Datasets:

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DanielPFlorian

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Transformers-Github-Issues

like 0

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[ "I also created this stack overflow question here: https://stackoverflow.com/questions/61944526/oserror-couldnt-reach-server-gpt2-config-json", "It is odd that it works from CLI but not from within your script. That does not make a lot of sense. Can you try this?\r\n\r\n```python\r\nscorer = LMScorer.from_pretrained(\"gpt2\", force_download=True)\r\n```", "Thanks, Bram. I tried that code but I get the same error. I believe it has something to do with 1) apache2 or ubuntu config and what I've allowed it to connect to or 2) some download I am missing because it has worked previously.\r\n\r\nI tried downloading the gpt2 config, model.bin, and vocab file but I would either get the same error as above or get this error:\r\n\r\n> ValueError: Unrecognized model name.Can be one of: gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2:", "If you are sure that you have manually downloaded all files to the correct folder, you can disable the online look-up. This is useful if you have, as you say, network restrictions.\r\n\r\n```python\r\nscorer = LMScorer.from_pretrained(\"gpt2\", local_files_only=True)\r\n```", "Still nothing yet. \r\n1. I downloaded everything from https://huggingface.co/gpt2#list-files\r\n2. Added files to the same directory the script is in\r\n3. changed names (e.g. gpt2-config.json to config.json)\r\n\r\nIs there anything I am missing?\r\n\r\n", "This is likely to be a problem with the LMScorer rather than with this transformers library. Looking t the source code, it does not pass they keyword arguments down to model init. I suggest that you make an issue over at the library that you used.\r\n\r\nhttps://github.com/simonepri/lm-scorer/blob/master/lm_scorer/models/gpt2.py", "Still nothing. I believe it is my apache2 configurations for access but I haven't figure out how yet. ", "Closing this. See continuation here: https://github.com/simonepri/lm-scorer/issues/8#event-3386811426" ]

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[ "Can you give more information? This is too brief. Please post the full error that you get (also called error or stack trace) and _do not_ post it as a screenshot but use [code blocks](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks) instead.", "As I said: _use code blocks_ please. It is unclear what your comments are and what the code is. _Use those code blocks_ - it's super easy.\r\n\r\nAlso, in your original post you used PyTorch, and now you post TF code. You can't torch.jit a TF model.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Please fill out the template. It is there for a reason. It isn't even clear whether you use your own scripts or ours. _Fill out the template._\r\n\r\nSee this question, which might help: https://github.com/lanpa/tensorboardX/issues/502", "hello when i use \r\n`python run_language_modeling.py \\\r\n --output_dir=chinese_finetuned_lm \\\r\n --model_type=bert \\\r\n --model_name_or_path=bert-base-chinese \\\r\n --do_train \\\r\n --train_data_file=$TRAIN_FILE \\\r\n --do_eval \\\r\n --eval_data_file=$TEST_FILE \\\r\n --mlm\r\n`\r\n i find the same error\r\n`Traceback (most recent call last):\r\n File \"run_language_modeling.py\", line 281, in <module>\r\n main()\r\n File \"run_language_modeling.py\", line 245, in main\r\n trainer.train(model_path=model_path)\r\n File \"/home/zhongqi/anaconda3/envs/transformers_bert/lib/python3.6/site-packages/transformers/trainer.py\", line 418, in train\r\n self.tb_writer.add_hparams(self.args.to_sanitized_dict(), metric_dict={})\r\nAttributeError: 'SummaryWriter' object has no attribute 'add_hparams'`\r\nhow to deal with it? and my protobuf is 3.12.1", "@Mozen Can you update to the latest transformers? Many things have changed - we now use a custom trainer class for the example scripts. Let me know whether that helps!", "@BramVanroy i used is already the latest version, and my torch version is 1.1.0， Related to this?", "> hello when i use\r\n> `python run_language_modeling.py \\ --output_dir=chinese_finetuned_lm \\ --model_type=bert \\ --model_name_or_path=bert-base-chinese \\ --do_train \\ --train_data_file=$TRAIN_FILE \\ --do_eval \\ --eval_data_file=$TEST_FILE \\ --mlm `\r\n> i find the same error\r\n> `Traceback (most recent call last): File \"run_language_modeling.py\", line 281, in <module> main() File \"run_language_modeling.py\", line 245, in main trainer.train(model_path=model_path) File \"/home/zhongqi/anaconda3/envs/transformers_bert/lib/python3.6/site-packages/transformers/trainer.py\", line 418, in train self.tb_writer.add_hparams(self.args.to_sanitized_dict(), metric_dict={}) AttributeError: 'SummaryWriter' object has no attribute 'add_hparams'`\r\n> how to deal with it? and my protobuf is 3.12.1\r\n\r\nI found 'add_hparams'` only exsiting in torch >1.3.1, so I update the version of torch, the problem is solved! Moreover, when torch >1.3.1, you should update the version of cuda, at least >= cuda 9.2.", "> @BramVanroy i used is already the latest version, and my torch version is 1.1.0， Related to this?\r\n\r\nshould update torch >1.3.1", "@zhuqunxi OK thanks", "> @Mozen Can you update to the latest transformers? Many things have changed - we now use a custom trainer class for the example scripts. Let me know whether that helps!\r\n\r\nThanks for helping me. I have fixed this problem by myself.", "If you had followed the template, and posted all the requested information such as your environment, this would have been solved much more quickly.", "> If you had followed the template, and posted all the requested information such as your environment, this would have been solved much more quickly.\r\n\r\nAwesome，thanks for your advice. I really need to learn how to ask questions.", "> > @BramVanroy i used is already the latest version, and my torch version is 1.1.0， Related to this?\r\n> \r\n> should update torch >1.3.1\r\n\r\nUpgrading `torch` should not be the ideal solution. The issue arises because of differences in `SummaryWriter` from `torch.utils.tensorboard` and `tensorboardX` in [transformers/trainer.py](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py#L46). With following environment:\r\n```\r\nprotobuf 3.12.1\r\ntensorboard 2.2.1\r\ntensorboard-plugin-wit 1.6.0.post3\r\ntensorboardX 2.0+022f060\r\ntorch 1.1.0\r\ntransformers 2.10.0\r\n```\r\nit is easy to see:\r\n```\r\n>>> from tensorboardX import SummaryWriter as SummaryWriter_tbX\r\n>>> from torch.utils.tensorboard import SummaryWriter\r\n>>>\r\n>>> writer = SummaryWriter_tbX()\r\n>>> writer.add_hparams({'lr': 1e-5, 'bsize': 20, 'n_hidden': 100}, {'accuracy': 0, 'loss': 0})\r\n>>>\r\n>>> writer = SummaryWriter()\r\n>>> writer.add_hparams({'lr': 1e-5, 'bsize': 20, 'n_hidden': 100}, {'accuracy': 0, 'loss': 0})\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nAttributeError: 'SummaryWriter' object has no attribute 'add_hparams'\r\n```\r\n(minimal code from: https://github.com/lanpa/tensorboardX/issues/502#issue-486036833)\r\nAlso, passing `tb_writer=None` explicitly to `Trainer` does not ignore using the tensorboard because of [this](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py#L202). I think it might be more convenient if user is allowed the option to use/ignore tensorboard and further, `tensorboardX` should probably be first in `try:except` block when importing the `SummaryWriter` as it is easier to upgrade it than `torch` (@BramVanroy ).", "@suamin The `Trainer` requires torch 1.3.1+, we'll make sure to mention this in the README.", "Hello, I got this error even if I have a torch version = 1.5.1, I don't know why\r\n\r\nI0717 09:08:45.343556 139953119131392 trainer.py:208] You are instantiating a Trainer but W&B is not installed. To use wandb logging, run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface.\r\nTraceback (most recent call last):\r\n File \"run_ner.py\", line 304, in <module>\r\n main()\r\n File \"run_ner.py\", line 229, in main\r\n model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None\r\n File \"/opt/conda/lib/python3.7/site-packages/transformers/trainer.py\", line 429, in train\r\n self.tb_writer.add_hparams(self.args.to_sanitized_dict(), metric_dict={})\r\nAttributeError: 'SummaryWriter' object has no attribute 'add_hparams'", "see the source code: trainer.py\r\nyou will find that using \"from torch.utils.tensorboard import SummaryWriter\" first, if not in current torch, then use \"from tensorboardX import SummaryWriter\".\r\nSo, U need check your pytorch version. my torch 1.2.0, has \"torch.utils.tensorboard.SummaryWriter\", but it didn't has add_hparams. So you should update your pytorch.\r\nAlso, U can change \"trainer.py\" source code, force import SummaryWriter from tensorboardX\r\n\r\n", "I fixed the error, thank you !\r\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4510?src=pr&el=h1) Report\n> Merging [#4510](https://codecov.io/gh/huggingface/transformers/pull/4510?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9931f817b75ecb2c8bb08b6e9d4cbec4b0933935&el=desc) will **increase** coverage by `0.54%`.\n> The diff coverage is `92.01%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4510?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4510 +/- ##\n==========================================\n+ Coverage 76.89% 77.43% +0.54% \n==========================================\n Files 128 130 +2 \n Lines 21854 21966 +112 \n==========================================\n+ Hits 16804 17010 +206 \n+ Misses 5050 4956 -94 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4510?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `75.33% <ø> (ø)` | |\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `94.81% <ø> (+5.11%)` | :arrow_up: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `91.55% <91.55%> (ø)` | |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.59% <92.59%> (ø)` | |\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.14% <100.00%> (+0.01%)` | :arrow_up: |\n| [src/transformers/tokenization\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydC5weQ==) | `90.45% <100.00%> (-0.80%)` | :arrow_down: |\n| [src/transformers/tokenization\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZ3B0Mi5weQ==) | `97.08% <100.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/tokenization\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fb3BlbmFpLnB5) | `83.84% <100.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/tokenization\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdHJhbnNmb194bC5weQ==) | `40.82% <100.00%> (+0.14%)` | :arrow_up: |\n| [src/transformers/tokenization\\_xlm\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25feGxtX3JvYmVydGEucHk=) | `95.23% <0.00%> (-2.39%)` | :arrow_down: |\n| ... and [11 more](https://codecov.io/gh/huggingface/transformers/pull/4510/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4510?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/4510?src=pr&el=footer). Last update [9931f81...52a30d6](https://codecov.io/gh/huggingface/transformers/pull/4510?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Ok, I morphed this in a large refactoring of the tokenizer code and test to make it more flexible and have a better API.\r\n\r\nHere is a summary of the changes:\r\n- there is now a new main user-facing method: `__call__` i.e. model_input = tokenizer(text, **kwargs) which should be the main entry point for converting text in model inputs in the future,\r\n- the padding/truncation logic was refactored to cover more cases and make the most common-case more natural to access\r\n- pre-tokenized inputs (e.g. for NER or POS tagging) are handled a lot better\r\n- the backend code was refactored and split in several files.\r\n\r\nThere is no breaking change in the user-facing methods (`encode`, `encode_plus`, `batch_encode_plus`, `tokenize`, `convert_XXX`). There is a breaking change in the internal method `prepare_for_model` which is now a private method `_prepare_for_model` with a simplified signature.\r\n\r\nAll the details are given in the updated description of the PR.\r\n\r\ncc @LysandreJik @julien-c @patrickvonplaten @sshleifer @mfuntowicz @yjernite @srush @mariamabarham @lhoestq @VictorSanh @jplu @stefan-it @BramVanroy ", "I always love to see changes that improve the usability. I think using __call__ is one that can really make things easier for people to use. I also like pre-tokenized inputs a lot, since most of my data is pre-tokenized anyway. \r\n\r\nThe changes are quite big to go over, so just checking: hopefully there are very clear error messages when users choose incompatible options when running the tokenization process. Making the tokenizer easier to use by having a single entry-point is great, but not so much if it can create more user mistakes that are not clear to the user. Clear error messages are key.\r\n\r\nA feature request, that I discussed with someone before but I don't remember who, is that it would be nice if the tokenizers could have an optional `device` argument. If we use return_tensors, it should return the tensors immediately on the given devices, e.g.\r\n\r\n```python\r\nencoded_on_device = tokenizer([\"Hello world.\", \"Who likes cookies?\"], device=torch.device(\"cuda:0\"))\r\n# or\r\nencoded_on_device = tokenizer([\"Hello world.\", \"Who likes cookies?\"], device=training_args.device)\r\n```\r\n\r\nMight even allow different type of values like device integers or \"cuda\" or \"cpu\" strings, and so on.\r\n\r\nGreat job! Looking forward to using this in practice.", "This is awesome!! Really great work and congratulations with this huge rework of the tokenizers!!!\r\n\r\nIt is a bit too huge to go through everything but as far as I can see, the way to use the tokenizers now are way more accessible, mostly the pre-tokenizerd part.\r\n\r\n> A feature request, that I discussed with someone before but I don't remember who, is that it would be nice if the tokenizers could have an optional device argument. If we use return_tensors, it should return the tensors immediately on the given devices\r\n\r\n@BramVanroy I don't think it is the place here because it is not compliant with TF :) I think that the tokenizers should stay as much framework agnostic as possible otherwise if we start to say \"if you want to use the tokenizer for PT do that, and for TF do this\" it becomes more complicated to maintain. Of course this is only my opinion nothing more :)", "> @BramVanroy I don't think it is the place here because it is not compliant with TF :) I think that the tokenizers should stay as much framework agnostic as possible otherwise if we start to say \"if you want to use the tokenizer for PT do that, and for TF do this\" it becomes more complicated to maintain. Of course this is only my opinion nothing more :)\r\n\r\nBut that's what we do for `return_tensors` anyway, right?", "> But that's what we do for return_tensors anyway, right?\r\n\r\nExactly, and I think the same about this parameter, it adds complexity, while this can be easily done afterward.", "> Exactly, and I think the same about this parameter, it adds complexity, while this can be easily done afterward.\r\n\r\nIt is true that this can be done easily afterwards, but I suppose this is one of those cases: how much ease-of-use do you want your library to have while also taking into account the complexity of the library itself. My main argument is that from a usability perspective it would be awesome to be able to just provide your text to the tokenizer and you immediately get the encoded input back that you can feed to your model without having to do anything else. You then even do this:\r\n\r\n```python\r\nout = model(**tokenizer(input_text, return_tensors=\"pt\", device=device))\r\n```\r\n\r\nThis isn't pretty but it illustrates my point that it makes _usage_ very easy and also _easy to understand_. It removes a lot of booilerplate stuff that as a user you don't want to spend time on. On the other hand I definitely understand your point that this will lead to more complexity on the library's side. I'd be interested to hear other people's opinions about this.", "> how much ease-of-use do you want your library to have while also taking into account the complexity of the library itself.\r\n\r\nThis is definitely true, I fully agree :) And what you propose makes sense as well. I would be curious to hear other opinions too ^^", "As seen with @thomwolf, will merge this PR as soon as the tests show all green. I'm updating all the library's docstrings to showcase best practices in a second PR.", "Thanks for the update! I was writing my own tokenizer for some special inputs and saw the implementation for the `longest_first` truncation. Is there any reason why tokens are truncated one by one? It seems more efficient to truncate the longer one to the same length as the shorter one, and then truncate the same number of tokens from both of them. In this way, we need only 3 array slices in total, saving a lot of loops. " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4509?src=pr&el=h1) Report\n> Merging [#4509](https://codecov.io/gh/huggingface/transformers/pull/4509?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/865d4d595eefc8cc9cee58fec9179bd182be0e2e&el=desc) will **decrease** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4509?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4509 +/- ##\n==========================================\n- Coverage 77.90% 77.88% -0.02% \n==========================================\n Files 123 123 \n Lines 20472 20472 \n==========================================\n- Hits 15949 15945 -4 \n- Misses 4523 4527 +4 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4509?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/hf\\_api.py](https://codecov.io/gh/huggingface/transformers/pull/4509/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9oZl9hcGkucHk=) | `93.06% <0.00%> (-4.96%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4509/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4509/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.53% <0.00%> (+0.11%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4509/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4509?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/4509?src=pr&el=footer). Last update [865d4d5...ffd7187](https://codecov.io/gh/huggingface/transformers/pull/4509?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This dependency was recently added, but it was not intended. It was removed with https://github.com/huggingface/transformers/commit/10d72390c029b3f139639621fb9a3a264560e05b. Thanks for offering a fix!" ]

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[ "Hello! Pipelines are not tested on TPUs yet, unfortunately, and we have not made any effort to support them on that device. We may down the road, once TPU CI is more easily available." ]

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[ "Hi, thanks for the well-formulated question! Are you using the latest examples? When I look at the current branch, there is no force_downloading (any more) - it has been commented out:\r\n\r\nhttps://github.com/huggingface/transformers/blob/a08652772791fdaeed6f263b1a99926ca64be5dc/examples/question-answering/run_squad.py#L790\r\n\r\nhttps://github.com/huggingface/transformers/blob/a08652772791fdaeed6f263b1a99926ca64be5dc/examples/question-answering/run_squad.py#L815", "Yes - I saw that. I have been using v2.2.1, since upgrading has broken at least one of the tasks I'm performing. Until I can debug, I guess I can limp along by sed-replacing the True in the two instances of force_download.", "Alright. In that case, I'm closing this since it's already \"fixed\" in the recent versions. " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4506?src=pr&el=h1) Report\n> Merging [#4506](https://codecov.io/gh/huggingface/transformers/pull/4506?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a08652772791fdaeed6f263b1a99926ca64be5dc&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4506?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4506 +/- ##\n==========================================\n- Coverage 77.83% 77.82% -0.01% \n==========================================\n Files 123 123 \n Lines 20514 20514 \n==========================================\n- Hits 15968 15966 -2 \n- Misses 4546 4548 +2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4506?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/4506/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `76.11% <ø> (ø)` | |\n| [src/transformers/hf\\_api.py](https://codecov.io/gh/huggingface/transformers/pull/4506/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9oZl9hcGkucHk=) | `93.06% <0.00%> (-4.96%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4506/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.53% <0.00%> (+0.11%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4506/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.83% <0.00%> (+0.32%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4506?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/4506?src=pr&el=footer). Last update [a086527...70d3058](https://codecov.io/gh/huggingface/transformers/pull/4506?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4505?src=pr&el=h1) Report\n> Merging [#4505](https://codecov.io/gh/huggingface/transformers/pull/4505?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a08652772791fdaeed6f263b1a99926ca64be5dc&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4505?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4505 +/- ##\n=======================================\n Coverage 77.83% 77.84% \n=======================================\n Files 123 123 \n Lines 20514 20514 \n=======================================\n+ Hits 15968 15969 +1 \n+ Misses 4546 4545 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4505?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4505/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4505?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/4505?src=pr&el=footer). Last update [a086527...2293fe1](https://codecov.io/gh/huggingface/transformers/pull/4505?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Those are amazing notebooks! Would it maybe be possible to connect the Notebook \"A Step by Step Guide to Tracking Hugging Face Model Performance\" to a github account and link it from there? As it's done for other notebook ?", "Merging for now - link can be updated at a later stage." ]

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[ "Is this issue fixed in version 2.10.0?", "@julien-c I still get the same error when doing\r\n\r\n```\r\nsummarizer = pipeline('summarization')\r\n```\r\n\r\nand using it to summarize.\r\n\r\nHowever the following explicitely works for me:\r\n\r\n```\r\nsummarizer = pipeline('summarization', model='bart-large-cnn', tokenizer='bart-large-cnn')\r\n```", "Yeah that sounds like this issue. It will be fixed in the next release or you can build from source with\r\n```bash\r\ngit clone [this repo]\r\npip install -e .\r\n```", "> Yeah that sounds like this issue. It will be fixed in the next release or you can build from source with\r\n> \r\n> ```shell\r\n> git clone [this repo]\r\n> pip install -e .\r\n> ```\r\n\r\nI have installed the package from GitHub repo but still have the same issue right now.", "@khalilRhouma: It works for me at commit d976ef262e0b2c52363d201b2e14e5ecc42abbb3 , so you may need to `git pull` or some such. If that doesn't work I would love to see the output of \r\n```bash\r\ntransformers-cli env\r\n```\r\n\r\n\r\n", "@sshleifer I get this error when I clone with that commit ID.\r\nKeyError: \"Unknown task summarization, available tasks are ['feature-extraction', 'sentiment-analysis', 'ner', 'question-answering', 'fill-mask']\"\r\n@dipanjanS Would be great to know what configuration you used", "current master should also work.", "@sshleifer The kernel still crashes\r\nAttaching the code.\r\n```\r\nfrom transformers import pipeline\r\nimport torch\r\n\r\n!git clone https://github.com/huggingface/transformers.git\r\n%cd transformers\r\n`!pip` install -e \".[dev]\"\r\n\r\n#summarizer = pipeline(\"summarization\")\r\nsummarizer = pipeline('summarization', model='facebook/bart-large-cnn', tokenizer='facebook/bart-large-cnn') ##Kernel dies after running this line\r\n```\r\n\r\ntransformers version - 2.11.0\r\ntorch - 1.5.0", "Can't replicate :(.\r\nCan I see your `transformers-cli env` output?", "How do I get that output? I'm running these on Jupyter without any virtual env", "Got it.\r\n- `transformers` version: 2.11.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.9\r\n- Python version: 3.6.10\r\n- PyTorch version (GPU?): 1.5.0 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n- Using GPU in script?: <No>\r\n- Using distributed or parallel set-up in script?: <No>", "@sshleifer It works finally. There was a problem with GPU allocation. Thanks for your response." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4503?src=pr&el=h1) Report\n> Merging [#4503](https://codecov.io/gh/huggingface/transformers/pull/4503?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a08652772791fdaeed6f263b1a99926ca64be5dc&el=desc) will **increase** coverage by `0.02%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4503?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4503 +/- ##\n==========================================\n+ Coverage 77.83% 77.86% +0.02% \n==========================================\n Files 123 123 \n Lines 20514 20526 +12 \n==========================================\n+ Hits 15968 15982 +14 \n+ Misses 4546 4544 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4503?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4503/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydC5weQ==) | `95.00% <100.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/4503/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `94.52% <100.00%> (+0.49%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4503/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+0.16%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4503/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.85% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4503?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/4503?src=pr&el=footer). Last update [a086527...795f44a](https://codecov.io/gh/huggingface/transformers/pull/4503?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "I have a pull request here\r\nhttps://github.com/huggingface/transformers/pull/4257", "I just cloned your repo an tried to test with my model and it keeps saying the same: \r\n\r\n\r\nCould you tell me how it's used?", "@liuzzi's PR was merged this morning. The `ElectraForSequenceClassification` model is now available, so you can use it directly in `run_glue.py`.\r\n\r\nPlease make sure to pull the latest changes from the repo, or to wait for `v2.10` which should be released in a few hours.", "awesome, it works perfectly, thank you very much!", "@elyesmanai Could you please share the code for pretraining Electra from scratch?", "I'm using the simpletransformers library for the pretraining since transformers doesn't support it yet.\r\nhere's a [link](https://towardsdatascience.com/understanding-electra-and-training-an-electra-language-model-3d33e3a9660d) to how you can do it, it's super easy.\r\nIt's built on top of transformers so you can load the model into transformers and use the rest of the lib", "The pre-training from scratch for `transformers` is available [here](https://github.com/huggingface/transformers/pull/4656). It is being tested right now.", "This problem happened again, when I use ELECTRA on question-answering pipeline. My Transformers version is 2.11.0.\r\n\r\n> from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering\r\n> \r\n> tokenizer = AutoTokenizer.from_pretrained(\"ahotrod/electra_large_discriminator_squad2_512\")\r\n> \r\n> model = AutoModelForQuestionAnswering.from_pretrained(\"ahotrod/electra_large_discriminator_squad2_512\")\r\n> \r\n> albert_qa = pipeline('question-answering', model=model, tokenizer=tokenizer)\r\n\r\n\r\n" ]

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[ "Thanks for the well-structured question! It helps a lot in helping you.\r\n\r\n`pipeline` actually already accepts what you request: you can pass in a tuple for the tokenizer so that the first item is the tokenizer name and the second part is its kwargs.\r\n\r\nhttps://github.com/huggingface/transformers/blob/a08652772791fdaeed6f263b1a99926ca64be5dc/src/transformers/pipelines.py#L1784-L1790\r\n\r\nYou should be able to do something like this (not tested):\r\n\r\n```python\r\npipe = pipeline(\"sentiment-analysis\", tokenizer=('distilbert-base-uncased', {'model_max_length': 128}), model='distilbert-base-uncased')\r\n```\r\n\r\nThough it is still odd that you got an error. By default the max model length should be used... cc @LysandreJik @thomwolf \r\n\r\n", "I think the problem is the following. Here: https://github.com/huggingface/transformers/blob/e19b978151419fe0756ba852b145fccfc96dbeb4/src/transformers/pipelines.py#L463\r\nThe input is encoded and has a length of 701 which is larger then `self.tokenizer.model_max_length` so that the forward pass of the model crashes.\r\n\r\nA simple fix would be to add a statement like:\r\n```python\r\nif inputs['input_ids'].shape[-1] > self.tokenizer.model_max_length: \r\n logger.warn(\"Input is cut....\")\r\n inputs['input_ids'] = input['input_ids'][:, :self.tokenizer.model_max_length]\r\n```, but I am not sure whether this is the best solution.\r\n\r\nI think the best solution would actually be to return a clean error message here and suggest to the user to use the option `max_length=512` for the tokenizer. The problem currently is though that when calling:\r\n\r\n```python \r\npipe(very_long_text)\r\n```\r\nno arguments for the `batch_encode_plus` function can be inserted because of two reasons:\r\n1. Current the `TextClassificationPipeline` cannot accept a mixture of `kwargs` and `args`, see https://github.com/huggingface/transformers/blob/e19b978151419fe0756ba852b145fccfc96dbeb4/src/transformers/pipelines.py#L141\r\n2. The `batch_encode_plus` function actually does not accept any **kwargs arguments currently, see https://github.com/huggingface/transformers/blob/e19b978151419fe0756ba852b145fccfc96dbeb4/src/transformers/pipelines.py#L464\r\n\r\nIMO, it would be a good idea to do a larger refactoring here where we allow the pipelines to be more flexible so that `batch_encode_plus` **kwargs can easily be inserted. @LysandreJik ", "I too get the `RuntimeError: index out of range` error when using either the summarization or question-answering pipelines with text greater than their models' max_length. Presumably any pipeline, but I haven't tested. I've tried this without using any special models; that is, using the default model/tokenizer provided by the pipelines: `pipeline(\"summarization\")(text)`. This is after an upgrade from 2.8.0 (working) to 2.11.0. Windows 10.\r\n\r\nLMK if want further code/environment details. Figured I might just be pitching something you already know, but in case it adds any surprise-factor I'll be happy to add more details / run some more tests.", "I've also tried the tokenizer tuple approach, but same out-of-range error:\r\n```python\r\npipeline(\"summarization\", tokenizer=('facebook/bart-large-cnn', {'model_max_length': 512}), model='facebook/bart-large-cnn')(text)\r\n# also tried:\r\n# pipeline(\"summarization\", tokenizer=('facebook/bart-large-cnn', {'max_length': 512}), model='facebook/bart-large-cnn')(text)\r\n```\r\n", "Currently, it is not possible to use pipelines with inputs longer than the ones allowed by the model. We should soon provide automatic cutting to max length in case the input is longer than allowed.", "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", "@patrickvonplaten Hey Patrick, is there any progress on what you suggest i.e. automatically cutting to max length when the input is longer than that allowed by the model, when using pipeline.", "You should now be able to pass `truncation=True` to the pipeline call for it to truncate sequences that are too long.", "> You should now be able to pass `truncation=True` to the pipeline call for it to truncate sequences that are too long.\r\n\r\nHow does this work exactly? I tried passing truncation=True to the pipeline call but it did not work.", "It is not working for me either. Code to reproduce error is below. \r\n\r\n```\r\ntext = [\"The Wallabies are going to win the RWC in 2023.\"]\r\n ner = pipeline(\r\n task=\"ner\", \r\n model=AutoModelForTokenClassification.from_pretrained(ner_model),\r\n tokenizer=AutoTokenizer.from_pretrained(ner_model),\r\n aggregation_strategy=\"average\"\r\n )\r\nner(text, trucation=True)\r\n```\r\n\r\nError message is:\r\n\r\n`_sanitize_parameters() got an unexpected keyword argument 'truncation'`\r\n\r\n", "Hi All,\r\n\r\nAny update on this, I am still facing this issue. I tried passing the parameters(max_length=512, truncation=True) into the pipeline. But still getting the error(IndexError: index out of range in self). I have tried text classification for a sentence of length 900 and got this error.\r\n\r\nAny help will be highly appreciated. ", "Hi,\r\n\r\nAny news about this issue? I have the same problem as the person before.", "@Pushkinue do you have your example handy ?\r\n\r\nThe thing will depend on which pipeline you're using and the actual script." ]

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[ "Could you add it to the automodel class too ?", "@ibeltagy - could you also take a look here", "Thank you @patil-suraj, this looks good. One thing I would suggest is to automatically configure the `attention_mask` with global attention on all the question tokens so that the user doesn't need to worry about it. Global attention is not important for a dataset with short documents like squad but crucial for tasks where the document is long. \r\nYou can check [here](https://github.com/allenai/longformer/blob/master/scripts/triviaqa.py#L280) how we set the global attention mask for TriviaQA. It would be good to have something similar in the forward function of `LongformerForQuestionAnswering`; \r\n\r\n```\r\nif attention_mask is None:\r\n attention_mask = some_function(input_ids) # All ones. Twos for question tokens. Zero for padding tokens\r\nelse:\r\n pass # do nothing\r\n```\r\n\r\nYou will need to assume that you know where the question is in the input, usually at the beginning of the sequence, and usually separated from the rest with a certain metatag. Maybe we need extra input from the user to specify the separator tag. \r\n\r\nNotes about the code [here](https://github.com/allenai/longformer/blob/master/scripts/triviaqa.py#L280):\r\n- you don't need the padding step, it is already implemented in `LongformerModel`\r\n- this code assumes that the question length is the same for all examples, but we can't make that assumption here", "@ibeltagy \r\nI did try creating `attention_mask ` automatically in forward method, but as you said this involves knowing where the question is (before or after the context) and the ids of `bos` and `sep` tokens. So model will need access to `tokenizer` to get ids or they'll need to be hardcoded. If we do this then I'm not sure how it will fit with the rest of the pipeline.\r\n\r\nSo can we provide this as a utility ? Or can we do this in the tokenizer where the user can provide indices in the original string for which global attention should be applied ?\r\n\r\n@patrickvonplaten Could you provide some feedback here ?", "good points, @patil-suraj. We already have access to `self.config.pad_token_id`, so maybe we can do the same to get access to `bos_token_id` and `sep_token_id`?", "Just checked, `self.config.bos_token_id` and `self.config.eos_token_id` are available but not `self.config.sep_token_id`. How about adding a new argument to the forward function that specifies the separator token? This is more general because there are cases where the user wants to use a different separator token from `sep_token_id`. ", "@ibeltagy \r\nYes,` self.config.bos_token_id` and `self.config.eos_token_id` are available. If I'm not wrong the `eos` and `sep` tokens are same for `LongformerTokenizer`.\r\nSo we can do it two ways, either make it available in `self.config` or pass explicitly to the forward function. \r\nIf we make it available in `self.config` then the existing `QuestionAnsweringPipeline` won't need to be modified, and the user can override the `self.config.sep_token_id` if its different from `sep_token_id`", "👍 sounds good to me. ", "Thanks @ibeltagy , I'll try this and let you know.", "In the long run we are planning on having a combined tokenizer and model config, so IMO it would be best to add a hardcoded `config.sep_token_id` to the Longformer config.", "Okay, so adding `sep_token_id` in `config` and assuming question is at the beginning, can we do it this way \r\n\r\n```\r\nattention_mask = torch.ones_like(input_ids)\r\n\r\nfor i in range(input_ids.shape[0]):\r\n sep_index = (input_ids[i, :] == self.config.sep_token_id).nonzero().min().item()\r\n attention_mask[i, :sep_index] = 2\r\n\r\n # set 0 for padding values if input is padded\r\n if self.config.pad_token_id in input_ids[i, :]:\r\n pad_index = (input_ids[i, :] == self.config.pad_token_id).nonzero().min().item()\r\n attention_mask[i, pad_index:] = 0\r\n```\r\n\r\ndoes this sound good to you ?", "> Okay, so adding `sep_token_id` in `config` and assuming question is at the beginning, can we do it this way\r\n> \r\n> ```\r\n> attention_mask = torch.ones_like(input_ids)\r\n> \r\n> for i in range(input_ids.shape[0]):\r\n> sep_index = (input_ids[i, :] == self.config.sep_token_id).nonzero().min().item()\r\n> attention_mask[i, :sep_index] = 2\r\n> \r\n> # set 0 for padding values if input is padded\r\n> if self.config.pad_token_id in input_ids[i, :]:\r\n> pad_index = (input_ids[i, :] == self.config.pad_token_id).nonzero().min().item()\r\n> attention_mask[i, pad_index:] = 0\r\n> ```\r\n> \r\n> does this sound good to you ?\r\n\r\nThanks a lot for your effort here @patil-suraj !\r\n\r\n1) I would prefer to not have a `for loop` here. I think it'd be nicer to just use tensor operations, exactly like @ibeltagy implemented it here: \r\nhttps://github.com/allenai/longformer/blob/e007ba9b52c550048e5981c8385980cc84359bc4/scripts/triviaqa.py#L411\r\nI think you only have to replace `self.tokenizer.eos_token_id` with `self.config.sep_token_id`. \r\n\r\n2) No need to pad the `input_ids` with \r\n```python \r\n # set 0 for padding values if input is padded\r\n if self.config.pad_token_id in input_ids[i, :]:\r\n pad_index = (input_ids[i, :] == self.config.pad_token_id).nonzero().min().item()\r\n attention_mask[i, pad_index:] = 0\r\n```\r\n\r\nI think you can remove that part of the code because our tokenizers automatically correctly put the 0 in `attention_mask`. \r\n\r\nThinking a bit more about I'm actually not anymore 100% sure whether this function should be in the `forward()` function of the `LongformerForQuestionAnswering`. Maybe it would be better to have it in the tokenizer function...not sure...will have to think about it.\r\n\r\nLet's implement it in the forward function as suggested for now :-) ", "Thanks, @patil-suraj ! If you don't mind, I want to suggest one more thing to add. I think it will be useful if this function alerts the user when the number of global attention tokens is large or the question is on the wrong side. It will be good to add something like: \r\n\r\n```\r\nif num of global attention positions > max(self.config.attention_window)`:\r\n logger.warning('something something')\r\n```\r\n\r\n@patrickvonplaten, I see why you are thinking it might be better to have it in the tokenizer, but I think that it can quickly get complicated because the global attention setting needs to change based on the task.", "@ibeltagy \r\nI think we will need to alert the user about that in all `Longformer` tasks, so can we add that warning in the base `LongformerModel` instead of `LongformerForQuestionAnswering` ?\r\n\r\n@patrickvonplaten \r\nI did tried to vectorize it, but that code assumes that all the questions in the batch have same length. So I'm not sure if we can make that assumption here . Also looking at this function\r\n\r\n``` \r\ndef _get_question_end_index(self, input_ids):\r\n eos_token_indices = (input_ids == self.tokenizer.eos_token_id).nonzero()\r\n assert eos_token_indices.ndim == 2\r\n assert eos_token_indices.size(0) == 2 * input_ids.size(0)\r\n assert eos_token_indices.size(1) == 2\r\n return eos_token_indices.view(input_ids.size(0), 2, 2)[:, 0, 1]\r\n```\r\nit seems that it makes the assumption that `eos_token_id/sep_token_id` occurs twice in the input, but if we use the default `sep_token_id` then it occurs three times in the input, if we encode que and context as input pair.\r\n\r\nSo looking at all this, would it be better if we just provide this as a utility and keep the `forward` method same?", "You are right about the variable number of global attention per batch, but it can still be vectorized,\r\n\r\n1) In the function you mentioned, the following line\r\n\r\n```\r\n return eos_token_indices.view(input_ids.size(0), 2, 2)[:, 0, 1]\r\n```\r\n\r\nneeds to change to the following because, as you said, you have 3 sep/eos tokens\r\n\r\n```\r\n return eos_token_indices.view(input_ids.size(0), 3, 2)[:, 0, 1]\r\n```\r\n\r\n2) Now given `question_end_index` you can set the `attention_mask` as follows: \r\n\r\n```\r\nquestion_end_index = question_end_index.unsqueeze(dim=1) # size: batch_size x 1\r\n# bool attention mask with True in locations of global attention\r\nattention_mask = torch.arange(input_ids.size(1)).expand_as(input_ids) < question_end_index\r\nattention_mask = attention_mask.int() + 1 # from True, False to 2, 1\r\n```\r\n", "Thanks ! @ibeltagy ", "Awesome work so far @patil-suraj - I think after the changes as discussed above, we can merge this :-) ", "Oh and one small thing I forgot to add. Could you add a test for `LongformerQuestionAnswering`. I think you can more or less copy this test here: \r\nhttps://github.com/huggingface/transformers/blob/a34a9896ac2a4a33ff9cd805c76eed914c8d8965/tests/test_modeling_bert.py#L311", "> Awesome work so far @patil-suraj - I think after the changes as discussed above, we can merge this :-)\r\n\r\nHappy to contribute 🤗", "> Awesome work so far @patil-suraj - I think after the changes as discussed above, we can merge this :-)\r\n\r\nAlso should we add support for `QuestionAnsweringPipeline` before merging or should that be done in another PR ?", "> > Awesome work so far @patil-suraj - I think after the changes as discussed above, we can merge this :-)\r\n> \r\n> Also should we add support for `QuestionAnsweringPipeline` before merging or should that be done in another PR ?\r\n\r\nLet's do this in another PR :-) ", "Ok great, I did a little change in the config @patil-suraj, but it looks good to merge for me now! @patil-suraj can you fix the code quality? It's actually quite easy to do:\r\n1) run `flake8 src/transformers/modeling_longformer.py` - it will show you exactly which lines need to be fixed. In your case, all errors are redundant white spaces.\r\nJust delete and re-add lines 791, 794 and 798 without a white space and delete the trailing (at the end of the line) white spaces in line 800.", "@ibeltagy - ok for you to be merged? ", "> Ok great, I did a little change in the config @patil-suraj, but it looks good to merge for me now! @patil-suraj can you fix the code quality? It's actually quite easy to do:\r\n> \r\n> 1. run `flake8 src/transformers/modeling_longformer.py` - it will show you exactly which lines need to be fixed. In your case, all errors are redundant white spaces.\r\n> Just delete and re-add lines 791, 794 and 798 without a white space and delete the trailing (at the end of the line) white spaces in line 800.\r\n\r\nSure.", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4500?src=pr&el=h1) Report\n> Merging [#4500](https://codecov.io/gh/huggingface/transformers/pull/4500?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **increase** coverage by `0.21%`.\n> The diff coverage is `94.44%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4500?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4500 +/- ##\n==========================================\n+ Coverage 77.87% 78.09% +0.21% \n==========================================\n Files 123 123 \n Lines 20566 20617 +51 \n==========================================\n+ Hits 16016 16100 +84 \n+ Misses 4550 4517 -33 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4500?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/4500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19sb25nZm9ybWVyLnB5) | `97.40% <94.00%> (+14.45%)` | :arrow_up: |\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/4500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.13% <100.00%> (ø)` | |\n| [src/transformers/configuration\\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/4500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2xvbmdmb3JtZXIucHk=) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/4500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `78.57% <100.00%> (ø)` | |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.50% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4500/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.41% <0.00%> (-0.12%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4500?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/4500?src=pr&el=footer). Last update [a34a989...a198607](https://codecov.io/gh/huggingface/transformers/pull/4500?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Ok great, all green - merging! Hope that's ok with you @ibeltagy ", "Looks great. Thanks, @patil-suraj.", "https://colab.research.google.com/drive/1ZwnA8NCKOM4HBvaRRpjuVmAaM--x92hN?usp=sharing\r\n\r\nTried to use the longformer with simpletransformers library and tried out the example but I am getting two different errors.\r\n\r\nthe first error with simpletransformers is an assertion-error\r\n`AssertionError: There should be exactly three separator tokens in every sample for questions answering`\r\n\r\nThe second error from example is and tensor error\r\n`TypeError: only integer tensors of a single element can be converted to an index`", "> This PR adds `LongformerForQuestionAnswering` following `RobertaForQuestionAnswering`.\r\n> \r\n> The code is almost identical to `RobertaForQuestionAnswering`, just had to remove `head_mask` parameter from forward method of `RobertaForQuestionAnswering`.\r\n> \r\n> Also trained the model to verify this. You can check inference [here](https://colab.research.google.com/drive/1WGgYuBEzGvkvhGOrQxB94Jr1nwCPyblu?usp=sharing)\r\n> \r\n> @patrickvonplaten\r\n\r\nBtw @patil-suraj, feel free to upload the model you trained on the model hub. It's a `longformer-base-4096` fine-tuned on Squad no? It'd be great if you can upload the model: https://huggingface.co/transformers/model_sharing.html", "> > This PR adds `LongformerForQuestionAnswering` following `RobertaForQuestionAnswering`.\r\n> > The code is almost identical to `RobertaForQuestionAnswering`, just had to remove `head_mask` parameter from forward method of `RobertaForQuestionAnswering`.\r\n> > Also trained the model to verify this. You can check inference [here](https://colab.research.google.com/drive/1WGgYuBEzGvkvhGOrQxB94Jr1nwCPyblu?usp=sharing)\r\n> > @patrickvonplaten\r\n> \r\n> Btw @patil-suraj, feel free to upload the model you trained on the model hub. It's a `longformer-base-4096` fine-tuned on Squad no? It'd be great if you can upload the model: https://huggingface.co/transformers/model_sharing.html\r\n\r\nYes, I'm training the model as we are speaking :). The previous model was trained with question at the end so I'm training it again " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4499?src=pr&el=h1) Report\n> Merging [#4499](https://codecov.io/gh/huggingface/transformers/pull/4499?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a08652772791fdaeed6f263b1a99926ca64be5dc&el=desc) will **decrease** coverage by `0.01%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4499?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4499 +/- ##\n==========================================\n- Coverage 77.83% 77.82% -0.02% \n==========================================\n Files 123 123 \n Lines 20514 20514 \n==========================================\n- Hits 15968 15964 -4 \n- Misses 4546 4550 +4 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4499?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/4499/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `83.53% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/4499/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90NS5weQ==) | `95.16% <100.00%> (ø)` | |\n| [src/transformers/hf\\_api.py](https://codecov.io/gh/huggingface/transformers/pull/4499/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9oZl9hcGkucHk=) | `93.06% <0.00%> (-4.96%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4499/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.85% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4499?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/4499?src=pr&el=footer). Last update [a086527...e9775b2](https://codecov.io/gh/huggingface/transformers/pull/4499?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi @ZhuBaohe, \r\n\r\nThansk for your PR! Can you explain a bit more in-detail what the fix is doing here? :-) ", "@patrickvonplaten \r\n\r\nI fixes a bug that the variable **encoder_decoder_position_bias** was incorrectly assigned by cross-attention weights, not by cross-attention position bias.\r\n\r\nSee Line 745 of the file modeling_t5.py as follow:\r\n```\r\n# layer_outputs = hidden-states, -> 0\r\n key-value-states, -> 1\r\n (self-attention weights), -> 2 \r\n (self-attention position bias), -> 3 \r\n (cross-attention weights), -> 4 \r\n (cross-attention position bias) -> 5 \r\n```\r\n**encoder_decoder_position_bias** should be assigned by layer_outputs[5] instead of layer_outputs[4] .", "Great, I agree with you. Previously the attention weights of the cross attention layer were taken instead of the bias. \r\n\r\n@LysandreJik @thomwolf I am quite surprised that we did not see an error earlier. I checked the slow tests and the summarization / translation results are equivalent as before. \r\n\r\nSo good to merge for me!", "Surprising indeed @patrickvonplaten , I did fix a similar bug when implementing T5.\r\n\r\nWe should switch to NamedTuples one day 😄 " ]

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[ "This is a general question and does not sound like a bug. With these large models it is sometimes hard to find a good set of hyperparameters to get the model to converge well. The same is true, and reported, for ALBERT. I don't think this is a bug.", "@shanybarhom A good start would be to use the hyper-parameters mentioned in the [ELECTRA](https://arxiv.org/abs/2003.10555) paper :) Just refer to table 7.\r\n\r\nSo your batch size, adam epsilon and epochs are very different compared to the ELECTRA parameters.", "Thanks, @stefan-it I've tried to use the same hyper-parameters as mentioned in the ELECTRA paper (lr=0.00005, batch_size=32, adam_epsilon=0.000001, epochs=3), but the electra-large model still doesn't converge (accuracy of ~50%). \r\n\r\n@BramVanroy I thought it is a bug since the electra-base and the electra-small converge quite quickly (90% accuracy after the first epoch) with the same code and data, while the large model is stuck on ~50% during the training, so it felt like a bug, but of course, it may not.", "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 am facing the same problem with ELECTRA-large. I really appreciate if there is any further direction for this." ]

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[ "Your use case seems specific, so maybe you should try a custom Tokenizer via the `tokenizers` library. I believe the results you're getting are the intended behavior.\r\n\r\nFor example, any generic sentence where someone forgets to put a space after the period would end up tokenized incorrectly otherwise:\r\n\r\n`I love lamp.No I really love lamp.` would leave you with a token `lamp.No`, which is incorrect, eh?", "tks~ it helps a lot ~" ]

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[ "You really need to put more effort in how you ask questions. Just throwing in some error trace and leaving it up to us to figure out what you want or where things go wrong is not the way to go. Use [**code blocks**](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks) and when using an example script, post your environment (as per the **template**) and post the command that you used.\r\n\r\nIn your case it seems that you wanted to load a tensorflow model with PyTorch. That won't work. If you need to use Tensorflow models, use [run_tf_glue.py](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py) instead.", "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 it ?" ]

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[ "That `layer_norm` should be None in `bart-large*`.\r\nSee [this comment](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bart.py#L266)\r\n\r\nNo final `layer_norm` was applied before `mbart` was added, afaict." ]

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[ "Love this idea. @thomwolf @julien-c what do you guys think about adding `nlp` as a dependency in `examples/requirements.txt`", "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/4493?src=pr&el=h1) Report\n> Merging [#4493](https://codecov.io/gh/huggingface/transformers/pull/4493?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/865d4d595eefc8cc9cee58fec9179bd182be0e2e&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4493?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4493 +/- ##\n=======================================\n Coverage 77.90% 77.91% \n=======================================\n Files 123 123 \n Lines 20472 20472 \n=======================================\n+ Hits 15949 15950 +1 \n+ Misses 4523 4522 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4493?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4493/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.76% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4493/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.53% <0.00%> (+0.11%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4493?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/4493?src=pr&el=footer). Last update [865d4d5...7ffd712](https://codecov.io/gh/huggingface/transformers/pull/4493?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Things could be clearer, but the only case where train_batch_size is different from per_gpu_train_batch_size is in `nn.DataParallel`. \r\n\r\nAnd in DataParallel, your dataloader's apparent batch size will be scattered amongst the devices, so I believe the `batch_size=self.args.train_batch_size` is correct\r\n\r\n", "(Note that DataParallel is not really recommended anymore as a way to utilize multiple GPUs, vs. torch.distributed)", "Gotcha, thanks for the context. Is the user expected to pass both `--train_batch_size` and `--per_gpu_train_batch_size` together then? In `examples/run_language_modeling.py` as it stands, the `--train_batch_size` affects the true batch size, but `--per_gpu_train_batch_size` is what is printed to stdout here: https://github.com/huggingface/transformers/blob/a08652772791fdaeed6f263b1a99926ca64be5dc/src/transformers/trainer.py#L419", "No, user would not pass a `--train_batch_size` – was it documented somewhere that they should?", "My misunderstanding then. There are [a few instances](https://grep.app/search?q=--train_batch_size&filter[repo][0]=huggingface/transformers) through the codebase where that arg is expected, but I see that in this example it's a derived property. Thanks for the help, closing the issue." ]

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[ "Hi. This is not a bug but is expected: since the model works on the character level, a tokenizer is not \"required\". You can read more in [the model card](https://huggingface.co/google/reformer-enwik8#reformer-language-model-on-character-level-and-trained-on-enwik8) on how you can encode/decode your data.", "@erickrf can you share how you got to train the \"reformer\" model. I´m trying to utilize the \"google/reformer-enwik8\" to train a Portuguese model but I just got the same error of \r\n`Model name 'google/reformer-enwik8' was not found in tokenizers`", "@bratao I answered this in my comment... Open the link thzt I posted and scroll down. They tell you how to do tokenisation. No need to load a tokenizer as usual. ", "@BramVanroy \r\n\r\nmy code is below \r\n```shell\r\npython examples/seq2seq/finetune_trainer.py --model_name_or_path google/reformer-enwik8 --do_train --do_eval --task translation_en_to_de --data_dir /lustre/dataset/wmt17_en_de/ --output_dir /home2/zhenggo1/checkpoint/reformer_translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate\r\n```\r\nand the bug is below,so what the reason? thks!\r\n```shell\r\nTraceback (most recent call last):\r\n File \"examples/seq2seq/finetune_trainer.py\", line 367, in <module>\r\n main()\r\n File \"examples/seq2seq/finetune_trainer.py\", line 206, in main\r\n cache_dir=model_args.cache_dir,\r\n File \"/home2/zhenggo1/LowPrecisionInferenceTool/examples/pytorch/huggingface_transformers/src/transformers/models/auto/tokenization_auto.py\", line 385, in from_pretrained\r\n return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)\r\n File \"/home2/zhenggo1/LowPrecisionInferenceTool/examples/pytorch/huggingface_transformers/src/transformers/tokenization_utils_base.py\", line 1760, in from_pretrained\r\n raise EnvironmentError(msg)\r\nOSError: Can't load tokenizer for 'google/reformer-enwik8'. Make sure that:\r\n\r\n- 'google/reformer-enwik8' is a correct model identifier listed on 'https://huggingface.co/models'\r\n\r\n- or 'google/reformer-enwik8' is the correct path to a directory containing relevant tokenizer files\r\n\r\n```\r\n", "@LeopoldACC Please post a new issue so that some one can have a look." ]

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[ "I cannot reproduce this. This works for me (same environment except Python 3.8 which should not make a difference). Can you try again but force_overwrite potentially corrupt files?\r\n\r\n```python\r\ntok = MarianTokenizer.from_pretrained(mname, force_download=True)\r\n```", "Hi, \n\nI rebased the transformers project just before running this and updated\nwith \"pip install --upgrade .\" in the root transformers directory. \n\nHere is the code as run:\n\nfrom transformers import MarianTokenizer, MarianMTModel\nfrom typing import List\nsrc = 'fr' # source language\ntrg = 'en' # target language\nsample_text = \"où est l'arrêt de bus ?\"\nmname = f'Helsinki-NLP/opus-mt-{src}-{trg}'\n\nmodel = MarianMTModel.from_pretrained(mname, force_download=True)\ntok = MarianTokenizer.from_pretrained(mname, force_download=True)\n\n# batch = tok.prepare_translation_batch(src_texts=[sample_text]) #\ndon't need tgt_text for inference\n# gen = model.generate(**batch) # for forward pass: model(**batch)\n# words: List[str] = tok.batch_decode(gen, skip_special_tokens=True) #\nreturns \"Where is the the bus stop ?\"\n\nHere is the terminal output:\n\n2020-05-22 05:45:15.204824: I\ntensorflow/stream_executor/platform/default/dso_loader.cc:44]\nSuccessfully opened dynamic library cudart64_101.dll \nDownloading: 100%|██████████| 1.13k/1.13k [00:00<00:00, 568kB/s] \nDownloading: 100%|██████████| 301M/301M [00:32<00:00, 9.34MB/s] \nDownloading: 100%|██████████| 802k/802k [00:00<00:00, 5.85MB/s] \nDownloading: 100%|██████████| 778k/778k [00:00<00:00, 5.71MB/s] \nDownloading: 100%|██████████| 1.34M/1.34M [00:00<00:00, 6.69MB/s] \nDownloading: 100%|██████████| 42.0/42.0 [00:00<00:00, 13.8kB/s] \nstdbuf was not found; communication with perl may hang due to stdio\nbuffering. \nTraceback (most recent call last): \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils.py\", line\n1055, in _from_pretrained \n tokenizer = cls(*init_inputs, **init_kwargs) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_marian.py\",\nline 89, in __init__ \n self._setup_normalizer() \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_marian.py\",\nline 95, in _setup_normalizer \n self.punc_normalizer = MosesPunctuationNormalizer(self.source_lang) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\mosestokenizer\\punctnormalizer.py\", line\n47, in __init__ \n super().__init__(argv) \n File \"C:\\Program Files\\Python\\lib\\site-packages\\toolwrapper.py\", line\n64, in __init__ \n self.start() \n File \"C:\\Program Files\\Python\\lib\\site-packages\\toolwrapper.py\", line\n108, in start \n env=env, \n File \"C:\\Program Files\\Python\\lib\\subprocess.py\", line 709, in\n__init__ \n restore_signals, start_new_session) \n File \"C:\\Program Files\\Python\\lib\\subprocess.py\", line 997, in\n_execute_child \n startupinfo) \nFileNotFoundError: [WinError 2] The system cannot find the file\nspecified \n\nDuring handling of the above exception, another exception occurred: \n\nTraceback (most recent call last): \n File\n\"C:/Development/Research/COVID-19-Misinfo2/src/translate_test_2.py\",\nline 9, in <module> \n tok = MarianTokenizer.from_pretrained(mname, force_download=True) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils.py\", line\n902, in from_pretrained \n return cls._from_pretrained(*inputs, **kwargs) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils.py\", line\n1058, in _from_pretrained \n \"Unable to load vocabulary from file. \" \nOSError: Unable to load vocabulary from file. Please check that the\nprovided vocabulary is accessible and not corrupted. \n\nProcess finished with exit code 1 \n\nI also tried this with 'Helsinki-NLP/opus-mt-ROMANCE-en' and had the\nsame results. I also stepped through the code in the debugger and\nmanually downloaded the files using my browser and pointed the\n*.from_retrained() methods to that directory. Here is the relevant code:\n\nmodel_name = 'Helsinki-NLP/opus-mt-ROMANCE-en'\n# see tokenizer.supported_language_codes for choices\nmodel =\nMarianMTModel.from_pretrained(\"./models/opus-mt-ROMANCE-en/model\")\n#model.save_pretrained(\"./models/opus-mt-ROMANCE-en/model\")\ntokenizer =\nMarianTokenizer.from_pretrained(\"./models/opus-mt-ROMANCE-en/model\")\n#tokenizer.save_pretrained(\"./models/opus-mt-ROMANCE-en/tokenizer\")\n\nAnd here is the directory list. I've also attached all these files\nexcept the pytorch.model.bin. If there is a problem with these files,\nplease send me the correct ones and I can try this locally\n\n Directory:\nC:\\Development\\Research\\COVID-19-Misinfo2\\src\\models\\opus-mt-ROMANCE-en\\model\n\n\nMode LastWriteTime Length Name \n---- ------------- ------ ---- \n-a---- 5/20/2020 5:52 PM 1163 config.json \n-a---- 5/20/2020 5:52 PM 312086495 pytorch_model.bin \n-a---- 5/20/2020 6:05 PM 800087 source.spm \n-a---- 5/20/2020 6:08 PM 265 tokenizer_config.json \n-a---- 5/20/2020 6:07 PM 1460304 vocab.json \n\nThis had the same effect as the remote download\n\n2020-05-22 05:58:34.251856: I\ntensorflow/stream_executor/platform/default/dso_loader.cc:44]\nSuccessfully opened dynamic library cudart64_101.dll \ndir = C:\\Development\\Research\\COVID-19-Misinfo2\\src \nTraceback (most recent call last): \n File\n\"C:/Development/Research/COVID-19-Misinfo2/src/translate_test_1.py\",\nline 15, in <module> \n tokenizer =\nMarianTokenizer.from_pretrained(\"./models/opus-mt-ROMANCE-en/model\") \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils.py\", line\n902, in from_pretrained \n return cls._from_pretrained(*inputs, **kwargs) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils.py\", line\n1055, in _from_pretrained \n tokenizer = cls(*init_inputs, **init_kwargs) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_marian.py\",\nline 84, in __init__ \n self.spm_target = load_spm(target_spm) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_marian.py\",\nline 236, in load_spm \n spm.Load(path) \n File \"C:\\Program Files\\Python\\lib\\site-packages\\sentencepiece.py\",\nline 118, in Load \n return _sentencepiece.SentencePieceProcessor_Load(self, filename) \nTypeError: not a string \n\nProcess finished with exit code 1 \n\nI have downloaded and used the GPT-2 model without these problems using\nvery similar code\n\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n\nHope this helps,\n\nPhil Feldman\n\n---\n\nOn 2020-05-22 05:34, Bram Vanroy wrote:\n\n> I cannot reproduce this. This works for me (same environment except Python 3.8 which should not make a difference). Can you try again but force_overwrite potentially corrupt files?\n> \n> tok = MarianTokenizer.from_pretrained(mname, force_download=True)\n> \n> --\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub [1], or unsubscribe [2].\n \n\nLinks:\n------\n[1]\nhttps://github.com/huggingface/transformers/issues/4491#issuecomment-632597198\n[2]\nhttps://github.com/notifications/unsubscribe-auth/ABPRJH7JIRH4PIEONBAXAULRSZBJXANCNFSM4NGLYESA", "Hi @pgfeldman, I initally faced the same error but was able to resolve it by downloading the model to a specified location using the below steps\r\n```\r\ncache_dir = \"/home/transformers_files/\"\r\ncache_dir_models = cache_dir + \"default_models/\"\r\ncache_dir_tokenizers = cache_dir + \"tokenizers/\"\r\nmodel_name = 'Helsinki-NLP/opus-mt-ROMANCE-en'\r\ntokenizer = MarianTokenizer.from_pretrained(model_name, cache_dir=cache_dir_tokenizers)\r\nmodel = MarianMTModel.from_pretrained(model_name, cache_dir=cache_dir_models)\r\n```", "Hi! I had the same issue after installing the mosestokenizer (as recommended) on Windows with Python 3.6. After I uninstalled it, it seemed to work fine! I think more investigation is needed there.", "@BramVanroy did it work for you on windows? I also can't reproduce.", "> @BramVanroy did it work for you on windows? I also can't reproduce.\r\n\r\nI still cannot reproduce this. I tried uninstall/reinstalling mosestokenizer and it works in both cases.\r\n\r\nFor everyone having problems, can you run the following and post its output here so that we can find similarities? @jpcorb20 @SAswinGiridhar @pgfeldman \r\n\r\n**This requires you to be on the latest master branch (on Windows at least) so install from source!**\r\n\r\n```bash\r\ntransformers-cli env\r\n```", "I deleted and re-installed transformers and installed from source \r\n\r\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two\r\nlast points. \r\n\r\n- `transformers` version: 2.11.0 \r\n- Platform: Windows-10-10.0.18362-SP0 \r\n- Python version: 3.6.4 \r\n- PyTorch version (GPU?): 1.5.0+cu101 (True) \r\n- Tensorflow version (GPU?): 2.1.0 (True) \r\n- Using GPU in script?: <fill in> \r\n- Using distributed or parallel set-up in script?: <fill in> \r\n\r\nI'm also attaching my package list\r\n[deleted by moderator for length]", "Hello, here's mine :\r\n\r\n- `transformers` version: 2.11.0\r\n- Platform: Windows-10-10.0.18362-SP0\r\n- Python version: 3.6.7\r\n- PyTorch version (GPU?): 1.5.0+cu101 (True)\r\n- Tensorflow version (GPU?): 2.0.0 (False)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No\r\n", "Does\r\n```python\r\ntokenizer = XLMRobertaTokenizer.from_pretrained(\"xlm-roberta-base\")\r\ntokenizer.batch_encode_plus(['stuff'])\r\n```\r\nwork?", "Yes! \n\nHere's the code as run:\n\nfrom transformers import XLMRobertaTokenizer\n\ntokenizer = XLMRobertaTokenizer.from_pretrained(\"xlm-roberta-base\")\ntokenizer.batch_encode_plus(['stuff'])\n\nprint(\"done\")\n\nHere's the output\n\n\"C:\\Program Files\\Python\\python.exe\"\nC:/Users/Phil/AppData/Roaming/JetBrains/IntelliJIdea2020.1/scratches/transformers_error_2.py\n\n2020-06-08 17:44:17.768004: I\ntensorflow/stream_executor/platform/default/dso_loader.cc:44]\nSuccessfully opened dynamic library cudart64_101.dll \nDownloading: 100%|██████████| 5.07M/5.07M [00:00<00:00, 9.57MB/s] \ndone \n\nProcess finished with exit code 0 \n\nHope this helps, \n\nPhil\n\n---\n\nOn 2020-06-08 17:13, Sam Shleifer wrote:\n\n> Does\n> \n> tokenizer = XLMRobertaTokenizer.from_pretrained(\"xlm-roberta-base\")\n> tokenizer.batch_encode_plus(['stuff'])\n> \n> work? \n> \n> --\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub [1], or unsubscribe [2].\n \n\nLinks:\n------\n[1]\nhttps://github.com/huggingface/transformers/issues/4491#issuecomment-640889916\n[2]\nhttps://github.com/notifications/unsubscribe-auth/ABPRJHZZ3BPH7DFC36FOYJTRVVIAVANCNFSM4NGLYESA", "Working for me too", "Can anyone help with this issue: #5040 ?", "> Can anyone help with this issue: #5040 ?\r\n\r\nPlease don't spam other topics like this in the future. We do our best to help where and when we can. Posting duplicate comments on different topics adds more noise than it is helpful.", "I think this bug may be fixed on master, but I can't verify because I don't have windows. Could 1 person check and post their results? Remember to be up to date with master, your git log should contain `3d495c61e Sam Shleifer: Fix marian tokenizer save pretrained (#5043)`", "Doesn't work on my PC, but I changed the library for the moses tokenizer in _setup_normalizer and it works:\r\n\r\n```\r\ndef _setup_normalizer(self):\r\n try:\r\n from sacremoses import MosesPunctNormalizer\r\n self.punc_normalizer = MosesPunctNormalizer(lang=self.source_lang).normalize\r\n except ImportError:\r\n warnings.warn(\"Recommended: pip install sacremoses\")\r\n self.punc_normalizer = lambda x: x\r\n```", "Hi Sam, \n\nI just rebased, verified the gitlog, and installed using \"pip install\n--upgrade .\" I'm attaching the console record of the install. \n\nI still get the same error(s) \n\n2020-06-17 05:40:43.980254: I\ntensorflow/stream_executor/platform/default/dso_loader.cc:44]\nSuccessfully opened dynamic library cudart64_101.dll \nstdbuf was not found; communication with perl may hang due to stdio\nbuffering. \nTraceback (most recent call last): \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils_base.py\",\nline 1161, in _from_pretrained \n tokenizer = cls(*init_inputs, **init_kwargs) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_marian.py\",\nline 81, in __init__ \n self._setup_normalizer() \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_marian.py\",\nline 87, in _setup_normalizer \n self.punc_normalizer = MosesPunctuationNormalizer(self.source_lang) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\mosestokenizer\\punctnormalizer.py\", line\n47, in __init__ \n super().__init__(argv) \n File \"C:\\Program Files\\Python\\lib\\site-packages\\toolwrapper.py\", line\n64, in __init__ \n self.start() \n File \"C:\\Program Files\\Python\\lib\\site-packages\\toolwrapper.py\", line\n108, in start \n env=env, \n File \"C:\\Program Files\\Python\\lib\\subprocess.py\", line 709, in\n__init__ \n restore_signals, start_new_session) \n File \"C:\\Program Files\\Python\\lib\\subprocess.py\", line 997, in\n_execute_child \n startupinfo) \nFileNotFoundError: [WinError 2] The system cannot find the file\nspecified \n\nDuring handling of the above exception, another exception occurred: \n\nTraceback (most recent call last): \n File\n\"C:/Users/Phil/AppData/Roaming/JetBrains/IntelliJIdea2020.1/scratches/transformers_error.py\",\nline 9, in <module> \n tok = MarianTokenizer.from_pretrained(mname) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils_base.py\",\nline 1008, in from_pretrained \n return cls._from_pretrained(*inputs, **kwargs) \n File \"C:\\Program\nFiles\\Python\\lib\\site-packages\\transformers\\tokenization_utils_base.py\",\nline 1164, in _from_pretrained \n \"Unable to load vocabulary from file. \" \nOSError: Unable to load vocabulary from file. Please check that the\nprovided vocabulary is accessible and not corrupted. \n\nProcess finished with exit code 1 \n\nHope this helps \n\nPhil\n\n---\n\nOn 2020-06-16 09:50, Sam Shleifer wrote:\n\n> I think this bug may be fixed on master, but I can't verify because I don't have windows. Could 1 person check and post their results? Remember to be up to date with master, your git log should contain 3d495c61e Sam Shleifer: Fix marian tokenizer save pretrained (#5043) - (HEAD -> master, upstream/master) (2 minutes ago) \n> \n> --\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub [1], or unsubscribe [2].\n \n\nLinks:\n------\n[1]\nhttps://github.com/huggingface/transformers/issues/4491#issuecomment-644778862\n[2]\nhttps://github.com/notifications/unsubscribe-auth/ABPRJH5BKWN3OBT7DOP4PVTRW52CRANCNFSM4NGLYESA", "Just upgraded to version 3.0, and everything is working!" ]

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[ "Now I'm using head_mask instead of prune_heads. So, I didn't actually prune heads." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4489?src=pr&el=h1) Report\n> Merging [#4489](https://codecov.io/gh/huggingface/transformers/pull/4489?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/14cb5b35faeda7881341656aacf89d12a8a7e07b&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4489?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4489 +/- ##\n==========================================\n- Coverage 78.04% 78.03% -0.01% \n==========================================\n Files 123 123 \n Lines 20477 20477 \n==========================================\n- Hits 15981 15980 -1 \n- Misses 4496 4497 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4489?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4489/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4489?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/4489?src=pr&el=footer). Last update [14cb5b3...a5ce320](https://codecov.io/gh/huggingface/transformers/pull/4489?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Indeed ! Thanks for spotting this @RensDimmendaal " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4488?src=pr&el=h1) Report\n> Merging [#4488](https://codecov.io/gh/huggingface/transformers/pull/4488?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6dc52c78d8f1f96ffd9b8f8178e142b7d4a77d14&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4488?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4488 +/- ##\n==========================================\n+ Coverage 78.02% 78.04% +0.01% \n==========================================\n Files 123 123 \n Lines 20477 20477 \n==========================================\n+ Hits 15978 15982 +4 \n+ Misses 4499 4495 -4 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4488?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4488/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.76% <0.00%> (+0.23%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4488/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.83% <0.00%> (+0.32%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4488/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.85% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4488?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/4488?src=pr&el=footer). Last update [6dc52c7...c4a85ea](https://codecov.io/gh/huggingface/transformers/pull/4488?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Great, thanks for adding this notice" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4487?src=pr&el=h1) Report\n> Merging [#4487](https://codecov.io/gh/huggingface/transformers/pull/4487?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6dc52c78d8f1f96ffd9b8f8178e142b7d4a77d14&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4487?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4487 +/- ##\n=======================================\n Coverage 78.02% 78.03% \n=======================================\n Files 123 123 \n Lines 20477 20477 \n=======================================\n+ Hits 15978 15980 +2 \n+ Misses 4499 4497 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4487?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4487/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+0.16%)` | :arrow_up: |\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4487/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.76% <0.00%> (+0.23%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4487?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/4487?src=pr&el=footer). Last update [6dc52c7...2260280](https://codecov.io/gh/huggingface/transformers/pull/4487?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This is the expected behaviour: `len(tokenizer)` shows you the actual size (including the added tokens), whereas `.vocab_size` tells you the original size.\r\n\r\nhttps://github.com/huggingface/transformers/blob/a08652772791fdaeed6f263b1a99926ca64be5dc/src/transformers/tokenization_utils.py#L2285-L2290\r\n\r\nPS: don't forget to update your model's embeddings!\r\n\r\n```python\r\nmodel.resize_token_embeddings(len(tokenizer))\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", "I find it confusing that the vocab_size doesn't get modified. Also, the Hugging Face documentation describes `tokenizer.add_tokens` as follows: \r\n\r\n> Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary." ]

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[ "Closing in favour of the better formulated question here: https://github.com/huggingface/transformers/issues/4491", "Can anyone help with this issue: #5040 ?" ]

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[ "Hi, could you please post a code sample and a textual error, rather than an image? Thanks.", "@LysandreJik yes, its very simple my code I just trying to run a transformers example.\r\n\r\n if __name__ == '__main__':\r\n import ipywidgets as widgets\r\n from transformers.pipelines import pipeline\r\n from transformers.modeling_auto import AutoModelForQuestionAnswering\r\n from transformers.tokenization_auto import AutoTokenizer\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(\"deepset/roberta-base-squad2\")\r\n model = AutoModelForQuestionAnswering.from_pretrained(\"deepset/roberta-base-squad2\")\r\n nlp_qa = pipeline('question-answering', model=model, tokenizer=tokenizer, device = 0)\r\n X = nlp_qa(context=\"text document.txt\", question='What is this project?')\r\n print(X)\r\n\r\nAnd runing with this albert or any another albert I got this error:\r\n\r\n File \"c:/Users/tioga/Desktop/Tranformers/transformers_test.py\", line 44, in <module>\r\n X = nlp_qa(context=st, question='What is this project?')\r\n File \"C:\\Python\\lib\\site-packages\\transformers\\pipelines.py\", line 1042, in __call__\r\n for s, e, score in zip(starts, ends, scores)\r\n File \"C:\\Python\\lib\\site-packages\\transformers\\pipelines.py\", line 1042, in <listcomp>\r\n for s, e, score in zip(starts, ends, scores)\r\n KeyError: 0\r\n", "I can reproduce this error, but it is working with other models for me. Pinging @tholor who might know what is going on.", "hi guys, anyone managed to understand what the above issue is? I am facing the same issue.\r\nThanks.", "I believe this was fixed in #4049, which is available in the latest release `v2.10.0`. What are your installed `transformers` versions?", "@LysandreJik I was using 2.7.0, but I still get the same error using 2.10.0", "Using the exact code sample mentioned above? Are you using different code?", "I have the exact issue with one of my Roberta models.. But I tried exact code now\r\n<img width=\"827\" alt=\"Screen Shot 2020-05-26 at 6 38 24 AM\" src=\"https://user-images.githubusercontent.com/3698879/82908070-50b28980-9f1c-11ea-8a12-ff70f862c46b.png\">\r\n", "It's hard for me to test if you give an image. Can you paste the code? If you already have `transformers==2.7.0` installed, your `!pip install transformers==2.10.0` won't work. You need to add the `--upgrade` or `-U` flag.\r\n\r\nCan you add\r\n\r\n```py\r\nfrom transformers import __version__\r\nprint(__version__)\r\n```\r\n\r\njust to make sure?", "@LysandreJik works for me. Thank you.", "Glad I could help!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Hi,\r\n\r\nWhen I use transformers 2.4.0, it is working without the error, but with 3.0.2 I will get the same error!\r\n\r\nSo when the context has the answer in it, everything is fine, when it has not, I get the same error.\r\nExample:\r\n\r\n```\r\nfrom transformers.pipelines import pipeline\r\nname=\"ktrapeznikov/albert-xlarge-v2-squad-v2\"\r\nnlp=pipeline('question-answering',model=name,tokenizer=name,device=-1)\r\n```\r\n\r\nThis example won't cause any errors and I get the right answer:\r\n\r\n```\r\nqa_input = {'question': 'Is the company listed on any stock exchange?', 'context': 'Roche Corporate Executive Committee on 31 December 2019. We are dedicated to long-term success. Roche is listed on New York stock exchange.'}\r\nqa_response = nlp(qa_input)\r\n```\r\n\r\nThis will cause the error:\r\n\r\n```\r\nqa_input = {'question': 'Is the company listed on any stock exchange?', 'context': 'Roche Corporate Executive Committee on 31 December 2019. We are dedicated to long-term success.'}\r\nqa_response = nlp(qa_input)\r\n```\r\n\r\nCan you verify that it is not working with 3.0.2 ?\r\nDo you have any solutions or I should just use older versions for now to work with?\r\n\r\nThanks!\r\n" ]

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[ "@dimi1357 out of curiosity, what does training this look like?", "> @dimi1357 out of curiosity, what does training this look like?\r\n\r\nThis is my training loop:\r\n```python\r\nx, encoder_attention_mask, y, decoder_attention_mask, _ = batch\r\nx = x.to(self.device)\r\ny = y.to(self.device)\r\nencoder_attention_mask = encoder_attention_mask.to(self.device)\r\ndecoder_attention_mask = decoder_attention_mask.to(self.device)\r\nmodel_kwargs = {\r\n \"attention_mask\": encoder_attention_mask,\r\n \"decoder_attention_mask\": decoder_attention_mask,\r\n \"lm_labels\": y\r\n}\r\nself.optimizer.zero_grad()\r\noutputs = self.model(input_ids=x, decoder_input_ids=y, **model_kwargs)\r\nloss = outputs[0]\r\nloss.backward()\r\nself.optimizer.step()\r\nif self.scheduler is not None:\r\n self.scheduler.step()\r\n```\r\n\r\nand I create the model this way:\r\n```pyhon\r\nconfig_decoder = AutoConfig.from_pretrained(decoder_model_name, is_decoder=True)\r\nconfig_encoder = AutoConfig.from_pretrained(encoder_model_name, is_decoder=False)\r\nconfig = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)\r\nres_model = EncoderDecoderModel(config=config)\r\n```", "@dimi1357 Did you finally make it work? Can you provide me the \"full changes\" in some way? I am also interested in using the GPT2 model as decoder.", "Thanks for the Feature request and the in-detail code! I will think a bit more about how to implement this and get back to you!", "> Thanks for the Feature request and the in-detail code! I will think a bit more about how to implement this and get back to you!\r\n\r\nI forgot to add the change I've made to `Block` class forward function (I've also edited the issue):\r\n```python\r\n def forward(self, x, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, encoder_hidden_states=None,\r\n encoder_attention_mask=None):\r\n output_attn = self.attn(\r\n self.ln_1(x),\r\n layer_past=layer_past,\r\n attention_mask=attention_mask,\r\n head_mask=head_mask,\r\n use_cache=use_cache,\r\n )\r\n a = output_attn[0] # output_attn: a, present, (attentions)\r\n outputs = []\r\n if self.is_decoder and encoder_hidden_states is not None:\r\n cross_attention_outputs = self.crossattention(\r\n a, layer_past, attention_mask, head_mask, encoder_hidden_states=encoder_hidden_states,\r\n encoder_attention_mask=encoder_attention_mask\r\n )\r\n a = cross_attention_outputs[0]\r\n outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights\r\n\r\n x = x + a\r\n m = self.mlp(self.ln_2(x))\r\n x = x + m\r\n\r\n outputs = [x] + output_attn[1:] + outputs\r\n\r\n return outputs # x, present, (attentions)\r\n```", "> @dimi1357 Did you finally make it work? Can you provide me the \"full changes\" in some way? I am also interested in using the GPT2 model as decoder.\r\n\r\nYou can add the code above to where you've installed the transformers package, but I'm still not sure that this implementation is correct, so I suggest you wait for an update from huggingface team if this is okay.", "Hey @dimi1357 . So I think the Encoder Decoder roadmap is as follows: \r\n- In ~2 weeks, we will open-source a clean notebook showing how a `Bert2Bert` model can be fine-tuned\r\n- After that, we will take a deeper look into hooking `GPT2` into the `EncoderDecoder` framework. \r\n\r\nI will keep your code sample here in mind for this :-) ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "> Hey @dimi1357 . So I think the Encoder Decoder roadmap is as follows:\r\n> \r\n> * In ~2 weeks, we will open-source a clean notebook showing how a `Bert2Bert` model can be fine-tuned\r\n> * After that, we will take a deeper look into hooking `GPT2` into the `EncoderDecoder` framework.\r\n> \r\n> I will keep your code sample here in mind for this :-)\r\n\r\nHi, \r\nIs there any updates regarding to BERT2GPT implementation.\r\nThanks!", "Hey, I will take a look at BERTGPT2 encoder-decoder probably on Monday next week", "@patrickvonplaten Can you please share a work in progress notebook/colab, or some code. I am willing to help with tests and datasets, in order to improve the BERT2GPT2 model. Thank you :D", "Will finish the PR tomorrow then it should be pretty easy to do BERT2GPT2.", "Hi @patrickvonplaten . I've used your latest commit to train BERT2GPT2 using your BERT2BERT training tutorial. It was straight forward, I only had to replace the \"bert\" from decoder with \"gpt2\". The training worked, but at inference time there was a code error in `prepare_inputs_for_generation` at line 299:\r\n> /transformers/modeling_encoder_decoder.py\r\n> 297 # first step\r\n> 298 if type(past) is tuple:\r\n> 299 encoder_outputs, _ = past <----\r\n> 300 else:\r\n> 301 encoder_outputs = (past,)\r\n> \r\n\r\n> \r\n\r\n> ValueError: too many values to unpack (expected 2)\r\n\r\nI do not know if the model requires a different evaluation approach. ", "> Will finish the PR tomorrow then it should be pretty easy to do BERT2GPT2.\r\n\r\nThanks for the implementation, I'm going to test it now.", "GPT2 is added and results on summariation look promising. Check out this model (Bert2GPT2 trained on CNN/Daily Mail) including train and eval script: https://huggingface.co/patrickvonplaten/bert2gpt2-cnn_dailymail-fp16 .", "Hi @patrickvonplaten, I used this model card to train on my custom dataset, but again the TypeError is been thrownback that `forward() got an unexpected keyword argument 'encoder_hidden_states'`\r\nhere is my code\r\n```\r\nimport nlp\r\nimport logging\r\nfrom transformers import BertTokenizer, GPT2Tokenizer, EncoderDecoderModel, Trainer, TrainingArguments\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nmodel = EncoderDecoderModel.from_encoder_decoder_pretrained(\"bert-base-uncased\", \"gpt2\")\r\n# cache is currently not supported by EncoderDecoder framework\r\nmodel.decoder.config.use_cache = False\r\nbert_tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\n# CLS token will work as BOS token\r\nbert_tokenizer.bos_token = bert_tokenizer.cls_token\r\n\r\n# SEP token will work as EOS token\r\nbert_tokenizer.eos_token = bert_tokenizer.sep_token\r\n\r\n\r\n# make sure GPT2 appends EOS in begin and end\r\ndef build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):\r\n outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]\r\n return outputs\r\n\r\n\r\nGPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens\r\ngpt2_tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n# set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id\r\ngpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token\r\n\r\n\r\n# set decoding params\r\nmodel.config.decoder_start_token_id = gpt2_tokenizer.bos_token_id\r\nmodel.config.eos_token_id = gpt2_tokenizer.eos_token_id\r\nmodel.config.max_length = 142\r\nmodel.config.min_length = 56\r\nmodel.config.no_repeat_ngram_size = 3\r\nmodel.early_stopping = True\r\nmodel.length_penalty = 2.0\r\nmodel.num_beams = 4\r\n\r\n# load train and validation data\r\ntrain_dataset = nlp.load_dataset('csv', data_files='data.csv',split='train[:80%]')\r\nval_dataset = nlp.load_dataset('csv', data_files='data.csv',split='train[80%:]')\r\n\r\n# load rouge for validation\r\nrouge = nlp.load_metric(\"rouge\", experiment_id=1)\r\n\r\nencoder_length = 512\r\ndecoder_length = 128\r\nbatch_size = 16\r\n\r\n\r\n# map data correctly\r\ndef map_to_encoder_decoder_inputs(batch): # Tokenizer will automatically set [BOS] <text> [EOS] \r\n # use bert tokenizer here for encoder\r\n inputs = bert_tokenizer.encode_plus(batch[\"Patient\"], padding=\"max_length\", truncation=True, max_length=encoder_length)\r\n # force summarization <= 128\r\n outputs = gpt2_tokenizer.encode_plus(batch[\"Doctor\"], padding=\"max_length\", truncation=True, max_length=decoder_length)\r\n\r\n batch[\"input_ids\"] = inputs.input_ids\r\n batch[\"attention_mask\"] = inputs.attention_mask\r\n batch[\"decoder_input_ids\"] = outputs.input_ids\r\n batch[\"labels\"] = outputs.input_ids.copy()\r\n batch[\"decoder_attention_mask\"] = outputs.attention_mask\r\n\r\n # complicated list comprehension here because pad_token_id alone is not good enough to know whether label should be excluded or not\r\n batch[\"labels\"] = [\r\n [-100 if mask == 0 else token for mask, token in mask_and_tokens] for mask_and_tokens in [zip(masks, labels) for masks, labels in zip(batch[\"decoder_attention_mask\"], batch[\"labels\"])]\r\n ]\r\n\r\n assert all([len(x) == encoder_length for x in inputs.input_ids])\r\n assert all([len(x) == decoder_length for x in outputs.input_ids])\r\n\r\n return batch\r\n\r\n\r\ndef compute_metrics(pred):\r\n labels_ids = pred.label_ids\r\n pred_ids = pred.predictions\r\n\r\n # all unnecessary tokens are removed\r\n pred_str = gpt2_tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\r\n labels_ids[labels_ids == -100] = gpt2_tokenizer.eos_token_id\r\n label_str = gpt2_tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\r\n\r\n rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\r\n\r\n return {\r\n \"rouge2_precision\": round(rouge_output.precision, 4),\r\n \"rouge2_recall\": round(rouge_output.recall, 4),\r\n \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\r\n }\r\n\r\n\r\n# make train dataset ready\r\ntrain_dataset = train_dataset.map(\r\n map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=[\"Patient\", \"Doctor\"],\r\n)\r\ntrain_dataset.set_format(\r\n type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\r\n)\r\n\r\n# same for validation dataset\r\nval_dataset = val_dataset.map(\r\n map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=[\"Patient\", \"Doctor\"],\r\n)\r\nval_dataset.set_format(\r\n type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\r\n)\r\n\r\n# set training arguments - these params are not really tuned, feel free to change\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./ambi\",\r\n per_device_train_batch_size=batch_size,\r\n per_device_eval_batch_size=batch_size,\r\n evaluate_during_training=True,\r\n do_train=True,\r\n do_eval=True,\r\n logging_steps=1000,\r\n save_steps=1000,\r\n eval_steps=1000,\r\n overwrite_output_dir=True,\r\n warmup_steps=2000,\r\n save_total_limit=10,\r\n fp16=True,\r\n)\r\n\r\n# instantiate trainer\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n compute_metrics=compute_metrics,\r\n train_dataset=train_dataset,\r\n eval_dataset=val_dataset,\r\n)\r\n\r\n# start training\r\ntrainer.train()\r\n```\r\nIf you can see it carefully you can find that an argument is missing in `TrainingArguments` module, I always get an error that why `predict_from_generate` is passed, I tried finding that attribute in [`training_args.py`](https://github.com/huggingface/transformers/blob/master/src/transformers/training_args.py), but it seems there is no such attribute available in it. Please clarify which version are you using, If it is above 2.11 then please clarify why my the above code is throwing this error.", "You need to switch to this branch: https://github.com/huggingface/transformers/tree/more_general_trainer_metric to make the training work. I am trying to integrate this branch into master soon :-) ", "Thanks for letting me know.", "Sorry to ask a question after a long period of time :-). I am still not very clear about the effect of **encoder attention mask** in GPT2. \r\n\r\nI understand that it is used only in the decoder of Encoder-Decoder model to make some change to the cross attention weights. Also, I notice the operation defined in the modelling_gpt2.py:\r\n`attention_mask = encoder_attention_mask`\r\n`...`\r\n`w=w+attention_mask`\r\n\r\nHowever, I am confused why we need this **encoder attention mask**. Is that also because the decoder can not see the whole sequence?\r\n\r\nThanks for help :-)\r\n", "> Hi @patrickvonplaten, I used this model card to train on my custom dataset, but again the TypeError is been thrownback that `forward() got an unexpected keyword argument 'encoder_hidden_states'` here is my code\r\n> \r\n> ```\r\n> import nlp\r\n> import logging\r\n> from transformers import BertTokenizer, GPT2Tokenizer, EncoderDecoderModel, Trainer, TrainingArguments\r\n> \r\n> logging.basicConfig(level=logging.INFO)\r\n> \r\n> model = EncoderDecoderModel.from_encoder_decoder_pretrained(\"bert-base-uncased\", \"gpt2\")\r\n> # cache is currently not supported by EncoderDecoder framework\r\n> model.decoder.config.use_cache = False\r\n> bert_tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n> \r\n> # CLS token will work as BOS token\r\n> bert_tokenizer.bos_token = bert_tokenizer.cls_token\r\n> \r\n> # SEP token will work as EOS token\r\n> bert_tokenizer.eos_token = bert_tokenizer.sep_token\r\n> \r\n> \r\n> # make sure GPT2 appends EOS in begin and end\r\n> def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):\r\n> outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]\r\n> return outputs\r\n> \r\n> \r\n> GPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens\r\n> gpt2_tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n> # set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id\r\n> gpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token\r\n> \r\n> \r\n> # set decoding params\r\n> model.config.decoder_start_token_id = gpt2_tokenizer.bos_token_id\r\n> model.config.eos_token_id = gpt2_tokenizer.eos_token_id\r\n> model.config.max_length = 142\r\n> model.config.min_length = 56\r\n> model.config.no_repeat_ngram_size = 3\r\n> model.early_stopping = True\r\n> model.length_penalty = 2.0\r\n> model.num_beams = 4\r\n> \r\n> # load train and validation data\r\n> train_dataset = nlp.load_dataset('csv', data_files='data.csv',split='train[:80%]')\r\n> val_dataset = nlp.load_dataset('csv', data_files='data.csv',split='train[80%:]')\r\n> \r\n> # load rouge for validation\r\n> rouge = nlp.load_metric(\"rouge\", experiment_id=1)\r\n> \r\n> encoder_length = 512\r\n> decoder_length = 128\r\n> batch_size = 16\r\n> \r\n> \r\n> # map data correctly\r\n> def map_to_encoder_decoder_inputs(batch): # Tokenizer will automatically set [BOS] <text> [EOS] \r\n> # use bert tokenizer here for encoder\r\n> inputs = bert_tokenizer.encode_plus(batch[\"Patient\"], padding=\"max_length\", truncation=True, max_length=encoder_length)\r\n> # force summarization <= 128\r\n> outputs = gpt2_tokenizer.encode_plus(batch[\"Doctor\"], padding=\"max_length\", truncation=True, max_length=decoder_length)\r\n> \r\n> batch[\"input_ids\"] = inputs.input_ids\r\n> batch[\"attention_mask\"] = inputs.attention_mask\r\n> batch[\"decoder_input_ids\"] = outputs.input_ids\r\n> batch[\"labels\"] = outputs.input_ids.copy()\r\n> batch[\"decoder_attention_mask\"] = outputs.attention_mask\r\n> \r\n> # complicated list comprehension here because pad_token_id alone is not good enough to know whether label should be excluded or not\r\n> batch[\"labels\"] = [\r\n> [-100 if mask == 0 else token for mask, token in mask_and_tokens] for mask_and_tokens in [zip(masks, labels) for masks, labels in zip(batch[\"decoder_attention_mask\"], batch[\"labels\"])]\r\n> ]\r\n> \r\n> assert all([len(x) == encoder_length for x in inputs.input_ids])\r\n> assert all([len(x) == decoder_length for x in outputs.input_ids])\r\n> \r\n> return batch\r\n> \r\n> \r\n> def compute_metrics(pred):\r\n> labels_ids = pred.label_ids\r\n> pred_ids = pred.predictions\r\n> \r\n> # all unnecessary tokens are removed\r\n> pred_str = gpt2_tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\r\n> labels_ids[labels_ids == -100] = gpt2_tokenizer.eos_token_id\r\n> label_str = gpt2_tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\r\n> \r\n> rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\r\n> \r\n> return {\r\n> \"rouge2_precision\": round(rouge_output.precision, 4),\r\n> \"rouge2_recall\": round(rouge_output.recall, 4),\r\n> \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\r\n> }\r\n> \r\n> \r\n> # make train dataset ready\r\n> train_dataset = train_dataset.map(\r\n> map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=[\"Patient\", \"Doctor\"],\r\n> )\r\n> train_dataset.set_format(\r\n> type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\r\n> )\r\n> \r\n> # same for validation dataset\r\n> val_dataset = val_dataset.map(\r\n> map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=[\"Patient\", \"Doctor\"],\r\n> )\r\n> val_dataset.set_format(\r\n> type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\r\n> )\r\n> \r\n> # set training arguments - these params are not really tuned, feel free to change\r\n> training_args = TrainingArguments(\r\n> output_dir=\"./ambi\",\r\n> per_device_train_batch_size=batch_size,\r\n> per_device_eval_batch_size=batch_size,\r\n> evaluate_during_training=True,\r\n> do_train=True,\r\n> do_eval=True,\r\n> logging_steps=1000,\r\n> save_steps=1000,\r\n> eval_steps=1000,\r\n> overwrite_output_dir=True,\r\n> warmup_steps=2000,\r\n> save_total_limit=10,\r\n> fp16=True,\r\n> )\r\n> \r\n> # instantiate trainer\r\n> trainer = Trainer(\r\n> model=model,\r\n> args=training_args,\r\n> compute_metrics=compute_metrics,\r\n> train_dataset=train_dataset,\r\n> eval_dataset=val_dataset,\r\n> )\r\n> \r\n> # start training\r\n> trainer.train()\r\n> ```\r\n> \r\n> If you can see it carefully you can find that an argument is missing in `TrainingArguments` module, I always get an error that why `predict_from_generate` is passed, I tried finding that attribute in [`training_args.py`](https://github.com/huggingface/transformers/blob/master/src/transformers/training_args.py), but it seems there is no such attribute available in it. Please clarify which version are you using, If it is above 2.11 then please clarify why my the above code is throwing this error.\r\n\r\n@AmbiTyga @patrickvonplaten Is this error fixed? I have switched to the branch \"more_general_trainer_metric.\" But it seems this error still exists when I am running codes in https://huggingface.co/patrickvonplaten/bert2gpt2-cnn_dailymail-fp16. ", "The code is a bit outdated there. You should be able to simply use the https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization example. In order to create a BERT2GPT2 checkpoint, you could a code that is similar to this one: https://huggingface.co/docs/transformers/v4.17.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel.forward\r\n\r\n(just replace one BERT by GPT2)\r\n\r\nSo to summarize,\r\n\r\n1. Create a warm-started bert-gpt2 checkpoint\r\n2. save checkpoint\r\n3. use summarization example to fine-tune the checkpoint\r\n\r\nI'll keep this issue open for now since we should probably create a nice \"How-to\" guide for this", "> The code is a bit outdated there. You should be able to simply use the https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization example. In order to create a BERT2GPT2 checkpoint, you could a code that is similar to this one: https://huggingface.co/docs/transformers/v4.17.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel.forward\r\n> \r\n> (just replace one BERT by GPT2)\r\n> \r\n> So to summarize,\r\n> \r\n> 1. Create a warm-started bert-gpt2 checkpoint\r\n> 2. save checkpoint\r\n> 3. use summarization example to fine-tune the checkpoint\r\n> \r\n> I'll keep this issue open for now since we should probably create a nice \"How-to\" guide for this\r\n\r\nThanks for your guidance! I try this method to create and ft a bert2gpt2 model, but it seems that \"tokenizer\" would be a problem: I can't load a single suitable tokenizer for this model in the summarization example. So is it necessary for me to defined tokenizer1 for bert and tokenizer2 for gpt2 and then change any code that is related to \"tokenizer\" in order to fix this problem? @patrickvonplaten ", "It's fine to load two tokenizers no? ", "> \r\n\r\nYeah，I use 2 tokenizers to replace \"tokenizer\" in run_summarization.py and also do some other changes, the code can work now(although I don't know whether it is right....). Here are my changes.\r\n\r\n1. change the resize_token_embeddings method`#model.resize_token_embeddings(len(tokenizer))`\r\n `model.encoder.resize_token_embeddings(len(tokenizer1))`\r\n `model.decoder.resize_token_embeddings(len(tokenizer2))`\r\n2. some special tokens settings according to [https://huggingface.co/patrickvonplaten/bert2gpt2-cnn_dailymail-fp16](url)\r\n3. facing problem like https://github.com/huggingface/transformers/issues/10646#issue-829065330, and used codes in [https://github.com/huggingface/transformers/blob/24e2fa1590faac894da3422daf56abf9770c9d81/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py#L555](url) line554-555 and line147-162\r\n4. Noticing that in bert base/large \"max_position_embeddings\" is 512, and default max_source_length in run_summarization.py is 1024, as a result if our input sequence length is over 512, we will get an error like https://github.com/huggingface/transformers/issues/15081#issue-1097193504. So let max_source_length=512.\r\n5. all codes segmentations of (tokenizer->tokenizer2) in run_summarization.py(**Not sure**)\r\n```\r\n # Setup the tokenizer for targets\r\n with tokenizer2.as_target_tokenizer():\r\n labels = tokenizer2(targets, max_length=max_target_length, padding=padding, truncation=True)\r\n\r\n # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\r\n # padding in the loss.\r\n if padding == \"max_length\" and data_args.ignore_pad_token_for_loss:\r\n labels[\"input_ids\"] = [\r\n [(l if l != tokenizer2.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\r\n ]\r\n\r\n model_inputs[\"labels\"] = labels[\"input_ids\"]\r\n return model_inputs\r\n```\r\n\r\n\r\n```\r\n def compute_metrics(eval_preds):\r\n preds, labels = eval_preds\r\n if isinstance(preds, tuple):\r\n preds = preds[0]\r\n decoded_preds = tokenizer2.batch_decode(preds, skip_special_tokens=True)\r\n if data_args.ignore_pad_token_for_loss:\r\n # Replace -100 in the labels as we can't decode them.\r\n labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\r\n decoded_labels = tokenizer2.batch_decode(labels, skip_special_tokens=True)\r\n\r\n```\r\n\r\n\r\n\r\n```\r\n if trainer.is_world_process_zero():\r\n if training_args.predict_with_generate:\r\n predictions = tokenizer2.batch_decode(\r\n predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True\r\n )\r\n predictions = [pred.strip() for pred in predictions]\r\n output_prediction_file = os.path.join(training_args.output_dir, \"generated_predictions.txt\")\r\n with open(output_prediction_file, \"w\") as writer:\r\n writer.write(\"\\n\".join(predictions))\r\n\r\n```\r\n> It's fine to load two tokenizers no?\r\n\r\n", "Hey everyone,\r\nDid this work go anywhere?\r\nI need a pre-trained gpt2 model based on nn.Linear instead of Conv1D layers for research purpose, Is the implementation above merged anywhere, or there exist some other gpt2 model based on nn.Linear?", "Can I work on this issue as a good first issue or is there no point?", "I don't think there is any point @Forpee ", "> For a generation problem, it is usually better to use GPT2 as the decoder, over BERT.\r\n\r\nWhy should this be the case, if you have enough data to train the new cross-attention parameters?\r\n\r\nThe paper for the encoderDecoderModel reports for the summarization task: \r\n\r\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4482?src=pr&el=h1) Report\n> Merging [#4482](https://codecov.io/gh/huggingface/transformers/pull/4482?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/efbc1c5a9d96048ab11f8d746fe51107cb91646f&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4482?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4482 +/- ##\n=======================================\n Coverage 78.03% 78.03% \n=======================================\n Files 123 123 \n Lines 20477 20477 \n=======================================\n Hits 15980 15980 \n Misses 4497 4497 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4482?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4482/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.50% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4482/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.85% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4482?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/4482?src=pr&el=footer). Last update [efbc1c5...82aef0f](https://codecov.io/gh/huggingface/transformers/pull/4482?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4481?src=pr&el=h1) Report\n> Merging [#4481](https://codecov.io/gh/huggingface/transformers/pull/4481?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/384f0eb2f9d42e44094dbfd0917ccf4e6ddb462a&el=desc) will **decrease** coverage by `0.08%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4481?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4481 +/- ##\n==========================================\n- Coverage 77.96% 77.88% -0.09% \n==========================================\n Files 120 120 \n Lines 20140 20140 \n==========================================\n- Hits 15703 15686 -17 \n- Misses 4437 4454 +17 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4481?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/4481/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `28.66% <0.00%> (-5.42%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4481?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/4481?src=pr&el=footer). Last update [384f0eb...4deb915](https://codecov.io/gh/huggingface/transformers/pull/4481?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I think 865d4d595eefc8cc9cee58fec9179bd182be0e2e might be a more \"correct\" way to fix this" ]

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[ "@patrickvonplaten Thank you your awesome work. I´m super excited about a char-lm. \r\nI´m trying to can train the \"reformer\" model, using the \"google/reformer-enwik8\".\r\n\r\nBut using this script or the run_language_modeling.py I get the error about the lack of tokenizer ( that is expected to a char only LM).\r\n`Model name 'google/reformer-enwik8' was not found in tokenizers`\r\n\r\nCan you give me some pointer how I could train it?", "Hi bratao! Good point I will consult with our team on how to include models that don't have don't need a tokenizer! Let me get back to you in a couple of days :-) " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4479?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@18d233d`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4479?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4479 +/- ##\n=========================================\n Coverage ? 78.20% \n=========================================\n Files ? 120 \n Lines ? 20083 \n Branches ? 0 \n=========================================\n Hits ? 15705 \n Misses ? 4378 \n Partials ? 0 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4479?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `78.74% <0.00%> (ø)` | |\n| [src/transformers/configuration\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX21hcmlhbi5weQ==) | `100.00% <0.00%> (ø)` | |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `89.23% <0.00%> (ø)` | |\n| [src/transformers/configuration\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3RyYW5zZm9feGwucHk=) | `92.45% <0.00%> (ø)` | |\n| [src/transformers/tokenization\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdHJhbnNmb194bC5weQ==) | `40.67% <0.00%> (ø)` | |\n| [src/transformers/data/processors/xnli.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMveG5saS5weQ==) | `29.54% <0.00%> (ø)` | |\n| [src/transformers/hf\\_argparser.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9oZl9hcmdwYXJzZXIucHk=) | `61.97% <0.00%> (ø)` | |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `100.00% <0.00%> (ø)` | |\n| [src/transformers/tokenization\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZmxhdWJlcnQucHk=) | `40.42% <0.00%> (ø)` | |\n| [src/transformers/data/processors/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvX19pbml0X18ucHk=) | `100.00% <0.00%> (ø)` | |\n| ... and [110 more](https://codecov.io/gh/huggingface/transformers/pull/4479/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4479?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/4479?src=pr&el=footer). Last update [18d233d...b3c4f81](https://codecov.io/gh/huggingface/transformers/pull/4479?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This looks reasonable to me. Thanks for looking into it.", "can you run \r\n\r\n```\r\npip uninstall -y isort black\r\npip install -e .[quality]\r\nmake style\r\n```\r\n?\r\n\r\nThanks!", "Thanks for noting how to run code reformatting! ", "Thanks!" ]

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[ "I have the same problem, but I'm not sure if it's really a problem. I thought the extreme speed before the fix was because it wasn't training properly, and that the slower speed now is supposed to be this way, but that's just my guess.", "Hi, you're right @LeonieWeissweiler. Previous to that fix, the optimizer wasn't actually adjusting weights, resulting in a major speed-up (but the script in itself wasn't working).\r\n\r\n@Colanim, do you mind specifying what exactly you're training on? When training on TPU there's a lot you should take into account: batch size, sequence length, number of cores being the most important. Do you mind giving a bit of context as to what you're trying to run?\r\n\r\n We've also merged this PR https://github.com/huggingface/transformers/pull/4467 which solves quite a few issues with the TPU training. Please make sure to install from source to benefit from that commit.\r\n\r\nFrom my tests, on TPU with 8 cores (v3-8), on MNLI I reach 22 minutes/epoch with a batch size of 8, but **6 minutes/epoch** (with a 2 minute tracing, that isn't necessary for the following epochs) with a batch size of 128 (which does train with a final accuracy of 81% using `bert-base-cased`, single epoch). ", "I'm training ELECTRA for Extractive Text Summarization.\r\n\r\nI will try to increase the batch size and see the results, thanks for the pointer.\r\n\r\nWhat bother me is that I used TFElectra and I could train the model at ~8 iterations per sec.\r\n\r\nSame model and same hyper-parameters on Pytorch and it's slower.\r\n\r\nBut I realized that in my model, since it's extractive summarization, I'm extracting the [CLS] representation of each sentence. These CLS position varies from sample to sample. Maybe that's why it's slower on pytorch-xla ?", "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/4477?src=pr&el=h1) Report\n> Merging [#4477](https://codecov.io/gh/huggingface/transformers/pull/4477?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/efbc1c5a9d96048ab11f8d746fe51107cb91646f&el=desc) will **increase** coverage by `0.08%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4477?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4477 +/- ##\n==========================================\n+ Coverage 78.03% 78.12% +0.08% \n==========================================\n Files 123 123 \n Lines 20477 20477 \n==========================================\n+ Hits 15980 15997 +17 \n+ Misses 4497 4480 -17 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4477?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/4477/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `96.00% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4477/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.50% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4477/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.85% <0.00%> (+0.41%)` | :arrow_up: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/4477/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `34.07% <0.00%> (+5.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4477?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/4477?src=pr&el=footer). Last update [efbc1c5...2e2abdc](https://codecov.io/gh/huggingface/transformers/pull/4477?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", "@misrasaurabh1 What solution do you use now for this encoding dialog data problem?", "I use something like\r\n`self.tokenizer.encode(input)[-self.block_size:]`\r\nThis throws a warning for length overflow so I deactivate it with logging. \r\nAlso one has to make attention masks separately as some models require this.", "Indeed, we should add an option to truncate on the left!\r\ncc @n1t0 for our sprint of September.", "perhaps add a truncation_side to https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer to be consistent with padding_side.", "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", "@thomwolf @n1t0 Any plan for this? I just saw this because of the bot.\r\n\r\nI think I can do this, seems like all the logic is here. \r\nhttps://github.com/huggingface/transformers/blob/07708793f20ec3a949ccab32cc4fe0c7272dcc4c/src/transformers/tokenization_utils_base.py#L2766\r\n\r\nBut how about fast 🤗 Tokenizers? Will I need to also change the rust code?\r\n\r\nAnd I noticed something that might be a bug, and can be improved:\r\n\r\nhttps://github.com/huggingface/transformers/blob/07708793f20ec3a949ccab32cc4fe0c7272dcc4c/src/transformers/tokenization_utils_base.py#L2816-L2831\r\n\r\nHere it loops `num_tokens_to_remove` times to decide how many tokens needs to be truncated for each sequence, which can be calculated without looping.\r\n\r\nAnd in case `stride` is not 0, it seems to return up to `stride`*`num_tokens_to_remove` extra tokens to `overflowing_tokens`.\r\nhttps://github.com/huggingface/transformers/blob/07708793f20ec3a949ccab32cc4fe0c7272dcc4c/src/transformers/tokenization_utils_base.py#L2801-L2803\r\nAlso it seems weird to me that `overflowing_tokens` will be mixed with tokens from `ids` and `pair_ids`. Perhaps it should be a tuple of list if `TruncationStrategy` is `longest_first`.\r\n\r\nNote to self: `overflowing_tokens` is used in squad to construct another pair if the doc is too long. `stride` is also used in squad. I can't find other use of `overflowing_tokens`.\r\n\r\nhttps://github.com/huggingface/transformers/blob/969859d5f67c7106de4d1098c4891c9b03694bbe/src/transformers/data/processors/squad.py#L154-L216", "One feedback about what's happening with this facility of left truncation being not available - its harder to use the datasets library and we have to do python Hackery which reduces the benefits of using the datasets library in the first place.", "I recently needed to do exactly this, but ran into this issue so I had to manually truncate the text. Simply doing `encoded_tensor[-max_length:]` would also truncate samples that are less than `max_length` since they are padded to the right. \r\n\r\nHere's the approach I used instead:\r\n```python\r\ndef encode_right_truncated(tokenizer, text, padding='max_length', max_length=512, add_special_tokens=True):\r\n tokenized = tokenizer.tokenize(text, padding=padding, max_length=max_length, add_special_tokens=add_special_tokens)\r\n \r\n if not add_special_tokens:\r\n truncated = tokenized[-max_length:]\r\n else:\r\n truncated = tokenized[0:1] + tokenized[-(max_length-1):]\r\n \r\n ids = tokenizer.convert_tokens_to_ids(truncated)\r\n \r\n return ids\r\n```\r\n\r\nHope this helps future people finding this from Google/DDG", "For anyone arriving here from search, note that this is now possible by setting [`truncation_side`](https://huggingface.co/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.truncation_side) \r\n```python\r\n# specify when initializing the tokenizer,\r\ntokenizer = AutoTokenizer.from_pretrained(..., truncation_side = \"left\")\r\n# or modify an already-initialized tokenizer, like\r\ntokenizer.truncation_side = \"right\"\r\n```\r\nsee https://github.com/huggingface/transformers/pull/12913" ]

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[ "Hi @rjsaito we are actually moving to serving all our files from the cloudfront powered cdn.huggingface.co", "Awesome! Do you have a current ETA when this change would be in place?", "It's already in place for model weights.", "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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[ "Signature is of `addcdiv_` is different between Pytorch 1.4 and 1.5\r\n\r\nhttps://pytorch.org/docs/1.4.0/tensors.html?highlight=addcdiv#torch.Tensor.addcdiv\r\nhttps://pytorch.org/docs/stable/tensors.html?highlight=addcdiv_#torch.Tensor.addcdiv\r\n\r\nI guess as long as Pytorch 1.4 is supported by `transformers` we can just ignore the Warning given when using Pytorch 1.5", "See #4477 for a fix that work for both PT1.4 and PT1.5" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4473?src=pr&el=h1) Report\n> Merging [#4473](https://codecov.io/gh/huggingface/transformers/pull/4473?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/48c3a70b4eaedab1dd9ad49990cfaa4d6cb8f6a0&el=desc) will **decrease** coverage by `0.42%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4473?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4473 +/- ##\n==========================================\n- Coverage 78.41% 77.98% -0.43% \n==========================================\n Files 123 123 \n Lines 20432 20432 \n==========================================\n- Hits 16021 15934 -87 \n- Misses 4411 4498 +87 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4473?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/4473/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `28.66% <0.00%> (-28.03%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4473/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4473?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/4473?src=pr&el=footer). Last update [48c3a70...8518af3](https://codecov.io/gh/huggingface/transformers/pull/4473?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Cool notebook! And a great complement to the [model card](https://huggingface.co/ncoop57/DiGPTame-medium) \r\n\r\ncc @patrickvonplaten \r\n\r\nMaybe you can use the `Trainer` in a v2 of the notebook =)\r\n\r\nAnd you could use the [nlp](https://github.com/huggingface/nlp) library to share the dataset, cc @thomwolf ", "Awesome! " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4472?src=pr&el=h1) Report\n> Merging [#4472](https://codecov.io/gh/huggingface/transformers/pull/4472?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/48c3a70b4eaedab1dd9ad49990cfaa4d6cb8f6a0&el=desc) will **decrease** coverage by `0.41%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4472?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4472 +/- ##\n==========================================\n- Coverage 78.41% 77.99% -0.42% \n==========================================\n Files 123 123 \n Lines 20432 20432 \n==========================================\n- Hits 16021 15936 -85 \n- Misses 4411 4496 +85 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4472?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/4472/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `28.66% <0.00%> (-28.03%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4472/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.53% <0.00%> (+0.11%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4472/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+0.16%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4472/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.85% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4472?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/4472?src=pr&el=footer). Last update [48c3a70...9152273](https://codecov.io/gh/huggingface/transformers/pull/4472?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This is not a bug but expected behaviour. The length of the tokenized input is only calculated after padding.\r\n\r\nhttps://github.com/huggingface/transformers/blob/a08652772791fdaeed6f263b1a99926ca64be5dc/src/transformers/tokenization_utils.py#L1981-L1982\r\n\r\nPerhaps you are right, though, and it would be more useful to get the size before padding!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "by the way, could you add a \r\n\r\n```\r\n---\r\nlanguage: ukrainian\r\n---\r\n```\r\n\r\nmetadata block on top, for the model to be surfaced in search etc.?", "Sure, I’ll add it\n\nOn Wed, 20 May 2020 at 18:05, Julien Chaumond <[email protected]>\nwrote:\n\n> by the way, could you add a\n>\n> ---\n> language: ukrainian\n> ---\n>\n> metadata block on top, for the model to be surfaced in search etc.?\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/pull/4470#issuecomment-631533257>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AAPCBFE27XTTMKNB2IIQTNTRSPWTPANCNFSM4NFJPXQA>\n> .\n>\n-- \n____________________________________\nЗ повагою, Бушковський Олександр\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4469?src=pr&el=h1) Report\n> Merging [#4469](https://codecov.io/gh/huggingface/transformers/pull/4469?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5856999a9f2926923f037ecd8d27b8058bcf9dae&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4469?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4469 +/- ##\n==========================================\n+ Coverage 77.98% 78.00% +0.01% \n==========================================\n Files 123 123 \n Lines 20436 20431 -5 \n==========================================\n Hits 15938 15938 \n+ Misses 4498 4493 -5 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4469?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/4469/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyX3RmLnB5) | `17.92% <0.00%> (+0.41%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4469/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4469/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4469/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.53% <0.00%> (+0.11%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4469?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/4469?src=pr&el=footer). Last update [5856999...095c8d2](https://codecov.io/gh/huggingface/transformers/pull/4469?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4468?src=pr&el=h1) Report\n> Merging [#4468](https://codecov.io/gh/huggingface/transformers/pull/4468?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5856999a9f2926923f037ecd8d27b8058bcf9dae&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4468?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4468 +/- ##\n==========================================\n- Coverage 77.98% 77.98% -0.01% \n==========================================\n Files 123 123 \n Lines 20436 20436 \n==========================================\n- Hits 15938 15937 -1 \n- Misses 4498 4499 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4468?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4468/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.41% <0.00%> (ø)` | |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4468/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4468?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/4468?src=pr&el=footer). Last update [5856999...7d9fd53](https://codecov.io/gh/huggingface/transformers/pull/4468?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Great!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4467?src=pr&el=h1) Report\n> Merging [#4467](https://codecov.io/gh/huggingface/transformers/pull/4467?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/07dd7c2fd8996fec2979555437dfeff0d38cbf28&el=desc) will **decrease** coverage by `0.09%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4467?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4467 +/- ##\n==========================================\n- Coverage 78.07% 77.97% -0.10% \n==========================================\n Files 123 123 \n Lines 20436 20439 +3 \n==========================================\n- Hits 15955 15937 -18 \n- Misses 4481 4502 +21 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4467?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4467/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.48% <0.00%> (-0.28%)` | :arrow_down: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/4467/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `28.66% <0.00%> (-4.78%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4467/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4467/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4467/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.41% <0.00%> (-0.12%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4467?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/4467?src=pr&el=footer). Last update [07dd7c2...11186c1](https://codecov.io/gh/huggingface/transformers/pull/4467?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Solid work, as discussed 👍", "Nice! I tried two days ago and just skipped the checkpoints but something with the eval seemed to be messing things up(ForMultipleChoice) as well. WandB created 8 runs. dont know if its useful, im still figuring out alot of the stuff." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4466?src=pr&el=h1) Report\n> Merging [#4466](https://codecov.io/gh/huggingface/transformers/pull/4466?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5856999a9f2926923f037ecd8d27b8058bcf9dae&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4466?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4466 +/- ##\n==========================================\n- Coverage 77.98% 77.98% -0.01% \n==========================================\n Files 123 123 \n Lines 20436 20436 \n==========================================\n- Hits 15938 15937 -1 \n- Misses 4498 4499 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4466?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4466/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.50% <0.00%> (-0.17%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4466?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/4466?src=pr&el=footer). Last update [5856999...760964e](https://codecov.io/gh/huggingface/transformers/pull/4466?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "nice example" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4464?src=pr&el=h1) Report\n> Merging [#4464](https://codecov.io/gh/huggingface/transformers/pull/4464?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8f1d0471489352ec01556ae61f8e8246002bbc58&el=desc) will **increase** coverage by `0.04%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4464?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4464 +/- ##\n==========================================\n+ Coverage 77.93% 77.98% +0.04% \n==========================================\n Files 123 123 \n Lines 20430 20426 -4 \n==========================================\n+ Hits 15922 15929 +7 \n+ Misses 4508 4497 -11 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4464?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/4464/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2xvbmdmb3JtZXIucHk=) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/4464/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19sb25nZm9ybWVyLnB5) | `82.94% <100.00%> (+0.18%)` | :arrow_up: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4464/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.41% <0.00%> (-0.12%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4464/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+1.80%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4464?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/4464?src=pr&el=footer). Last update [8f1d047...e53e5dc](https://codecov.io/gh/huggingface/transformers/pull/4464?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Looks good!\r\n\r\nI just improved consistency with other scripts we have (in particular, `run_ner.py`) by:\r\n- using an enum instead of two boolean flags\r\n- I also always append the actual label name in the predictions file, which removes the need for a new arg\r\n\r\nLet me know if that works for you and I'll merge to master soon", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4463?src=pr&el=h1) Report\n> Merging [#4463](https://codecov.io/gh/huggingface/transformers/pull/4463?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8f1d0471489352ec01556ae61f8e8246002bbc58&el=desc) will **decrease** coverage by `0.01%`.\n> The diff coverage is `46.03%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4463?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4463 +/- ##\n==========================================\n- Coverage 77.93% 77.91% -0.02% \n==========================================\n Files 123 123 \n Lines 20430 20474 +44 \n==========================================\n+ Hits 15922 15953 +31 \n- Misses 4508 4521 +13 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4463?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/data/processors/glue.py](https://codecov.io/gh/huggingface/transformers/pull/4463/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvZ2x1ZS5weQ==) | `49.62% <31.81%> (-1.41%)` | :arrow_down: |\n| [src/transformers/data/datasets/glue.py](https://codecov.io/gh/huggingface/transformers/pull/4463/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFzZXRzL2dsdWUucHk=) | `86.15% <77.77%> (-4.05%)` | :arrow_down: |\n| [src/transformers/data/processors/utils.py](https://codecov.io/gh/huggingface/transformers/pull/4463/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvdXRpbHMucHk=) | `27.63% <100.00%> (+0.47%)` | :arrow_up: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4463/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.41% <0.00%> (-0.12%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4463/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.50% <0.00%> (+1.64%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4463?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/4463?src=pr&el=footer). Last update [8f1d047...d172a3b](https://codecov.io/gh/huggingface/transformers/pull/4463?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks Julien for the code improvement! This look very good to me. ", "Thank you for contributing this!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=h1) Report\n> Merging [#4462](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8f1d0471489352ec01556ae61f8e8246002bbc58&el=desc) will **increase** coverage by `0.04%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4462 +/- ##\n==========================================\n+ Coverage 77.93% 77.98% +0.04% \n==========================================\n Files 123 123 \n Lines 20430 20430 \n==========================================\n+ Hits 15922 15932 +10 \n+ Misses 4508 4498 -10 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4462/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.41% <0.00%> (-0.12%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4462/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+1.80%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4462?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/4462?src=pr&el=footer). Last update [8f1d047...ca0d2a0](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "That's awesome. Thanks @patil-suraj! @mariamabarham @lhoestq - might it be interesting to add `emotion classification` and `swag` to `nlp`? ", "Reworded the description a bit - hope that's ok @patil-suraj ", "> Reworded the description a bit - hope that's ok @patil-suraj\r\n\r\n@patrickvonplaten yes, it's more clear now. Thank you!" ]

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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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[ "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/4458?src=pr&el=h1) Report\n> Merging [#4458](https://codecov.io/gh/huggingface/transformers/pull/4458?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/384f0eb2f9d42e44094dbfd0917ccf4e6ddb462a&el=desc) will **decrease** coverage by `0.08%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4458?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4458 +/- ##\n==========================================\n- Coverage 77.96% 77.88% -0.09% \n==========================================\n Files 120 120 \n Lines 20140 20140 \n==========================================\n- Hits 15703 15686 -17 \n- Misses 4437 4454 +17 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4458?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4458/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.82% <0.00%> (ø)` | |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/4458/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `28.66% <0.00%> (-5.42%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4458/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.50% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4458/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.85% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4458?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/4458?src=pr&el=footer). Last update [384f0eb...568d3f1](https://codecov.io/gh/huggingface/transformers/pull/4458?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I am not quite sure who the right person to review and include this PR would be. But commenting to open it up back nonetheless. " ]

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[ "Thanks for the extensive bug report! I can confirm that you are correct and that the issue does not occur when using the slow option.\r\n\r\nPinging @n1t0 " ]

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[ "cc @jplu, might be of interest :)", "Hello!\r\n\r\nLike this there is not enough details to see what is the issue. Can you provide the piece of code you are trying to run? plz :)\r\n\r\nAlso if you are looking for how to properly use the trainer I suggest you to look at the already existing examples:\r\n\r\n- [Question-answering](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_tf_squad.py)\r\n- [Token classification](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_tf_ner.py)\r\n- [Sequence classification](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py)\r\n- [Multiple Choice](https://github.com/huggingface/transformers/blob/master/examples/multiple-choice/run_tf_multiple_choice.py)\r\n\r\nThey are all working well with CPU/Single GPU/Multiple GPU, only TPU need to be further tested for now.\r\n\r\nBasically the score is created the first time when you call the `strategy` property of the `tf_training_args.py`. Be careful if you try to translate the PT script to TF there are quite a lot of differences to care about. The training arguments are one of them.\r\n\r\nNevertheless, thanks a lot for trying to make a language modeling with TF2 and will be happy to help in case you need some.", "Thanks for your reply! @jplu \r\n\r\nSorry for that i'm not looking at the examples.In the examples, it initializes the model with trainng_args.strategy.scope(),and in my script, i initialize the model in TFTrainer.\\__init\\__ with the self.args.strategy.scope(). It looks the same.\r\n\r\nIf we initianize the model in trainng_args.strategy.scope(), it's be ok ! At first, I think we should pass the model_args to TFTrainer, not the model, and initialize the model in \\__init\\__. Therefore i think maybe we should improve the design. \r\n\r\nThanks again for your reply!\r\n" ]

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[ "Sure, these are called hidden states! Here's the [documentation of the XLNet model](https://huggingface.co/transformers/model_doc/xlnet.html?highlight=output_hidden_states#transformers.XLNetModel).\r\n\r\nPlease note the third return:\r\n\r\n> **hidden_states (tuple(torch.FloatTensor), optional, returned when config.output_hidden_states=True):**\r\n> \r\n> Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).\r\n> \r\n> Hidden-states of the model at the output of each layer plus the initial embedding outputs.\r\n" ]

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[ "Can you check the Dmoz[Dmoz] \r\nthe database in sql he cost 1000$ to change it (https://idmoz.org) ", "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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[ "Hello!\r\n\r\nIf you want to train over SQuAD I suggest you to use the [run_tf_squad.py](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_tf_squad.py) example that uses the TF Trainer or to check the following [Colab](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=kxZQ9Ms_vSV1) that uses the new `nlp` framework with a `.fit()` method.", "Hey. \r\nDid you see my examples? \r\nAt \"Try 2\" I explained the problems using the new `nlp` framework with a `.fit()` method\r\nI need to use a custom dataset.\r\n\r\nRegarding `run_tf_squad.py`, I still have problems with it. \r\nI want to use the `fit` method. \r\nI'm using this code instead of the `VFTrainer` in the same file `run_tf_squad.py`.\r\nThis is the only change I made - same dataset, same examples, same features. Just trying to use `fit`. \r\n```python\r\nloss_fn = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE, from_logits=True)\r\n opt = tf.keras.optimizers.Adam(learning_rate=3e-5)\r\n\r\nmodel.compile(optimizer=opt,\r\n loss={'output_1': loss_fn, 'output_2': loss_fn},\r\n loss_weights={'output_1': 1., 'output_2': 1.},\r\n metrics=['accuracy'])\r\n\r\nhistory = model.fit(train_dataset, validation_data=eval_dataset, epochs=1)\r\n```\r\n\r\nAnd it's the same problem that occurs:\r\n```python\r\nTraceback (most recent call last):\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/run_squad_tf.py\", line 257, in <module>\r\n main()\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/run_squad_tf.py\", line 242, in main\r\n history = model.fit(train_dataset, validation_data=eval_dataset, epochs=1)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py\", line 819, in fit\r\n use_multiprocessing=use_multiprocessing)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py\", line 235, in fit\r\n use_multiprocessing=use_multiprocessing)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py\", line 593, in _process_training_inputs\r\n use_multiprocessing=use_multiprocessing)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py\", line 706, in _process_inputs\r\n use_multiprocessing=use_multiprocessing)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/data_adapter.py\", line 702, in __init__\r\n x = standardize_function(x)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py\", line 660, in standardize_function\r\n standardize(dataset, extract_tensors_from_dataset=False)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py\", line 2360, in _standardize_user_data\r\n self._compile_from_inputs(all_inputs, y_input, x, y)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py\", line 2580, in _compile_from_inputs\r\n target, self.outputs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_utils.py\", line 1341, in cast_if_floating_dtype_and_mismatch\r\n if target.dtype != out.dtype:\r\nAttributeError: 'str' object has no attribute 'dtype'\r\n```\r\n\r\nI will add this to the post as a failing example - Try 4", "Sorry, misunderstanding, what I meant is that I proposed you to check how the features are built, if you want to use `.fit()` the features have to be built differently than in `squad_convert_examples_to_features`, also you have to use TF 2.2. Otherwise if you want to use this method, you have to pass by the trainer.\r\n\r\nAlso why using `TFBertForSequenceClassification` instead of `TFBertForQuestionAnswering`?", "Thank you for the answer. I prefare to use `fit`, you dont support it?\r\nAnyway, this is the status with the `VFTrainer`: \r\n\r\nI've used tensorflow 2.1.0 and I've now upgradeed to 2.2.0. \r\nI still have problems: \r\n\r\n```python\r\n trainer = TFTrainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=dev_dataset)\r\n print(f\"Created TFTrainer\")\r\n trainer.train()\r\n```\r\n\r\nIt does create the `TFTrainer`, but when getting to the `.train()` cmd it fails: \r\n```python\r\nCreated TFTrainer\r\nWARNING:tensorflow:From /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py:364: StrategyBase.experimental_run_v2 (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nrenamed to `run`\r\nWARNING:tensorflow:From /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nIf using Keras pass *_constraint arguments to layers.\r\n/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\r\n \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\r\nTraceback (most recent call last):\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/my_squad_tf_with_trainer.py\", line 112, in <module>\r\n main()\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/my_squad_tf_with_trainer.py\", line 34, in main\r\n trainer.train()\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py\", line 277, in train\r\n for training_loss in self._training_steps():\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py\", line 323, in _training_steps\r\n self._apply_gradients()\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 580, in __call__\r\n result = self._call(*args, **kwds)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 627, in _call\r\n self._initialize(args, kwds, add_initializers_to=initializers)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 506, in _initialize\r\n *args, **kwds))\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 2446, in _get_concrete_function_internal_garbage_collected\r\n graph_function, _, _ = self._maybe_define_function(args, kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 2777, in _maybe_define_function\r\n graph_function = self._create_graph_function(args, kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 2667, in _create_graph_function\r\n capture_by_value=self._capture_by_value),\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\", line 981, in func_graph_from_py_func\r\n func_outputs = python_func(*func_args, **func_kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 441, in wrapped_fn\r\n return weak_wrapped_fn().__wrapped__(*args, **kwds)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 3299, in bound_method_wrapper\r\n return wrapped_fn(*args, **kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\", line 968, in wrapper\r\n raise e.ag_error_metadata.to_exception(e)\r\nValueError: in user code:\r\n\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py:329 _apply_gradients *\r\n self.args.strategy.experimental_run_v2(self._step)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py:343 _step *\r\n self.optimizer.apply_gradients(list(zip(gradients, vars)))\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/optimization_tf.py:135 apply_gradients *\r\n return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars), name=name,)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:478 apply_gradients **\r\n self._create_all_weights(var_list)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:663 _create_all_weights\r\n self._create_slots(var_list)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/adam.py:156 _create_slots\r\n self.add_slot(var, 'm')\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:716 add_slot\r\n .format(strategy, var))\r\n\r\n ValueError: Trying to create optimizer slot variable under the scope for tf.distribute.Strategy (<tensorflow.python.distribute.one_device_strategy.OneDeviceStrategy object at 0x7f2d141aec50>), which is different from the scope used for the original variable (<tf.Variable 'tf_bert_for_question_answering/bert/embeddings/word_embeddings/weight:0' shape=(28996, 768) dtype=float32, numpy=\r\n array([[-0.00054784, -0.04156886, 0.01308366, ..., -0.0038919 ,\r\n -0.0335485 , 0.0149841 ],\r\n [ 0.01688265, -0.03106827, 0.0042053 , ..., -0.01474032,\r\n -0.03561099, -0.0036223 ],\r\n [-0.00057234, -0.02673604, 0.00803954, ..., -0.01002474,\r\n -0.0331164 , -0.01651673],\r\n ...,\r\n [-0.00643814, 0.01658491, -0.02035619, ..., -0.04178825,\r\n -0.049201 , 0.00416085],\r\n [-0.00483562, -0.00267701, -0.02901638, ..., -0.05116647,\r\n 0.00449265, -0.01177113],\r\n [ 0.03134822, -0.02974372, -0.02302896, ..., -0.01454749,\r\n -0.05249038, 0.02843569]], dtype=float32)>). Make sure the slot variables are created under the same strategy scope. This may happen if you're restoring from a checkpoint outside the scope\r\n```\r\nThank you", "This error means that you haven't created the model in the proper scope. Did you use the scope created in the TrainerArgs?\r\n\r\nWhat gives you the following command line without touching to the initial code:\r\n```\r\npython examples/question-answering/run_tf_squad.py \\\r\n --model_name_or_path bert-base-uncased \\\r\n --output_dir model \\\r\n --max-seq-length 384 \\\r\n --num_train_epochs 2 \\\r\n --per_gpu_train_batch_size 8 \\\r\n --per_gpu_eval_batch_size 16 \\\r\n --do_train \\\r\n --logging_dir logs \\\r\n --mode question-answering \\\r\n --logging_steps 10 \\\r\n --learning_rate 3e-5 \\\r\n --doc_stride 128 \\\r\n --optimizer_name adamw\r\n```", "That code works, **but** I need one extra thing: evaluation/prediction on test dataset, and it doesn't work for me. \r\n\r\nI took the `run_tf_squad.py` and added simple changes: \r\n```python\r\ntest_examples = processor.get_dev_examples(data_args.data_dir, filename='test-v1.1.json')\r\ntest_dataset = (\r\n squad_convert_examples_to_features(\r\n examples=test_examples,\r\n tokenizer=tokenizer,\r\n max_seq_length=data_args.max_seq_length,\r\n doc_stride=data_args.doc_stride,\r\n max_query_length=data_args.max_query_length,\r\n is_training=False,\r\n return_dataset=\"tf\",\r\n )\r\n )\r\n```\r\n\r\nThat is, only adding the test dataset.\r\nNow I want to evalute my final model on it. I tried with both predict and evaluate and it doesn't work. \r\n\r\nTry 1 - \r\n```python\r\nresults = trainer.evaluate(test_dataset)\r\n```\r\nTrace:\r\n```python\r\n05/24/2020 10:55:39 - INFO - transformers.trainer_tf - ***** Running Evaluation *****\r\n05/24/2020 10:55:39 - INFO - transformers.trainer_tf - Batch size = 16\r\nTraceback (most recent call last):\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/my_run_squad_tf_with_trainer.py\", line 208, in <module>\r\n main()\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/my_run_squad_tf_with_trainer.py\", line 203, in main\r\n # results = trainer.evaluate(test_dataset)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py\", line 246, in evaluate\r\n output = self._prediction_loop(eval_dataset, description=\"Evaluation\")\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py\", line 195, in _prediction_loop\r\n loss, logits = self._evaluate_steps(features, labels)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 580, in __call__\r\n result = self._call(*args, **kwds)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 627, in _call\r\n self._initialize(args, kwds, add_initializers_to=initializers)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 506, in _initialize\r\n *args, **kwds))\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 2446, in _get_concrete_function_internal_garbage_collected\r\n graph_function, _, _ = self._maybe_define_function(args, kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 2777, in _maybe_define_function\r\n graph_function = self._create_graph_function(args, kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 2667, in _create_graph_function\r\n capture_by_value=self._capture_by_value),\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\", line 981, in func_graph_from_py_func\r\n func_outputs = python_func(*func_args, **func_kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\", line 441, in wrapped_fn\r\n return weak_wrapped_fn().__wrapped__(*args, **kwds)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/eager/function.py\", line 3299, in bound_method_wrapper\r\n return wrapped_fn(*args, **kwargs)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\", line 968, in wrapper\r\n raise e.ag_error_metadata.to_exception(e)\r\nValueError: in user code:\r\n\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py:171 _evaluate_steps *\r\n per_replica_loss, per_replica_logits = self.args.strategy.experimental_run_v2(\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py:400 _run_model *\r\n logits = self.model(features, training=training)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/modeling_tf_bert.py:1163 call *\r\n outputs = self.bert(inputs, **kwargs)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/modeling_tf_bert.py:548 call *\r\n extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py:984 _slice_helper\r\n name=name)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py:1150 strided_slice\r\n shrink_axis_mask=shrink_axis_mask)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py:10179 strided_slice\r\n shrink_axis_mask=shrink_axis_mask, name=name)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:744 _apply_op_helper\r\n attrs=attr_protos, op_def=op_def)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py:595 _create_op_internal\r\n compute_device)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/ops.py:3327 _create_op_internal\r\n op_def=op_def)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/ops.py:1817 __init__\r\n control_input_ops, op_def)\r\n /home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/tensorflow/python/framework/ops.py:1657 _create_c_op\r\n raise ValueError(str(e))\r\n\r\n ValueError: Index out of range using input dim 1; input has only 1 dims for '{{node tf_bert_for_question_answering/bert/strided_slice}} = StridedSlice[Index=DT_INT32, T=DT_INT32, begin_mask=9, ellipsis_mask=0, end_mask=9, new_axis_mask=6, shrink_axis_mask=0](per_replica_features, tf_bert_for_question_answering/bert/strided_slice/stack, tf_bert_for_question_answering/bert/strided_slice/stack_1, tf_bert_for_question_answering/bert/strided_slice/stack_2)' with input shapes: [128], [4], [4], [4] and with computed input tensors: input[3] = <1 1 1 1>.\r\n```\r\n\r\nTry 2: \r\n```python\r\npredictions = trainer.predict(test_dataset)\r\n```\r\nTrace:\r\n```python\r\n05/24/2020 11:06:50 - INFO - transformers.trainer_tf - ***** Running Prediction *****\r\n05/24/2020 11:06:50 - INFO - transformers.trainer_tf - Batch size = 16\r\nTraceback (most recent call last):\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/my_run_squad_tf_with_trainer.py\", line 208, in <module>\r\n main()\r\n File \"/home/ec2-user/yonatab/ZeroShot/transformers_experiments/src/my_run_squad_tf_with_trainer.py\", line 201, in main\r\n predictions = trainer.predict(test_dataset)\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py\", line 430, in predict\r\n return self._prediction_loop(test_dataset, description=\"Prediction\")\r\n File \"/home/ec2-user/anaconda3/envs/yonatan_env_tf2/lib/python3.6/site-packages/transformers/trainer_tf.py\", line 213, in _prediction_loop\r\n preds = logits.numpy()\r\nAttributeError: 'tuple' object has no attribute 'numpy'\r\n\r\n```", "That's normal, the evaluation/prediction are not implemented yet. I have to make the example compliant with the SQuAD metric from the `nlp` framework. It means that for now only training is possible.\r\n\r\nBut if you want to make this integration yourself and do a PR, you are very welcome to do it :) Otherwise I think to be able to do it in the next 2 coming weeks. Really sorry for that.", "Thank you for the answers. \r\n\r\nThat's why I tried to use the normal tensorflow `fit` and `predict` methods as shown here https://blog.tensorflow.org/2019/11/hugging-face-state-of-art-natural.html.\r\n\r\nBasically I just want to do training and evaluation during training, and then testing on the test dataset.\r\nI succeed to do it with the pytorch model (`run_squad.py`), and I now tried to do it with the tensorflow model as well. If it will be implemented in the future it will be great, I will wait.\r\n\r\nThanks :) 👍 ", "I very quickly coded this so it is not really tested but it can gives you an idea of how to use `.fit()` method. It is based on the Colab version proposed for the `nlp` framework.\r\n\r\n```python\r\nfrom transformers import (\r\n BertTokenizerFast,\r\n TFBertForQuestionAnswering,\r\n)\r\nimport tensorflow_datasets as tfds\r\nimport tensorflow as tf\r\n\r\nds = tfds.load(\"squad\")\r\n\r\ntokenizer = BertTokenizerFast.from_pretrained(\"bert-base-cased\")\r\nmodel = TFBertForQuestionAnswering.from_pretrained(\"bert-base-cased\")\r\n\r\ndef get_correct_alignement(context, gold_text, start_idx):\r\n end_idx = start_idx + len(gold_text)\r\n if context[start_idx:end_idx] == gold_text:\r\n return start_idx, end_idx # When the gold label position is good\r\n elif context[start_idx-1:end_idx-1] == gold_text:\r\n return start_idx-1, end_idx-1 # When the gold label is off by one character\r\n elif context[start_idx-2:end_idx-2] == gold_text:\r\n return start_idx-2, end_idx-2 # When the gold label is off by two character\r\n else:\r\n raise ValueError()\r\n\r\ndef convert_to_tf_features(example, training=True):\r\n encodings = tokenizer.encode_plus(example[\"context\"].numpy().decode(\"utf-8\"), example[\"question\"].numpy().decode(\"utf-8\"), pad_to_max_length=True, max_length=512)\r\n start_positions, end_positions = [], []\r\n \r\n if training:\r\n start_idx, end_idx = get_correct_alignement(example[\"context\"].numpy().decode(\"utf-8\"), example[\"answers\"][\"text\"][0].numpy().decode(\"utf-8\"), example[\"answers\"][\"answer_start\"][0].numpy())\r\n start = encodings.char_to_token(0, start_idx)\r\n end = encodings.char_to_token(0, end_idx-1)\r\n \r\n if start is None or end is None:\r\n return None, None\r\n \r\n start_positions.append(start)\r\n end_positions.append(end)\r\n else:\r\n for i, start, text in enumerate(zip(example[\"answers\"][\"answer_start\"], example[\"answers\"][\"text\"])):\r\n start_idx, end_idx = get_correct_alignement(example[\"context\"].numpy().decode(\"utf-8\"), example[\"context\"].numpy().decode(\"utf-8\"), text.numpy().decode(\"utf-8\"), start.numpy())\r\n \r\n start = encodings.char_to_token(0, start_idx)\r\n end = encodings.char_to_token(0, end_idx-1)\r\n \r\n if start is None or end is None:\r\n return None, None\r\n \r\n start_positions.append(start)\r\n end_positions.append(end)\r\n \r\n if start_positions and end_positions:\r\n encodings.update({'output_1': start_positions,\r\n 'output_2': end_positions})\r\n \r\n return encodings, {'output_1': start_positions, 'output_2': end_positions}\r\n\r\ntrain_features = {}\r\ntrain_labels = {}\r\nfor item in ds[\"train\"]:\r\n feature, label = convert_to_tf_features(item)\r\n if feature is not None and label is not None:\r\n for k, v in feature.items():\r\n train_features.setdefault(k, []).append(v)\r\n for k, v in label.items():\r\n train_labels.setdefault(k, []).append(v)\r\n\r\ntrain_tfdataset = tf.data.Dataset.from_tensor_slices((train_features, train_labels)).batch(8)\r\n\r\nloss_fn = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE, from_logits=True)\r\nopt = tf.keras.optimizers.Adam(learning_rate=3e-5)\r\nmodel.compile(optimizer=opt,\r\n loss={'output_1': loss_fn, 'output_2': loss_fn},\r\n loss_weights={'output_1': 1., 'output_2': 1.},\r\n metrics=['accuracy'])\r\n\r\nmodel.fit(train_tfdataset, epochs=1, steps_per_epoch=3)\r\n```", "Thank for the help :) \r\n\r\nI've succeded to use your code as reference with my dataset, converting examples to features: \r\n\r\n```python\r\ndef get_tf_dataset(args, processor, tokenizer, dataset_type):\r\n filename_by_case = {'train': args.train_file, 'dev': args.dev_file, 'test': args.test_file}\r\n func_by_case = {'train': processor.get_train_examples, 'dev': processor.get_dev_examples, 'test': processor.get_dev_examples}\r\n examples = func_by_case[dataset_type](args.data_dir, filename=filename_by_case[dataset_type])\r\n\r\n train_features = {}\r\n train_labels = {}\r\n for item in examples:\r\n feature, label = convert_to_tf_features(item, tokenizer)\r\n if feature is not None and label is not None:\r\n for k, v in feature.items():\r\n train_features.setdefault(k, []).append(v)\r\n for k, v in label.items():\r\n train_labels.setdefault(k, []).append(v)\r\n\r\n tfdataset = tf.data.Dataset.from_tensor_slices((train_features, train_labels)).batch(8)\r\n return tfdataset\r\n\r\n\r\ndef convert_to_tf_features(example, tokenizer, training=True):\r\n context = example.context_text # example[\"context\"].numpy().decode(\"utf-8\")\r\n question = example.question_text # example[\"question\"].numpy().decode(\"utf-8\")\r\n encodings = tokenizer.encode_plus(context, question, pad_to_max_length=True, max_length=512)\r\n start_positions, end_positions = [], []\r\n\r\n first_answer = example.answers[0] if len(example.answers) > 0 else \"\" # example[\"answers\"][\"text\"][0].numpy().decode(\"utf-8\")\r\n first_answer_start = example.start_position # example[\"answers\"][\"answer_start\"][0].numpy()\r\n start_idx, end_idx = get_correct_alignement(context,\r\n first_answer,\r\n first_answer_start)\r\n start = encodings.char_to_token(0, start_idx)\r\n end = encodings.char_to_token(0, end_idx - 1) if end_idx > 0 else 0\r\n\r\n if start is None or end is None:\r\n return None, None\r\n\r\n start_positions.append(start)\r\n end_positions.append(end)\r\n\r\n if start_positions and end_positions:\r\n encodings.update({'output_1': start_positions,\r\n 'output_2': end_positions})\r\n\r\n return encodings, {'output_1': start_positions, 'output_2': end_positions}\r\n```\r\n\r\nI will check how to deal with the impossible answers by another references. In this example its empty string \"\" when no answer and `end_position = 0`. Thanks.", "Hi， how did you solve the **Try 1** problem？ \r\nAttributeError: 'NoneType' object has no attribute 'strip'" ]

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[ "Not expected, but shouldn't be an issue. Feel free to open a PR swapping args in https://github.com/huggingface/transformers/blob/31eedff5a0fc47d60609089627af6698c21da88d/src/transformers/optimization.py#L165" ]

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

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

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[ "Hello!\r\n\r\nThanks for the fix! Just one suggestion above :)", "Thanks for the feedback! Sounds like a good idea, added that. Are the failing ci tests an issue? Everything passes on my machine. ", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4448?src=pr&el=h1) Report\n> Merging [#4448](https://codecov.io/gh/huggingface/transformers/pull/4448?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2d184cb553ee20943b03b253f44300e466357871&el=desc) will **increase** coverage by `0.84%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4448?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4448 +/- ##\n==========================================\n+ Coverage 77.30% 78.14% +0.84% \n==========================================\n Files 120 120 \n Lines 20027 20027 \n==========================================\n+ Hits 15481 15651 +170 \n+ Misses 4546 4376 -170 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4448?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `79.24% <ø> (ø)` | |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.44% <0.00%> (-0.42%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.08% <0.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `43.25% <0.00%> (+1.10%)` | :arrow_up: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `81.78% <0.00%> (+1.37%)` | :arrow_up: |\n| [src/transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `75.73% <0.00%> (+2.29%)` | :arrow_up: |\n| [src/transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jdHJsLnB5) | `97.81% <0.00%> (+2.62%)` | :arrow_up: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `95.71% <0.00%> (+10.00%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4448/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9weXRvcmNoX3V0aWxzLnB5) | `89.93% <0.00%> (+81.20%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4448?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/4448?src=pr&el=footer). Last update [2d184cb...d99d65c](https://codecov.io/gh/huggingface/transformers/pull/4448?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Awesome! LGTM :)\r\n\r\n/cc @julien-c and @LysandreJik " ]