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DanielPFlorian

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Transformers-Github-Issues

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4112?src=pr&el=h1) Report\n> Merging [#4112](https://codecov.io/gh/huggingface/transformers/pull/4112?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d713cfc5ebfb1ed83de1fce55dd7279f9db30672&el=desc) will **decrease** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4112?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4112 +/- ##\n==========================================\n- Coverage 78.84% 78.84% -0.01% \n==========================================\n Files 114 114 \n Lines 18688 18688 \n==========================================\n- Hits 14735 14734 -1 \n- Misses 3953 3954 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4112?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4112/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.14% <0.00%> (-0.42%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4112?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/4112?src=pr&el=footer). Last update [d713cfc...a442791](https://codecov.io/gh/huggingface/transformers/pull/4112?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Really cool. [Model page](https://huggingface.co/wptoux/albert-chinese-large-qa)" ]

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[ "The encoder-decoder or bart may be what you want.", "I think you should take a look into the ``encoder-decoder`` framework: https://huggingface.co/transformers/model_doc/encoderdecoder.html", "Note that currently only Bert2Bert is possible." ]

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[ "Uh, I guess it should be `reshape(-1, 1)` instead of `reshape(-1,1)` regarding code quality issues, but I'm not sure whether it's the correct fix.", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4109?src=pr&el=h1) Report\n> Merging [#4109](https://codecov.io/gh/huggingface/transformers/pull/4109?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d713cfc5ebfb1ed83de1fce55dd7279f9db30672&el=desc) will **not change** coverage.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4109?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4109 +/- ##\n=======================================\n Coverage 78.84% 78.84% \n=======================================\n Files 114 114 \n Lines 18688 18688 \n=======================================\n Hits 14735 14735 \n Misses 3953 3953 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4109?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/4109/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `74.94% <100.00%> (ø)` | |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4109/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.14% <0.00%> (-0.42%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4109/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.77% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4109?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/4109?src=pr&el=footer). Last update [d713cfc...e12b3d5](https://codecov.io/gh/huggingface/transformers/pull/4109?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4107?src=pr&el=h1) Report\n> Merging [#4107](https://codecov.io/gh/huggingface/transformers/pull/4107?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/58cca47c16149e43d1b516623d59e3c5d97f695e&el=desc) will **decrease** coverage by `1.21%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4107?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4107 +/- ##\n==========================================\n- Coverage 77.83% 76.62% -1.22% \n==========================================\n Files 141 141 \n Lines 24634 24634 \n==========================================\n- Hits 19175 18876 -299 \n- Misses 5459 5758 +299 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4107?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `95.00% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.04% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `26.92% <0.00%> (-68.47%)` | :arrow_down: |\n| [src/transformers/tokenization\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25feGxtLnB5) | `16.26% <0.00%> (-66.67%)` | :arrow_down: |\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `68.14% <0.00%> (-25.67%)` | :arrow_down: |\n| [src/transformers/tokenization\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdHJhbnNmb194bC5weQ==) | `33.56% <0.00%> (-8.93%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `82.95% <0.00%> (-2.26%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `92.44% <0.00%> (-1.17%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `75.82% <0.00%> (-0.18%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/4107/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `98.76% <0.00%> (+32.51%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4107?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/4107?src=pr&el=footer). Last update [58cca47...21fae42](https://codecov.io/gh/huggingface/transformers/pull/4107?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Creating new one with all checks passed." ]

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[ "Closing in favor of #4154 " ]

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[ "Hi all\r\n\r\nI am gonna run \"run_language_modeling.py\" on 1 GPU 1080 Ti \r\nand loading distill bert from directory \r\n\r\nand using fp16 , apex\r\n\r\nit is run OK \r\nand i am getting trace back of \r\n\r\n\r\n05/01/2020 22:24:38 - WARNING - __main__ - Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: store_true\r\n05/01/2020 22:24:38 - INFO - transformers.configuration_utils - loading configuration file ./save_pre_trn_model/config.json\r\n05/01/2020 22:24:38 - INFO - transformers.configuration_utils - Model config DistilBertConfig {\r\n \"_num_labels\": 2,\r\n \"activation\": \"gelu\",\r\n \"architectures\": [\r\n \"DistilBertModel\"\r\n ],\r\n \"attention_dropout\": 0.1,\r\n \"bad_words_ids\": null,\r\n \"bos_token_id\": null,\r\n \"decoder_start_token_id\": null,\r\n \"dim\": 768,\r\n \"do_sample\": false,\r\n \"dropout\": 0.1,\r\n \"early_stopping\": false,\r\n \"eos_token_id\": null,\r\n \"finetuning_task\": null,\r\n \"hidden_dim\": 3072,\r\n \"id2label\": {\r\n \"0\": \"LABEL_0\",\r\n \"1\": \"LABEL_1\"\r\n },\r\n \"initializer_range\": 0.02,\r\n \"is_decoder\": false,\r\n \"is_encoder_decoder\": false,\r\n \"label2id\": {\r\n \"LABEL_0\": 0,\r\n \"LABEL_1\": 1\r\n },\r\n \"length_penalty\": 1.0,\r\n \"max_length\": 20,\r\n \"max_position_embeddings\": 512,\r\n \"min_length\": 0,\r\n \"model_type\": \"distilbert\",\r\n \"n_heads\": 12,\r\n \"n_layers\": 6,\r\n \"no_repeat_ngram_size\": 0,\r\n \"num_beams\": 1,\r\n \"num_return_sequences\": 1,\r\n \"output_attentions\": false,\r\n \"output_hidden_states\": false,\r\n \"output_past\": true,\r\n \"pad_token_id\": 0,\r\n \"prefix\": null,\r\n \"pruned_heads\": {},\r\n \"qa_dropout\": 0.1,\r\n \"repetition_penalty\": 1.0,\r\n \"seq_classif_dropout\": 0.2,\r\n \"sinusoidal_pos_embds\": false,\r\n \"task_specific_params\": null,\r\n \"temperature\": 1.0,\r\n \"tie_weights_\": true,\r\n \"top_k\": 50,\r\n \"top_p\": 1.0,\r\n \"torchscript\": false,\r\n \"use_bfloat16\": false,\r\n \"vocab_size\": 30000\r\n}\r\n", "Is it OK to see \r\n16-bits training:True (i have written \"store_true\" in the code of it instead of True but those are doing the same thing)\r\nand\r\n\"use_bfloat16\": false\r\n\r\nwith each other ?\r\n\r\nwhat does \"use_bfloat16\": false means ?", "[As far as I can see](https://github.com/huggingface/transformers/search?q=use_bfloat16&type=Code) `bfloat16` is only relevant for Tensorflow XLNet, so no need to worry.", "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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[ "@simonepri can you point to the examples that need fixing?", "https://github.com/huggingface/transformers/blob/master/examples/glue/run_pl_glue.py\r\nhttps://github.com/huggingface/transformers/blob/master/examples/ner/run_pl_ner.py\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "I also have this error, anyone has the solution?" ]

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[ "I don't fully understand the question, but love the color theme :)", "> I don't fully understand the question, but love the color theme :)\r\n\r\nHey thanks😄its cobalt on vscode. Have edited the question with tldr pls let me know on it.", "That being said, please don't use screenshots. They are hard to read (especially on phones) and make it impossible to copy-and-paste your code. Use [code blocks](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks) instead. I also don't quite understand your question. Do you have a general question about how attention works? Please use [Stack Overflow](https://stackoverflow.com/) for this, as the template clearly mentions.", "> That being said, please don't use screenshots. They are hard to read (especially on phones) and make it impossible to copy-and-paste your code. Use [code blocks](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks) instead. I also don't quite understand your question. Do you have a general question about how attention works? Please use [Stack Overflow](https://stackoverflow.com/) for this, as the template clearly mentions.\r\n\r\nSure will follow the guidelines, closing the issue." ]

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[ "Awesome :-) " ]

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[ "Gunna merge this at 7pm EST barring objections @thomwolf @julien-c ", "Pinging @thomwolf especially as he'd want to review this imo" ]

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[ "Defaults are at https://github.com/huggingface/transformers/blob/master/src/transformers/pipelines.py#L1459 . There is often a different default for tf and pt (pytorch).", "Closing this as i think the question is resolved (also it's probably a better match for Stack Overflow)", "What is the default model for the 'fill-mask' pipeline? I'm not able to tell from the previous answer in this thread. \r\n\r\nAny assistance much appreciated.", "The defaults are defined [here](https://github.com/huggingface/transformers/blob/master/src/transformers/pipelines.py#L2987-L3087). The `fill-mask` pipeline uses the `distilroberta-base` checkpoint.", "The above answers are out of date.\r\nThe defaults are now defined in [pipelines/__init__.py](https://github.com/huggingface/transformers/blob/master/src/transformers/pipelines/__init__.py) (in the values of the SUPPORTED_TASKS dictionary)." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4095?src=pr&el=h1) Report\n> Merging [#4095](https://codecov.io/gh/huggingface/transformers/pull/4095?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b8686174be75220d2c26a961597a39ef4921b616&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `50.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4095?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4095 +/- ##\n=======================================\n Coverage 78.84% 78.84% \n=======================================\n Files 114 114 \n Lines 18691 18693 +2 \n=======================================\n+ Hits 14737 14739 +2 \n Misses 3954 3954 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4095?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4095/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.68% <50.00%> (-0.15%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4095/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.61% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4095/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `43.90% <0.00%> (+0.34%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4095/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.55% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4095?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/4095?src=pr&el=footer). Last update [b868617...d506143](https://codecov.io/gh/huggingface/transformers/pull/4095?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Indeed :)", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi, I can't reproduce this on `master`, could you upgrade your library to the latest version and let me know if you face the same issue?", "I think torch-1.5.0 and pytorch-transformers-1.2.0 are the latest versions, no?\r\nI upgraded to python3.7 and tried the above again and still get the same issue. ", "That's because `pytorch-transformers` became `transformers` in September!", "Thanks! It works now. Sorry for asking such a dum question...\r\n", "No worries, gald you could make it work!", "Cool! Problem solved. Will close this issue. \r\nNice weekend!", "> That's because `pytorch-transformers` became `transformers` in September!\r\n\r\nwhat does that mean? Sorry, I didn't get your point and I have the same issue.", "> > That's because `pytorch-transformers` became `transformers` in September!\r\n> \r\n> what does that mean? Sorry, I didn't get your point and I have the same issue.\r\n\r\nInstead of install and use pytorch-transformers, install and use transformers. For examples,\r\n\r\n> from transformers import *" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4093?src=pr&el=h1) Report\n> Merging [#4093](https://codecov.io/gh/huggingface/transformers/pull/4093?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b8686174be75220d2c26a961597a39ef4921b616&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4093?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4093 +/- ##\n=======================================\n Coverage 78.84% 78.85% \n=======================================\n Files 114 114 \n Lines 18691 18691 \n=======================================\n+ Hits 14737 14738 +1 \n+ Misses 3954 3953 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4093?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4093/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `43.90% <0.00%> (+0.34%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4093?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/4093?src=pr&el=footer). Last update [b868617...d0f7228](https://codecov.io/gh/huggingface/transformers/pull/4093?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Not sure when it got broken, but this looks ok at first glance to me. I'll pull it down tonight as a sanity check to run it. \r\n\r\nThank you! ", "LGTM too 😄 Thank you again, @simonepri " ]

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[ "+1. I'm also confused on how to structure the lm_labels and the decoder_input_ids.", "Given T5's universal text-to-text objective, I'm under the impression that the [T5 summarization](https://github.com/huggingface/transformers/tree/master/examples/summarization/t5) example should be applicable for all T5 tasks, as long as the input and target sequences are correctly structured for the specified task. Hope this can be confirmed!\r\n\r\nSample input and target structures for specific tasks can be found at Appendix D in the T5 [paper](https://arxiv.org/pdf/1910.10683.pdf).", "To correctly train T5 one should follow the instructions at https://huggingface.co/transformers/model_doc/t5.html#training . \r\n\r\nFor training, there is no need to provide the `decoder_input_ids` - they are created automatically. One only has to provide the `lm_labels`.\r\n\r\nAs @enzoampil, Appendix D of the paper gives good input/output examples. \r\n\r\n", "@patrickvonplaten What exactly would be the `lm_labels` for something like summarization?\r\n\r\n**Example Usecase**\r\nText: \"ABC\" with maximum length 500\r\nSummary: \"XYZ\" with maximum length 50\r\n\r\nI understand that we can prepare `input_ids` and `attention_mask` like this for the document.\r\n```python\r\nx = tokenizer.encode_plus(sentence, \r\n max_length=500, \r\n pad_to_max_length=True, \r\n return_tensors='pt')\r\n```\r\nNow for the lm_labels i.e. summary, **is simply doing this enough**?\r\n```python\r\nlm_labels = tokenizer.encode(summary, \r\n return_tensors='pt', \r\n max_length=50, \r\n pad_to_max_length=True)\r\n```\r\n\r\nAnd the model as \r\n```python\r\nmodel = T5ForConditionalGeneration.from_pretrained('t5-small')\r\nmodel(input_ids=..., lm_labels=lm_labels, attention_mask=...)\r\n```\r\n\r\nIn your examples folder for summarization, I've seen some preprocessing like this for lm_labels. I didn't understand why this is being done.\r\n```python\r\ny_ids = y[:, :-1].contiguous()\r\nlm_labels = y[:, 1:].clone()\r\nlm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\r\n```", "Hi @amitness,\r\n\r\nFor T5 summarization you will have to append the prefix \"summarize: \" to every input data. But you are more or less right. All you have to do is:\r\n1. Prepare input data\r\n```python\r\nx = tokenizer.encode_plus(\"summarize: \" + sentence, \r\n max_length=500, \r\n pad_to_max_length=True, \r\n return_tensors='pt')\r\n```\r\n2. Prepare labels\r\n```python\r\nlm_labels = tokenizer.encode_plus(summary, \r\n return_tensors='pt', \r\n max_length=50, \r\n pad_to_max_length=True)\r\n```\r\n3. For tokens that are padded (which is only relevant if you train with batch_size > 1) you need to make sure that no loss is calculated on those tokens, so\r\n```python\r\nlm_labels[lm_labels == tokenizer.pad_token_id] = -100\r\n```\r\n\r\nThere is no need to shift the tokens as you show at the end of your comment because T5 does that automatically - see https://github.com/huggingface/transformers/blob/6af3306a1da0322f58861b1fbb62ce5223d97b8a/src/transformers/modeling_t5.py#L1063.\r\n\r\nThis is also explained in https://huggingface.co/transformers/model_doc/t5.html#training .", "Thanks for this clarification @patrickvonplaten ! Finally got it to work from my side 😄 \r\n\r\nGotcha for me was that the `decoder_input_ids` at inference should be prepended by the padding token as stated in the [docs](https://huggingface.co/transformers/model_doc/t5.html#t5forconditionalgeneration) for `T5ForConditionalGeneration`.", "@enzoampil Can you give an example code of what you meant by prepending padding token at inference time?", "@patrickvonplaten Thank you.\r\n\r\nBesides the inbuilt prefix like summarize:, translate: etc, can I train with my own prefix? Let's say there is a prefix called \"simplify:\" and I have pairs of datasets. Is adding the prefix and preparing data in the format you mentioned above enough?", "@amitness \r\n\r\nE.g. in your summarization case, it would look something like:\r\n\r\n```\r\nfrom transformers import T5Tokenizer, T5Model\r\n\r\ntokenizer = T5Tokenizer.from_pretrained('t5-small')\r\nmodel = T5Model.from_pretrained('t5-small')\r\ninput_ids = tokenizer.encode(\"summarize: Hello, my dog is cute\", return_tensors=\"pt\")\r\ndecoder_input_ids = tokenizer.encode(\"<pad>\", return_tensors=\"pt\") \r\noutputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)\r\noutputs[0]\r\n```\r\n\r\nDo note that `T5ForConditionalGeneration` already prepends the padding by default. Above is only necessary if you're doing a forward pass straight from `T5Model`.\r\n\r\nRegarding your question about making your own prefix, yes, you should be able to train on your own prefix. This is the whole point of T5's text-to-text approach. You should be able to specify any problem through this kind of approach (e.g. Appendix D in the T5 paper).", "@enzoampil Makes sense. Thank you so much.", "> @patrickvonplaten Thank you.\r\n> \r\n> Besides the inbuilt prefix like summarize:, translate: etc, can I train with my own prefix? Let's say there is a prefix called \"simplify:\" and I have pairs of datasets. Is adding the prefix and preparing data in the format you mentioned above enough?\r\n\r\nSure, you can train with your own prefix.", "> Thanks for this clarification @patrickvonplaten ! Finally got it to work from my side \r\n> \r\n> Gotcha for me was that the `decoder_input_ids` at inference should be prepended by the padding token as stated in the [docs](https://huggingface.co/transformers/model_doc/t5.html#t5forconditionalgeneration) for `T5ForConditionalGeneration`.\r\n\r\nYeah that's actually a bit hidden in the code. So to clarify: \r\nDuring training, there is no need to prepend the padding token since this is done automatically in T5 when `lm_labels` is provided. \r\nDuring evaluation, one has to prepend the PAD token as you stated in your example. \r\n\r\nAfter training, the mode can be used with the `generate()` method (which actually powers the `summarization`, `translation` and `text-generation` pipeline).\r\nIn the `generate()` method, the padding token is automatically prepended.", "@patrickvonplaten One thing I've noticed is the discrepancy between huggingface's and the original google-research tokenization. \r\n\r\nIn the official colab by the paper authors, they seem to add `</s>` when tokenizing to the end of each text. But, when we use tokenizers from hugging face, it is not added. Not sure if it is a problem or not. \r\nHere is an excerpt from their official [colab](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb#scrollTo=I64TqHGxbOJ2)\r\n```python\r\n'inputs_plaintext': b'trivia question: what is the population of fayetteville north carolina?', 'inputs': array([22377, 822, 10, 125, 19, 8, 2074, 13, 3,\r\n 89, 9, 63, 1954, 1420, 3457, 443, 12057, 9,\r\n 58, 1])\r\n```\r\nYou can see 1 added at the end of the token_ids. But if we tokenize this same sentence with huggingface tokenizer, we don't get 1 at end.\r\n```python\r\ntokenizer.encode('trivia question: what is the population of fayetteville north carolina?')\r\n# [22377, 822, 10, 125, 19, 8, 2074, 13, 3, 89, 9, 63, 1954, 1420, 3457, 443, 12057, 9, 58]\r\n```\r\n\r\nWhen I was prototyping with the models, I tried preparing data like this to solve it. This adds 1 to the end. Not sure if we need to do this or not.\r\n```\r\ntokenizer.encode(\"summarize: Hello world</s>\", return_tensors=\"pt\")\r\n```\r\n", "Yes you are right, you should add the `</s>` token to the end of a sentence. I think this is also shown in the docs: https://huggingface.co/transformers/model_doc/t5.html#training. ", "Thanks to @patrickvonplaten for all clarification and others for their further questions that led to more details on the subject. ", "Hello everyone, \r\n\r\nI am currently working on finetuning the TFT5ForConditionalGeneration model on a parallel dataset.\r\nQuestions:\r\n\r\n1. Can I call model.fit like this - `model.fit([x,y])` where x is input_ids and y is lm_labels?\r\n\r\nIf not, how do I pass in lm_labels and train with the model. \r\n\r\nThanks. ", "@patrickvonplaten ", "For the tensorflow version you have to input `input_ids`, `decoder_input_ids` and `lm_labels` yourself. The model should work fine with the `keras` framework!", "I will soon add more documentation for T5 for tensorflow. It's true that there is not enough documentation for TF at the moment.", "Okay, I would appreciate that. So, do I add the `input_ids`, `decoder_input_ids `and `lm_labels` as keywords when calling `model.fit`(which I doubt) or when do I do that?\r\n\r\n", "I have not looked trained tensorflow using keras `model.fit` function yet. The forward pass in tensorflow's T5 implementation needs both `input_ids` and `decoder_input_ids` as you can see when going through this function: \r\nhttps://github.com/huggingface/transformers/blob/fd2174664c8879c747ada3e6e0a2486858808421/src/transformers/modeling_tf_t5.py#L980\r\n\r\nSo, depending on your code you will have to create `input_ids`, `decoder_input_ids` and `lm_labels` yourself. Feel free to share your code here if you have a working training pipeline for TFT5 :-) ", "Hi Patrick. Got it to work with Pytorch. However, I have a question:\r\n\r\nIs it possible to use a different vocab size with this pretrained model? I have a trained sentence piece model and it only works with this pretrained t5 when I use a beam size of 1. I have manually changed the vocab size by setting `model.config.vocab_size = tokenizer.vocab_size` . However, the beam size problem still persists and it returns a shape mismatch error. \r\n\r\nPlease let me know if this is possible, thanks. \r\n\r\n\r\n", "@patrickvonplaten ", "I think it will work in case the targets pieces from the new vocab is the same in the old one.\r\nBesides, what is the benefit from the pretrained T5 if the sentence piece targets changed ?!!", "Created a little repo for NMT finetuning https://github.com/keleog/finetune_huggingace_t5", "> I have not looked trained tensorflow using keras `model.fit` function yet. The forward pass in tensorflow's T5 implementation needs both `input_ids` and `decoder_input_ids` as you can see when going through this function:\r\n> https://github.com/huggingface/transformers/blob/fd2174664c8879c747ada3e6e0a2486858808421/src/transformers/modeling_tf_t5.py#L980\r\n> \r\n> So, depending on your code you will have to create `input_ids`, `decoder_input_ids` and `lm_labels` yourself. Feel free to share your code here if you have a working training pipeline for TFT5 :-)\r\n\r\nHi @patrickvonplaten, I was able to create a data source with the input data and labels as you described.\r\nNow I'm trying to use that data for keras fit with the loss function `tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)`\r\nThe shape of the labels is (batch_size, seq_len), and I would expect that the model `TFT5ForConditionalGeneration` would return the logits of shape (batch_size, seq_len, vocab_size). However its call method returns this:\r\n` return decoder_outputs + encoder_outputs\r\n`\r\nso I get an error:\r\n `ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 27 array(s), for inputs ['output_1', 'output_2', 'output_3', 'output_4', 'output_5', 'output_6', 'output_7', 'output_8', 'output_9', 'output_10', 'output_11', 'output_12', 'output_13', 'output_14', 'output_15', 'output_16', 'output_17', 'output_18', 'output_19', 'output_20', 'output_21', 'output_22', 'output_23', 'output_24', 'output_25', 'output_26', 'output_27'] but instead got the following list of 1 arrays: [<tf.Tensor 'args_4:0' shape=(32, 128) dtype=int32>]...`\r\n\r\nI can think of two solutions, neither sounds good:\r\n1. override `call` method in a subclass and return only the decoder outputs\r\n2. use a custom loss function that extracts the decoder outputs from the model output\r\n\r\nWhat would you advice?", "Hi @patrickvonplaten ,\r\nI am working on one question answering task using TFT5. I have done a text encoding step.\r\nMy raw input is question and target is the answer shown in the below image\r\n\r\n\r\nHow should I configure input so that I can pass it in model.fit() method\r\nlike this way!! I am able to get input_id and input mask.\r\n\r\n```\r\nmodel = TFT5ForConditionalGeneration.from_pretrained(\"t5-small\")\r\noptimizer = keras.optimizers.Adam(lr=5e-5)\r\nmodel.compile(optimizer=optimizer)\r\nmodel.fit(\r\n x_train,\r\n y_train,\r\n epochs=1, \r\n verbose=2,\r\n batch_size=2,\r\n)\r\n```\r\n\r\nHere is the [Colab Notebook](https://github.com/bhadreshpsavani/EfficientQAExperiments/blob/master/NaturalQAT5TF.ipynb)", "I'll start working on a TFT5 notebook this week. Related issues: \r\nhttps://discuss.huggingface.co/t/how-to-train-tft5forconditionalgeneration-model/888\r\nhttps://discuss.huggingface.co/t/how-to-train-t5-with-tensorflow/641/6\r\nhttps://github.com/huggingface/transformers/issues/6876", "Hello @patrickvonplaten \r\n\r\nI am working with T5 for paraphrase generation, but I wanted to know if there is a way to use my own custom defined loss function for training? \r\n\r\n", "Sure! You can just output the language head logits with T5 and build your own loss with it :-) " ]

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[ "I don't think we have any character-level language models available on [huggingface.co/models](https://huggingface.co/models), but they should be pretty straightforward to train.\r\n\r\nBTW I'm sure you saw it already but [PyTorch's nn.Transformer tuto](https://pytorch.org/tutorials/beginner/transformer_tutorial.html) is a char-level language model.", "Hi, that's actually is word-level model.\r\nDo you know of any pretrained bidirectional transformer-based character level language models that I can use?", "No I'm not aware of any pretrained one.", "We will soon have a pretrained ReformerLM model on character level ", "@patrickvonplaten just for my curiosity would this be the model trained on the \"Crime and Punishment\" from their Colab (but the vocab is not char-only), or do you have an own trained model 🤔 ", "Yeah that was my bad definition of char-only I guess :D. The vocab has 320 tokens, so it's more like on \"very\" small word units level. \r\n\r\nExample: \r\n```python \r\ntok = ReformerTokenizer.from_pretrained(\"google/reformer-crime-and-punishment\")\r\ntokens = tok.encode(\"This is a test sentence\") # [108, 265, 24, 111, 4, 3, 249, 7, 76, 25, 69]\r\nprint([tok.decode(token) for token in tokens]) # ['T', 'h', 'is', 'is', 'a', 't', 'est', 's', 'ent', 'en', 'ce']\r\n```", "\"True\" char-level is cool because obviously the tokenizer is then pretty trivial :)", "Btw, we now have a \"True\" char-level reformer model here: https://huggingface.co/google/reformer-enwik8 :-) ", "Any chance of having a TF version of the Reformer model?", "Yes in ~2 month I would guess", "@patrickvonplaten Is there any notebook/doc on how to fine tune the char level model using reformer-enwiki8 model? Having a doc showing how to pass training data to fine tune it would be helpful.", "You should be able to leverage the code shown on the model card of reformer-enwik8 here: https://huggingface.co/google/reformer-enwik8#reformer-language-model-on-character-level-and-trained-on-enwik8 . It shows how data is passed to the model.", "I don't understand how to use that code in place of a Tokenizer object. For example, to train a masked language model in [this example script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) the tokenizer is used in the data collator to do a lot more than just mapping to `input_ids`. I've tried loading tokenizers from character level models like CANINE but that doesn't work either. I could try instantiating an untrained tokenizer and passing all the characters in the corpus as special characters [like this person did](https://discuss.huggingface.co/t/character-level-tokenizer/12450) but it seems like I shouldn't have to do that to achieve something so basic." ]

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[ "Awesome! These instructions might help: \r\n\r\nhttps://github.com/huggingface/transformers#Quick-tour-of-model-sharing", "Hi @LoreDema, thanks for sharing GePpeTto, it's really cool!\r\n\r\nYes you can juste create a user account and an organization on huggingface.co and share the weights there. (and add a model card here if you can)", "Awesome, just pushed the model and created a pull request for the card https://github.com/huggingface/transformers/pull/4099" ]

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[ "Hey @zirlman,\r\n\r\nThanks a lot for catching the error and the detailed error description. The PR that will fix the error is linked to the issue :-) ", "@patrickvonplaten that's great. Thank you 😁" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4087?src=pr&el=h1) Report\n> Merging [#4087](https://codecov.io/gh/huggingface/transformers/pull/4087?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e73595bd649a149879816a59b56f57c8b37c73d0&el=desc) will **increase** coverage by `0.92%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4087?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4087 +/- ##\n==========================================\n+ Coverage 77.99% 78.92% +0.92% \n==========================================\n Files 114 114 \n Lines 18667 18667 \n==========================================\n+ Hits 14559 14732 +173 \n+ Misses 4108 3935 -173 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4087?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4087/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `43.90% <0.00%> (+0.69%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4087/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.61% <0.00%> (+0.82%)` | :arrow_up: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/4087/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/4087/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/4087/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/4087/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/4087/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/4087?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/4087?src=pr&el=footer). Last update [e73595b...a4a673a](https://codecov.io/gh/huggingface/transformers/pull/4087?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "You should rebase instead of merging `upstream/master`, this would make merging this easier :)\r\n\r\nAnyways, cherrypicked in 8829ace4aac6b2ebef0eb8753e6b58d7eafc7734, thanks!" ]

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[ "Do you have a link to a TensorBoard or any other experiment tracking for your training?\r\n\r\nYou can also add the `--evaluate_during_training` flag to see the evolution of the eval metric during training.", "Have you tried with fewer epochs? (e.g ```--num_train_epochs 3.0``` as stated in the repo's example)\r\n\r\nAlso what you need to consider is seed hyper-parameter.\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "@patrickvonplaten ", "Can you install transformers from source?", "Thanks worked for biomed-roberta\r\n\r\nHowever \r\n\r\n```\r\nnlp1 = pipeline(\"fill-mask\", model=\"allenai/scibert_scivocab_cased\", tokenizer=\"allenai/scibert_scivocab_cased\")\r\n\r\nnlp1(\"coinfection by multiple quasispecies is not uncommon in human <mask>, and that passage to Vero cells may either generate new mutations at a low rate, or titrates out one quasispecies in the transition.\")\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/nas/home/tuhinc/miniconda3/envs/robertamed/lib/python3.7/site-packages/transformers/pipelines.py\", line 743, in __call__\r\n masked_index = (input_ids == self.tokenizer.mask_token_id).nonzero().item()\r\nValueError: only one element tensors can be converted to Python scalars\r\n```", "It’s not using the same mask token :)\r\n\r\nTry [MASK] here" ]

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[ "This is quite a general question, and I fear there is no one right answer. You can see the \"best layer to use\" as another hyperparameter to tune in your task: the right value will depend on your task, dataset, and query. Oftentimes paper do some kind of evaluation of this. For instance, in the original [BERT paper](https://arxiv.org/pdf/1810.04805.pdf) they use BERT for feature extraction and evaluate which layer combinations worked best (Table 7). They find that the second-to-last layer performs better than the last layer, but that a concatenation of the last four layers is best. But again, that is using BERT, on a specific task, with a specific dataset. Your results may differ, so it's best to test and tune to your problem.", "@BramVanroy @kaushaltrivedi How do you customize what layers go into the feature representation (via the pipeline)?" ]

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[ "In my past experience, I noticed that masked tokens would get very low attention scores(that's what self attention's supposed to do) and would not contribute to the model's accuracy. I wonder how would it be in a transformer architecture.", "Not all models requires this feature, mainly Roberta so it includes XLM-R, Camembert, etc.", "I think your question is why are the `attention_mask` model inputs optional?\r\nThis is because you need attention masks only when padding. If you're not batching your inputs, you do not need to pad and therefore do not need to specify an attention mask.", "@LysandreJik I wonder if there is padding and the user does not provide `attention_mask`, does the model think there is no padding or there is a mechanism to figure it out uder the hood (maybe treat token ID 0 as padded token). I am just trying to fully understand the model's behavior. Thanks", "No, if you don't provide any `attention_mask` then the model will attend to every token the same way. You absolutely should use the `attention_mask` as soon as you're padding, or the model will output flawed results!", "Got it, thanks a lot." ]

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

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[ "I would use the version installed from source but training a tokenizer is substantially slower than the pip install version. When I run the code snippet below, the pip install version finishesin ~5 minutes but the install-from-source version takes 20+ (I didn't wait for it to finish). For some reason there's a new \"Reading files\" stage which takes forever to finish.\r\n\r\n```import os\r\nfrom tokenizers import ByteLevelBPETokenizer, BertWordPieceTokenizer\r\n\r\n\r\ntokenizer = ByteLevelBPETokenizer(lowercase=False)\r\n\r\npaths = [\"./data.txt\"]\r\n\r\ntokenizer.train(files=paths, vocab_size=52000, min_frequency=2, special_tokens=[\r\n \"<s>\",\r\n \"<pad>\",\r\n \"</s>\",\r\n \"<unk>\",\r\n \"<mask>\",\r\n])\r\n\r\nout_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"token\")\r\nos.makedirs(out_path, exist_ok=True)\r\n\r\ntokenizer.save(out_path)```", "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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[ "How did you resolve this? I am facing this issue as well.", "@shikharsingla, what is your exact bug? Which tokenizer are you using, with which code?", "Just saw your issue https://github.com/huggingface/transformers/issues/4868, looking into it." ]

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[ "Hi @antoniomastro1996, \r\n\r\nCan you post your environment information here as written in the \"Issue\" template. Also are you sure that the path: `/Users/antonio/Downloads/tf_path/model.ckpt-306400.data-00000-of-00002` exist? \r\n\r\nAlso can you add the link the Raffel example script? ", "Hi @patrickvonplaten this is my environment:\r\n\r\n<b>\r\n- `transformers` version: 2.8.0<br>\r\n- Platform: Darwin-19.4.0-x86_64-i386-64bit<br>\r\n- Python version: 3.7.6<br>\r\n- PyTorch version (GPU?): 1.4.0 (False)<br>\r\n- Tensorflow version (GPU?): 2.2.0-rc2 (False)<br>\r\n- Using GPU in script?: no<br>\r\n- Using distributed or parallel set-up in script?: no<br>\r\n</b>\r\n<br>\r\n\r\nAbout the training process, I have adapted the following Colab link: https://tiny.cc/t5-colab\r\nThis is the one provided by Raffel et all on their GitHub page.\r\n\r\nAbout the path, yes is fine, it exists. To me seem strange the error manifest itself on this global_step param. Furthermore I have seen the t5_modeling code and this global_step alongside with other variables is in a sort of black list if we perform fine-tuning.", "Hi @patrickvonplaten\r\nI've just solved, I changed the pre-trained model and worked fine.\r\n\r\nHowever, if I try to load the model with the following command:\r\nmodel = T5ForConditionalGeneration.from_pretrained(\"/content/drive/My Drive/t5_model.bin\")\r\n\r\nI get the following error: \r\n<b>UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte</b>\r\n\r\nLike I said in the previous comment, I've used this config file\r\nhttps://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json\r\nwhere I have just removed the part related to the task in such a way the final output appears like this:\r\n\r\n{\r\n \"architectures\": [\r\n \"T5WithLMHeadModel\"\r\n ],\r\n \"d_ff\": 2048,\r\n \"d_kv\": 64,\r\n \"d_model\": 512,\r\n \"decoder_start_token_id\": 0,\r\n \"dropout_rate\": 0.1,\r\n \"eos_token_id\": 1,\r\n \"initializer_factor\": 1.0,\r\n \"is_encoder_decoder\": true,\r\n \"layer_norm_epsilon\": 1e-06,\r\n \"model_type\": \"t5\",\r\n \"n_positions\": 512,\r\n \"num_heads\": 8,\r\n \"num_layers\": 6,\r\n \"output_past\": true,\r\n \"pad_token_id\": 0,\r\n \"relative_attention_num_buckets\": 32,\r\n \"vocab_size\": 32128\r\n}\r\n\r\nWhere am I going wrong?", "Sorry to answer this late! Loading the model via:\r\n```python\r\nmodel = T5ForConditionalGeneration.from_pretrained(\"/content/drive/My Drive/\")\r\n```\r\n\r\nshould work :-) " ]

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[ "Oh no, there's something wrong with the label alignment (can also be seen in the `config.json`):\r\n\r\n```json\r\n\"id2label\": {\r\n \"0\": \"LABEL_0\",\r\n \"1\": \"LABEL_1\",\r\n \"2\": \"LABEL_2\",\r\n \"3\": \"LABEL_3\",\r\n \"4\": \"LABEL_4\",\r\n \"5\": \"LABEL_5\",\r\n \"6\": \"LABEL_6\",\r\n \"7\": \"LABEL_7\",\r\n \"8\": \"LABEL_8\"\r\n },\r\n \"initializer_range\": 0.02,\r\n \"intermediate_size\": 3072,\r\n \"label2id\": {\r\n \"LABEL_0\": 0,\r\n \"LABEL_1\": 1,\r\n \"LABEL_2\": 2,\r\n \"LABEL_3\": 3,\r\n \"LABEL_4\": 4,\r\n \"LABEL_5\": 5,\r\n \"LABEL_6\": 6,\r\n \"LABEL_7\": 7,\r\n \"LABEL_8\": 8\r\n },\r\n```\r\n\r\nThanks for reporting, I'll try to fix it :)\r\n\r\nIn the meantime, you could use our large model `dbmdz/bert-large-cased-finetuned-conll03-english` 😅", "Also I forgot to add, I see the [CLS], [SEP] tokens and also words like \"is\" , \"a\" in the NER results which are not present when I use the default dbmdz/bert-large-cased-finetuned-conll03-english.", "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", "https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/bert-base-cased-finetuned-conll03-english/config.json\r\nThis file needs fixing.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I'm having the same problem with the model: dbmdz/bert-base-multilingual-cased-finetuned-conll03-spanish", "> I'm having the same problem with the model: dbmdz/bert-base-multilingual-cased-finetuned-conll03-spanish\r\n\r\nOne fix is to download the model and config.json to your local and then just manually edit the \"config.json\" file.\r\nThis is what it should actually look like :- \r\nhttps://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/bert-large-cased-finetuned-conll03-english/config.json", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "fixed in Marian PR" ]

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[ "Also remove the now-unused bias variable on https://github.com/huggingface/transformers/blob/50c9c3e98235d447a7a2d0efebc9ff4e426fce2d/src/transformers/modeling_tf_albert.py#L467", "You're absolutely right, thanks @jarednielsen !" ]

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[ "Which language do you want to use?\r\nYou can load a model that pre-trained with a certain language or you can use mBERT / XLM instead.", "> Which language do you want to use?\r\n> You can load a model that pre-trained with a certain language or you can use mBERT / XLM instead.\r\n\r\nThank for reply\r\ni want to use BERT for Vietnamese language", "Unfortunately, there is no BERT for Vietnamese available for now.\r\nYou will have to wait for somebody to pre-trained the Vietnamese model or you can use Multilingual-BERT (mBERT) instead:\r\n```bert-base-multilingual-cased``` or ```bert-base-multilingual-uncased```\r\n\r\nThese models were pre-trained with [top 100 languages in Wikipedia](https://meta.wikimedia.org/wiki/List_of_Wikipedias)." ]

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[ "So the issue is fixed for you then? " ]

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[ "Hi, our API has its own saving function: `model.save_pretrained(\"directory\")`, which saves the model alongside its configuration file. Can you let me know if this method fits your use case?", "Hi, using that method I've managed to save the model. Unfortunately, when I convert the model into `model.json` load it later in my JS file I receive the following error: \r\n```\r\nTypeError: Cannot read property 'model_config' of null\r\n at models.ts:287\r\n at common.ts:14\r\n at Object.next (common.ts:14)\r\n at a (common.ts:14)\r\n```\r\nBellow you can steps I've followed (to try) to load the model into the JS file\r\n\r\nModel saving:\r\n```\r\nmodel_path = \"/content/t5_base_model\"\r\ntfjs_path = \"/content/t5_base_model_js\"\r\n\r\nmodel.save_pretrained(model_path)\r\nmodel_path += \"/tf_model.h5\"\r\n```\r\nConversion:\r\n```\r\n!tensorflowjs_converter --input_format keras \\\r\n $model_path \\\r\n $tfjs_path \r\n```\r\n\r\nJS file:\r\n```\r\nvar model;\r\nconst model_path = \"../libraries/models/t5_base_model_js/model.json\";\r\n\r\ndocument.querySelector(\"#msg\").innerText = \"[LOADING TF MODEL]\";\r\ntf.loadLayersModel(model_path)\r\n.then((loadedModel) => {\r\n model = loadedModel;\r\n document.querySelector(\"#msg\").innerText = \"[MODEL LOADED]\";\r\n}).catch(err => console.log(err));\r\n```\r\n\r\nI assume the problem occurs because `model.save_pretrained(dir)` saves the config in a separate file. I've tried to figure out if maybe `tf-converter` has a parameter for additional files to bundle into model.json, but with no success. @LysandreJik any idea what I could do to fix this error?\r\n\r\nP.S. I've tried to load mobilenet from TF-Hub to make sure my script works fine and it successfully loaded it.\r\n\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Any updates on this? Stuck on saving mobilebert as a tf layers model" ]

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[ "Hello! You're pointing the script to a file or folder `bert-base-256/checkpoint-10000`. Is that a folder containing model file, or is that a file in itself?", "> Hello! You're pointing the script to a file or folder `bert-base-256/checkpoint-10000`. Is that a folder containing model file, or is that a file in itself?\r\n\r\nIt is the folder created by the script containing these files:\r\n- optimizer.pt and scheduler.pt\r\n- pytorch_model.bin and training_args.bin\r\n- config.json\r\n\r\n", "Ah, I think I know where the issue stems from. We've changed the paradigm for our examples, which now rely on a `Trainer` abstraction. Until c811526 five days ago, the tokenizer was unfortunately not saved, which is fixed now.\r\n\r\nI would recommend you use the lastest script so that it doesn't happen anymore. \r\n\r\nTo fix this error, you could manually save your tokenizer in that folder as so:\r\n\r\n```py\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")\r\ntokenizer.save_pretrained(\"/content/drive/My Drive/Colab Notebooks/NER/Batteria/transformers-master_2020_04_27/examples/ner/bert-base-256/checkpoint-10000\")\r\n```\r\n\r\nThis will save the tokenizer file in the appropriate folder. Please keep in mind that this is to reload the original `bert-base-cased` tokenizer. If you have modified your tokenizer in any way, you should save that tokenizer in the aforementioned folder. Please let me know if I can be of further help!", "> Ah, I think I know where the issue stems from. We've changed the paradigm for our examples, which now rely on a `Trainer` abstraction. Until [c811526](https://github.com/huggingface/transformers/commit/c81152600452ad1bec4ab705356788d29a3573ee) five days ago, the tokenizer was unfortunately not saved, which is fixed now.\r\n> \r\n> I would recommend you use the lastest script so that it doesn't happen anymore.\r\n> \r\n> To fix this error, you could manually save your tokenizer in that folder as so:\r\n> \r\n> ```python\r\n> from transformers import BertTokenizer\r\n> \r\n> tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")\r\n> tokenizer.save_pretrained(\"/content/drive/My Drive/Colab Notebooks/NER/Batteria/transformers-master_2020_04_27/examples/ner/bert-base-256/checkpoint-10000\")\r\n> ```\r\n> \r\n> This will save the tokenizer file in the appropriate folder. Please keep in mind that this is to reload the original `bert-base-cased` tokenizer. If you have modified your tokenizer in any way, you should save that tokenizer in the aforementioned folder. Please let me know if I can be of further help!\r\n\r\nI downloaded transformers library on 27 April, two days ago, after [c811526](https://github.com/huggingface/transformers/commit/c81152600452ad1bec4ab705356788d29a3573ee). So, does this problem still persist?", "@ChessMateK \r\nThis might sound a bit silly but did you check if the drive was mounted in colab when you ran that command ?", "@patil-suraj Yes, I checked. Furthermore, if I insert as output path the folder where all the checkpoints are saved, even if they are not loaded, the \"startup\" time of the script increases considerably.", "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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[ "That would be awesome – and might be of interest to @mfuntowicz among others.\r\n\r\nWe'll help you if needed.", "Awesome! Thanks for the upvotes and interest, very appreciated\r\n@julien-c, thanks for your support of the idea. \r\n\r\nI will create a PR and get started on a basic first version and cross-reference it here. \r\n@all: Feel free to chime in on the PR to help shape the interface :)", "@MFreidank I was curious if you had an update here! I've implemented a version of a PyTorch serve handler that operates on the https://huggingface.co/transformers/main_classes/pipelines.html abstraction for a currently unreleased project, as opposed to a distinct model or tokenizer. I think it's a nice abstraction for this use-case, what do you think? ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi @ivokun – this was actually on purpose as someone was using it as an API\r\n\r\nWe'll secure it down the line if it gets abused." ]

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[ "This seems reasonable, what do you think @suvrat96?", "(never mind the failing CI test)", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Yes, you can use the `run_language_modeling.py` script for pre-training e.g. BERT from scratch:\r\n\r\nhttps://huggingface.co/transformers/examples.html#language-model-training\r\n\r\n(Just leave the `model_name_or_path` parameter empty for pre-training from scratch)", "@stefan-it thank u.", "@xealml Curious whether you managed to train XLNet? If so, any pointers you could share? ", "@jbmaxwell @xealml I am getting lost on this as well. How do you train XLNet from scratch? I can't find any field for inputting raw data to it" ]

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[ "Hi @lx-kika, \r\n\r\nCan you post a code snippet that calculates the different loss types you mention? \r\nAlso, I think it should be fine to just use the first method to calculate the loss - I don't really see a reason as why to use the second method, no? " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4066?src=pr&el=h1) Report\n> Merging [#4066](https://codecov.io/gh/huggingface/transformers/pull/4066?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6faca88ee0c472de8207e648b0999a1ee01ff127&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4066?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4066 +/- ##\n=======================================\n Coverage 78.91% 78.92% \n=======================================\n Files 114 114 \n Lines 18670 18670 \n=======================================\n+ Hits 14734 14735 +1 \n+ Misses 3936 3935 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4066?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4066/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.93% <0.00%> (+0.16%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4066?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/4066?src=pr&el=footer). Last update [6faca88...2af7f81](https://codecov.io/gh/huggingface/transformers/pull/4066?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/4065?src=pr&el=h1) Report\n> Merging [#4065](https://codecov.io/gh/huggingface/transformers/pull/4065?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6faca88ee0c472de8207e648b0999a1ee01ff127&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4065?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4065 +/- ##\n=======================================\n Coverage 78.91% 78.91% \n=======================================\n Files 114 114 \n Lines 18670 18670 \n=======================================\n Hits 14734 14734 \n Misses 3936 3936 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4065?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/4065?src=pr&el=footer). Last update [6faca88...5f06af6](https://codecov.io/gh/huggingface/transformers/pull/4065?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/4064?src=pr&el=h1) Report\n> Merging [#4064](https://codecov.io/gh/huggingface/transformers/pull/4064?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6faca88ee0c472de8207e648b0999a1ee01ff127&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4064?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4064 +/- ##\n=======================================\n Coverage 78.91% 78.92% \n=======================================\n Files 114 114 \n Lines 18670 18670 \n=======================================\n+ Hits 14734 14735 +1 \n+ Misses 3936 3935 -1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4064?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4064/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.55% <0.00%> (+0.41%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4064?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/4064?src=pr&el=footer). Last update [6faca88...0c16603](https://codecov.io/gh/huggingface/transformers/pull/4064?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/4063?src=pr&el=h1) Report\n> Merging [#4063](https://codecov.io/gh/huggingface/transformers/pull/4063?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6faca88ee0c472de8207e648b0999a1ee01ff127&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4063?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4063 +/- ##\n=======================================\n Coverage 78.91% 78.91% \n=======================================\n Files 114 114 \n Lines 18670 18670 \n=======================================\n Hits 14734 14734 \n Misses 3936 3936 \n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4063?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/4063?src=pr&el=footer). Last update [6faca88...4030892](https://codecov.io/gh/huggingface/transformers/pull/4063?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/4062?src=pr&el=h1) Report\n> Merging [#4062](https://codecov.io/gh/huggingface/transformers/pull/4062?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6faca88ee0c472de8207e648b0999a1ee01ff127&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4062?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4062 +/- ##\n=======================================\n Coverage 78.91% 78.91% \n=======================================\n Files 114 114 \n Lines 18670 18670 \n=======================================\n Hits 14734 14734 \n Misses 3936 3936 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4062?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4062/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `92.61% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4062/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `91.04% <0.00%> (+0.12%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4062?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/4062?src=pr&el=footer). Last update [6faca88...6f20b16](https://codecov.io/gh/huggingface/transformers/pull/4062?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Have you got two blank lines consecutive? Look at the end of the file, maybe you got an extra blank line or an empty line at the end of the file.", "Additionally, the dataset format must be in a `Token Label` (delimiter is a space) format.\r\n\r\nThe error message shows, that an unexpected label ( `\"`) was found, so I could imagine, that the dataset format is not consistent.\r\n\r\nTo check this, just do the following on your training, development and test set:\r\n\r\n```bash\r\ncat train.txt dev.txt test.txt | grep -v \"^$\" | cut -d \" \" -f 2 | sort | uniq\r\n```\r\n\r\nThis should give you all labels. If you see other tokens, then there's something wrong in the dataset.\r\n\r\n", "This problem also happened to me when I trained with my dataset.\r\n\r\nPossible causes:\r\n1. There is an extra character in labels\r\n1. The word and label is not separated by space but tab space `\\t` <- my main culprit", "Hi can I look into this issue. I am very interested to start contributing in this project", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi, I had the same issue and I solved it by downloading the required files locally with the steps below.\r\n1. Download [vocab.txt](https://github.com/huggingface/transformers/blob/455c6390938a5c737fa63e78396cedae41e4e87e/src/transformers/tokenization_bert_japanese.py#L33), [config.json](https://github.com/huggingface/transformers/blob/455c6390938a5c737fa63e78396cedae41e4e87e/src/transformers/configuration_bert.py#L42), [pytorch_model.bin](https://github.com/huggingface/transformers/blob/455c6390938a5c737fa63e78396cedae41e4e87e/src/transformers/modeling_bert.py#L51) from the source URL\r\n\r\n2. Enter the folder containing the three files in the from_pretrained method\r\ne.g.\r\n```\r\nmodel = BertModel.from_pretrained ('./models/bert-base-japanese/')\r\nconfig = BertConfig('./models/bert-base-japanese/')\r\ntokenizer = BertJapaneseTokenizer.from_pretrained('./models/bert-base-japanese/')\r\n```\r\nwhere\r\n```\r\n─ models\r\n └- bert-base-japanese\r\n ├- vocab.txt\r\n      ├- config.json\r\n      └- pytorch_model.bin\r\n```\r\nI think this is probably an obstacle caused by a change in the path on S3 due to this commit. The version of transformers installed by pip is old and you may be pointing to the wrong path.\r\nhttps://github.com/huggingface/transformers/commit/455c6390938a5c737fa63e78396cedae41e4e87e\r\n\r\nReinstall with the latest version of transformers and it should work.\r\n```\r\ngit clone [email protected]: huggingface/transformers.git\r\npip install ./transformers\r\n```", "I apologize, it's my fault. I `mv`ed files around instead of `copy`ing them as we do usually, so I broke backward compatibility for the `bert-base-japanese` models.\r\n\r\nAs @reo11 said, you'll need to install from source for now. You can also do: \r\n`pip install git+git://github.com/huggingface/transformers.git`\r\n\r\nSorry about that.", "@reo11 Thank you so much!\r\n@julien-c Thank you for your response. Since a lot of us trying to use transformers in production too, please consider having stable workflow. (Anyways you guys doing great!)" ]

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[ "Hi @weiarqq, \r\n\r\nWhere does the code come from? What is the application? What do you mean by \"training of dropout\"? the `training=kwargs.get(\"training\", False)` is a flag for pytorch's dropout to switch it on and off depending on whether you are in training module \"training=True\" or not." ]

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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/4057?src=pr&el=h1) Report\n> Merging [#4057](https://codecov.io/gh/huggingface/transformers/pull/4057?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/c99fe0386be118bceaab1c85cdb8309eb8cb8208&el=desc) will **increase** coverage by `0.06%`.\n> The diff coverage is `96.87%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4057?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4057 +/- ##\n==========================================\n+ Coverage 78.31% 78.37% +0.06% \n==========================================\n Files 120 120 \n Lines 19854 19916 +62 \n==========================================\n+ Hits 15549 15610 +61 \n- Misses 4305 4306 +1 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4057?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/4057/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.10% <ø> (ø)` | |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/4057/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `77.23% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/4057/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `68.62% <ø> (ø)` | |\n| [src/transformers/modeling\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/4057/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hbGJlcnQucHk=) | `77.20% <94.73%> (+1.89%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/4057/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `87.19% <100.00%> (+0.93%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4057?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/4057?src=pr&el=footer). Last update [c99fe03...0171b91](https://codecov.io/gh/huggingface/transformers/pull/4057?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hi! This is really cool, thanks for taking the time to do that! A few notes on your remarks:\r\n\r\n> TFBertForPreTraining does not include an option to calculate the loss, but BertForPreTraining does. \r\n\r\nThis is not a bug, it's done on purpose. We try to respect as much as possible each library's paradigm. With PyTorch you can compute the loss both inside and outside the model, so we make it easy for the user to supply the labels and compute a standard loss.\r\n\r\nFor TensorFlow however, the loss is usually computed outside the model, especially with Keras. None of our TensorFlow models computes the loss for that very reason.\r\n\r\n> TFAlbertMLMHead adds two biases to the final output: self.decoder_bias and self.bias. TFBertMLMHead only adds one bias: self.bias.\r\n\r\nThat's, unfortunately, a mistake on my side. https://github.com/huggingface/transformers/pull/4076\r\n\r\n\r\nI'm taking a look at your PR, and will see if we can upload weights on S3 containing the SOP weights as well.", "Hi @LysandreJik, any updates on your side? I know last week was ICLR. We're prepping a code release and blog post, so it would be great to have this merged in soon :)", "Hi @jarednielsen, I took a look today, and it's in good shape! There's a few things to update, however, as this is an opportunity to update our checkpoints to include the SOP.\r\n\r\nIn order to do this, I had to do a few changes, especially removing the `AlbertPreTrainingHeads` class so that the masked lm layer (previously `AlbertForPreTraining.cls.predictions`) could be at the same level than the `AlbertForMaskedLM` class: `AlbertForMaskedLM.predictions`.\r\n\r\nThere's a few other changes that need to be done in the conversion utility. I'll look into doing TensorFlow tomorrow and will try merge tomorrow evening + upload the updated checkpoints on the S3; is this timeline okay with you?\r\n\r\nDo you mind if I push directly on your fork, so that all the changes are visible in this PR?", "Thanks for the prompt response! Yes, that timeline sounds great; feel free to push directly onto my fork.", "Cool, will do that. We're releasing transformers `v2.9` today so it won't be in it, but it will be in the next version which should be released sometimes next week.", "Thanks a lot for your contribution! Will upload the weights on S3 in the coming days." ]

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[ "Yeah that's quite a big breaking change 🤣\r\nwe may want to be a little bit careful on backward compatibility", "I think this would be a very welcome update. Maybe adding an alias to the method signature would be sufficient to maintain compatibility?\r\n\r\nWe could deprecate the old ones as well, with a warning and remove them in a future version.", "Yes adding an alias would be good for me.\r\n\r\nOne possible option to deprecate progressively (that Keras use for instance) is to move the old parameters in `**kwargs` so they are not advocated anymore and add a deprecation warning later on." ]

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[ "Sorry, it's been a while, but this has since been fixed in #3716." ]

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[ "Hi @jarednielsen - thanks a lot for the clean-up :-) " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4050?src=pr&el=h1) Report\n> Merging [#4050](https://codecov.io/gh/huggingface/transformers/pull/4050?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9a0a8c1c6f4f2f0c80ff07d36713a3ada785eec5&el=desc) will **increase** coverage by `0.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/4050?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #4050 +/- ##\n=======================================\n Coverage 78.87% 78.88% \n=======================================\n Files 111 111 \n Lines 18536 18533 -3 \n=======================================\n- Hits 14621 14620 -1 \n+ Misses 3915 3913 -2 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4050?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/4050/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `88.88% <ø> (+7.93%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4050/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.02% <0.00%> (ø)` | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4050?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/4050?src=pr&el=footer). Last update [9a0a8c1...16e9e66](https://codecov.io/gh/huggingface/transformers/pull/4050?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@mkaze hello, what's your tensorflow version and transformers version?", "@etveritas When I was working on this, I was using the latest versions at the time, i.e. TF 2.1 and Transformers 2.8. ", "@mkaze oh, thank 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", "> the concatenation of CLS token representation for all the last four layers (as suggested in BERT paper);\r\n\r\nWhy are you using this 4-layer implementation suggestion from the paper? The paper clearly recommends this logic only for feature-based version of the classifier (which is mentioned only for comparison purposes), and not for the primary fine-tuning version of the model. I don't really get it.\r\n" ]

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[ "I guess you are right. The same is true for transformer-xl as well. \r\nCode: https://github.com/bajajahsaas/transformers/blob/master/src/transformers/modeling_transfo_xl.py#L855", "Yes, I think you guys are right! Thanks for pointing this out. I think we should do a general name cleaning here. \r\n\r\nSee linked PR.", "This was fixed by #4711 " ]