Datasets:

---

DanielPFlorian

/

Transformers-Github-Issues

like 0

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1911?src=pr&el=h1) Report\n> Merging [#1911](https://codecov.io/gh/huggingface/transformers/pull/1911?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/26db31e0c09a8b5e1ca7a61c454b159eab9d86be?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1911?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1911 +/- ##\n=======================================\n Coverage 84.04% 84.04% \n=======================================\n Files 97 97 \n Lines 14333 14333 \n=======================================\n Hits 12046 12046 \n Misses 2287 2287\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1911?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/tokenization\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/1911/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rva2VuaXphdGlvbl9ncHQyLnB5) | `96.72% <ø> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1911?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/1911?src=pr&el=footer). Last update [26db31e...65c5080](https://codecov.io/gh/huggingface/transformers/pull/1911?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Great, thanks!" ]

[]

[]

[ "Hi, this is due to the original BERT's tokenization. You can try it using the [google-research's implementation](https://github.com/google-research/bert):\r\n\r\n```py\r\nraw_text = 'text with percentage%'\r\ntokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=True)\r\ntokens = tokenizer.tokenize(raw_text)\r\n\r\nprint(tokens) # ['text', 'with', 'percentage', '%']\r\n```\r\n\r\nThe goal is to be as close as possible to the original implementation, hence the similar behavior concerning special tokens." ]

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

[]

[ "Hi! You can simply use `tf.constant` to build your input tensors, like this:\r\n\r\n```py\r\nraw_text = \"Here comes the sun\"\r\ntokens = tokenizer.encode(raw_text, add_special_tokens=False)\r\ninputs = {'input_ids': tf.constant(tokens)}\r\noutputs = model(inputs)\r\n```\r\n\r\nYou can use datasets when using building a custom loop or using keras.fit for example, as these will generally feed the tensors directly to the model, instead of feeding the `tf.data.Dataset` directly. Here's how I would go about starting a basic custom loop using a `tf.data.Dataset`:\r\n\r\n```py\r\nimport tensorflow as tf\r\nimport numpy as np\r\nfrom transformers import *\r\n\r\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\r\nmodel = TFGPT2LMHeadModel.from_pretrained('gpt2')\r\n\r\nraw_text = \"Here comes the sun\"\r\ntokens = tokenizer.encode(raw_text, add_special_tokens=False)\r\ninputs = tf.data.Dataset.from_tensor_slices( np.array([tokens]) )\r\n\r\nfor input_value in inputs:\r\n outputs = model(input_value)\r\n```\r\n\r\nPlease notice I converted to a numpy array by adding a dimension (`[tokens]`) otherwise you would end up with individual IDs held by the dataset rather than sequences of ids.", "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 think yes, \r\nJust load the model (class) and then start training. As it is mentioned [here](https://huggingface.co/transformers/model_doc/transformerxl.html#transformers.TransfoXLModel), model classes are just a PyTorch torch.nn.Module sub-class.\r\n\r\n> This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.", "Is it also possible to train tensorflow model?", "If we think about [this](https://huggingface.co/transformers/model_doc/transformerxl.html#tftransfoxlmodel) expression, yes. \r\n\r\n> This model is a tf.keras.Model tf.keras.Model sub-class. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.\r\n\r\nAlso, check [this](https://github.com/huggingface/transformers#quick-tour-tf-20-training-and-pytorch-interoperability) out.", "Thanks very much!" ]

[]

[]

[ "How do you know you have exact identical `pytorch_model.bin` files? Do you just compare file sizes? IF so, it is not a qualified method just because weights usually are just float numbers and they (almost) always occupy same size on the disk. You can compare the hashes of files to make sure.", "Yes, I just thought of comparing the files naively by comparing their sizes. \r\n\r\nI see, yes, \"hashes\" sounds a much better way of comparing files, thanks. I'll post here if that works.\r\n\r\nAlso, do you have any beginner suggestions on generating the hashes quickly and efficiently?", "I used md5sum pytorch_model.bin to generate the hashes of the files and both are different. Anyway, thanks, again!" ]

[]

[]

[ "According to the code (https://github.com/huggingface/transformers/blob/master/transformers/tokenization_gpt2.py#L183) the docstring is incomplete. \r\n\r\nThe `add_prefix_space` parameter should be passed to `tokenizer.tokenize()` as well as `tokenizer.encode()` or `tokenizer.decode()`.\r\n\r\nAs well, the docstring should actually say:\r\n\r\n```\r\nOtherwise, this tokenizer encode and decode method will not conserve a space at the beginning of a string: tokenizer.decode(tokenizer.encode(“ Hello”)) = ”Hello”\r\n```\r\n\r\nAccording to #1380, the Roberta/GPT2 tokenizer expects sequences to start with a space. Simply prepending a space to the input sequence doesn't give the same result because `GPT2Tokenizer` only overrides the internally-used `_tokenizer()` method and `PreTrainedTokenizer`'s `tokenize()` method (which is called by the user) does it's own preprocessing." ]

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

[]

[ "I think so. The script called `run_summarization_finetuning.py` has the goal of extracting text summary given as input a text document. This code shows how to fine-tune a text summarizer model (called **BertSum**) with two different datasets: CNN and Daily Mail.\r\n\r\n> ## Questions & Help\r\n> Hi\r\n> Does your code run_summarization_finetuning.py implement the abstractive summarization approach described in \"Text summarization with pretrained encoders.\" by Liu, Yang, and Mirella Lapata?\r\n> Thanks", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[]

[]

[ "Yes, feel free to open a PR to fix this Patrick ;-)" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1903?src=pr&el=h1) Report\n> Merging [#1903](https://codecov.io/gh/huggingface/transformers/pull/1903?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0cdfcca24b5739e25b584c05b866baa19ea382ef?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1903?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1903 +/- ##\n=======================================\n Coverage 84.04% 84.04% \n=======================================\n Files 97 97 \n Lines 14333 14333 \n=======================================\n Hits 12046 12046 \n Misses 2287 2287\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1903?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/1903?src=pr&el=footer). Last update [0cdfcca...66fc8d2](https://codecov.io/gh/huggingface/transformers/pull/1903?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "> * Loss logging no longer accumulates (why was that?) but shows raw loss\r\n\r\nWith the revised commits, this is no longer true (but I'm not able to edit @JuhaKiili's original message, and he's AFK at Slush :) )\r\n\r\n" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1902?src=pr&el=h1) Report\n> Merging [#1902](https://codecov.io/gh/huggingface/transformers/pull/1902?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e70cdf083ddb8bfe298d43e6d70d698a3a2f56d3?src=pr&el=desc) will **decrease** coverage by `0.03%`.\n> The diff coverage is `14.28%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1902?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1902 +/- ##\n==========================================\n- Coverage 84.08% 84.04% -0.04% \n==========================================\n Files 97 97 \n Lines 14316 14323 +7 \n==========================================\n+ Hits 12037 12038 +1 \n- Misses 2279 2285 +6\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1902?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/1902/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2F1dG8ucHk=) | `31.81% <14.28%> (-1.52%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1902?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/1902?src=pr&el=footer). Last update [e70cdf0...75ef125](https://codecov.io/gh/huggingface/transformers/pull/1902?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Great, thanks @Evpok !" ]

[]

[]

[ "Did you check if the methods are in `modeling_utils.py` in your local package?\r\nSee #1837 . As @julien-c said, the version of the lib you use may not be in sync with the scripts you run.\r\nTry to install the lib from `master`:\r\n`pip install git+https://github.com/huggingface/transformers`\r\n", "The doc you linked to is for the `master` version, if I'm not mistaken. Maybe we should make that clearer, cc @LysandreJik \r\n\r\nYes, @loveritsu929 is right – install from source if you want to use those methods right now. Thanks!" ]

[]

[]

[ "You don't need to do anything in order to make it predict the next word. That's what it is. But you may consider finetuning it with your own dataset.", "Hello,\r\n\r\nThank you for your reply.\r\nso the **GPT2DoubleHeadsModel** (NOT GPT2LMHeadModel but the DoubleHeadsModel), without any adjustment on its head, can be used for any \"non-multiple-choice-based\" next token prediction, as well as for the multiple-choice questions?\r\n\r\nThank you,", "Edit:\r\nI'm not sure. I did missread the GPT2DoubleHeadsModel.", "If GPT2DoubleHeadsModel can process both multiple-choice questions as well as non-multiple-choice next token predictions without any adjustment, why did HuggingFace make 2 different GPT2 models -- GPT2DoubleHeadsModel and GPT2LMHeadModel ?\r\n\r\nCan GPT2DoubleHeadsModel process both multiple-choice questions as well as non-multiple-choice next token predictions without any further adjustment(s) [e.g. adjustment on its head, etc,]?", "So?" ]

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

[]

[ "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" ]

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

[]

[ "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" ]

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

[]

[ "Do you need pretrain distilgpt2 from scratch? You can consider just finetuning it.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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

[]

[ "Please check this out: https://github.com/huggingface/transformers/issues/1816#issuecomment-554759751\r\nThis finetuning script https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py and the library do almost everything for you. Thus you don't need to split the text into fixed blocksize etc. Just give it one datafile for training and one for evaluating.", "I was using the script but when generating samples it seemed like it was mixing information from multiple examples in the training set, when they should be treated as individual examples. Is there a way to avoid this?", "Like in this thread it mentions not concatenating different articles in the same input: https://github.com/huggingface/transformers/issues/1145#issuecomment-527225854? Is there a way to do that with single examples (if they are not correlated)", "Language models look for entire corpus. You can seperate them via `<|endoftext|>` special token. The model learns (and already learned if you based it on a pretrained one while finetuning) to separate contexts by `<|endoftext|>` token. This is a common case for language models. For example, XLNet uses `<eod>` as separator. This is also implied at the original GPT paper while covering how to train GPT for document classification etc. See figure one: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf\r\nBut, still if you want to make sure that different rows in the data are not even connected while training, then you can pad each line until fill `block_size`. For example, GPT2 has 1024 input size and you have a data long 1540 (=1024+516) as tokens. Then you could have: \r\n<1024 tokens><516 tokens + 508 padding><nextdata..>\r\nThus you can make sure that GPT2 doesn't get mixed data as input.", "Also, please check this tutorial http://jalammar.github.io/illustrated-gpt2/ by Jay Alammar", "I thought GPT-2 didn't use padding tokens https://github.com/huggingface/transformers/issues/1435#issuecomment-538779523 and it's unclear to me how to use the attention mask. I'll try just using the <|endoftext|> token however", "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" ]

[]

[]

[ "Could you give at least some explanation in the title and the body of your post explaining what fixed or changed? Now it's very vague. ", "> Could you give at least some explanation in the title and the body of your post explaining what fixed or changed? Now it's very vague.\r\n\r\nSorry about the vagueness, I'll do this for any new pull requests. As for this one, it's obsolete now since this script has been refactored. " ]

[]

[]

[ "Indeed, thank you!" ]

[]

[]

[ "Great, thanks @jysohn23 !", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1893?src=pr&el=h1) Report\n> Merging [#1893](https://codecov.io/gh/huggingface/transformers/pull/1893?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/454455c695ff38df1ed3670a43677fdd1abcedf3?src=pr&el=desc) will **decrease** coverage by `0.02%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1893?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1893 +/- ##\n==========================================\n- Coverage 84.05% 84.03% -0.03% \n==========================================\n Files 97 94 -3 \n Lines 14316 14032 -284 \n==========================================\n- Hits 12034 11792 -242 \n+ Misses 2282 2240 -42\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1893?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rva2VuaXphdGlvbl91dGlscy5weQ==) | `91.2% <0%> (-0.95%)` | :arrow_down: |\n| [transformers/configuration\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL2NvbmZpZ3VyYXRpb25fYXV0by5weQ==) | `58.82% <0%> (-0.64%)` | :arrow_down: |\n| [transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3JvYmVydGEucHk=) | `89.9% <0%> (-0.53%)` | :arrow_down: |\n| [transformers/modeling\\_tf\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3hsbmV0LnB5) | `87.82% <0%> (-0.37%)` | :arrow_down: |\n| [transformers/modeling\\_tf\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3RyYW5zZm9feGwucHk=) | `92.21% <0%> (-0.33%)` | :arrow_down: |\n| [transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3V0aWxzLnB5) | `92.4% <0%> (-0.28%)` | :arrow_down: |\n| [transformers/modeling\\_tf\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX2N0cmwucHk=) | `97.75% <0%> (-0.11%)` | :arrow_down: |\n| [transformers/tests/modeling\\_distilbert\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX2Rpc3RpbGJlcnRfdGVzdC5weQ==) | `99.09% <0%> (-0.09%)` | :arrow_down: |\n| [transformers/modeling\\_tf\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3hsbS5weQ==) | `90.39% <0%> (-0.08%)` | :arrow_down: |\n| [transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX2Rpc3RpbGJlcnQucHk=) | `98.59% <0%> (-0.07%)` | :arrow_down: |\n| ... and [19 more](https://codecov.io/gh/huggingface/transformers/pull/1893/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1893?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/1893?src=pr&el=footer). Last update [454455c...2a9df0c](https://codecov.io/gh/huggingface/transformers/pull/1893?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

[ { "id": 1108649070, "node_id": "MDU6TGFiZWwxMTA4NjQ5MDcw", "url": "https://api.github.com/repos/huggingface/transformers/labels/Need%20more%20information", "name": "Need more information", "color": "d876e3", "default": false, "description": "Further information is requested" }, { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

[]

[ "We can't help you without more information.\r\nPlease fill in the issues templates required information.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1891?src=pr&el=h1) Report\n> Merging [#1891](https://codecov.io/gh/huggingface/transformers/pull/1891?src=pr&el=desc) into [xlnet](https://codecov.io/gh/huggingface/transformers/commit/1b35d05d4b3c121a9740544aa6f884f1039780b1?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1891?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## xlnet #1891 +/- ##\n=====================================\n Coverage 78.9% 78.9% \n=====================================\n Files 34 34 \n Lines 6181 6181 \n=====================================\n Hits 4877 4877 \n Misses 1304 1304\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1891?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/1891?src=pr&el=footer). Last update [1b35d05...dcbea8c](https://codecov.io/gh/huggingface/transformers/pull/1891?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Same as #1890 " ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1890?src=pr&el=h1) Report\n> Merging [#1890](https://codecov.io/gh/huggingface/transformers/pull/1890?src=pr&el=desc) into [xlnet](https://codecov.io/gh/huggingface/transformers/commit/1b35d05d4b3c121a9740544aa6f884f1039780b1?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1890?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## xlnet #1890 +/- ##\n=====================================\n Coverage 78.9% 78.9% \n=====================================\n Files 34 34 \n Lines 6181 6181 \n=====================================\n Hits 4877 4877 \n Misses 1304 1304\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1890?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/1890?src=pr&el=footer). Last update [1b35d05...5365fbd](https://codecov.io/gh/huggingface/transformers/pull/1890?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi, we don't have finetune_on_pregenerated.py in the examples anymore but a simpler script, `run_lm_finetuning`. Closing for now." ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1889?src=pr&el=h1) Report\n> Merging [#1889](https://codecov.io/gh/huggingface/transformers/pull/1889?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/454455c695ff38df1ed3670a43677fdd1abcedf3?src=pr&el=desc) will **increase** coverage by `0.02%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1889?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1889 +/- ##\n==========================================\n+ Coverage 84.05% 84.08% +0.02% \n==========================================\n Files 97 97 \n Lines 14316 14316 \n==========================================\n+ Hits 12034 12037 +3 \n+ Misses 2282 2279 -3\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1889?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/tests/modeling\\_tf\\_common\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1889/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2NvbW1vbl90ZXN0LnB5) | `97.08% <0%> (+1.45%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1889?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/1889?src=pr&el=footer). Last update [454455c...5cd8487](https://codecov.io/gh/huggingface/transformers/pull/1889?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Cool, this is important. I feel like installing from source without cloning (e.g. using `pip install git+https://github.com/huggingface/transformers`) would put less strain on the users' set-up, is there a reason we prefer cloning (I know the aforementioned method also clones, but it puts it in a temporary folder)? This is fine as it is, I'm just curious.", "That way you also cover the case of people who clone the repository to execute the examples (I would believe the majority of users), and encourage this behavior which is a much better practice than copy/pasting.\r\nDoing so you are absolutely sure the version of the library is the exact same as the version of the examples. This may not be true for `pip install git+https://github.com/huggingface/transformers` if the library has since changed.", "> That way you also cover the case of people who clone the repository to execute the examples (I would believe the majority of users), and encourage this behavior which is a much better practice than copy/pasting.\r\n> Doing so you are absolutely sure the version of the library is the exact same as the version of the examples. This may not be true for `pip install git+https://github.com/huggingface/transformers` if the library has since changed.\r\n\r\nin fact, passing a branch name, a commit hash, a tag name or a git ref is possible like so:\r\n\r\n```\r\n[-e] git://git.example.com/MyProject.git@master#egg=MyProject\r\n[-e] git://git.example.com/[email protected]#egg=MyProject\r\n[-e] git://git.example.com/MyProject.git@da39a3ee5e6b4b0d3255bfef95601890afd80709#egg=MyProject\r\n[-e] git://git.example.com/MyProject.git@refs/pull/123/head#egg=MyProject\r\n```\r\nAccording to https://pip.pypa.io/en/stable/reference/pip_install/#git ", "@LysandreJik Maybe we can list both options i.e. also reference `pip install git+https://github.com/huggingface/transformers`\r\n\r\nAlso small nitpick, don't hesitate to squash commits when merging very related changes.", "Fair nitpick" ]

[]

[]

[ "Maybe you should start with [XLNet](https://arxiv.org/abs/1906.08237).", "@iedmrc Thanks, I'm sure that most of these models could do this. I'm looking for guidance on adjusting the architecture of these transformer models to look for context form surrounding sentences to classify the current sentence. XLNetForSequenceClassification takes in a document as a whole for classification.\r\n\r\nI imagine many others have confronted this problem, so I'm reaching out for guidance on where to start.", "@pydn You are probably looking for a model like this: https://github.com/allenai/sequential_sentence_classification", "@armancohan Thank you! This is exactly what I was looking for." ]

[]

[]

[ "We have not released a new Pypi/conda version yet, but will do so in the next few days. You can install from source if you want to use GPT2-xl before that!", "Via docker:\r\nhttps://github.com/huggingface/transformers/blob/master/docker/Dockerfile\r\n\r\nVia pip:\r\nhttps://github.com/huggingface/transformers/blob/master/docker/Dockerfile\r\nor \r\nhttps://github.com/huggingface/transformers/issues/1837#issuecomment-554594306" ]

[]

[]

[ "This should be fixed in #1860 which should be merged shortly" ]

[]

[]

[ "Hi, there have been a few fixes done on the tokenizers recently that haven't been released on Pypi yet. When I run your script with the library installed from source, I obtain: \r\n\r\n```py\r\n[SEP] 50258\r\n[PAD] 50257\r\nAll special tokens: ['[PAD]', '[SEP]', '<|endoftext|>']\r\nAll special ids: [50257, 50258, 50256]\r\n```\r\n\r\nWould you mind installing from source using `pip install git+https://github.com/huggingface/transformers` and let me know if it fixes your problem,?", "That fixed it, thank you!" ]

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

[]

[ "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" ]

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

[]

[ "I got the same situation. However, when I changed the learning rate to 1e-5, the problem was solved.", "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" ]

[]

[]

[ "Ok, I found why : the padding is done before the sentence, and not after. " ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1881?src=pr&el=h1) Report\n> Merging [#1881](https://codecov.io/gh/huggingface/transformers/pull/1881?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f3386d938348628c91457fc7d8650c223317a053?src=pr&el=desc) will **increase** coverage by `1.35%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1881?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1881 +/- ##\n==========================================\n+ Coverage 82.72% 84.08% +1.35% \n==========================================\n Files 97 97 \n Lines 14316 14316 \n==========================================\n+ Hits 11843 12037 +194 \n+ Misses 2473 2279 -194\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1881?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1881/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rva2VuaXphdGlvbl91dGlscy5weQ==) | `92.14% <100%> (ø)` | :arrow_up: |\n| [transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/1881/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX29wZW5haS5weQ==) | `82% <0%> (+1.33%)` | :arrow_up: |\n| [transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/1881/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2N0cmwucHk=) | `96.46% <0%> (+2.21%)` | :arrow_up: |\n| [transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/1881/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3hsbmV0LnB5) | `73.61% <0%> (+2.43%)` | :arrow_up: |\n| [transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/1881/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3JvYmVydGEucHk=) | `71.76% <0%> (+12.35%)` | :arrow_up: |\n| [transformers/tests/modeling\\_tf\\_common\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1881/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2NvbW1vbl90ZXN0LnB5) | `97.08% <0%> (+15.53%)` | :arrow_up: |\n| [transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1881/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3B5dG9yY2hfdXRpbHMucHk=) | `92.95% <0%> (+83.09%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1881?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/1881?src=pr&el=footer). Last update [f3386d9...821ba9e](https://codecov.io/gh/huggingface/transformers/pull/1881?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi, thanks for looking into it! What's your use-case for adding 207 special tokens?", "In the kaggle Tensorflow 2 Nature question competition. I try to add some additional Sequence Embedding Such as [Tableid=13] and split short sentence. ", "I may misunderstand, but why not use the `add_tokens` method rather than the `add_special_tokens` method, which is reserved for tokens like CLS or MASK?", "Yes, `add_special_tokens` method is reserved for a limited number of tokens with special properties and usage like CLS or MASK. For other uses, go for `add_tokens`.\r\n", "Here is how we solved the performance issue when adding custom vocabulary: In the `add_tokens` method, we simply integrate `new_tokens` into the `self.vocab`.\r\n\r\n```\r\nfrom transformers import BertTokenizer, WordpieceTokenizer\r\nfrom collections import OrderedDict\r\n\r\n\r\nclass CustomVocabBertTokenizer(BertTokenizer):\r\n def add_tokens(self, new_tokens):\r\n new_tokens = [token for token in tokens if not (token in self.vocab or token in self.all_special_tokens)]\r\n\r\n self.vocab = OrderedDict([\r\n *self.vocab.items(),\r\n *[\r\n (token, i + len(self.vocab))\r\n for i, token in enumerate(new_tokens)\r\n ]\r\n ])\r\n\r\n self.ids_to_tokens = OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])\r\n self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)\r\n\r\n return len(new_tokens)\r\n```" ]

[]

[]

[ "Actually, both 616 and 1820 are indices for `my`. The difference lies in the prefix space that was added: in the case that it was not added (1820), it is identified as being the beginning of the sentence or part of a word.\r\n\r\nIn the case that it was added (616), it is identified as being the beginning of a word in a sentence.\r\n\r\nYou can check the behavior by calling `tokenize` instead of `encode`, for example:\r\n\r\n```py\r\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\r\n\r\nprint(tokenizer.tokenize(\"my son is jeremy\",add_prefix_space=True))\r\n# ['Ġmy', 'Ġson', 'Ġis', 'Ġj', 'ere', 'my']\r\n\r\nprint(tokenizer.tokenize(\"my son is my son\",add_prefix_space=False))\r\n# ['my', 'Ġson', 'Ġis', 'Ġmy', 'Ġson']\r\n```", "Thank you! ", "still don't understand what is the difference in \r\n```\r\ntokenizer = tokenizers.ByteLevelBPETokenizer(\r\n vocab_file=PATH+'vocab-roberta-base.json', \r\n merges_file=PATH+'merges-roberta-base.txt',\r\n lowercase=True,\r\n add_prefix_space=True\r\n)\r\n```", "The argument you mention is for the initialization of a tokenizer from the `huggingface/tokenizers` library, you would have better luck opening an issue there." ]

[]

[]

[ "Great, thanks for letting us know!" ]

[]

[]

[ "duplicated \r\nhttps://github.com/huggingface/transformers/issues/1837#issue-523145270", "I checked the other comment but did not work. Any idea ? \r\n```\r\n__init__() got an unexpected keyword argument 'num_warmup_steps'\r\n```\r\n", "Hi, could you specify which versions of Python and Transformers is your environment running on?", "Thanks\r\n```\r\npython: 3.6.8\r\nTransformers: 2.1.1\r\n```\r\n", "Did you install transformers from source or from pypi?", "Could you try installing from source and telling me if it fixes your problem?", "Thanks it worked", "worked with building from the source and this \r\n```\r\nfrom transformers import AdamW,get_linear_schedule_with_warmup\r\n```", "Great to hear!", "whats the meaning of installing from source ,i am a little bit confused. could you please tell me ,thank you.\r\ni've tried 'pip install git+https://github.com/huggingface/transformers' or pip install transformers \r\nor pip install pytorhc-transformers. But they all doesn't work.", "> Hello,\r\n> I tried to import this:\r\n> \r\n> `from transformers import AdamW, get_linear_schedule_with_warmup`\r\n> but got error : model not found\r\n> but when i did this, it worked:\r\n> \r\n> ```\r\n> from transformers import AdamW\r\n> from transformers import WarmupLinearSchedule as get_linear_schedule_with_warmup\r\n> ```\r\n> \r\n> however when I set the scheduler like this :\r\n> \r\n> ```\r\n> scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total) \r\n> ```\r\n> \r\n> I got this error :\r\n> \r\n> ```\r\n> __init__() got an unexpected keyword argument 'num_warmup_steps'\r\n> ```\r\n\r\ncan you kindly tell me how to install from source.", "@TLCFYBJJHYYSND See https://github.com/huggingface/transformers#from-source", "In order to install Transformers library, you have to open your command line and enter:\r\n```\r\npip install git+https://github.com/huggingface/transformers.git\r\n```\r\n> > Hello,\r\n> > I tried to import this:\r\n> > `from transformers import AdamW, get_linear_schedule_with_warmup`\r\n> > but got error : model not found\r\n> > but when i did this, it worked:\r\n> > ```\r\n> > from transformers import AdamW\r\n> > from transformers import WarmupLinearSchedule as get_linear_schedule_with_warmup\r\n> > ```\r\n> > \r\n> > \r\n> > however when I set the scheduler like this :\r\n> > ```\r\n> > scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total) \r\n> > ```\r\n> > \r\n> > \r\n> > I got this error :\r\n> > ```\r\n> > __init__() got an unexpected keyword argument 'num_warmup_steps'\r\n> > ```\r\n> \r\n> can you kindly tell me how to install from source.", "Thanks for that, it really works!!!" ]

[]

[]

[]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1876?src=pr&el=h1) Report\n> Merging [#1876](https://codecov.io/gh/huggingface/transformers/pull/1876?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f3386d938348628c91457fc7d8650c223317a053?src=pr&el=desc) will **increase** coverage by `1.35%`.\n> The diff coverage is `0%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1876?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1876 +/- ##\n==========================================\n+ Coverage 82.72% 84.08% +1.35% \n==========================================\n Files 97 97 \n Lines 14316 14316 \n==========================================\n+ Hits 11843 12037 +194 \n+ Misses 2473 2279 -194\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1876?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1876/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3V0aWxzLnB5) | `92.68% <0%> (ø)` | :arrow_up: |\n| [transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/1876/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX29wZW5haS5weQ==) | `82% <0%> (+1.33%)` | :arrow_up: |\n| [transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/1876/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2N0cmwucHk=) | `96.46% <0%> (+2.21%)` | :arrow_up: |\n| [transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/1876/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3hsbmV0LnB5) | `73.61% <0%> (+2.43%)` | :arrow_up: |\n| [transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/1876/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3JvYmVydGEucHk=) | `71.76% <0%> (+12.35%)` | :arrow_up: |\n| [transformers/tests/modeling\\_tf\\_common\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1876/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2NvbW1vbl90ZXN0LnB5) | `97.08% <0%> (+15.53%)` | :arrow_up: |\n| [transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1876/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3B5dG9yY2hfdXRpbHMucHk=) | `92.95% <0%> (+83.09%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1876?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/1876?src=pr&el=footer). Last update [f3386d9...3de31f8](https://codecov.io/gh/huggingface/transformers/pull/1876?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks a lot @LysandreJik!" ]

[]

[]

[ "Hi, what exactly is your task? Is it question answering, sequence classification, language modeling, other?", "> Hi, what exactly is your task? Is it question answering, sequence classification, language modeling, other?\r\n\r\nHi LysandreJik\r\n\r\nI have two tasks: First one is sequence classification. Second one is Relation Extraction.\r\n", "For sequence classification, you can indeed take inspiration from the script `run_glue.py`. Those scripts are meant as examples showcasing how to manage models for different tasks, so that users may train our models in any way they see fit. The models are standard PyTorch models so they can be trained like any other model.\r\n\r\nFor relation extraction, there are no models specifically targeting this task but feel free to adapt a standard model by adding a few layers on top. For training there are examples in our [HMTL repository](https://github.com/huggingface/hmtl) which may be of help." ]

[ { "id": 1834056761, "node_id": "MDU6TGFiZWwxODM0MDU2NzYx", "url": "https://api.github.com/repos/huggingface/transformers/labels/Core:%20Modeling", "name": "Core: Modeling", "color": "FF8446", "default": false, "description": "Internals of the library; Models." } ]

[]

[ "We would need more information", "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", "Also cc @joeddav ", "Ok, I've reproduced an issue here.\r\n\r\n```python\r\nsentence = ' My favorite type of cheese is <mask>!'\r\nroberta_fairseq = torch.hub.load('pytorch/fairseq', 'roberta.base')\r\nroberta_hf = pipeline('fill-mask', model='roberta-base')\r\n\r\nroberta_fairseq.fill_mask(sentence)\r\n\"\"\"[(' My favorite type of cheese is goat!', 0.10264863073825836, ' goat'),\r\n (' My favorite type of cheese is cream!', 0.07233648002147675, ' cream'),\r\n (' My favorite type of cheese is broccoli!', 0.057516228407621384,' broccoli'),\r\n (' My favorite type of cheese is bacon!', 0.037444233894348145, ' bacon'),\r\n (' My favorite type of cheese is ham!', 0.03281955048441887, ' ham')]\"\"\"\r\n\r\nroberta_hf(sentence)\r\n\"\"\"[{'sequence': '<s> My favorite type of cheese is goat!</s>',\r\n 'score': 0.09398240596055984,\r\n 'token': 24791},\r\n {'sequence': '<s> My favorite type of cheese is cream!</s>',\r\n 'score': 0.07240654528141022,\r\n 'token': 6353},\r\n {'sequence': '<s> My favorite type of cheese is broccoli!</s>',\r\n 'score': 0.06303773820400238,\r\n 'token': 34803},\r\n {'sequence': '<s> My favorite type of cheese is bacon!</s>',\r\n 'score': 0.04124978929758072,\r\n 'token': 18599},\r\n {'sequence': '<s> My favorite type of cheese is jack!</s>',\r\n 'score': 0.03125162795186043,\r\n 'token': 10267}]\"\"\"\r\n```\r\n\r\nThey don't align when I use roberta large either. cc @sshleifer.", "Probably want to compare the encoded input IDs. If they are the same, then there is a model difference (which is a big deal). Otherwise it can be attributed to a different tokenisation (e.g. difference in spaces or difference in adding special tokens).", "The input IDs do match (again, when an empty space is prepended in fairseq's case): `[ 0, 1308, 2674, 1907, 9, 7134, 16, 50264, 328, 2]`. \r\n\r\nI doesn't look like a pipelines issue:\r\n\r\n```python\r\ntokenizer = RobertaTokenizer.from_pretrained('roberta-base')\r\nroberta_hf = RobertaForMaskedLM.from_pretrained('roberta-base')\r\nroberta_fairseq = torch.hub.load('pytorch/fairseq', 'roberta.base')\r\nroberta_fairseq.model.eval()\r\n\r\nsequence = 'My favorite type of cheese is gouda!'\r\ntokens = torch.tensor([tokenizer.encode(sequence)])\r\nhf_out = roberta_hf.forward(tokens)[0]\r\nfairseq_out = roberta_fairseq.model.forward(tokens)[0]\r\n\r\ntorch.mean(torch.abs(fairseq_out - hf_out)).item()\r\n# 0.053489409387111664\r\n```\r\n\r\nMasked LM outputs differ by an average of `0.053`.", "I don't have any time to look into this further, but from looking at the named parameters of both implementations, it seems that there is a difference in the LM head, that is, everything that comes after the encode (embedding + 12 layers). I can't pinpoint it exactly, but it seems note-worthy that the weight LM head in fairseq, comes from the token embedding weights\r\n\r\nhttps://github.com/pytorch/fairseq/blob/4923f34790761f41170fd88cd06e4d00ab0c527c/fairseq/models/roberta/model.py#L286-L291\r\n\r\nI am not sure in how far that is the same in transformers, but it seemed peculiar. Particularly, it seems that `transformers` does the 'None' case here, whereas it should actually take the token embedding weights.\r\n\r\nhttps://github.com/pytorch/fairseq/blob/4923f34790761f41170fd88cd06e4d00ab0c527c/fairseq/models/roberta/model.py#L211-L213\r\n\r\nI might be completely wrong, though. Other input welcome.\r\n\r\n**Tl;dr**\r\n\r\nTransformers does this\r\n\r\n```python\r\nself.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\r\n```\r\n\r\nwhereas fairseq does\r\n\r\n```python\r\nself.lm_head = RobertaLMHead(\r\n embed_dim=args.encoder_embed_dim,\r\n output_dim=len(dictionary),\r\n activation_fn=args.activation_fn,\r\n weight=self.sentence_encoder.embed_tokens.weight,\r\n)\r\n...\r\n# `weight` is **not** None\r\nif weight is None:\r\n weight = nn.Linear(embed_dim, output_dim, bias=False).weight\r\nself.weight = weight\r\n```\r\n\r\nI'd be very interested to know _why_ fairseq does this. Why would you want specifically those weights to be the same? ", "I implemented my findings in https://github.com/huggingface/transformers/pull/2928.\r\n\r\nThe following snippet (based on the problem stated above) runs as expected, with identical results for both `transformers` and `fairseq`.\r\n\r\n```python\r\nimport torch\r\nfrom transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline\r\n\r\n# init HuggingFace\r\ntokenizer_hf = RobertaTokenizer.from_pretrained('roberta-base')\r\nroberta_hf = RobertaForMaskedLM.from_pretrained('roberta-base')\r\n# init fairseq\r\nroberta_fairseq = torch.hub.load('pytorch/fairseq', 'roberta.base')\r\nroberta_fairseq.model.eval()\r\n\r\n# LM test\r\nsequence = 'My favorite type of cheese is gouda!'\r\ntokens = torch.tensor([tokenizer_hf.encode(sequence)])\r\nhf_out = roberta_hf.forward(tokens)[0]\r\nfairseq_out = roberta_fairseq.model.forward(tokens)[0]\r\nprint(torch.mean(torch.abs(fairseq_out - hf_out)).item())\r\n# should be 0.0\r\n\r\n# ---\r\n# pipeline test fill-mask\r\nsentence = ' My favorite type of cheese is <mask>!'\r\nprint(roberta_fairseq.fill_mask(sentence))\r\nroberta_hf = pipeline('fill-mask', tokenizer=tokenizer_hf, model='roberta-base')\r\nprint(roberta_hf(sentence))\r\n# should have identical predictions\r\n```", "Resolved by #2958" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1873?src=pr&el=h1) Report\n> Merging [#1873](https://codecov.io/gh/huggingface/transformers/pull/1873?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f3386d938348628c91457fc7d8650c223317a053?src=pr&el=desc) will **increase** coverage by `1.35%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1873?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1873 +/- ##\n==========================================\n+ Coverage 82.72% 84.08% +1.35% \n==========================================\n Files 97 97 \n Lines 14316 14316 \n==========================================\n+ Hits 11843 12037 +194 \n+ Misses 2473 2279 -194\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1873?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/tokenization\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rva2VuaXphdGlvbl9kaXN0aWxiZXJ0LnB5) | `100% <ø> (ø)` | :arrow_up: |\n| [transformers/configuration\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL2NvbmZpZ3VyYXRpb25fZGlzdGlsYmVydC5weQ==) | `89.74% <ø> (ø)` | :arrow_up: |\n| [transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2Rpc3RpbGJlcnQucHk=) | `95.87% <ø> (ø)` | :arrow_up: |\n| [transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX29wZW5haS5weQ==) | `82% <0%> (+1.33%)` | :arrow_up: |\n| [transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2N0cmwucHk=) | `96.46% <0%> (+2.21%)` | :arrow_up: |\n| [transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3hsbmV0LnB5) | `73.61% <0%> (+2.43%)` | :arrow_up: |\n| [transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3JvYmVydGEucHk=) | `71.76% <0%> (+12.35%)` | :arrow_up: |\n| [transformers/tests/modeling\\_tf\\_common\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2NvbW1vbl90ZXN0LnB5) | `97.08% <0%> (+15.53%)` | :arrow_up: |\n| [transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1873/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3B5dG9yY2hfdXRpbHMucHk=) | `92.95% <0%> (+83.09%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1873?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/1873?src=pr&el=footer). Last update [f3386d9...da06afa](https://codecov.io/gh/huggingface/transformers/pull/1873?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "fyi I just adjusted the permissions on the s3 objects.", "Perfect!" ]

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

[]

[ "Just to clarify, I am asking this because I originally tried to create a custom vocab/merges files by giving a new path for tokenizer.from_pretrained for my new files. Failure to do so led me to notice that giving a new path for the original files downloaded from S3 also fail in the new path. If I put my custom files to the path used for the S3 cache (with identical filenames used by the cache, everything works fine. In other words, if I replace the contents of the following files with my custom content, everything works:\r\n\r\n`11/19/2019 18:31:15 - INFO - transformers.tokenization_utils - loading file https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-vocab.json from cache at /root/.cache/torch/transformers/d0c5776499adc1ded22493fae699da0971c1ee4c2587111707a4d177d20257a2.ef00af9e673c7160b4d41cfda1f48c5f4cba57d5142754525572a846a1ab1b9b\r\n11/19/2019 18:31:15 - INFO - transformers.tokenization_utils - loading file https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-merges.txt from cache at /root/.cache/torch/transformers/b35e7cd126cd4229a746b5d5c29a749e8e84438b14bcdb575950584fe33207e8.70bec105b4158ed9a1747fea67a43f5dee97855c64d62b6ec3742f4cfdb5feda` ", "Have you learned anything about this? I'm currently running to the same issue trying to specify an existing model. I noticed that the cache_lm_* file that it creates is only 5 bytes, and gedit says the files is corrupt.", "Yes, so bizarrely even if you have the original hugggingface files in a new location, an empty data frame (the cache_lm file) will be created. Try replacing the original hugging face files downloaded from S3 with your custom vocab/merges files. They should be in /root/.cache/torch/transformers\r\n\r\nIf you still run into the same problem, then either your data is too small or the vocab/merges files are not in the correct format.", "@AVSuni I get the same issue when using a checkpoint generated by huggingface/transformers. Even when using cached versions of the merge/vocab files from `/root/.cache/torch/transformers/`. Even when I delete the `cache_lm_*` file, specifying the model rather than giving a model name to be used causes this issue.", "Maybe I'm being superstitious, but setting a block size seems to alleviate the issue. I didn't bother tracing the code, but I did notice that when I set `block_size`, the cache file is set with the number of the block size. When I do not set the block size, the temp file has some absurdly long number in the cache file where the block size would be. I don't really see that documented anywhere, but here we are.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1870?src=pr&el=h1) Report\n> Merging [#1870](https://codecov.io/gh/huggingface/transformers/pull/1870?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f3386d938348628c91457fc7d8650c223317a053?src=pr&el=desc) will **increase** coverage by `1.37%`.\n> The diff coverage is `88.4%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1870?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1870 +/- ##\n==========================================\n+ Coverage 82.72% 84.09% +1.37% \n==========================================\n Files 97 97 \n Lines 14316 14383 +67 \n==========================================\n+ Hits 11843 12096 +253 \n+ Misses 2473 2287 -186\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1870?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/tests/modeling\\_tf\\_xlnet\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX3hsbmV0X3Rlc3QucHk=) | `96.05% <100%> (+0.3%)` | :arrow_up: |\n| [transformers/modeling\\_tf\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3hsbmV0LnB5) | `88.53% <100%> (+0.34%)` | :arrow_up: |\n| [transformers/tests/modeling\\_xlnet\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3hsbmV0X3Rlc3QucHk=) | `94.28% <81.81%> (-1.85%)` | :arrow_down: |\n| [transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3hsbmV0LnB5) | `73.95% <82.6%> (+2.77%)` | :arrow_up: |\n| [transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX29wZW5haS5weQ==) | `82% <0%> (+1.33%)` | :arrow_up: |\n| [transformers/modeling\\_ctrl.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2N0cmwucHk=) | `96.46% <0%> (+2.21%)` | :arrow_up: |\n| [transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3JvYmVydGEucHk=) | `71.76% <0%> (+12.35%)` | :arrow_up: |\n| [transformers/tests/modeling\\_tf\\_common\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2NvbW1vbl90ZXN0LnB5) | `97.08% <0%> (+15.53%)` | :arrow_up: |\n| [transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1870/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3RmX3B5dG9yY2hfdXRpbHMucHk=) | `92.95% <0%> (+83.09%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1870?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/1870?src=pr&el=footer). Last update [f3386d9...4193aa9](https://codecov.io/gh/huggingface/transformers/pull/1870?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This looks good to me, thank you @alexzubiaga !", "This is great, thanks a lot @alexzubiaga (and nice work adding the tests!).\r\nMerging" ]

[]

[]

[ "Hi, you might be interested in freezing some layers rather than initializing a model with only 3 layers. Freezing some layers mean that they won't be affected by the backpropagation.\r\n\r\nIf that's what you're looking for, @BramVanroy's answer may be of help: https://github.com/huggingface/transformers/issues/1431\r\n\r\nWe do not have an NSP example in our examples." ]

[]

[]

[]

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

[]

[ "I think you should check out pytorch dataloaders https://pytorch.org/docs/stable/data.html\r\nAlso, gradient accumulation is helpful.", "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" ]

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

[]

[ "Why not just use flair? Flair has integrated BERT.", "BERT was performing sentence embedding better than Flair(tired all different type of stacked embedding ) but less when compared to USC . Flair had a functionality that gave NER tagging directly . i was expecting the same will would be available in BERT but its not available directly .It would be good if BERT gives a direct plug and play functionality for NER task . ", "@ajbot2019 \r\n\r\n1. compute prediction list\r\nhttps://github.com/huggingface/transformers/blob/master/examples/run_ner.py#L242\r\n\r\n2. print it with \r\nhttps://github.com/huggingface/transformers/blob/master/examples/run_ner.py#L503\r\n\r\nshell scripts to train and evaluate for CoNLL 2003 (english) dataset.\r\nhttps://github.com/dsindex/transformers_examples\r\nthis may help you.", "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" ]

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

[]

[ "Your copy&pasta is broken. Perhaps Roberto can help you? [Text classification with RoBERTa](https://rsilveira79.github.io/fermenting_gradients/machine_learning/nlp/pytorch/text_classification_roberta/)", "> Your copy&pasta is broken. Perhaps Roberto can help you? [Text classification with RoBERTa](https://rsilveira79.github.io/fermenting_gradients/machine_learning/nlp/pytorch/text_classification_roberta/)\r\n\r\nhahaha, actually i solve this problem last night. It's because WarmupLinearSchedule, and I set a t_total args which is smaller than warmup steps , so the lr is nearly zero actually.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[]

[]

[ "This bug has been reviewed and corrected in this PR #1876!\r\nClose this issue." ]

[]

[]

[ "You can initialize a model by calling its constructor with a configuration:\r\n\r\n```py\r\nfrom transformers import OpenAIGPTDoubleHeadsModel, OpenAIGPTConfig\r\n\r\nconfig = OpenAIGPTConfig()\r\nmodel = OpenAIGPTDoubleHeadsModel(config)\r\n```", "Cool, so if I instantiate the model with the default config, it will use the same vocab as the pre-trained model but it won't initialize the pre-trained weights, is that correct?\r\n\r\nAlso, could you please point me to where the model weights are initialized when we don't use pre-trained weights?", "If you instantiate a model with a default configuration without specifying any argument, it will instantiate your model according to the default configuration values, [visible here](https://github.com/huggingface/transformers/blob/master/transformers/configuration_openai.py#L59).\r\n\r\nThe model weights are initialized in the `OpenAIGPTPreTrainedModel`'s [`_init_weights` method](https://github.com/huggingface/transformers/blob/master/transformers/modeling_openai.py#L267)." ]

[]

[]

[ "Hi, to retrieve all layers you can set the `output_hidden_states` flag to `True` in your model configuration:\r\n\r\n```py\r\nfrom transformers import BertConfig, BertModel\r\n\r\nconfig = BertConfig.from_pretrained(\"bert-base-uncased\")\r\nconfig.output_hidden_states = True\r\nmodel = BertModel.from_pretrained(\"bert-base-uncased\", config=config)\r\n\r\noutputs = model(inputs)\r\nhidden_states = outputs[-1]\r\n```\r\n\r\nThe model will now output an additional value which will be a tuple of size 1 + n_layer:\r\n`hidden_states[0]`: output of the embedding layer\r\n`hidden_states[1]`: output of the first layer\r\n...\r\n`hidden_states[12]`: output of the last layer\r\n" ]

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

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1861?src=pr&el=h1) Report\n> Merging [#1861](https://codecov.io/gh/huggingface/transformers/pull/1861?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a5a06a851e1da79138e53978aa079a093f243dde?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1861?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1861 +/- ##\n=======================================\n Coverage 81.43% 81.43% \n=======================================\n Files 122 122 \n Lines 18338 18338 \n=======================================\n Hits 14933 14933 \n Misses 3405 3405\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1861?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/1861?src=pr&el=footer). Last update [a5a06a8...0f730de](https://codecov.io/gh/huggingface/transformers/pull/1861?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" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=h1) Report\n> Merging [#1860](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3916b334a86484af8442d1cfdb2f15695feae581?src=pr&el=desc) will **decrease** coverage by `0.03%`.\n> The diff coverage is `55.55%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1860 +/- ##\n==========================================\n- Coverage 84.08% 84.04% -0.04% \n==========================================\n Files 97 97 \n Lines 14316 14333 +17 \n==========================================\n+ Hits 12037 12046 +9 \n- Misses 2279 2287 +8\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/modeling\\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/1860/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX2NhbWVtYmVydC5weQ==) | `100% <100%> (ø)` | :arrow_up: |\n| [transformers/tokenization\\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/1860/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rva2VuaXphdGlvbl9jYW1lbWJlcnQucHk=) | `36.92% <38.46%> (+0.38%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1860?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/1860?src=pr&el=footer). Last update [3916b33...56c8486](https://codecov.io/gh/huggingface/transformers/pull/1860?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This looks good to me @stefan-it 👌 \r\n\r\nWe'll also need to do the TF models but that can be in a different PR.\r\n\r\ncc @LysandreJik @thomwolf ", "I tried to replicate the results for PoS tagging on ParTUT dataset (using the tagged 2.2 version from [here](https://github.com/UniversalDependencies/UD_French-ParTUT)). I could replicate the results mentioned in the CamemBERT paper for the multilingual (cased) BERT model: `run_ner` outputs F1-score, so I added the `accuracy_score` metric from the `seqeval` module for debugging ;) \r\n\r\nHowever, the CamemBERT model is ~10% behind it. I'm trying to figure out why, loss is really high compared to mBERT...\r\n", "LGTM thanks!" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1859?src=pr&el=h1) Report\n> Merging [#1859](https://codecov.io/gh/huggingface/transformers/pull/1859?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0477b307c7501ea76e01b03cb387a2312db752b3?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1859?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1859 +/- ##\n=======================================\n Coverage 84.08% 84.08% \n=======================================\n Files 97 97 \n Lines 14316 14316 \n=======================================\n Hits 12037 12037 \n Misses 2279 2279\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1859?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/1859?src=pr&el=footer). Last update [0477b30...63af013](https://codecov.io/gh/huggingface/transformers/pull/1859?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks! Slightly reworded in next commit" ]

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

[]

[ "Logits are the classification scores before softmax.\r\nI don't see why the results of your equation should add up to 1.\r\nYou may try to use the softmax function directly.", "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" ]

[]

[]

[ "Duplicate of #1842 " ]

[]

[]

[ "from the documentation of the class,\r\nhttps://github.com/huggingface/transformers/blob/933841d903a032d93b5100220dc72db9d1283eca/pytorch_transformers/modeling_bert.py#L1100\r\n\r\nI understand that I could use the ```scores```, as input to an additional module that I could stack on top of BertForTokenClassification, for example for a second task. Is that correct? \r\n\r\n```loss, scores = outputs[:2] ```\r\n\r\nBut what I am thinking right now, is that scores could have small dimensions, so probably I would need the weights of the last layer. How could I extract them?\r\n\r\nAlways your thoughts on that would be much appreciated!", "for every other suffered person that needs an answer on that last question above, I think that the way to extract those weights, is if you open the black box of the implementation of this class, and here it is what you want:\r\noutputs = self.bert(..)\r\nso I think that reimplementation/enhancment is needed to support my needs as was given above.\r\nAm I missing something if I reimplement this class adding more functionality? I think that no, and that it is safe. ", "Hi, there are several things you can do to obtain the last layer representation. \r\n\r\n- First of all, you can use a standard `BertModel` on which you add your own classifier for token classification (that's what's done with `BertForTokenClassification`). This will allow you to easily switch the heads for your multi-task setup.\r\n\r\n- You could also use the `BertForTokenClassification`, as you have said, and use the inner model (model.bert) to obtain the last layer.\r\n\r\n- Finally, the cleanest way would be to output hidden states directly by specifying the option in the configuration:\r\n\r\n```py\r\nfrom transformers import BertConfig, BertForTokenClassification\r\nimport torch\r\n\r\nconfig = BertConfig.from_pretrained(\"bert-base-cased\")\r\nconfig.output_hidden_states = True\r\n\r\nmodel = BertForTokenClassification.from_pretrained(\"bert-base-cased\", config=config)\r\n\r\ninputs = torch.tensor([[1, 2, 3]])\r\n\r\noutputs = model(inputs)\r\ntoken_classification_outputs, hidden_states = outputs\r\n\r\nlast_layer_hidden_states = hidden_states[-1]\r\n```\r\n\r\nThe variable `last_layer_hidden_states` is of shape `[batch_size, seq_len, hidden_size]` and is the output of the last transformer layer.\r\n\r\nI hope this clears things up.", "https://github.com/jiant-dev/jiant" ]

[]

[]

[ "Hi!\r\n\r\nFirst, please check the `run_summarization.py` script in the `example-summarization` branch as it is more up-to-date. To answer your questions:\r\n\r\na) We are following this implementation: https://arxiv.org/pdf/1908.08345.pdf where they add an extra [CLS] token for each new sentence.\r\n\r\nb) I would need to triple-check the authors’ code, but I hope it does not matter.\r\n\r\nc) Good point. If you look at the way the encoder-decoder is implemented you will see that masks passed to the decoder are automatically turned into causal (“look-ahead”), so the code does have the expected behavior :)", "Thank you so much." ]

[]

[]

[ "Could you please respect the issue template so that we can help you better? It's hard to help without seeing the versions or the full error." ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1853?src=pr&el=h1) Report\n> Merging [#1853](https://codecov.io/gh/huggingface/transformers/pull/1853?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0477b307c7501ea76e01b03cb387a2312db752b3?src=pr&el=desc) will **decrease** coverage by `0.09%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1853?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1853 +/- ##\n=========================================\n- Coverage 84.08% 83.98% -0.1% \n=========================================\n Files 97 97 \n Lines 14316 14316 \n=========================================\n- Hits 12037 12023 -14 \n- Misses 2279 2293 +14\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1853?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/tests/modeling\\_tf\\_auto\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2F1dG9fdGVzdC5weQ==) | `96.36% <0%> (-1.82%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_transfo\\_xl\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX3RyYW5zZm9feGxfdGVzdC5weQ==) | `93% <0%> (-1%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_ctrl\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2N0cmxfdGVzdC5weQ==) | `93.06% <0%> (-1%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_distilbert\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2Rpc3RpbGJlcnRfdGVzdC5weQ==) | `98.16% <0%> (-0.92%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_openai\\_gpt\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX29wZW5haV9ncHRfdGVzdC5weQ==) | `93.85% <0%> (-0.88%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_gpt2\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2dwdDJfdGVzdC5weQ==) | `93.85% <0%> (-0.88%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_xlm\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX3hsbV90ZXN0LnB5) | `94.35% <0%> (-0.81%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_roberta\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX3JvYmVydGFfdGVzdC5weQ==) | `74.4% <0%> (-0.8%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_xlnet\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX3hsbmV0X3Rlc3QucHk=) | `95.03% <0%> (-0.71%)` | :arrow_down: |\n| [transformers/tests/modeling\\_tf\\_bert\\_test.py](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL3Rlc3RzL21vZGVsaW5nX3RmX2JlcnRfdGVzdC5weQ==) | `95.59% <0%> (-0.63%)` | :arrow_down: |\n| ... and [2 more](https://codecov.io/gh/huggingface/transformers/pull/1853/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1853?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/1853?src=pr&el=footer). Last update [0477b30...de3cb08](https://codecov.io/gh/huggingface/transformers/pull/1853?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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

[]

[ "What parameters should I exactly pass for XLNet QA model?", "More details would be nice but please check #1849 #1848 and #1805 . Also set `CUDA_LAUNCH_BLOCKING=1` environment variable to have more details about the error, from the CUDA.", "Thanks. It worked after changing the input sequence limit to 128.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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

[]

[ "Same.\r\nmodel =roberta-base\r\ntotal batch size=8\r\ntrain num epochs=5\r\nfp16 =False\r\nmax seq length =512\r\neval_acc = 0.29568242275424594\r\neval_loss = 1.3862943781258896\r\n@spolu ", "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", "In my experience, it's because loss not getting down.\r\nrefer to fb repo issue https://github.com/pytorch/fairseq/issues/1114#issue-490144245\r\nIt seems that learning rate needs to be smaller and require larger batch size\r\n\r\nUse the config below:\r\n```\r\npython ./examples/run_multiple_choice.py --model_type roberta --task_name race --model_name_or_path roberta-base-openai-detector --do_eval --data_dir $RACE_DIR --learning_rate 1e-5 --num_train_epochs 10 --max_seq_length 512 --output_dir ./roberta-base-openai-race --model_name_or_path ./roberta-base-openai-race/ --per_gpu_eval_batch_size=9 --per_gpu_train_batch_size=9 --gradient_accumulation_steps 2 --save_steps 4882 --eval_all_checkpoints --fp16 --seed 77 --do_lower_case\r\n```\r\n\r\nI can get \r\n***** Eval results checkpoint-24410 is test:False ***** \r\neval_acc = 0.6758747697974218 \r\neval_loss = 0.9408586282721337 \r\n\r\nIt‘s getting close, but still faraway from current result......", "Thank you very much, I will try it.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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

[]

[ "Hi! Do you succeed in loading your pre-trained model in Fairseq?", "Yes, it work well in Fairseq~", "My script:\r\npython ./transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py --roberta_checkpoint_path ./checkpoint_best.pt --pytorch_dump_folder_path ./race_base/roberta-convert-checkpoint/", "you will need --classification-head to load the final layer i guess", "Hi, I also come across this issue, have you solved it?", "No, I give up :(", "Hi, I found that --roberta_checkpoint_path should be ./ not ./checkpoint_best.pt", "Is there a way to convert from a PyTorch bin (obtained with Huggin Face) to a Roberta model? When I run this script:\r\n```\r\nfrom fairseq.models.roberta import RobertaModel\r\nroberta= RortaModel.from_pretrained('/path/to/checkpoint/folder/',checkpoint_file='pytorch_model.bin')\r\n```\r\n\r\nI got this error:\r\n```\r\nFile \"/usr/local/lib/python3.6/dist-packages/fairseq/checkpoint_utils.py\", line 162, in load_checkpoint_to_cpu\r\n args = state[\"args\"]\r\nKeyError: 'args'\r\n```\r\n\r\nI think I have the opposite problem, but I don't find a script for this.", "@paulthemagno https://github.com/pytorch/fairseq/issues/1514#issuecomment-567934059", "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", "`--roberta_checkpoint_path` should be path to folder with checkpoint named as model.pt.\r\nI am getting:\r\n```\r\nTraceback (most recent call last):\r\n File \"convert_roberta_original_pytorch_checkpoint_to_pytorch.py\", line 176, in <module>\r\n convert_roberta_checkpoint_to_pytorch(\r\n File \"convert_roberta_original_pytorch_checkpoint_to_pytorch.py\", line 54, in convert_roberta_checkpoint_to_pytorch\r\n roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)\r\n File \"venv/lib/python3.8/site-packages/fairseq/models/roberta/model.py\", line 244, in from_pretrained\r\n x = hub_utils.from_pretrained(\r\n File \"venv/lib/python3.8/site-packages/fairseq/hub_utils.py\", line 70, in from_pretrained\r\n models, args, task = checkpoint_utils.load_model_ensemble_and_task(\r\n File \"venv/lib/python3.8/site-packages/fairseq/checkpoint_utils.py\", line 279, in load_model_ensemble_and_task\r\n state = load_checkpoint_to_cpu(filename, arg_overrides)\r\n File \"venv/lib/python3.8/site-packages/fairseq/checkpoint_utils.py\", line 231, in load_checkpoint_to_cpu\r\n setattr(args, arg_name, arg_val)\r\nAttributeError: 'NoneType' object has no attribute 'bpe'\r\n```", "@djstrong I'm getting the same issue. were you able to get around this problem?" ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1849?src=pr&el=h1) Report\n> Merging [#1849](https://codecov.io/gh/huggingface/transformers/pull/1849?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0477b307c7501ea76e01b03cb387a2312db752b3?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1849?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1849 +/- ##\n=======================================\n Coverage 84.08% 84.08% \n=======================================\n Files 97 97 \n Lines 14316 14316 \n=======================================\n Hits 12037 12037 \n Misses 2279 2279\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1849?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/1849?src=pr&el=footer). Last update [0477b30...a76db2e](https://codecov.io/gh/huggingface/transformers/pull/1849?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Looks good to me, thanks for looking into it!", "You are welcome and thanks @LysandreJik for interested. Do you mind approve and merge, if there is no problem?" ]

[]

[]

[ "Indeed, this seems to be a problem. Thanks for the PR!", "fixed at #1849 " ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1847?src=pr&el=h1) Report\n> Merging [#1847](https://codecov.io/gh/huggingface/transformers/pull/1847?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0477b307c7501ea76e01b03cb387a2312db752b3?src=pr&el=desc) will **increase** coverage by `<.01%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1847?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1847 +/- ##\n==========================================\n+ Coverage 84.08% 84.08% +<.01% \n==========================================\n Files 97 97 \n Lines 14316 14317 +1 \n==========================================\n+ Hits 12037 12038 +1 \n Misses 2279 2279\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1847?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1847/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3V0aWxzLnB5) | `89.67% <100%> (+0.02%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1847?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/1847?src=pr&el=footer). Last update [0477b30...07bda77](https://codecov.io/gh/huggingface/transformers/pull/1847?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I think this bug is already fixed on master.", "It should be fixed the latest release (2.2.1).\r\nFeel free to reopen if it's not the case." ]

[]

[]

[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1846?src=pr&el=h1) Report\n> Merging [#1846](https://codecov.io/gh/huggingface/transformers/pull/1846?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0477b307c7501ea76e01b03cb387a2312db752b3?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/1846?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #1846 +/- ##\n=======================================\n Coverage 84.08% 84.08% \n=======================================\n Files 97 97 \n Lines 14316 14316 \n=======================================\n Hits 12037 12037 \n Misses 2279 2279\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/1846?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/1846/diff?src=pr&el=tree#diff-dHJhbnNmb3JtZXJzL21vZGVsaW5nX3V0aWxzLnB5) | `89.64% <100%> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/1846?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/1846?src=pr&el=footer). Last update [0477b30...d08a338](https://codecov.io/gh/huggingface/transformers/pull/1846?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Yes, thanks!" ]

[]

[]

[ "Thanks!" ]