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DanielPFlorian

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Transformers-Github-Issues

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[ "Why don't give the full path of the train/dev file instead of giving data_dir?", "My thought was not the change much of the argument interface. If we support full path, then the `data_dir` will not be required and considered. So I wasn't sure if that change is the way to go. Sure, we can change it like that as well.\r\n\r\nPersonally, I do agree that full path for train/dev is more convenient while using." ]

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[ "You should do `from pytorch_transformers.modeling_bert import BertPreTrainedModel`\r\n\r\nI'll add these to the main `__init__.py`", "Thank you for the answer @thomwolf . It solved that error but now I'm getting another one (which wasn't there when I was using previous versions of the repository): `TypeError: unhashable type: 'BertConfig'` what could be wrong ? ", "We need a full error log and more details." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/907?src=pr&el=h1) Report\n> Merging [#907](https://codecov.io/gh/huggingface/pytorch-transformers/pull/907?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/46cc9dd2b51a152b2e262ec12e40dddd13235aba?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/907?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #907 +/- ##\n=======================================\n Coverage 79.03% 79.03% \n=======================================\n Files 34 34 \n Lines 6234 6234 \n=======================================\n Hits 4927 4927 \n Misses 1307 1307\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/907?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/pytorch-transformers/pull/907?src=pr&el=footer). Last update [46cc9dd...09ecf22](https://codecov.io/gh/huggingface/pytorch-transformers/pull/907?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Ok thanks David!" ]

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[ "Try to implement gradient accumulation during training, instead of updating parameters in each iteration. Please check this nice and easy-to-follow tutorial by @thomwolf [here](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) . I used this technique with GPT-2 small, with a dataset of ~350k, with single GPU and it worked completely fine.", "thanks @sajidrahman \r\ni will go through it ", "Edit: There is a parameter now for `gradient_accumulation_steps`... this can be adjusted to achieve gradient accumulation? ", "The problem is about batch size 20. Batch sizes more than 4 are something that doesn't fit most of (single) gpu's for many models. Check this: https://github.com/huggingface/transformers/issues/2016#issuecomment-561093186 . Some cases you cannot make fit even 1 batch to memory. As @sajidrahman mentioned, [this](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) is a good point to start.\r\nThe issue can be closed if everything is clear?", "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", "Even in my case problem was my batch size of 8, worked after changing it to 2." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/905?src=pr&el=h1) Report\n> Merging [#905](https://codecov.io/gh/huggingface/pytorch-transformers/pull/905?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/46cc9dd2b51a152b2e262ec12e40dddd13235aba?src=pr&el=desc) will **increase** coverage by `<.01%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/905?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #905 +/- ##\n==========================================\n+ Coverage 79.03% 79.03% +<.01% \n==========================================\n Files 34 34 \n Lines 6234 6235 +1 \n==========================================\n+ Hits 4927 4928 +1 \n Misses 1307 1307\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/905?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [pytorch\\_transformers/tokenization\\_gpt2.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/905/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvdG9rZW5pemF0aW9uX2dwdDIucHk=) | `96.69% <100%> (+0.02%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/905?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/pytorch-transformers/pull/905?src=pr&el=footer). Last update [46cc9dd...f8d9977](https://codecov.io/gh/huggingface/pytorch-transformers/pull/905?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "You don't need to use this method here because the models have built-in losses computation.\r\nJust feed the labels and you will get the loss back (see the doc/docstrings of the models).", "Hi @thomwolf, thanks for the suggestion. After following your advice, I'm not getting the error anymore, but now I'm a bit confused about the `backward()` pass. Now the **losses** variable contains a list of tensors of loss calculations per gpu and I'm not sure how I can enforce each individual model sitting in each gpu to perform backprop. Following is a sample code of what I've done so far and the sample output:\r\n\r\n```\r\nlosses:[[tensor(98.5968, device='cuda:0', grad_fn=<NllLossBackward>), tensor(0.7206, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)], [tensor(100.5673, device='cuda:1', grad_fn=<NllLossBackward>), tensor(0.6629, device='cuda:1', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)]] \r\n lm_loss: (tensor(98.5968, device='cuda:0', grad_fn=<NllLossBackward>), tensor(100.5673, device='cuda:1', grad_fn=<NllLossBackward>))\r\nclf_loss:(tensor(0.7206, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>), tensor(0.6629, device='cuda:1', grad_fn=<BinaryCrossEntropyWithLogitsBackward>))\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n<ipython-input-55-10ac591c2408> in <module>\r\n 26 lm_loss, clf_loss = zip(*losses)\r\n 27 print('losses:{} \\n lm_loss: {}\\nclf_loss:{}\\n'.format(losses, lm_loss, clf_loss))\r\n 28 \r\n---> 29 loss = (args.lm_coef * lm_loss.to(device) + clf_loss.to(device)).to(device)\r\n 30 \r\n 31 print(loss)\r\n\r\nAttributeError: 'tuple' object has no attribute 'to'\r\n```\r\nObviously I can deal with this 'tuple' error, but I'm confused what should I do next with this? Should I call `loss.backward()` per each cuda devices? How will I then gather gradient values in that case? Please excuse me for any naive assumptions I'm making here. Your input would be highly appreciated :)", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Hi @thomwolf , I also experience this imbalanced GPU usage when using the trainer function. Can I ask the reason that the DataParallelModel that you discussed on the Medium post, is not applied as default in the trainer function? Thank you." ]

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[ "i have the same issue as you. Do you have a good solution for it？", "I met the same problem in multi-labels classification task, have no idea about the problem!", "@zsk423200 Maybe you can try the \"bert-base-multilingual-cased-pytorch_model\" , it's performance seems better a lot than the pure Chinese ver in my task, just a temporal solu.", "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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[ "2 possible reasons:\r\n1. the first time you run `forward` will do some preheating work, maybe you should exclude the first run.\r\n2. try exclude `toTuple`\r\n\r\nAccording to my experience, jit with python or c++ will cost almost the same time.", "@Meteorix Forward is called once before the loop, are you talking about something else. \r\nExcluding `toTuple` doesn't help. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Had the same issue when passing the exact path of the vocabulary file. Fixed it by just passing the name of the directory that contains the vocabulary file (in my case it was `vocab.txt`).", "Good catch.\r\n\r\nFor non-BPE models with a single vocabulary file (Bert, XLNet, Transformer-XL) we can fix this workflow so you can provide a direct path.\r\n\r\nUpdating this." ]

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[ "are we going to get this? :) thanks :)", "Fyi https://github.com/mandarjoshi90/coref#pretrained-coreference-models describes how to obtain the coreference models that should contain SpanBERT.\r\n", "@ArneBinder Thanks for that hint!\r\n\r\nI downloaded the *SpanBERT* (base) model. Unfortunately, the TF checkpoint conversion throws the following error message:\r\n\r\n```bash\r\nINFO:pytorch_transformers.modeling_bert:Loading TF weight width_scores/output_weights/Adam_1 with shape [3000, 1]\r\nINFO:pytorch_transformers.modeling_bert:Skipping antecedent_distance_emb\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/pytorch_transformers\", line 11, in <module>\r\n load_entry_point('pytorch-transformers', 'console_scripts', 'pytorch_transformers')()\r\n File \"/mnt/pytorch-transformers/pytorch_transformers/__main__.py\", line 30, in main\r\n convert_tf_checkpoint_to_pytorch(TF_CHECKPOINT, TF_CONFIG, PYTORCH_DUMP_OUTPUT)\r\n File \"/mnt/pytorch-transformers/pytorch_transformers/convert_tf_checkpoint_to_pytorch.py\", line 36, in convert_tf_checkpoint_to_pytorch\r\n load_tf_weights_in_bert(model, config, tf_checkpoint_path)\r\n File \"/mnt/pytorch-transformers/pytorch_transformers/modeling_bert.py\", line 111, in load_tf_weights_in_bert\r\n assert pointer.shape == array.shape\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\", line 591, in __getattr__\r\n type(self).__name__, name))\r\nAttributeError: 'BertForPreTraining' object has no attribute 'shape'\r\n```\r\n\r\nI think some variables must be skipped, so a debugging session is unavoidable 😅 ", "Hi @stefan-it, the SpanBERT authors shared their (~`pytorch-transformers`-compatible) weights with us, so if you'd be interested we can send them your way so you can experiment/integrate them here.\r\n\r\nLet me know!", "@julien-c this would be awesome 🤗 I would really like to do some experiments (mainly NER and PoS tagging) - would be great if you can share the weights (my mail is `[email protected]`) - thank you in advance :heart: ", "Hi @julien-c, I would also like to receive the spanbert pytorch-compatible weights for semantic tasks like coref. could you send it to me too? my mail is [email protected]. many thanks.", "You can have a look here, the official implementation has just been released: https://github.com/facebookresearch/SpanBERT", "Well, two preliminary experiments (SpanBERT base) on CoNLL-2003 show a difference of ~7.8% compared to a BERT (base, cased) model 😱 So maybe this has something to do with the named entity masking 🤔 But I'll investigate that further this weekend...", "Update on that: I tried SpanBERT for PoS tagging and the results are pretty close to DistilBERT. Here's one run over the Universal Dependencies v1.2:\r\n\r\n| Model | Dev | Test\r\n| ---------------------------------------------------------- | --------- | ---------\r\n| RoBERTa (large) | **97.80** | **97.75**\r\n| SpanBERT (large) | 96.48 | 96.61\r\n| BERT (large, cased) | 97.35 | 97.20\r\n| DistilBERT (uncased) | 96.64 | 96.70\r\n| [Plank et. al (2016)](https://arxiv.org/abs/1604.05529) | - | 95.52\r\n| [Yasunaga et. al (2017)](https://arxiv.org/abs/1711.04903) | - | 95.82", "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/pytorch-transformers/pull/899?src=pr&el=h1) Report\n> Merging [#899](https://codecov.io/gh/huggingface/pytorch-transformers/pull/899?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/067923d3267325f525f4e46f357360c191ba562e?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/899?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #899 +/- ##\n=======================================\n Coverage 79.03% 79.03% \n=======================================\n Files 34 34 \n Lines 6234 6234 \n=======================================\n Hits 4927 4927 \n Misses 1307 1307\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/899?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/pytorch-transformers/pull/899?src=pr&el=footer). Last update [067923d...e1e2ab3](https://codecov.io/gh/huggingface/pytorch-transformers/pull/899?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "yes, I fixed it in #896 and waiting for author to merge..\r\nbut I still can't figure out why fp16 didn't save memory and didn't speed up....", "Merged", "close" ]

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[ "Duplicate to #882 " ]

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[ "Thanks, can you update `run_squad` similarly?", "> Thanks, can you update `run_squad` similarly?\r\n\r\nupdated already.", "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/896?src=pr&el=h1) Report\n> Merging [#896](https://codecov.io/gh/huggingface/pytorch-transformers/pull/896?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/067923d3267325f525f4e46f357360c191ba562e?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/896?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #896 +/- ##\n=======================================\n Coverage 79.03% 79.03% \n=======================================\n Files 34 34 \n Lines 6234 6234 \n=======================================\n Hits 4927 4927 \n Misses 1307 1307\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/896?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/pytorch-transformers/pull/896?src=pr&el=footer). Last update [067923d...f0aeb7a](https://codecov.io/gh/huggingface/pytorch-transformers/pull/896?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks a lot!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/895?src=pr&el=h1) Report\n> Merging [#895](https://codecov.io/gh/huggingface/pytorch-transformers/pull/895?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/067923d3267325f525f4e46f357360c191ba562e?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `0%`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/895?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #895 +/- ##\n=======================================\n Coverage 79.03% 79.03% \n=======================================\n Files 34 34 \n Lines 6234 6234 \n=======================================\n Hits 4927 4927 \n Misses 1307 1307\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/895?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [pytorch\\_transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/895/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvdG9rZW5pemF0aW9uX3V0aWxzLnB5) | `86.53% <0%> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/895?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/pytorch-transformers/pull/895?src=pr&el=footer). Last update [067923d...c9a7b29](https://codecov.io/gh/huggingface/pytorch-transformers/pull/895?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks, that was fixed in #893 " ]

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[ "https://github.com/google-research/bert/issues/27#issuecomment-435265194", "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/pytorch-transformers/pull/893?src=pr&el=h1) Report\n> Merging [#893](https://codecov.io/gh/huggingface/pytorch-transformers/pull/893?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/067923d3267325f525f4e46f357360c191ba562e?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `0%`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/893?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #893 +/- ##\n=======================================\n Coverage 79.03% 79.03% \n=======================================\n Files 34 34 \n Lines 6234 6234 \n=======================================\n Hits 4927 4927 \n Misses 1307 1307\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/893?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [pytorch\\_transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/893/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvdG9rZW5pemF0aW9uX3V0aWxzLnB5) | `86.53% <0%> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/893?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/pytorch-transformers/pull/893?src=pr&el=footer). Last update [067923d...ae152ce](https://codecov.io/gh/huggingface/pytorch-transformers/pull/893?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Indeed, thanks Joel!" ]

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[ "How did you add a new vocab.txt file? ", "I actually figured it out. I manually replaced one of the unused tokens in the vocab file with [NEW] and added \"additiona_special_tokens\": \"[NEW]\" to the special_tokens.json file in the same directory as the vocab.txt file. It works, but I realized that adding new tokens without the ability to do further pretraining isn't all that useful, especially given small dataset size. I decided not to do it." ]

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[ "Fixed in #882" ]

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[ "Yes, this is a nice idea, I was thinking about implementing something like this for another reason (simplifying the task of maintaining `torch.hub` configuration files).\r\n\r\nRegarding the library architecture, I think it's better to make a new (very simple) class, something like `AutoTokenizer` in a new file `tokenizer_auto.py` deriving from `PreTrainedTokenizer` (no need to avoid circular dependencies in this case).\r\n\r\nThen the idea would be to make a new file `modeling_auto.py` as well with something like `AutoModel`, pretty much like `AutoTokenizer`, and `AutoModelForSequenceClassification`, `AutoModelForQuestionAnswering` that would encapsulate standard architectures on top of each model.\r\n\r\nMaybe the `AutoXXX` is not the best name, also thought about `GenericXXX` or `UniversalXXX` but they convey meanings that could be misleading.", "`{Generic,Universal,Auto}` Tokenizer and Model interfaces would be awesome (I'm also highly interested in that, as I'm currently working on Flair to add support for all six architectures) :heart: ", "Ok, I've hacked something together for an internal hackathon this week. I'll see if I can pick this up the way you suggest next week, if no one else gets to it first. I also don't know much about your PR requirements, so if someone who's more familiar with this repo wants to pick it up, I wouldn't complain =).", "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, this is the initial embedding layer (i.e. this is layers \"0\" through 12 or 24). There are a number of small changes that haven't been migrated into the documentation yet. " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/888?src=pr&el=h1) Report\n> Merging [#888](https://codecov.io/gh/huggingface/pytorch-transformers/pull/888?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/067923d3267325f525f4e46f357360c191ba562e?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/888?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #888 +/- ##\n=======================================\n Coverage 79.03% 79.03% \n=======================================\n Files 34 34 \n Lines 6234 6234 \n=======================================\n Hits 4927 4927 \n Misses 1307 1307\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/888?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [pytorch\\_transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/888/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvbW9kZWxpbmdfb3BlbmFpLnB5) | `74.76% <ø> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/888?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/pytorch-transformers/pull/888?src=pr&el=footer). Last update [067923d...66b15f7](https://codecov.io/gh/huggingface/pytorch-transformers/pull/888?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Yes, thanks @rococode!" ]

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[ "Yes, the LM fine-tuning example will be refactored.\r\n\r\nAdding the removal of gradient clipping to the list of breaking changes, thanks.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi, I was wondering how you managed to resolve this issue? I'm running into a similar problem. :) ", "Hi, the model outputs are well documented, they're *always* tuples, even if there's a single return value. You can check the documentation [here](https://huggingface.co/transformers/main_classes/output.html).", "@jacqueline-he did you resolve the issue?", "@WeeHyongTok Yes I have! I only needed to access what's inside the returned tuple. @LysandreJik's recommendation was very helpful. " ]

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[ "@ereday Take the `simple_lm_finetuning.py` script for example. It has a `--bert_model` argument. When your target domain is German, then you should use the recently introduced [BERT model for german](https://github.com/huggingface/pytorch-transformers/pull/688) via passing `bert-base-german-cased`. \r\n\r\nThis should fine-tune the German BERT model :)", "@stefan-it thanks alot. I wasn't aware of german specific bert model. Awesome!" ]

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[ "@lixin4ever I have the same question. Do you solve it?", "I have solve it. Thank you!", "@searchlink How do you solve this problem?", "For reference, the updated resource link mentioned in the original post can be now found [here](https://huggingface.co/transformers/_modules/transformers/modeling_bert.html#BertForTokenClassification); It was affected by the renaming into `transformers`, too.", "But I can't see any difference between \"pytorch-transformers\" and \"transformers\" except the line initializing BERT parameters. Have you met the same problem? @dennlinger ", "I was just pointing to the up-to-date reference. I'm currently looking into token classification using BERT (or in my case, I would prefer RoBERTa or other iterations of BERT, but unfortunately they seem not available yet).", "So Token classification using BERT does not work?\r\n", "As you can see below, `BertForTokenClassification` works as expected with **PyTorch 1.3.1** and **Transformers 2.2.2** installed with `pip install transformers`.\r\n```\r\nPython 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \r\n[GCC 7.3.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import transformers\r\n>>> from transformers import BertForTokenClassification\r\n>>> from transformers import BertTokenizer\r\n>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n>>> model = BertForTokenClassification.from_pretrained('bert-base-uncased')\r\n>>> text='Hello, my dog is cute'\r\n>>> import torch\r\n>>> input_ids = torch.tensor(tokenizer.encode(text, add_special_tokens=True)).unsqueeze(0) # Batch size 1\r\n>>> input_ids\r\ntensor([[ 101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])\r\n>>> labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1\r\n>>> labels\r\ntensor([[1, 1, 1, 1, 1, 1, 1, 1]])\r\n>>> outputs = model(input_ids, labels=labels)\r\n>>> outputs\r\n(tensor(0.7529, grad_fn=<NllLossBackward>), tensor([[[ 0.5078, 0.1628],\r\n [-0.0593, 0.0163],\r\n [ 0.0308, -0.2312],\r\n [ 0.0863, -0.1000],\r\n [-0.2833, -0.2656],\r\n [-0.2014, -0.5225],\r\n [-0.2912, -0.1220],\r\n [-0.2781, -0.2919]]], grad_fn=<AddBackward0>))\r\n>>> len(outputs)\r\n2\r\n>>> loss=outputs[0]\r\n>>> scores=outputs[1]\r\n>>> loss\r\ntensor(0.7529, grad_fn=<NllLossBackward>)\r\n>>> scores\r\ntensor([[[ 0.5078, 0.1628],\r\n [-0.0593, 0.0163],\r\n [ 0.0308, -0.2312],\r\n [ 0.0863, -0.1000],\r\n [-0.2833, -0.2656],\r\n [-0.2014, -0.5225],\r\n [-0.2912, -0.1220],\r\n [-0.2781, -0.2919]]], grad_fn=<AddBackward0>)\r\n>>>\r\n```\r\n\r\nIt's working with **TensorFlow 2.0.0** and **Transformers 2.2.2** too!\r\n```\r\nPython 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \r\n[GCC 7.3.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\n/home/vidiemme/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\r\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\r\n/home/vidiemme/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\r\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\r\n/home/vidiemme/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\r\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\r\n/home/vidiemme/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\r\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\r\n/home/vidiemme/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\r\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\r\n/home/vidiemme/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\r\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\r\n>>> import transformers\r\n>>> from transformers import BertTokenizer\r\n>>> from transformers import TFBertForTokenClassification\r\n>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n>>> model = TFBertForTokenClassification.from_pretrained('bert-base-uncased')\r\n2019-12-17 12:54:37.120123: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\r\n2019-12-17 12:54:37.320081: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3408000000 Hz\r\n2019-12-17 12:54:37.320815: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a42296edd0 executing computations on platform Host. Devices:\r\n2019-12-17 12:54:37.320841: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version\r\n>>> text='Hello, my dog is cute'\r\n>>> input_ids = tf.constant(tokenizer.encode(text))[None, :] # Batch size 1\r\n>>> input_ids\r\n<tf.Tensor: id=6056, shape=(1, 8), dtype=int32, numpy=\r\narray([[ 101, 7592, 1010, 2026, 3899, 2003, 10140, 102]],\r\n dtype=int32)>\r\n>>> outputs = model(input_ids)\r\n>>> len(outputs)\r\n1\r\n>>> outputs\r\n(<tf.Tensor: id=7961, shape=(1, 8, 2), dtype=float32, numpy=\r\narray([[[ 0.09657212, -0.51087016],\r\n [ 0.28020248, -0.25160134],\r\n [-0.09995201, 0.0843759 ],\r\n [-0.12110823, 0.20886022],\r\n [-0.03617962, 0.00401567],\r\n [-0.03330922, 0.01042 ],\r\n [-0.21674895, -0.1601235 ],\r\n [ 0.1076538 , 0.19144017]]], dtype=float32)>,)\r\n>>> scores = outputs[0]\r\n>>> scores\r\n<tf.Tensor: id=7961, shape=(1, 8, 2), dtype=float32, numpy=\r\narray([[[ 0.09657212, -0.51087016],\r\n [ 0.28020248, -0.25160134],\r\n [-0.09995201, 0.0843759 ],\r\n [-0.12110823, 0.20886022],\r\n [-0.03617962, 0.00401567],\r\n [-0.03330922, 0.01042 ],\r\n [-0.21674895, -0.1601235 ],\r\n [ 0.1076538 , 0.19144017]]], dtype=float32)>\r\n```\r\n\r\n> So Token classification using BERT does not work?", "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 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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[ "Just saw run_glue has the new one." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/882?src=pr&el=h1) Report\n> Merging [#882](https://codecov.io/gh/huggingface/pytorch-transformers/pull/882?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/067923d3267325f525f4e46f357360c191ba562e?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/882?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #882 +/- ##\n=======================================\n Coverage 79.03% 79.03% \n=======================================\n Files 34 34 \n Lines 6234 6234 \n=======================================\n Hits 4927 4927 \n Misses 1307 1307\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/882?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/pytorch-transformers/pull/882?src=pr&el=footer). Last update [067923d...a7fce6d](https://codecov.io/gh/huggingface/pytorch-transformers/pull/882?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks indeed, this should fix #891" ]

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[ "```\r\npython convert.py --tf_checkpoint_path=./uncased_L-12_H-768_A-12/bert_model.ckpt --bert_config_file=./uncased_L-12_H-768_A-12/bert_config.json --pytorch_dump_path=./uncased_L-12_H-768_A-12/bert_model.bin\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 } ]

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[ "Are you running in jupyter? This might be an artifact of how `tqdm` is interacting with whatever shell you're running it in. If you don't want to print anything, you could simply drop the `tdqm` wrapper and just iterate over `train_dataloader`. ", "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/pytorch-transformers/pull/879?src=pr&el=h1) Report\n> Merging [#879](https://codecov.io/gh/huggingface/pytorch-transformers/pull/879?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/067923d3267325f525f4e46f357360c191ba562e?src=pr&el=desc) will **decrease** coverage by `0.01%`.\n> The diff coverage is `0%`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/879?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #879 +/- ##\n==========================================\n- Coverage 79.03% 79.02% -0.02% \n==========================================\n Files 34 34 \n Lines 6234 6235 +1 \n==========================================\n Hits 4927 4927 \n- Misses 1307 1308 +1\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/879?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [pytorch\\_transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/879/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvdG9rZW5pemF0aW9uX3V0aWxzLnB5) | `86.17% <0%> (-0.36%)` | :arrow_down: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/879?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/pytorch-transformers/pull/879?src=pr&el=footer). Last update [067923d...31bc1dd](https://codecov.io/gh/huggingface/pytorch-transformers/pull/879?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Closing this for now. Feel free to re-open if you want to continue with this PR." ]

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[ "Hi, how do you solve this problem? If we set `pretrained_model_name_or_path` as a path to vocab.txt, it still need the two files: added_tokens.json, special_tokens_map.json. Where can we get these files? ", "> Hi, how do you solve this problem? If we set `pretrained_model_name_or_path` as a path to vocab.txt, it still need the two files: added_tokens.json, special_tokens_map.json. Where can we get these files?\r\n\r\nYou can ignore these files: added_tokens.json, special_tokens_map.json. All you need to do is to modify some code lines in the file: tokenization_utils.py. I have modified it in my forked repository as you can see [here](https://github.com/xijiz/pytorch-transformers/commit/31bc1ddf4f68ad790da9874a3623cf22d62dc186). ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "The `labels` input for the model is not the number of labels but the tensor of labels (see the docstrings and doc).", "> The `labels` input for the model is not the number of labels but the tensor of labels (see the docstrings and doc).\r\n\r\nThank you for the answer. I'm trying to train the model to do polarity classification for google reviews, this is how the code that computes \"logits\" looked like before in pytorch-pretrained-bert:\r\n`logits = model(input_ids, segment_ids, input_mask, labels=None)`\r\nDo I do after the migration now:\r\n\r\n`output = model(input_ids,labels=None)`\r\n`loss, logits = output[:2]`\r\n\r\nin order to match the similar behaviors? Thanks.\r\np.s. I'm new to the field, picked up the code from the street and trying to figure out how to make it work, I'm sorry if the question is dumb.", "How was this solved? I have the same problem and for me : \r\n\r\noutput = model(input_ids,labels=None)\r\nloss, logits = output[:2]\r\n\r\ndoes not solve it" ]

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[ "Hi,\r\nBERT out-of-the-box is not the best option for this task, as the run-time in your setup scales with the number of sentences in your corpus. I.e., if you have 10,000 sentences/articles in your corpus, you need to classify 10k pairs with BERT, which is rather slow.\r\n\r\nA better option is to generate sentence embeddings: Every sentence / article is mapped to a fixed sized vector. You need to map your 3k articles only once to a vector.\r\n\r\nA new query is then also mapped to a vector. In this setup, you only need to run BERT for one sentence (at inference), independent how large your corpus is.\r\n\r\nThen, you can use cosine-similiarity, or manhatten / euclidean distance to find sentence embeddings that are closest = that are the most similar.\r\n\r\nI released today a framework which uses pytorch-transformers for exactly that purpose:\r\nhttps://github.com/UKPLab/sentence-transformers\r\n\r\nI also uploaded an example for semantic search, where each sentence in a corpus is mapped to a vector and than cosine-similarity is used to find the most similar sentences / vectors:\r\nhttps://github.com/UKPLab/sentence-transformers/blob/master/examples/application_semantic_search.py\r\n\r\nLet me know if you have further questions.", "I think you can use [faiss](https://github.com/facebookresearch/faiss) for storing and finding similar embeddings. ", "@nreimers Amazing!! Thank you so much. What you created is a real-life savior! Can this be used for finding similar news(given title and abstract)? I ran the code and I have the following doubts. \r\nWhich model should I use? \r\n bert-large-nli-stsb-mean-tokens vs bert-base-nli-mean-tokens vs bert-large-nli-mean-tokens (what are the datasets on which all these models are trained on?) \r\n\r\nCan I use [faiss](https://github.com/facebookresearch/faiss) to compute the search/distance of the vectors instead of L2/Manhattan/Cosine distances? \r\n\r\nMany thanks to @stefan-it for introducing me to [faiss](https://github.com/facebookresearch/faiss).\r\n\r\n\r\n", "@nreimers I don't think scipy.spatial.distance.cdist is good enough, it takes a lot of time to compute the results, almost 10 minutes on a corpus of 3.9k news articles. I think I should try using [faiss](https://github.com/facebookresearch/faiss). I don't know anything about [faiss](https://github.com/facebookresearch/faiss) but I will try.", "Hi @Raghavendra15,\r\nregarding the model I sadly cannot be helpful, you would need to test them. In general, sentence embeddings methods (like Inference, Universal Sentence Encoder or my git) work well for short text, i.e., for sentences. For longer text with multiple sentences their performance often decrease and average word embeddings or tf-idf is in many case a much better choice. For longer texts, all these sentence embeddings methods are not really needed.\r\n\r\nIt would be great if you have some training data. Then, it would be quite easy to fine-tune a model specifically for your task. It should achieve much better performances than the pre-trained models.\r\n\r\nI think the issue is not scipy.spatial.distance.cdist. On a corpus with 100k embeddings and 1024 embedding size, it requires about 0.2 seconds per query (if you can batch queries, even less time is needed).\r\n\r\nI think the issue might be the generation of the 4k sentence embeddings? Transformer networks like BERT are extremely slow on CPUs. However, on a GPU, the implementation can process about 2000 sentences per seconds. On a GPU, only about 40 sentences.\r\n\r\nBut the corpus must only be processed once and can then be stored & loaded from disk. At inference, you just need to generate one embedding for the respective query.\r\n\r\nYou can of course combine this with faiss. Faiss generates index structures that allow a quick search in vector space and is especially suitable if you have a high number (millions) of vectors. For 4k vectors, scipy takes about 0.008 seconds per queries to find the most similar vectors. \r\n\r\nSo either something is really strange with scipy on your computer, or the long run-time comes from the generation of the embeddings.", "@nreimers Thank you very much for your response. You're absolutely right, most of the time taken is for generating the embedding for 4k sentences. I'm now confused between choosing this model over [XLNet](https://github.com/zihangdai/xlnet), XLNet has achieved the state of the art results.\r\n\r\nBy your comments on faiss, As long as I have a smaller dataset, results from faiss and scipy won't make any difference? However, If I had millions or billions of news articles then using faiss makes sense right? For smaller datasets, there is no difference in terms of quality of matches between faiss and scipy(the results are the same for computing the distances)? \r\n\r\nI have one important question, If I want to train the model as you suggested which would yield better results, In that case, I should have labeled dataset right? However, for news, I only have titles and abstract about that news. Is there a way to train them without the labels? ", "Hi,\r\nXLNet achieved state-of-the-art performance for supervised tasks like classification. But it is unclear if it generates also good embeddings for unsupervised tasks.\r\n\r\nIn the framework you can choose XLNet, but I was only able to produce results that are slightly below those of BERT.\r\n\r\nOthers also have problems getting a good performance with Xlnet for supervised tasks, as it appears that it is extremely sensitive to the hyper parameters.\r\n\r\nIf you have millions of docs, faiss makes sense. With scipy, you get exact scores. With faiss, the scores are fuzzy and the returned most similar vectors must not necessarily be the actual most similar vectors. There can be small variations. But I think the difference will be small.\r\n\r\nOften you have in your data some structure, like categories or links between news articles. This structure can be used to fine-tune a model. Let's say you have links linking to similar events. Than you train the network with triplet loss with the two linked articles and one random other article as negative example.\r\n\r\nThis will give you a vector space where (possibly) linked articles are close. ", "@nreimers Thank you very much for your quick response. \r\nAre the existing model \"bert-large-nli-stsb-mean-tokens\" better than the google news word2vec [google_news_300](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing), they claim that-\" We are publishing pre-trained vectors trained on part of Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases.\"\r\nIs the pretrained \"bert-large-nli-stsb-mean-tokens\" better than google's pre-trained news vectors?\r\n\r\nFor training the existing model to improve results for news similarity, the problem I have is I can't create a dataset to compute triplet loss. For triplet loss to work in the case of news similarity for query news ['**a**'], I need to find a news article ['**b**'] which is similar as a positive example and a dissimilar news article ['**c**'] as a negative example. Like <a,b> positive example and <a,c> negative example.\r\n\r\nHowever, If I run the news every day then, new entities/topics are going to pop up every single day? I need to update my embeddings right? I don't know how to handle this situation. \r\n", "Google News vectors are just word vectors, you still need a strategy to derive sentence embeddings from these. But as mentioned earlier, average word embeddings is a promising idea for your task. Note, average word embeddings models will be added soon to the repository.\r\n\r\nConstantly updating of the model is not needed. News are changing, but the used words remain the same. So training once should give you a model that can be used for a long time. ", "@nreimers Thank you very much! Any tentative date by when the average word embeddings will be added to the repository? \r\n\r\nI want to know how to evaluate the results of similar sentences numerically, for example when I use your model to evaluate for a given news, finding similar news in the corpus. \r\n\r\nIs there a way to measure numerically how good the similar sentences are in the below example? I used BLEU score, but the problem is, it's not an accurate measure of similarity. BLEU score doesn't consider the context of the sentence, it just blindly counts whether a word in the query sentence is present in the similar sentence regardless of where the word is placed.\r\n\r\nFor an item, I get related items.\r\nIn the below example, the first title in relatedItems is similar, however, the second item in \"relatedItems\" is not at all similar which talks about Stephen Colbert and Joe Biden. \r\nSuppose I use word2vec model for the above task it might give me two totally different sentences as relatedItems, In that case, how can I evaluate both the models and claim numerically which one is better?\r\n\r\nExample:\r\n\r\n{\"title\": \"Google Is Rolling Out A New Version Of Android Auto - Here's What You Can Expect\",\r\n\"abstract\": \"The new Android Auto. Google If you use Android Auto, you're about to receive to a nice upgrade.\",\r\n}\r\n\"relatedItems\": \r\n[{\r\n\r\n \"title\": \"New Android ransomware is spreading through text messages\",\r\n \"abstract\": \"There\\u2019s a new type of Android ransomware making the rounds that leverages SMS to spread, according to a new report from cyberappsecurity com\",\r\n},\r\n{\r\n \"title\": \"Stephen Colbert Brings Curtain Down On Democratic Debates With Joe Biden Tweaks\",\r\n \"abstract\": \"Stephen Colbert closed his second of two live Late Show monologues with a spree of zingers directed at Joe Biden, mixing in plenty for the o\",\r\n\r\n}\r\n]}\r\n\r\n\r\n\r\n", "Bleu wouldn't be a good measure, because the best similarity metric to find similar news would be: Bleu (of course). \r\n\r\nWhat you would need is an annotated Corpus. For a given article, get for example the 20 articles with the highest tf idf similarity. Then annotate every pair as similar or not.\r\n\r\nWith this data you can compare different methods with Ndcg about how well they rank the 20 candidate articles. \r\n\r\nAvg. Word embeddings should be included within the next two weeks to the repo. ", "@nreimers When you say -\"Bleu wouldn't be a good measure, because the best similarity metric to find similar news would be: Bleu (of course).\" \r\nDo you mean when I get similar news like in the above example, BLEU is the best metric to measure how similar the two news articles are? Please correct me if I understood this wrong.\r\n\r\nIn the STS benchmark, I saw a pair in the training dataset with gold-standard human evaluated scores. The following paid had a score of 5, however, when I use BLEU scores for 1gram they don't get a score of 1. Instead, they get the following scores. BLEU looks for the exact word to be present in the reference sentence that's the problem I feel. There's no notion of similarity.\r\n\r\ns=word_tokenize(\"The polar bear is sliding on the snow\")\r\nreference = [s]\r\ncandidate =word_tokenize(\"The polar bear is sliding across the snow\") \r\nprint('Individual 1-gram: %f' % sentence_bleu(reference, candidate, weights=(1, 0, 0, 0)))\r\nprint('Individual 2-gram: %f' % sentence_bleu(reference, candidate, weights=(0, 1, 0, 0)))\r\nprint('Individual 3-gram: %f' % sentence_bleu(reference, candidate, weights=(0, 0, 1, 0)))\r\nprint('Individual 4-gram: %f' % sentence_bleu(reference, candidate, weights=(0, 0, 0, 1)))\r\n\r\nIndividual 1-gram: 0.875000\r\nIndividual 2-gram: 0.714286\r\nIndividual 3-gram: 0.500000\r\nIndividual 4-gram: 0.400000\r\n\r\nreference sentence has 8 words out of which the candidate matches exactly 7 words, so 7/8 score for 1-gram matches.\r\n\r\nI'm not sure how the STS benchmarks are evaluated, I'm currently looking into them. If you have any leads or a document I would be more than happy to read them. \r\n\r\nThank you very much for your help :)\r\n\r\n", "No, BLEU is a terrible idea for evaluation.\r\n\r\nSTS is usually evaluated using Pearson correlation between gold and predicted labels. But Pearson correlation is also a bad idea:\r\nhttps://aclweb.org/anthology/C16-1009\r\n\r\nI strongly recommend to use Spearman correlation for comparison. ", "@nreimers Kudos on the COLING paper! It's very well written. In the paper, you have mentioned How Pearson correlation can be misleading or ill-suited for the semantic text-similarity task. However, you did not suggest to use Spearman correlation instead of Pearson correlation. But for me, you suggested me to use Spearman correlation why? (That's my current understanding of the paper)\r\n\r\nCan I use the Spearman rank correlation from scipy?\r\nBasically, I want to compare the BERT output sentences from your model and output from word2vec to see which one gives better output.\r\nSo there is a reference sentence and I get a bunch of similar sentences as I mentioned in the previous example [ please refer to the JSON output in the previous comments].\r\n\r\nWill the below code is the right way to do the comparison? \r\nIn your sentence transformer, you have used the same below package in SentenceEvaluator class. I couldn't figure out how to use that class for my comparison. \r\n\r\nWill you please give me some idea in this regard?\r\n\r\nExample code:\r\nfrom scipy.stats import spearmanr\r\nx = [1, 2, 3] ---> I will use BERT and word2vec embeddings here.\r\nx_corr = [2, 4, 6]\r\ncorr, p_value = spearmanr(x, x_corr)\r\nprint (corr)\r\n", "Hi @Raghavendra15 \r\nThe issue with pearson correlation is, that it assumes a linear correlation between the system output and gold labels. Adding a montone function to the system output can change the scores (make them better or worse), which does not really make sense in applications.\r\n\r\nAssume you have a systems that predicts the perfect gold scores, however, the output is output=sqrt(gold_label).\r\n\r\nThis system would get a really low Pearson correlation. However, for every application, this system would be perfect, as it predicts the gold labels. With Spearman correlation, you don't have this issue. There, just the ranking of the scores are important.\r\n\r\nIn general I think the STS tasks (or the STS benchmark) are not really well suited to evaluated approaches. The STS tasks with Pearson/Spearman correlation weights every score similar, but in applications, we are often only interested in certain examples.\r\n\r\nFor example, if we search for pairs with the highest similarity, then we don't care how the scores are for low similarity pairs. A system that gives a perfect score for high similarity pairs and a random score for low similarity pairs would be great for this application. However, this system would get a low Pearson/Spearman correlation, as it fails to correctly order the somewhat-similar and unsimilar pairs.\r\n\r\nIf you want so estimate the similarity of two vectors, you should use cosine-similarity or Manhatten/Euclidean distance. \r\n\r\nSpearman correlation is only used for the comparison to gold scores.\r\n\r\nAssume you have the pairs:\r\nx_1, y_1\r\nx_2, y_2\r\n\r\n...\r\nfor every (x_i, y_i) you have a score s_i from 0 ... 1 indicating a gold label score for their similarity. \r\n\r\nYou can check how good the embeddings are by computing the cosine similarity between the embeddings for (x_i, y_i) and then you compute the Spearman correlation between these computes cosine similarity scores and the gold score s_i.\r\n\r\nNote: Currently I add methods to compute average word embeddings and similar methods to the repository. So a comparison will become easier.", "@nreimers Last week you added the methods to compute average word embeddings should I use that method when I get a sentence embedding or will there be a pre-trained average word embedding weights? \r\nIn the below code I will get the embeddings once I pass the input strings. Should I use the compute avg word embedding method on top of this?\r\n\r\ncorpus = ['A man is eating a food.',\r\n 'A man is eating a piece of bread.' ]\r\ncorpus_embeddings = embedder.encode(corpus)\r\nor \r\nBy any chance, pre-trained avg-word embedding weights will be uploaded to the repository by any time this week. ", "Hi @Raghavendra15 \r\nI just uploaded v0.2.0 to github and PyPi:\r\nhttps://github.com/UKPLab/sentence-transformers\r\n\r\nYou can update with pip install -U sentence-transformers\r\n\r\nI added an example for average word embeddings (+a DAN layer that is trainable):\r\nhttps://github.com/UKPLab/sentence-transformers/blob/master/examples/training_stsbenchmark_avg_word_embeddings.py\r\n\r\nYou can also use it without the DAN layer. There is also a tokenizer implemented that allows the usage of the word2vec Google News vectors. These vectors contain phrases like 'New_York'. These phrases are detected by the tokenizer and mapped to the correct embedding for New_York. But there is currently no example for this in the repo. If you need help, let me know.\r\n\r\nTo get avg. word embeddings only (without DAN), the code must look like this:\r\n```\r\n# Map tokens to traditional word embeddings like GloVe\r\nword_embedding_model = models.WordEmbeddings.from_text_file('glove.6B.300d.txt.gz')\r\n\r\n# Apply mean pooling to get one fixed sized sentence vector\r\npooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),\r\n pooling_mode_mean_tokens=True,\r\n pooling_mode_cls_token=False,\r\n pooling_mode_max_tokens=False)\r\nmodel = SentenceTransformer(modules=[word_embedding_model, pooling_model])\r\n\r\ncorpus_embeddings = model.encode(corpus)\r\n```\r\n\r\nNext release will update include support for RoBERTa and add other sentence embeddings methods (like USE, LASER), which will be trainable.", "@nreimers Thank you very much! You spoke my mind with RoBERTa, I was about to ask you about it. But with the avg-embedding approach, I won't be using BERT at all right? \r\n\r\nIn addition to that, I won't be training the model. I don't think I fully understand this. Earlier I would pass a pretrained weight model into SentenceTransformer, however now I won't pass anything related to BERT, does that mean I won't be using BERT?", "@Raghavendra15 \r\nThe framework offers you a lot of flexibility. You can choose between the following embedding approaches:\r\n- BERT or XLNet (RoBERTa and other will follow)\r\n- Traditional word embeddings like GloVe, word2vec etc.\r\n\r\nThen, you can choose between different pooling modes: Mean pooling, max pooling, usage of the CLS token for BERT / XLNet.\r\n\r\nFinally, if you like, you can add feed-forward networks to create a deep-averaging network.\r\n\r\nIf you have training data, I can recommend this combination:\r\nBERT + mean-pooling\r\n\r\nThis gave the best performance for many cases. \r\n\r\nIf you have training data, you need a low computation time and performance is not that important, choose this combination:\r\nGloVe embeddings (or something similar) + mean-pooling + 1 or 2 dense layers\r\n\r\nIf you don't have training data, choose:\r\nGloVe embeddings (or something similar) + mean-pooling \r\n\r\nAs you can see, there are various options you can choose from, depending if you have training data and how important is a high speed vs. a good performance.\r\n\r\nOnce I have RoBERTa integrated, how suitable it is for the generation of sentence embeddings. My experiences with XLNet was that the performance is slightly below the performance of BERT for sentence embeddings. Maybe RoBERTa is better for sentence embeddings, maybe not.\r\n\r\nAveraging BERT without fine-tuning on data gave really poor results. However, what you can of course try, is to use one of the existent pretrained BERT models like 'bert-base-nli-mean-tokens', which is BERT+mean-pooling, fine-tuned on NLI data to generate meaningful sentence embeddings.\r\n", "@nreimers Thank you very much! Why didn't you choose (word2vec) Google news vectors? Is there any particular reason for choosing Glove embedding over word2vec? I'm curious to know how RoBERTa will perform! 😃", "@Raghavendra15 \r\nThere are two reasons:\r\n1) Google news word2vec is quite large, it requires about 12 GB of RAM to read it in. Not that ideal for an example script. GloVe embeddings are about 10 times smaller.\r\n2) In most of my experiments, the Google news word2vec vectors did not yield good performances. GloVe embeddings were often a bit better. I especially like the embeddings by Levy et al (trained on dependencies) and by Komninos. I also conducted a larger comparison between word embeddings (https://arxiv.org/abs/1707.06799, Table 5).\r\n\r\nBut note, using the Google news word2vec vectors is quite easy. In training_stsbenchmark_avg_word_embeddings.py replace\r\n```\r\nword_embedding_model = models.WordEmbeddings.from_text_file('glove.6B.300d.txt.gz')\r\n```\r\nwith\r\n```\r\nword_embedding_model = models.WordEmbeddings.from_text_file('GoogleNews-vectors-negative300.txt.gz')\r\n```\r\n\r\nFirst experiments with RoBERTa are done: On STSbenchmark, it increases the Spearman correlation by about 1 - 2 percentage points. I will see how it will perform on other datasets.\r\n\r\nBest, Nils Reimers", "This issue is very interesting, thanks for sharing your experiments and framework @nreimers!", "@nreimers I read your paper on word embedding comparison, however, when I saw the GLEU scoreboard for STS benchmark Glove scored very less compared to word2vec, Isn't it contradictory to your paper? Also in your paper, the comparisons are on a certain set of tasks like Entity Recognition, NER but not on Semantic Textual Similarity. I don't know much about it, I'm trying to learn. Do my questions make sense?\r\n \r\nIs there any significant difference between using glove.840B.300d.zip (contains 840 billion words vectors trained on the common crawl ) vs glove.6B.300d.txt.gz (contains 6 billion words vectors wikipedia+Gigaword), Is it like more words the better? also, they're trained on different datasets, will that make a huge difference when applied to news similarity?", "See the GloVe website / paper for the differences. 6B was trained on 6 billion words from Wikipedia, 840B was trained on 840 Billion words from common crawl.\r\n\r\nIt depends on the task and data which one is more suitable. If you have a lot of rare words, and those play an important role for your task, 840B is often better. If you have clean data / only common words are important for your task, 6B often works better. \r\n\r\nHowever, the differences are often only minor between the two versions. \r\n\r\nIn my paper I only compare embeddings for supervised task, only for sequence tagging. \r\n\r\nIn unsupervised tasks, you can get completely different results. Further, how word embeddings are averaged has a big impact. Some authors don't ignore stop words, instead they propose some complicated weighting scheme. If stop words are ignored, performances can be improved up tp 10 percentage points, sometimes outperforming complex weighting approaches. \r\n\r\nBest, \r\nNils Reimers ", "Thank you for your work, Nils, it is brillant! \r\n\r\nI would like to design a sentence level semantic search engine using email data (Enron dataset). \r\n\r\nI am still a little bit confused about how I should be fine-tuning models on such dataset (maybe I am missing something obvious).\r\n\r\nThanks.\r\n\r\nGogan\r\n", "@ggndtes In general BM25 will be really hard to beat on this type of task. See this paper where they compare sentence embeddings with BM25 on an end-to-end retrieval task (given: question, find similar / duplicate questions in a large corpus):\r\nhttps://arxiv.org/pdf/1811.08008.pdf\r\n\r\nA complex sentence embedding method only achieves 1 - 2 percentage points improvement against BM25 (Table 2, Dual Encoder Paralex vs. Okapi BM 25).\r\n\r\nEspecially if you have more than just a sentence, carefully constructed BM25 for example with Elasticsearch is really really hard to beat. If you are interested in a production system, I would highly recommend to first try Elasticsearch (or similar), beating it will be difficult.\r\n\r\nBack to your question how you can tune it:\r\nThe big question narrows down to: What are your queries, what are your documents. Are your documents complete emails? Or only email subjects? Or only sentences within emails?\r\n\r\nAre your queries inputs from the user, email subjects or complete emails?\r\n\r\nIn general you would need to construct same sort of similarity. Currently I can only think of imperfect method to create similarity labels. One option would be: Triplet loss with 2 emails from the same inbox vs. one random other subject. But this would I think create rather bad embeddings.\r\n\r\nCurrently I can't think of a good method to create similarity labels for that dataset. And as mention, even with perfect labels, it will be really hard to beat BM25. \r\n\r\nBest,\r\n-Nils Reimers\r\n \r\n\r\n\r\n", "@nreimers The sentence encoder actually takes quite a lot of time to load the Glove embeddings, Is there a way where I can make it load from the disk or make it faster?", "@Raghavendra15 When you run the code the first time, the embeddings are downloaded and stored in the path of the script. In follow-up executions, the embeddings file is loaded from disk.\r\n\r\nGloVe embeddings are quite large, so loading it can take some time.\r\n\r\nThere are two ways to speed it up:\r\n1) Limit the vocab size, i.e., don't load all the ~400k embeddings. Pass the parameter 'max_vocab_size' to the method 'from_text_file' when called.\r\n2) Save the WordEmbeddings model to disc. In follow-up executions, you can load the (binary) model directly from disc and you don't have to read in and parse in the text file.\r\n\r\nShould work something like this:\r\n```\r\nword_model = WordEmbeddings.from_text_file('my-glove-file.txt')\r\nword_model.save('my/output/folder/GloveWordModel')\r\n\r\n# In follow-up calls, should be faster\r\nword_model = WordEmbeddings.load('my/output/folder/GloveWordModel')\r\n```", "@nreimers Wow!! It works blazingly fast! \r\nI was trying to play with the below code. Thank you very much for the help :) \r\nCode in In WordEmbeddings.py file:\r\n```\r\n with gzip.open(embeddings_file_path, \"rt\", encoding=\"utf8\") if embeddings_file_path.endswith('.gz') else open(embeddings_file_path, encoding=\"utf8\") as fIn:\r\n iterator = tqdm(fIn, desc=\"Load Word Embeddings\", unit=\"Embeddings\")\r\n for line in iterator:\r\n\r\n```\r\nAlso, can I load the model similar to that for BERT pre-trained weights? such as the below code?\r\n\r\n`embedder = SentenceTransformer('bert-large-nli-stsb-mean-tokens')`\r\nCan I load the above pre-trained weights somehow just like you have `load` method for glove weights?\r\n\r\nIs the avg embedding with Glove better than \"bert-large-nli-stsb-mean-tokens\" the BERT pre-trained model you have loaded in the repository? How's RoBERTa doing? Your work is amazing! Thank you so much again! \r\n", "@Raghavendra15 Sure you can:\r\n```\r\nword_embedding_model = models.WordEmbeddings.from_text_file('glove.6B.300d.txt.gz')\r\n\r\n# Apply mean pooling to get one fixed sized sentence vector\r\npooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),\r\n pooling_mode_mean_tokens=True,\r\n pooling_mode_cls_token=False,\r\n pooling_mode_max_tokens=False)\r\n\r\n\r\nmodel = SentenceTransformer(modules=[word_embedding_model, pooling_model])\r\nmodel.save('my/output/folder/avg-glove-embeddings')\r\n\r\n# Load the Model:\r\nmodel = SentenceTransformer('my/output/folder/avg-glove-embeddings')\r\n```\r\n\r\nWhich model is better depends extremely on your data and on your task. The BERT models work good if you have clean data, which is not too domain specific and rather descriptive. This is due to the nature on which data it was fine-tuned (on NLI dataset).\r\n\r\nAverage GloVe embeddings works I think better if you have noisy data, really domain specific data or very short sentences or very large paragraphs. \r\n\r\nExperiments with RoBERTa are finished. Paper will be uploaded next week to arxiv. In my experiments, I could not observe a major difference between BERT and RoBERTa for sentence embeddings: Sometimes BERT is a little bit better, sometimes RoBERTa. But nothing that is significant. XLNet was so far in general worse than BERT.\r\n\r\nBest\r\n-Nils Reimers" ]

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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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[ "Having the same question - how to use bert for generation? ", "the same problem， how to train my own data for text generation?", "We'll add an example for fine-tuning this month.", "@thomwolf as I read in other issues, BERT model cannot be used to generate text directly (your reply https://github.com/huggingface/pytorch-transformers/issues/401#issuecomment-477111518).\r\nWhat exact examples are you planning to add? Thanks.", "@Bruno-bai did you figure out how to train own data?", "Not really. Would appreciate a tutorial:)\n\nOn Mon, Aug 19, 2019 at 5:22 AM Vedang Mandhana <[email protected]>\nwrote:\n\n> @Bruno-bai <https://github.com/Bruno-bai> did you figure out how to train\n> own data?\n>\n> —\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/pytorch-transformers/issues/874?email_source=notifications&email_token=AHZ2KL5Q2RIKUTGARMX6EY3QFINYTA5CNFSM4IGHELH2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD4RUR4I#issuecomment-522406129>,\n> or mute the thread\n> <https://github.com/notifications/unsubscribe-auth/AHZ2KL3FS6CDZQN75KNXVPDQFINYTANCNFSM4IGHELHQ>\n> .\n>\n", "this would be very useful example to have. to finetune gpt2, xlnet, ... and run generation from the finetuned model. Don't know whether bert supports generation or not, but the ones that do..", "I am too struggling with similar problem. I want to train a non-english (hindi) language model on my custom dataset and use it for text generation. From what I understood, BERT sucks at text generation as it uses MLM for training. The ones that do well (gpt,trans-xl,xlnet) don't have a pretrained multilingual model available.\r\n\r\n@Bruno-bai @sakalouski are you looking for training own data for language generation? Coz I have done it for classification and can help with that.", "Hi @thomwolf \r\n\r\n> We'll add an example for fine-tuning this month.\r\n\r\nHas this example been added yet?\r\n\r\nThanks", "Hi @amin-nejad, the example has been added and is available [here](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_lm_finetuning.py).", "Thanks @LysandreJik. Will this also work with Transformer-XL if we just modify the source code to include the Transformer-XL Config, LMHeadModel and Tokenizer as a model class? Or will it require more substantial changes?", "Using `run_lm_finetuning.py` seemingly works for Transformer-XL if we additionally import the Transformer-XL Config, LMHeadModel and Tokenizer and modify the `MODEL_CLASSES` to include them. We also need to provide the `block_size` as a command line parameter. Training curves look reasonable and decoding also happens without errors using `run_generation.py` but the model output is pretty much always just a bunch of equals signs e.g. `= = = = = = = = =` etc. for me at least anyway. Clearly more substantial changes are required to `run_lm_finetuning.py` to make it work. If anyone knows what/why, please let me know", "One thing we should do (maybe when we have some bandwidth for that with @LysandreJik) is to push back a PR to PyTorch repo to add an option to have biases on all clusters of PyTorch's Adaptive Softmax so we can rely on the official Adaptive Softmax implementation instead of having our own.\r\n\r\nThat would make the job of maintaining and upgrading Transformer-XL a lot easier as it's currently the most cumbersome code base to maintain.", "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/pytorch-transformers/pull/872?src=pr&el=h1) Report\n> Merging [#872](https://codecov.io/gh/huggingface/pytorch-transformers/pull/872?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/268c6cc160ba046d6a91747c5f281f82bd88a4d8?src=pr&el=desc) will **increase** coverage by `0.12%`.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/872?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #872 +/- ##\n==========================================\n+ Coverage 78.9% 79.03% +0.12% \n==========================================\n Files 34 34 \n Lines 6192 6228 +36 \n==========================================\n+ Hits 4886 4922 +36 \n Misses 1306 1306\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/872?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [pytorch\\_transformers/tests/optimization\\_test.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/872/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvdGVzdHMvb3B0aW1pemF0aW9uX3Rlc3QucHk=) | `98.97% <100%> (+0.4%)` | :arrow_up: |\n| [pytorch\\_transformers/optimization.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/872/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvb3B0aW1pemF0aW9uLnB5) | `96.62% <100%> (+0.33%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/872?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/pytorch-transformers/pull/872?src=pr&el=footer). Last update [268c6cc...0740e63](https://codecov.io/gh/huggingface/pytorch-transformers/pull/872?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Duplicate of #868" ]

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[ "That's a strange error, what are the exact process you are using and full error log?", "> That's a strange error, what are the exact process you are using and full error log?\r\n\r\nHi, thanks for the reply.\r\nI used distributed training in one node with 2GPUs and my command is:\r\nexport SWAG_DIR=SWAG; export export CUDA_VISIBLE_DEVICES=2,3; python -m torch.distributed.launch --nproc_per_node=2 run_bert_swag.py --bert_model bert-base-uncased --do_train --do_lower_case --do_eval --data_dir $SWAG_DIR/data --train_batch_size 16 --learning_rate 2e-5 --num_train_epochs 1.0 --max_seq_length 80 --output_dir /home/disk1/chengqinyuan/pt_transformer_examples/swag_output --gradient_accumulation_steps 1\r\n\r\nI modified run_swag.py with follow lines:\r\n if args.do_train:\r\n # Save a trained model, configuration and tokenizer\r\n model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self\r\n\r\n # If we save using the predefined names, we can load using `from_pretrained`\r\n output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)\r\n output_config_file = os.path.join(args.output_dir, CONFIG_NAME)\r\n\r\n # torch.save(model.state_dict(), output_model_file)\r\n model_to_save.save_pretrained(args.output_dir)\r\n model_to_save.config.to_json_file(output_config_file)\r\n tokenizer.save_vocabulary(args.output_dir)\r\n\r\n # Load a trained model and vocabulary that you have fine-tuned\r\n model = BertForMultipleChoice.from_pretrained(args.output_dir, num_choices=4)\r\n tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)\r\n else:\r\n model = BertForMultipleChoice.from_pretrained(args.output_dir, num_choices=4)\r\n tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)\r\n model.to(device)\r\n\r\nAnd the error log is:\r\nTraceback (most recent call last):\r\n File \"run_bert_swag.py\", line 571, in <module>\r\n main()\r\n File \"run_bert_swag.py\", line 505, in main\r\n model = BertForMultipleChoice.from_pretrained(args.output_dir)\r\n File \"/home/chengqinyuan/anaconda3/envs/py3/lib/python3.6/site-packages/pytorch_transformers-1.0.0-py3.6.egg/pytorch_transformers/modeling_utils.py\", line 406, in from_pretrained\r\n File \"/home/chengqinyuan/anaconda3/envs/py3/lib/python3.6/site-packages/torch/serialization.py\", line 387, in load\r\n return _load(f, map_location, pickle_module, **pickle_load_args)\r\n File \"/home/chengqinyuan/anaconda3/envs/py3/lib/python3.6/site-packages/torch/serialization.py\", line 581, in _load\r\n deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly)\r\nRuntimeError: storage has wrong size: expected -4807048246308659860 got 589824\r\n\r\nBy the way, when I used the script to save a pre_trained model without fine-tune(I skipped the training process and then saved the model), it can normally load the saved model and do evaluation. But the error would occur when I loaded a fine-tuned model.\r\n\r\nOr could you please give me a brief guideline to execute run_swag.py with pytorch_transformers. I followed the guideline at https://huggingface.co/pytorch-transformers/examples.html and encounter many bugs. Thank you very much!", "I solved this error by use multi-GPU training instead of distributed training, it seems like something wrong in distributed training setting, thanks for your reply : )", "> I solved this error by use multi-GPU training instead of distributed training, it seems like something wrong in distributed training setting, thanks for your reply : )\r\n\r\n我在用单机多卡训练后保存模型也遇到了这个问题，请问你是怎么解决的，用DataParallel还是DistributedDataParallel？\r\n" ]

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[ "This was added in PyTorch 1.1.0 (see [changelog here](https://github.com/pytorch/pytorch/tree/v1.1.0) :)\r\n\r\nSo I guess you just have to update your PyTorch version!", "Oh yes, I guess we can add a replacement to keep older PyTorch compatibility.\r\nWould be sad to lose backward compatibility just for this.", "Is this replacement added in any of the newer versions?" ]

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[ "Indeed, thanks! Fixed in master" ]

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[ "You can train the model on a downstream task to get a sentence vector related to your task or you can get a sentence vector by averaging or max-pooling the output sequence of token hidden-states.", "Try doing:\r\n\r\n```Python\r\nmodel = model_class.from_pretrained(pretrained_weights,\r\n output_hidden_states=True,\r\n output_attentions=True)\r\n\r\nsequence_summary = SequenceSummary(model.config)\r\n\r\nes = torch.tensor([tokenizer.encode(s)])\r\n# sentence embedding\r\nt = sequence_summary(model(es)[0])", "A simple strategy is to take the concatentation of the last hidden state, the mean-pooling, and the max-pooling (tends to be a reasonably good baseline pooling strategy, e.g. [ULMFit](https://arxiv.org/pdf/1801.06146.pdf))", "@cpcdoy hey, and I have a question why the sentence is not stationary.", "So, we should pool over all hidden states, and not just use the hidden state corresponding to `[CLS]`?", "I would go with @rishibommasani's solution for a general \"semantic\" sentence embeddings or fine-tuning and using the `[CLS]` for a task-specific sentence embedding.", "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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[ "Hmm, well that's embarrassing. I'll inspect the failing tests some more to see what's up", "Regarding python 2, yes we want to keep supporting it and thanks for taking care of it.\r\n\r\nGoogle (which is still using python 2) is a major supplier of pretrained model and architectures and having python 2 support in the library make the job of re-implementing the models a lot easier (I can load TF and PT models side-by-side) :)", "I have updated the readme breaking change section on this (ba52fe6)", "Thanks for the feedback: In my latest commits I've updated the documentation as requested and renamed the `return_unused_args` parameter to `return_unused_kwargs` to remove any ambiguity.\r\n\r\nI also removed the unused `*args` parameter from `PreTrainedConfig.from_pretrained`, which is the only actual interface/logic change", "Looks good to me, thanks a lot @xanlsh!" ]

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[ "gtx 1080. Is there any other way to make the predictions faster?", "Hi, you need at least a Volta GPU to get benefits from fp16 unfortunately.", "@thomwolf Does P100 applicable?", "I don't think so", "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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[ "Indeed thanks. We'll subsume this PR with #866 which add a few other stuff.\r\n\r\nI agree with you on the `pop` pattern. We'll move away from this when the first one of these two events happens: (i) google stop open-sourcing interesting new models or (ii) google stop using python 2 internally ;)", "Okay! :+1: " ]

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[ "Fix with #866" ]

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[ "I have borrowed most of the ideas from [utils](https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py#L391) in one of the examples to create a [script to extract embeddings](https://gist.github.com/shubhamagarwal92/37ccb747f7130a35a8e76aa66d60e014). \r\n\r\nHowever, I am still curious if there is any way where we can pass the vocabulary (and tensors) directly instead of passing raw strings?\r\n\r\n", "Hi, I think your example is nice.\r\n\r\nI'm not sure to understand what you are referring to when you want to \"pass the vocabulary (and tensors) directly instead of passing raw strings\". ", "Currently, I am trying to get the Bert embeddings in my encoder before I use `nn.Embedding` instead of pre-computing it. \r\n\r\nThus, I have to convert the tensors to raw strings using `vocab` before passing it through the bert model and hence the gist. ", "Sorry,I want to know why we pass the sentence(Hello, my dog is cute) directly instead of adding some tokens([CLS] Hello, my dog is cute [SEP]) in the @thomwolf .\r\n\r\n```\r\n >>> config = BertConfig.from_pretrained('bert-base-uncased')\r\n >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n >>> model = BertModel(config)\r\n >>> input_ids = torch.tensor(tokenizer.encode(\"Hello, my dog is cute\")).unsqueeze(0) # Batch size 1\r\n >>> outputs = model(input_ids)\r\n >>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple\r\n```", "If you use the function such as [this](https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py#L391), it appends the special tokens automatically. I guess the example you mentioned needs to append these tokens. \r\n\r\n@thomwolf could verify that for you! ", "Yeah we'll add the option to automatically add control tokens. It can be useful.", "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", "> If you use the function such as [this](https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py#L391), it appends the special tokens automatically. I guess the example you mentioned needs to append these tokens.\r\n> \r\n> @thomwolf could verify that for you!\r\n\r\nYes, I understand one can set \"add_special_tokens=True\" for the same when encoding the document. " ]

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[ "Models are usually located under `~/.cache/torch/pytorch_pretrained_bert` (older version of this library) or `~/.cache/torch/pytorch_transformers` (now) :)", "Thank you! I found it elsewhere, actually. What worked for me (quaintly) was:\r\n find . -type f -size +1G -print 2>/dev/null\r\n", "hey, do you guys know where it is stored on windows? thanks", "In my case, it gets stored in \" /tmp/torch\"", "In my case I found a variable containing the default cache path.\r\nRun the following in python:\r\n```\r\nfrom transformers import file_utils\r\nprint(file_utils.default_cache_path)\r\n```\r\n\r\nIf it is not there, check your environmental variables.\r\n\r\nIn my current transformers version `PYTORCH_PRETRAINED_BERT_CACHE`, `PYTORCH_TRANSFORMERS_CACHE` and `TRANSFORMERS_CACHE` can overwrite the default cache path." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/860?src=pr&el=h1) Report\n> Merging [#860](https://codecov.io/gh/huggingface/pytorch-transformers/pull/860?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/2f869dc6651f9cf9253f4c5a43279027a0eccfc5?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `100%`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/860?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #860 +/- ##\n======================================\n Coverage 78.9% 78.9% \n======================================\n Files 34 34 \n Lines 6192 6192 \n======================================\n Hits 4886 4886 \n Misses 1306 1306\n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/860?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [pytorch\\_transformers/tokenization\\_bert.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/860/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvdG9rZW5pemF0aW9uX2JlcnQucHk=) | `93.2% <100%> (ø)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/860?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/pytorch-transformers/pull/860?src=pr&el=footer). Last update [2f869dc...bef0c62](https://codecov.io/gh/huggingface/pytorch-transformers/pull/860?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Great, thanks!" ]

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[ "If you are loading the chinese model, this is probably related to #860 and #825.\r\nShould be fixed now.", "thanks!" ]

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[ "Yes, this one was also mentioned in https://github.com/huggingface/pytorch-transformers/issues/810#issuecomment-512991164.\r\n\r\nIt is fixed now." ]

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[ "You can do that using `XLNetLMHeadModel` and custom masks as shown in the [`run_generation` example](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_generation.py#L115-L121).\r\n\r\nBut note that XLNet is rather bad on short text input completions as I discussed in https://github.com/huggingface/pytorch-transformers/issues/846#issuecomment-514228565", "> #846 (comment)\r\n\r\nThanks for your reply! However, I mean the `XLM` version of BERT, instead of XLNet. Is it also convenient to do that for XLM?\r\n\r\nThanks!", "Oh, right! You can just use `XLMWithLMHeadModel` with an input sequence containing XLM masked token `tokenizer.mask_token` (which is `<special1>` for XLM).", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "If you don't want/cannot to use the built-in download/caching method, you can download both files manually, save them in a directory and rename them respectively `config.json` and `pytorch_model.bin`\r\n\r\nThen you can load the model using `model = BertModel.from_pretrained('path/to/your/directory')`", "What if I try to run a GPT-2 example from docs Quickstart:\r\n```\r\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\r\n...\r\nmodel = GPT2LMHeadModel.from_pretrained('gpt2')\r\n```\r\nand get this\r\n```\r\nINFO:pytorch_transformers.file_utils:https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json not found in cache, downloading to C:\\Users\\KHOVRI~1\\AppData\\Local\\Temp\\tmprm150emm\r\nERROR:pytorch_transformers.tokenization_utils:Couldn't reach server to download vocabulary.\r\n```\r\n\r\nWhere should I put vocab file and get other files for GPT-2? I work under corporate proxy, maybe there is a way to write this proxy into the sort of config?", "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", "> 错误：pytorch_transformers.modeling_utils：无法到达位于https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json的服务器，无法下载经过预先​​训练的模型配置文件。\r\n> 错误：pytorch_transformers.modeling_utils：无法到达位于“ https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin ”的服务器以下载预先训练的权重。\r\n> 错误：pytorch_transformers.tokenization_utils：无法访问服务器以下载词汇。\r\n> \r\n> 如果手动将这两个文件下载到某个路径，如何指向这两个文件？\r\nI also encountered such a problem, the network speed is very slow, can not get off.However, I ran several times, about 10 times, and finally successfully ran without any error\r\n", "Same question. Thank you.", "> If you don't want/cannot to use the built-in download/caching method, you can download both files manually, save them in a directory and rename them respectively `config.json` and `pytorch_model.bin`\r\n> \r\n> Then you can load the model using `model = BertModel.from_pretrained('path/to/your/directory')`\r\n\r\nFor posterity, those who get errors because of missing vocab.txt despite doing above, you can get it at `https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt` and also rename it to `vocab.txt` in desired folder. Resolved my errors.", "Hi swayson,\r\n\r\nmodel = BertModel.from_pretrained('path/to/your/directory')\r\n\r\nwhere we need to add above line of code for loading model?", "You can find all the models here [https://stackoverflow.com/a/64280935/251674](https://stackoverflow.com/a/64280935/251674)", "> If you don't want/cannot to use the built-in download/caching method, you can download both files manually, save them in a directory and rename them respectively `config.json` and `pytorch_model.bin`\r\n> \r\n> Then you can load the model using `model = BertModel.from_pretrained('path/to/your/directory')`\r\n\r\nso great!", "I tried downloading these models and then upload it in Jupyter lab to use in `Styleformer ` package. But the result seems to be broken. It works fine in Google Colab but fails when I try to manually upload and run. \r\n\r\nModels: https://huggingface.co/prithivida/informal_to_formal_styletransfer\r\n https://huggingface.co/prithivida/parrot_adequacy_on_BART", "In my case, I want to load gpt2 pretrained model locally.\r\n- First I download config.json and pytorch_model.bin from [hugginface model zoo](https://huggingface.co/gpt2/tree/main)\r\nWhen I execute code below:\r\n`gpt2_tok = GPT2Tokenizer.from_pretrained(myfolderpath, do_lower_case=False)`\r\nSome error occurs like:\r\n`TypeError: expected str bytes or os.pathlike object not nonetype`\r\n\r\n- Later I follow the instruction @swayson provides to download vocab.json.\r\nSame error occurs again but it indicates that I may need some kind \"merges\" file:\r\n`with open(merges_file, encoding=\"utf-8\") as merges_handle\r\n...\r\nTypeError: expected str bytes or os.pathlike object not nonetype`\r\n\r\n- So I go back to [hugginface model zoo](https://huggingface.co/gpt2/tree/main) and download merges.txt.\r\nFinally the code is executed successfully and the encoding process also works.\r\n\r\nHope this helps someone who also suffers from the internet connection problem." ]

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[ "I don't understand your question.\r\n\r\nCan you give more details and point to the exact code lines you are referring to?\r\n\r\nYou cannot provide specific position indices to XLNet if that's what you are trying to do. You have to use the built-in relative embeddings.", "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", "For new PyTorch 1.0, the syntax should be `torch.eisum('i,d->id', pos_seq, inv_freq) `", "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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[ "Have you read in detail the [migration guide](https://github.com/huggingface/pytorch-transformers#migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) of the readme?\r\n\r\nThere is also a new `run_glue` example which is an updated version of the previous `run_classifier` and that you can use as a starting point for designing your own fine-tuning scripts.", "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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[ "Do you have a file named `biobert_v1.1_pubmed/config.json` as mentioned in the error?", "Oh thanks. There is a config file name \"bert_config.json\" in the directory \"biobert_v1.1_pubmed/\". I changed the file name to \"config.json\" and it works! \r\nI am wondering why the pytorch-pretrained-bert can load the checkpoint. Maybe it reads \"bert_config.json\" and pytorch-transformer reads \"config.json\"?" ]

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[ "It should be fine. There are probably your output losses.", "@thomwolf Thanks for your reply. Could you explain more about why this happens ? I am still confused though", "Maybe it is caused by calculating the loss in the model's forward function.\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I have the same problem in version 2.2.1, what can it be?", "In my case, it doesn’t influence the results\n\nOn Mon, Jan 6, 2020 at 5:19 AM calusbr <[email protected]> wrote:\n\n> I have the same problem in version 2.2.1, what can it be?\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/transformers/issues/852?email_source=notifications&email_token=AKRMVV4J4UT6HCXEEMOYBGDQ4MVV3A5CNFSM4IFUKP3KYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEIFNJAI#issuecomment-571135105>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AKRMVV37AGO5AXJNPSGUVLTQ4MVV3ANCNFSM4IFUKP3A>\n> .\n>\n", "In my case, the speed of training with 4 GPU is the same as 1 GPU. How solve the speed issues?", "👀", "> In my case, the speed of training with 4 GPU is the same as 1 GPU. How solve the speed issues?\r\n\r\n@sociengineer , sorry for bothering you, may I ask if you have solved this problem as I meet the same one. Thanks!", "> > In my case, the speed of training with 4 GPU is the same as 1 GPU. How solve the speed issues?\r\n> \r\n> @sociengineer , sorry for bothering you, may I ask if you have solved this problem as I meet the same one. Thanks!\r\n\r\n@XLechter I couldn't resolve the problem 😭" ]

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[ "Which model were you resizing?", "I'm working on chinese BertForPreTraining model", "This is strange because Bert's LM head has no bias...\r\n\r\nWould need to have a more complete error message to be able to understand.", "```python\r\nclass BertLMPredictionHead(nn.Module):\r\n def __init__(self, config):\r\n super(BertLMPredictionHead, self).__init__()\r\n self.transform = BertPredictionHeadTransform(config)\r\n\r\n # The output weights are the same as the input embeddings, but there is\r\n # an output-only bias for each token.\r\n self.decoder = nn.Linear(config.hidden_size,\r\n config.vocab_size,\r\n bias=False)\r\n\r\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\r\n\r\n def forward(self, hidden_states):\r\n hidden_states = self.transform(hidden_states)\r\n hidden_states = self.decoder(hidden_states) + self.bias\r\n return hidden_states\r\n```\r\n```python\r\nself.bias = nn.Parameter(torch.zeros(config.vocab_size))\r\n```\r\nFor example, we load the pretrained model whose vocab size is 23189, and i add 1000 tokens and call resize_token_embeddings. Since the decoder weight is actually embedding weight, they are reshaped to (24189, hidden_size), but the bias is still 23189.", "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, I'll add a detailed example for this method in the coming weeks (update of the bertology script).\r\n\r\nThis can be used to remove heads in the model following the work of [Michel et al. (Are Sixteen Heads Really Better than One?)](http://arxiv.org/abs/1905.10650) among others.", "Thanks a lot！", "Hi @thomwolf, would it be possible to provide an example on how to prune or select some heads for a layer? when i just change the config file by setting \r\nconfig.pruned_heads = {11:[1,2,3]} and use it in initializing the model, it throws an error.\r\n```\r\nsize mismatch for bert.encoder.layer.11.attention.self.query.weight: copying a param with shape torch.Size([768\r\nurrent model is torch.Size([576, 768]). and more. \r\n```\r\nso, the default query,key and vaule are set with 768 dim. \r\nI assume we can not just prune heads and still load the pre-trained model because the word embedding and layer norm was setup up with 768 dim. ", "meanwhile i came across bertology.py script and realize that we can save a model after pruning. that works fine for me. now, i'm trying to load the saved model, and I get the opposite error. \r\n```\r\nsize mismatch for bert.encoder.layer.11.attention.self.query.weight: copying a param with shape torch.Size([576, 768]) from checkpoint, the sh\r\nape in current model is torch.Size([768, 768]).\r\n```\r\nthe error wouldn't go away after even changing the config file. " ]

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[ "It's hard to tell what could be the source of the error without describing with some minimum details what you tried to do/run and which version of the code you are running...\r\n\r\n_If_ you are running `run_glue.py` in the examples, make sure that [`utils_glue.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py) is present in the same working directory in which you are executing the script.", "Thanks David. Turns out that the import in e.g., `run_glue.py` should be \r\n\r\n`from examples.utils_glue import ...`\r\n\r\ninstead of \r\n\r\n`from utils_glue import ...`\r\n\r\nMaybe it's a problem of PyCharm IDE", "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. At the moment, this library is designed for loading pretrained models mostly and no one has open-sourced a Transformer-XL pretrained model using something else than adaptive softmax so I have not spent time adding these options.\r\n\r\nHappy to welcome PR though.\r\n\r\nThe main thing of interest here, if you want to give it a try would be to add bias to all clusters in PyTorch official [`AdaptiveLogSoftmaxWithLoss`](https://pytorch.org/docs/stable/nn.html?highlight=adaptivelogsoftmaxwithloss#torch.nn.AdaptiveLogSoftmaxWithLoss) module so we could just use the official implementation without maintaining ours.", "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/pytorch-transformers/pull/847?src=pr&el=h1) Report\n> Merging [#847](https://codecov.io/gh/huggingface/pytorch-transformers/pull/847?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/a615499076a67dceb8907ecdf8eadaff04bb8d6a?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/847?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #847 +/- ##\n======================================\n Coverage 78.9% 78.9% \n======================================\n Files 34 34 \n Lines 6192 6192 \n======================================\n Hits 4886 4886 \n Misses 1306 1306\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/847?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/pytorch-transformers/pull/847?src=pr&el=footer). Last update [a615499...76be189](https://codecov.io/gh/huggingface/pytorch-transformers/pull/847?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Indeed!" ]

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[ "I solved one of the problems, using another way to load the model described bellow, but still it works way worse than BERT.\r\n \r\n tokenizer = XLNetTokenizer.from_pretrained(\"xlnet-large-cased\")\r\n model = XLNetLMHeadModel.from_pretrained(\"xlnet-large-cased\")\r\n model.eval()\r\n if torch.cuda.is_available(): model.to('cuda') #if we have a GPU \r\n target_id = 5\r\n input_ids = torch.tensor(tokenizer.encode(\"I believe my sister is <mask> because she eats a lot of vegetables .\")).unsqueeze(0) # We will predict the masked token\r\n perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)\r\n perm_mask[:, :, target_id] = 1.0 # Previous tokens don't see last token\r\n target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float) # Shape [1, 1, seq_length] => let's predict one token\r\n target_mapping[0, 0, target_id] = 1.0 # Our first (and only) prediction will be the last token of the sequence (the masked token)\r\n \r\n input_ids_tensor = input_ids.to(\"cuda\")\r\n target_mapping_tensor = target_mapping.to(\"cuda\")\r\n perm_mask_tensor = perm_mask.to(\"cuda\")\r\n \r\n with torch.no_grad():\r\n predictions = model(input_ids_tensor, perm_mask=perm_mask_tensor, target_mapping=target_mapping_tensor)\r\n \r\n predicted_k_indexes = torch.topk(predictions[0][0][0],k=10)\r\n predicted_logits_list = predicted_k_indexes[0] \r\n predicted_indexes_list = predicted_k_indexes[1] \r\n \r\n \r\n print (\"predicted word:\",tokenizer.decode(input_ids[0][target_id].item()))\r\n for i,item in enumerate(predicted_indexes_list):\r\n the_index = predicted_indexes_list[i].item()\r\n print(\"word and logits\",tokenizer.decode(the_index),predicted_logits_list[i].item())\r\n\r\n\r\nBut the output is not so good, i believe Bert is better. I hope this is correct code to get masked word inside a sentence.\r\n\r\nI am not sure if this line should be any different:\r\n \r\n perm_mask[:, :, target_id] = 1.0 # Previous tokens don't see last token\r\n\r\n\r\n\r\n\r\noutput:\r\n\r\n sentence: \"I believe my sister is <mask> because she is a blonde .\"\r\n predicted word: <mask>\r\n word and logits is -30.468482971191406\r\n word and logits the -33.0710334777832\r\n word and logits was -34.586158752441406\r\n word and logits because -34.74900436401367\r\n word and logits in -34.762718200683594\r\n word and logits that -34.86489486694336\r\n word and logits but -34.97043991088867\r\n word and logits and -35.04599380493164\r\n word and logits if -35.07524108886719\r\n word and logits not -35.1640510559082\r\n\r\nwhen i do not use perm_mask and call only: \r\n\r\n predictions = model(input_ids_tensor, target_mapping=target_mapping_tensor)\r\n\r\nI get a better, but still quite bad results, but it is at least interesting.\r\n \r\n sentence: \"I believe my sister is <mask> because she is a blonde .\"\r\n predicted word: <mask>\r\n word and logits Colombian 25.14841651916504\r\n word and logits a 25.1247615814209\r\n word and logits the 25.11375617980957\r\n word and logits Venezuelan 25.041296005249023\r\n word and logits I 24.912843704223633\r\n word and logits Beyonce 24.855722427368164\r\n word and logits Jessica 24.557470321655273\r\n word and logits in 24.518535614013672\r\n word and logits paranoid 24.407917022705078\r\n word and logits not 24.374282836914062\r\n\r\n\r\nWith bert base you get much better output, that makes much more sense [mainly adjectives]:\r\n\r\n [('beautiful', 7.622010231018066), ('attractive', 6.6926116943359375), ('special', 6.309513568878174), ('crazy', 6.045520782470703), ('pretty', 5.968326091766357), ('lucky', 5.951317310333252), ('famous', 5.942074775695801), ('different', 5.920231819152832), ('gorgeous', 5.897611141204834), ('blonde', 5.834926605224609)]\r\n \r\n \r\n\r\n", "I also did comparasion with Bert, so far just one example, but I found that Bert is much much better. I am not sure why is that ... but there must be a reason. \r\n\r\n", "Agreed! There is a chance we are not using the permutation mask and target mapping correctly, but I am suspicious as the documentation's example is not working very well either.", "The main reason you get bad performance is that XLNet is not good on short inputs (comes from the way it is pretrained, always having a long memory and only guessing a few words in the sequence).\r\n\r\nThe `run_generation` example [here](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_generation.py) will show you how to get better performances by adding a random text as initiator.\r\n\r\nAman Rusia also wrote a blog post about that [here](https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e). We are using his solution in the `run_generation` example.\r\n ", "Thanks, I am going to try the generation method and post the results here. Hope prediction is going to improve, but i guess that ading a lot of padding is make to slow the execution down a lot.", "@Oxi84 Any luck with your results ? I still get pretty random results even while using this trick", "Thank you suggestions. \r\n\r\nAfter adding padding text, result is much more reasonable for predicting both middle masked token and text generation.\r\n\r\nSome texting sample:\r\nInput\r\n`text = 'The quick brown fox jumps <mask> the lazy dog.'`\r\nOutput\r\n```\r\nThe quick brown fox jumps above the lazy dog.\r\nThe quick brown fox jumps across the lazy dog.\r\n```\r\n\r\nInput\r\n`text = 'The <mask> brown fox jumps over the lazy dog.'`\r\nOutput\r\n```\r\nThe rapid brown fox jumps over the lazy dog.\r\nThe slow brown fox jumps over the lazy dog.\r\n```\r\n", "hey guys, \r\n\r\nQ1)\r\ncan someone give some more insight what @thomwolf explaining about?\r\n'''\r\nhttps://github.com/huggingface/transformers/issues/846\r\nThe main reason you get bad performance is that XLNet is not good on short inputs (comes from the way it is pretrained, always having a long memory and only guessing a few words in the sequence).\r\n\r\nThe run_generation example here will show you how to get better performances by adding a random text as initiator.\r\nAman Rusia also wrote a blog post about that here. We are using his solution in the run_generation example.\r\n'''\r\n I can't understand the difference the way both Bert and XLnetLM works for LMhead task. \r\nAren't both model having disadvantages if they have short sentence? \r\n\r\nIt seems he said **XLnet has huge disadvantage** on short input sentence \r\nwhile Bert does not(or has less disadvantage). Any detail explanation could be useful ! \r\n\r\nQ2)\r\nAlso, I can't get the point of adding extra padding or adding random padding things to improve XLnetLMHead model. Any snippet or explanation could be appreciated too...(saw the link but could not fully understood). I experimented by just adding extra strings of line:'I believe my sister is <mask> because she is a blonde ' + '<eod> </s> <eos>' and it gives much better result than not having <eod> </s> <eos> at the end....\r\n\r\nQ3)\r\nhttps://github.com/huggingface/transformers/issues/846#issuecomment-513514039\r\nLastly, why do we have better result when we don't use perm_mask ? above link response shows that \r\nnot having perm_mask option does give at least better result...But isn't perm_mask supposed to help to get better prediction and what author of paper used for SOTA ?\r\n\r\nisn't perm_mask allow model to not seeing the next <mask> tokens in the given input while can see the previous <mask> tokens? According to the paper and the original code, I could see that if permute order is 3->4->1->2, mask=1,3, then model cannot see masked<1> when it tried to predict masked<3> but the reverse is possible.\r\n\r\nMany thanks in advance ! ", "I think these questions are not directly related to this repo. Maybe you should check out the [paper](https://arxiv.org/abs/1906.08237) or ask on [quora](https://www.quora.com/) or on [researchgate](https://explore.researchgate.net/display/support/Asking+questions)", "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/pytorch-transformers/pull/845?src=pr&el=h1) Report\n> Merging [#845](https://codecov.io/gh/huggingface/pytorch-transformers/pull/845?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/a615499076a67dceb8907ecdf8eadaff04bb8d6a?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/845?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #845 +/- ##\n======================================\n Coverage 78.9% 78.9% \n======================================\n Files 34 34 \n Lines 6192 6192 \n======================================\n Hits 4886 4886 \n Misses 1306 1306\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/845?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/pytorch-transformers/pull/845?src=pr&el=footer). Last update [a615499...f63ff53](https://codecov.io/gh/huggingface/pytorch-transformers/pull/845?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "LGTM, thanks @rabeehk" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/844?src=pr&el=h1) Report\n> Merging [#844](https://codecov.io/gh/huggingface/pytorch-transformers/pull/844?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/a615499076a67dceb8907ecdf8eadaff04bb8d6a?src=pr&el=desc) will **not change** coverage.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/pytorch-transformers/pull/844?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #844 +/- ##\n======================================\n Coverage 78.9% 78.9% \n======================================\n Files 34 34 \n Lines 6192 6192 \n======================================\n Hits 4886 4886 \n Misses 1306 1306\n```\n\n\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/844?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/pytorch-transformers/pull/844?src=pr&el=footer). Last update [a615499...6b3d9ad](https://codecov.io/gh/huggingface/pytorch-transformers/pull/844?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]