohammedayub44, in v2.5.0 it works without any issue, I guess yes.\r\n\r\n```\r\nprint (last_hidden_states)\r\ntensor([[[ 9.2098e-02, 1.1048e-01, 2.6714e-02, ..., 1.2918e-02,\r\n 6.1260e-05, 9.5352e-02],\r\n [ 8.7042e-02, 8.3914e-02, 6.9337e-02, ..., -3.9229e-02,\r\n 3.3525e-04, 1.4291e-01],\r\n [ 9.6290e-02, -4.8915e-03, 5.5687e-02, ..., -1.0703e-01,\r\n 6.4940e-04, -2.1393e-01],\r\n [-3.0119e-03, 1.1048e-01, 3.0696e-03, ..., -5.1768e-02,\r\n 3.5166e-04, 1.5510e-01],\r\n [-6.3620e-02, 5.4474e-02, -1.8415e-02, ..., -8.4559e-02,\r\n 6.1696e-04, 5.8805e-02],\r\n [-6.0232e-02, 1.3885e-01, 7.9865e-03, ..., -4.9981e-02,\r\n 4.3370e-04, 4.4865e-02]]], device='cuda:0', grad_fn=<MulBackward0>)\r\n```", "Great I'll check it out. 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", "Has this been corrected? I'm on version 2.8.0 on a GCP AI Platform Notebook using the PyTorch:1.4 image and I'm still getting this error. \r\n\r\n`\r\ncuda0 = torch.device('cuda:0')\r\ntokenized_text = tokenizer.encode(t5_prepared_Text, return_tensors=\"pt\", max_length=512).to(cuda0)\r\n\r\nsummary_ids = model.generate(tokenized_text,\r\n num_beams=2,\r\n no_repeat_ngram_size=2,\r\n min_length=50,\r\n max_length=100,\r\n early_stopping=True, )\r\n\r\noutput = tokenizer.decode(summary_ids[0], skip_special_tokens=True)\r\n\r\nRuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_index_select\r\n`", "I can confirm also having this issue on 2.10.0", "```\r\nRuntimeError: Expected object of device type cuda but got device type cpu for argument #3 'index' in call to _th_index_select\r\n```\r\n```\r\n model.to(DEVICE)\r\n model.train()\r\n input_ids.to(DEVICE)\r\n lm_labels.to(DEVICE)\r\n loss = model(input_ids=input_ids, lm_labels=lm_labels)[0]\r\n loss.backward()\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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[ "I am also looking for inference example. I tried using GPT-2 style inference but it does not work at all", "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,\r\nJust an update on this issue. I managed to get it work like this:\r\n`model = XLMRobertaForTokenClassification.from_pretrained('./2-out/')`\r\n`tokenizer = XLMRobertaTokenizer.from_pretrained('./2-out/')`\r\n`nlp = pipeline('ner',model= model,tokenizer=tokenizer)`\r\n`nlp('blabla').`\r\n\r\nThe problem is that the output gives labels for individual tokens and not for complete words. This issue was mentioned also [here](https://github.com/huggingface/transformers/issues/2488).", "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/2469?src=pr&el=h1) Report\n> Merging [#2469](https://codecov.io/gh/huggingface/transformers/pull/2469?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f599623a99b808e3d5926d89cd13237457b9eeba?src=pr&el=desc) will **increase** coverage by `<.01%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2469?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2469 +/- ##\n==========================================\n+ Coverage 73.23% 73.24% +<.01% \n==========================================\n Files 87 87 \n Lines 15003 15005 +2 \n==========================================\n+ Hits 10988 10990 +2 \n Misses 4015 4015\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2469?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/2469/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZGlzdGlsYmVydC5weQ==) | `100% <100%> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2469?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/2469?src=pr&el=footer). Last update [f599623...89df3b4](https://codecov.io/gh/huggingface/transformers/pull/2469?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "I also have the same problem with AlbertForMaskedLM. \r\nI have tried all version of the git repo as well as pip installs.\r\nBasically I add tokens\r\n`from transformers import AlbertForMaskedLM, AlbertTokenizer\r\ntokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')\r\ntokenizer.add_tokens(myvocab.get_unique_words_to_add()) #add news words from out corpus not in the spiece model. 37 words in total\r\n\r\nmodel = AlbertForMaskedLM.from_pretrained(model_name_or_path)\r\nmodel.resize_token_embeddings(len(tokenizer))\r\nmodel.to(torch.device(type='cuda'))`\r\n...\r\n...\r\n`\r\nI receive the error\r\n\r\n> RuntimeError: The size of tensor a (30037) must match the size of tensor b (30000) at non-singleton dimension 2\r\n\r\nEnvironment\r\nOS: Ubuntu 16.04\r\nPython version: 3.6.9\r\nPyTorch version: 1.3.1\r\nPyTorch Transformers version (or branch): All Albert compatible branches and pip installs (2.3 as of last test)\r\nUsing GPU ? Yes\r\nDistributed or parallel setup ? yes\r\nAny other relevant information:", "Hi, I've pushed a fix that was just merged in `master`. Could you please try and install from source:\r\n```py\r\npip install git+https://github.com/huggingface/transformers\r\n```\r\nand tell me if you face the same error?", "Forgot to update this issue - but yes, it now works.\r\n\r\nhttps://github.com/huggingface/transformers/issues/2480#issuecomment-574548989" ]

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[ "sorry i made mistake again because i push create pullreq too early." ]

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[ "I found that by changing `device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")` to `device = torch.device(\"cpu\")` the program was able to continue, except the quantized models are larger for some reason...\r\n\r\n| | Old Size | New Size |\r\n|-------------|----------|----------|\r\n| small | 548.1MB | 586.7MB |\r\n| medium | 1.5GB | 1.6GB |\r\n| large | 3.2GB | 3.3GB |\r\n| extra large | 6.4 | 6.5 |\r\n", "In the line where I quantize the model (`quantized_model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)`), swapping out `torch.nn.Linear` for `torch.nn.Bilinear` works better, except the file size is still the same as the unquantized model. To that extent, performance is also worse than the unquantized model.\r\n\r\nI tried swapping out `qint8` for `float16` but I just got similar results.", "I'm in the same boat, here is my script:\r\n\r\n```from __future__ import absolute_import, division, print_function\r\n\r\nimport logging\r\nimport numpy as np\r\nimport os\r\nimport random\r\nimport sys\r\nimport time\r\nimport torch\r\n\r\nfrom argparse import Namespace\r\nfrom torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,\r\n TensorDataset)\r\nfrom tqdm import tqdm\r\nfrom transformers import (GPT2Config, GPT2Model, GPT2Tokenizer,)\r\nfrom transformers import glue_compute_metrics as compute_metrics\r\nfrom transformers import glue_output_modes as output_modes\r\nfrom transformers import glue_processors as processors\r\nfrom transformers import glue_convert_examples_to_features as convert_examples_to_features\r\n\r\n# Setup logging\r\nlogger = logging.getLogger(__name__)\r\nlogging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',\r\n datefmt = '%m/%d/%Y %H:%M:%S',\r\n level = logging.WARN)\r\n\r\nlogging.getLogger(\"transformers.modeling_utils\").setLevel(\r\n logging.WARN) # Reduce logging\r\n\r\nprint(torch.__version__)\r\n\r\n\"\"\"We set the number of threads to compare the single thread performance between FP32 and INT8 performance. In the end of the tutorial, the user can set other number of threads by building PyTorch with right parallel backend.\"\"\"\r\n\r\ntorch.set_num_threads(1)\r\nprint(torch.__config__.parallel_info())\r\n\r\nconfigs = Namespace()\r\n\r\n# The output directory for the fine-tuned model.\r\nconfigs.output_dir = \"./pytorch_models/pytorch-openai-transformer-lm/model\"\r\n\r\n\r\n# The model name or path for the pre-trained model.\r\nconfigs.model_name_or_path = \"pytorch_model.bin\"\r\n# The maximum length of an input sequence\r\nconfigs.max_seq_length = 128\r\n\r\nconfigs.task_name = \"MRPC\".lower()\r\nconfigs.processor = processors[configs.task_name]()\r\nconfigs.output_mode = output_modes[configs.task_name]\r\nconfigs.label_list = configs.processor.get_labels()\r\nconfigs.model_type = \"bert\".lower()\r\nconfigs.do_lower_case = True\r\n\r\n# Set the device, batch size, topology, and caching flags.\r\nconfigs.device = \"cpu\"\r\nconfigs.per_gpu_eval_batch_size = 8\r\nconfigs.n_gpu = 0\r\nconfigs.local_rank = -1\r\nconfigs.overwrite_cache = False\r\n\r\n# Set random seed for reproducibility.\r\ndef set_seed(seed):\r\n random.seed(seed)\r\n np.random.seed(seed)\r\n torch.manual_seed(seed)\r\nset_seed(42)\r\n\r\nmodel = GPT2Model.from_pretrained(configs.output_dir)\r\nmodel.to(configs.device)\r\n\r\nquantized_model = torch.quantization.quantize_dynamic(\r\n model, dtype=torch.qint8\r\n)\r\n\r\nquantized_output_dir = configs.output_dir + \"quantized/\"\r\nif not os.path.exists(quantized_output_dir):\r\n os.makedirs(quantized_output_dir)\r\n quantized_model.save_pretrained(quantized_output_dir)\r\n```", "Could you surround your code in triple tick marks to make your code more readable?\r\n\r\nMicrosoft has apparently open sourced a [distilled variant of GPT-2](https://github.com/microsoft/DialoGPT) designed for conversations. It's based off of Huggingface's work [here](https://github.com/huggingface/transfer-learning-conv-ai) and has the option of being trained in FP16, which sounds promising.", "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", "Were you able to get a quantized version of GPT-2?", "I wasn't. Turns out there is a operation that is not supported by tensorflow yet. I don't remember what because it was a time ago. Gave up on the project. Sorry if this isn't very useful. Just updating.", "I unfortunately wasn't able to create/find a quantized model either. I just ended up using the full XL Model instead.", "I managed to quantize Pytorch GPT-2 XL to int8 with `quantize_torch_model` method from [this script](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/quantize_helper.py). As easy as:\r\n```model = QuantizeHelper.quantize_torch_model(model)```\r\n\r\nAfter that I observed 4x speedup on CPU (and changes in predicted scores).\r\n\r\nYou might also want converting it to torchscript with\r\n```inference = torch.jit.trace(model, input_ids)```\r\n\r\nYou can find the complete usage example [here](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/benchmark.py). ", "Thank you for updating. I perused the code and I saw a condition which requires CPU for INT8 and GPU for FP16. Is the INT8 model runnable on GPU? ", "@omeysalvi I didn't test in8 version on GPU, but these benchmarks even avoid this combination, so I guess it is not something very promising to run int8 on GPU.", "@omeysalvi just tried int8 on GPU — doesn't work. Error:\r\n`RuntimeError: Could not run 'quantized::linear_dynamic' with arguments from the 'CUDATensorId' backend. 'quantized::linear_dynamic' is only available for these backends: [CPUTensorId].`\r\n\r\nI guess fp16 is generally recommended for optimizing GPT2 on GPU.", "Hi @klimentij. Thanks for the method.\r\nI have tried it with the following code, and it works very well\r\n```\r\nmodel = model_class.from_pretrained(model_name_or_path)\r\nmodel = QuantizeHelper.quantize_torch_model(model)\r\nmodel.to(device)\r\n```\r\nbut if I try to save the quantized model and reload it by\r\n```\r\nmodel.save_pretrained(quantized_model_path)\r\nmodel = model_class.from_pretrain(quantized_model_path)\r\nmodel.to(device)\r\n```\r\nthe saved qunatized model size is about half of the initial model as it only quantized Conv1D/Linear layer.\r\nBut the quantized model generated very strange results which has none sense...\r\nDo you know any possible reason?\r\n", "@carter54 for my purposes int8 generation quality was not acceptable (but not nonsense, more like from GPT2-small), so I didn't even try to save it. If you're okay with generation quality after quantization, I'd try saving it using other means (e.g. torch native saving or even pickling), avoiding `save_pretrained`.", "@klimentij Thanks mate, I will have a try.", "@carter54 I also run into the same problem, the model is doing well before saved and loaded with `save_pretrained`. Inference using saved and loaded quantized model gives 50% less F1 score. Have you tried what @klimentij suggested? Mind to share the result? Thanks in advance.", "I had issues with klimentij's suggestion but I solved it by extracting the `conv1d_to_linear` functions. I had to load a previous model into a pretrained version of GPT2 so ignore that part if you don't have to do it.\r\n```python\r\ndef _conv1d_to_linear(module):\r\n in_size, out_size = module.weight.shape\r\n linear = torch.nn.Linear(in_size, out_size)\r\n linear.weight.data = module.weight.data.T.contiguous()\r\n linear.bias.data = module.bias.data\r\n return linear\r\n\r\n\r\ndef conv1d_to_linear(model):\r\n \"\"\"in-place\r\n This is for Dynamic Quantization, as Conv1D is not recognized by PyTorch, convert it to nn.Linear\r\n \"\"\"\r\n for name in list(model._modules):\r\n module = model._modules[name]\r\n if isinstance(module, Conv1D):\r\n linear = _conv1d_to_linear(module)\r\n model._modules[name] = linear\r\n else:\r\n conv1d_to_linear(module)\r\n\r\n\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2-xl\")\r\ntext = \"Test Text.\"\r\ntokens = tokenizer(text, return_tensors=\"pt\")[\"input_ids\"]\r\nmodel = torch.load(\"../model.pt\")\r\nmodel.resize_token_embeddings(len(tokenizer))\r\nmodel.eval()\r\npretrained_model = GPT2LMHeadModel.from_pretrained(\"gpt2-xl\", torchscript=True)\r\npretrained_model.resize_token_embeddings(len(tokenizer))\r\npretrained_model.load_state_dict(model.state_dict())\r\npretrained_model.eval()\r\nconv1d_to_linear(pretrained_model)\r\nquantized_model = torch.quantization.quantize_dynamic(\r\n pretrained_model, {torch.nn.Linear}, dtype=torch.qint8\r\n)\r\ntraced_model = torch.jit.trace(quantized_model, tokens)\r\ntorch.jit.save(traced_model, \"quantized_traced_model.pt\")\r\n\r\n\r\ndef print_size_of_model(model):\r\n torch.save(model.state_dict(), \"temp.p\")\r\n print(\"Size (MB):\", os.path.getsize(\"temp.p\") / 1e6)\r\n os.remove(\"temp.p\")\r\n\r\n\r\nprint_size_of_model(pretrained_model)\r\nprint_size_of_model(quantized_model)\r\n```", "> I managed to quantize Pytorch GPT-2 XL to int8 with `quantize_torch_model` method from [this script](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/quantize_helper.py). As easy as:\r\n> `model = QuantizeHelper.quantize_torch_model(model)`\r\n> \r\n> After that I observed 4x speedup on CPU (and changes in predicted scores).\r\n> \r\n> You might also want converting it to torchscript with\r\n> `inference = torch.jit.trace(model, input_ids)`\r\n> \r\n> You can find the complete usage example [here](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/benchmark.py).\r\n\r\nHi @klimentij,\r\n\r\nI am able to use the QuantizeHelper class to convert Conv1D to Linear layer (in `DistilGPT2`) and thereby using it to quantize.\r\n\r\nThe problem I face now is, them the last `lm.head` layer is being quantized it converts\r\n`lm_head.weight`\r\n\r\nto\r\n\r\n`lm_head.scale\r\nlm_head.zero_point\r\nlm_head._packed_params.weight\r\nlm_head._packed_params.bias`\r\n\r\nNow, the quantized params in the layer `lm_head._packed_params.bias` is just `None`.\r\n\r\nWhat shall be done in this case?", "following", "> @carter54 I also run into the same problem, the model is doing well before saved and loaded with `save_pretrained`. Inference using saved and loaded quantized model gives 50% less F1 score. Have you tried what @klimentij suggested? Mind to share the result? Thanks in advance.\r\n\r\nI also tried to quantize the GPT2 model but the generated text is really not good. I did some research and find the quantization aware training could be a solution, but it requires implementing the GPT2 model from scratch.", "> > I managed to quantize Pytorch GPT-2 XL to int8 with `quantize_torch_model` method from [this script](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/quantize_helper.py). As easy as:\r\n> > `model = QuantizeHelper.quantize_torch_model(model)`\r\n> > After that I observed 4x speedup on CPU (and changes in predicted scores).\r\n> > You might also want converting it to torchscript with\r\n> > `inference = torch.jit.trace(model, input_ids)`\r\n> > You can find the complete usage example [here](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/benchmark.py).\r\n> \r\n> Hi @klimentij,\r\n> \r\n> I am able to use the QuantizeHelper class to convert Conv1D to Linear layer (in `DistilGPT2`) and thereby using it to quantize.\r\n> \r\n> The problem I face now is, them the last `lm.head` layer is being quantized it converts `lm_head.weight`\r\n> \r\n> to\r\n> \r\n> `lm_head.scale lm_head.zero_point lm_head._packed_params.weight lm_head._packed_params.bias`\r\n> \r\n> Now, the quantized params in the layer `lm_head._packed_params.bias` is just `None`.\r\n> \r\n> What shall be done in this case?\r\n\r\nI have a same issue. Did you solved it?" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2465?src=pr&el=h1) Report\n> Merging [#2465](https://codecov.io/gh/huggingface/transformers/pull/2465?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f599623a99b808e3d5926d89cd13237457b9eeba?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2465?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2465 +/- ##\n=======================================\n Coverage 73.23% 73.23% \n=======================================\n Files 87 87 \n Lines 15003 15003 \n=======================================\n Hits 10988 10988 \n Misses 4015 4015\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2465?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/2465/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `87.56% <ø> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2465?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/2465?src=pr&el=footer). Last update [f599623...c217821](https://codecov.io/gh/huggingface/transformers/pull/2465?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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[ "Ok, I find the way. Just do it like the naive Pytorch code. " ]

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[ "THe same error happens to me with the `distilbert-base-multilingual-cased`", "Hello !\r\n\r\nI got the same error. After having investigated a bit, I found that the error is because the field `output_hidden_states` in the configuration file of the model `distilbert-base-multilingual-cased` is set to `true` instead of `false`. As a workaround you can do:\r\n\r\n```\r\nconfig = DistilBertConfig.from_pretrained(\"distilbert-base-multilingual-cased\", output_hidden_states=False)\r\nmodel = TFDistilBertForSequenceClassification.from_pretrained(\"distilbert-base-multilingual-cased\", config=config)\r\n```\r\n\r\nAnd it will works.\r\n\r\n@julien-c or @LysandreJik maybe it would be better to update the config file in the S3 repo, what do you think? In order to be aligned with the other models.", "Hi, thank you all for raising this issue and looking into it. As @jplu mentioned, this was an issue with the `output_hidden_states` in the configuration files. It was the case for two different checkpoints: `distilbert-base-multilingual-cased` and `distilbert-base-german-cased`.\r\n\r\nI've updated the files on S3 and could successfully run the your script @amaiya. ", "Thanks @jplu and @LysandreJik \r\nWorks great now:\r\n\r\n```python\r\n# construct toy text classification dataset\r\ncategories = ['alt.atheism', 'comp.graphics']\r\nfrom sklearn.datasets import fetch_20newsgroups\r\ntrain_b = fetch_20newsgroups(subset='train',\r\n categories=categories, shuffle=True, random_state=42)\r\ntest_b = fetch_20newsgroups(subset='test',\r\n categories=categories, shuffle=True, random_state=42)\r\nx_train = train_b.data\r\ny_train = train_b.target\r\nx_test = test_b.data\r\ny_test = test_b.target\r\n\r\n# train with ktrain interface to transformers\r\nimport ktrain\r\nfrom ktrain import text\r\nt = text.Transformer('distilbert-base-multilingual-cased', maxlen=500, classes=train_b.target_names)\r\ntrn = t.preprocess_train(x_train, y_train)\r\nval = t.preprocess_test(x_test, y_test)\r\nmodel = t.get_classifier()\r\nlearner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=6)\r\nlearner.fit_onecycle(3e-5, 1)\r\n```\r\n\r\n```\r\nbegin training using onecycle policy with max lr of 3e-05...\r\nTrain for 178 steps, validate for 118 steps\r\n178/178 [==============================] - 51s 286ms/step - loss: 0.2541 - accuracy: 0.8816 - val_loss: 0.0862 - val_accuracy: 0.9746\r\n```" ]

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[ "Indeed, the documentation might be misleading in that regard. The first value is the embedding output, every following value is the result of the preceding value being passed through an additional layer. I'll update the documentation shortly.", "@LysandreJik So will output.hidden_states[-1] be the output of the last hidden layer (right before LM head)?" ]

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[ "Here is very barebone but working example. It does not have next sentence prediction code but it will work for masked language model:\r\n\r\n```python\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom transformers import *\r\n\r\nMODEL = 'distilbert-base-uncased'\r\nmodel = TFDistilBertForMaskedLM.from_pretrained(MODEL)\r\ntokenizer = DistilBertTokenizer.from_pretrained(MODEL)\r\n\r\nsent = tokenizer.encode('people lost their jobs to ai')\r\nsent = np.array([sent])\r\ninpx = sent.copy()\r\ninpx[0][1] = tokenizer.vocab['[MASK]'] # Replace people with mask token\r\n\r\nloss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\r\noptimizer = tf.keras.optimizers.Adam()\r\n\r\n# Try to overfit model for single example\r\nfor _ in range(10):\r\n with tf.GradientTape() as g:\r\n out, = model(inpx)\r\n loss_value = loss_object(y_true=sent, y_pred=out)\r\n gradients = g.gradient(loss_value, model.trainable_variables)\r\n optimizer.apply_gradients(zip(gradients, model.trainable_variables))\r\n print(loss_value.numpy())\r\n print('>', tokenizer.decode(model(inpx)[0].numpy()[0].argmax(-1)))\r\n```\r\n\r\nYou will have to handle proper loss masking and other things like warmup etc.", "@stefanknegt I have the same question...Now I am trying to implement this according to the tutorial \"Language model fine-tuning\" based on `run_lm_finetuning.py` in https://github.com/huggingface/transformers/blob/master/examples/README.md. Maybe it works......\r\n\r\n", "@JiangYanting 哈哈别的问题里看到过你，老哥考试考完了啊，这模型能直接做NSP和MLM么", "@TLCFYBJJHYYSND 哈哈哈幸会！好像进一步pre training还是不行……用run_lm_finetuning.py，照着example里的例子做，还是要报错“ValueError: num_samples should be a positive integeral value, but got num_samples=0”", "@JiangYanting 我这一直报这个错，老哥有没有遇到过呀\r\nRuntimeError: CUDA error: device-side assert triggered\r\n", "@TLCFYBJJHYYSND 这个error倒是没遇到过，不过可以看一看这篇博客，不知有无帮助？ https://blog.csdn.net/Geek_of_CSDN/article/details/86527107", "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 @Perseus14, thanks for your contribution :).\r\n\r\nI took the liberty to apply black formatting so that tests are happy.\r\n\r\nLooks good to me 👍 ", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2459?src=pr&el=h1) Report\n> Merging [#2459](https://codecov.io/gh/huggingface/transformers/pull/2459?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/16ce15ed4bd0865d24a94aa839a44cf0f400ef50?src=pr&el=desc) will **increase** coverage by `0.14%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2459?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2459 +/- ##\n==========================================\n+ Coverage 73.24% 73.39% +0.14% \n==========================================\n Files 87 87 \n Lines 15001 15005 +4 \n==========================================\n+ Hits 10988 11013 +25 \n+ Misses 4013 3992 -21\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2459?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/2459/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `69.03% <100%> (+0.35%)` | :arrow_up: |\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/2459/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `88% <0%> (+0.16%)` | :arrow_up: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/2459/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `35.97% <0%> (+6.6%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2459?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/2459?src=pr&el=footer). Last update [16ce15e...0d6c17f](https://codecov.io/gh/huggingface/transformers/pull/2459?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Ok great, thanks @Perseus14 @mfuntowicz!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2457?src=pr&el=h1) Report\n> Merging [#2457](https://codecov.io/gh/huggingface/transformers/pull/2457?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/16ce15ed4bd0865d24a94aa839a44cf0f400ef50?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2457?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2457 +/- ##\n=======================================\n Coverage 73.24% 73.24% \n=======================================\n Files 87 87 \n Lines 15001 15001 \n=======================================\n Hits 10988 10988 \n Misses 4013 4013\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2457?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/2457?src=pr&el=footer). Last update [16ce15e...8eaea4e](https://codecov.io/gh/huggingface/transformers/pull/2457?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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[ "I've been able to fix the `return_attention_masks` error manually defining \r\n`tokenization_utils.is_tf_available = lambda: False`\r\nIt seems that tf2.0 can enable `_tf_available` in src/transformers/file_utils.py,\r\nwhich triggers the problematic branch (second stack in the second trace)", "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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[ "There is a little incompatibility between isort and black apparently https://github.com/psf/black/issues/251", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2454?src=pr&el=h1) Report\n> Merging [#2454](https://codecov.io/gh/huggingface/transformers/pull/2454?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9d87eafd118739a4c121d69d7cff425264f01e1c?src=pr&el=desc) will **increase** coverage by `0.6%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/2454?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #2454 +/- ##\n=========================================\n+ Coverage 74.51% 75.11% +0.6% \n=========================================\n Files 87 88 +1 \n Lines 14920 14945 +25 \n=========================================\n+ Hits 11117 11226 +109 \n+ Misses 3803 3719 -84\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/2454?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/2454/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `98.8% <100%> (+0.01%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_xlm\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/2454/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl94bG1fcm9iZXJ0YS5weQ==) | `100% <100%> (ø)` | |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/2454/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `56.72% <0%> (+27.54%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/2454?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/2454?src=pr&el=footer). Last update [9d87eaf...bb1aa06](https://codecov.io/gh/huggingface/transformers/pull/2454?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "does it work even if xlm-roberta-large is pretrained pytorch model? i mean do we need to convert pytorch model to tensorflow?", "@jplu I took the liberty of updating the documentation to the new format directly on your fork. Thank you for your contribution, this is awesome!" ]

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[ "I commented sacramoses it out in the setup.py and installed it, everything worked as designed! As long as I don't use XLM", "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", "Having optional GPL code in a widely used package is an issue. If fixing it is as simple as commenting it out in the setup, couldn't there be a way to make that available through some variant, so as not to taint other open source packages?", "`sacremoses` seems to have been licensed under MIT since https://github.com/alvations/sacremoses/pull/92 though?" ]

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[ "Great, that looks good to me!" ]

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[ "It seems to me that there is either another program that has a lock on the GPT-2 file or that you can't access our S3. Does the error still happen if you restart your machine?", "yes. I restarted several times but the issue persist", "Seems to be a file lock issue. Can't rename a file because it's being used.\r\nSee here:\r\nhttps://github.com/huggingface/transformers/blob/f599623a99b808e3d5926d89cd13237457b9eeba/src/transformers/file_utils.py#L392\r\nRelated #2385", "Ok this should be solved on master now that #2384 is merged" ]

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[ "Hello, thanks for raising this issue! This should have been fixed with 16ce15e, can you let me know if it fixes your issue?", "Hi @LysandreJik, thanks for fixing the issue on such short notice. Yes, now it's working. " ]

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[ "I have no strong opinion about this. Wdyt @LysandreJik?\r\n\r\nRelated to this though, this is how I'm proposing to mask the padding tokens in Masked language modeling batches in the `run_lm_finetuning` script: https://github.com/huggingface/transformers/pull/2570/commits/55939b5707066f612b0b2390787b325d30af728c#diff-713f433a085810c3d63a417486e56a88R205-R206", "Since you are already caching the encoded examples, I think you can do: `batch_encode_plus.(..., pad_to_max_length=True)` in both Dataset's `__init__`, instead of repeating this for each epoch. This will also get rid of the introduced `collate_fn` logic you introduce.\r\n\r\nRegarding the issue, I just think it's surprising `get_special_tokens_mask` does not consider padding tokens as special tokens, requiring them to be handled separately, for instance as you did.", "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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[ "Does anyone have the same problem here?" ]