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https://api.github.com/repos/huggingface/transformers/issues/108 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/108/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/108/comments | https://api.github.com/repos/huggingface/transformers/issues/108/events | https://github.com/huggingface/transformers/issues/108 | 389,346,652 | MDU6SXNzdWUzODkzNDY2NTI= | 108 | Does max_seq_length specify the maxium number of words | {
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"`max_seq_length` specifies the maximum number of tokens of the input. The number of token is superior or equal to the number of words of an input. \r\n\r\nFor example, the following sentence:\r\n\r\n```\r\nThe man hits the saxophone and demonstrates how to properly use the racquet.\r\n```\r\n\r\nis tokenized as fo... | 1,544 | 1,689 | 1,544 | NONE | null | I'm trying to figure out how the `--max_seq_length` parameter works in [run_classifier](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py). Based on the source, it seems like it represents the number of words? Is that correct? | {
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https://api.github.com/repos/huggingface/transformers/issues/107 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/107/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/107/comments | https://api.github.com/repos/huggingface/transformers/issues/107/events | https://github.com/huggingface/transformers/pull/107 | 389,227,363 | MDExOlB1bGxSZXF1ZXN0MjM3MjYyNzEy | 107 | Fix optimizer to work with horovod | {
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"Great thanks!"
] | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/106 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/106/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/106/comments | https://api.github.com/repos/huggingface/transformers/issues/106/events | https://github.com/huggingface/transformers/issues/106 | 389,201,876 | MDU6SXNzdWUzODkyMDE4NzY= | 106 | Picking max_sequence_length in run_classifier.py CoLA task | {
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"As mentioned in #89, the maximum value of `max_sequence_length` is 512. ",
"@rodgzilla thanks!"
] | 1,544 | 1,544 | 1,544 | NONE | null | Is there an upper bound for the max_sequence_length parameter when using run_classifier.py with CoLA task?
When I tested with the default max_sequence_length of 128, everything worked good, but once I changed it to something else, eg 1024, it started the training and failed on the first iteration with the error show... | {
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https://api.github.com/repos/huggingface/transformers/issues/105 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/105/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/105/comments | https://api.github.com/repos/huggingface/transformers/issues/105/events | https://github.com/huggingface/transformers/issues/105 | 388,994,586 | MDU6SXNzdWUzODg5OTQ1ODY= | 105 | weights initialized two times | {
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"I think it required for both the places. Because both of them can be used individually. As it is mentioned in the README.md file, the model can be loaded with 7 classes. In fact if you check `BertForMaskedLM` and `BertForNextSentencePrediction` classes it also has the weights initialised.\r\n\r\nPlease correct me ... | 1,544 | 1,544 | 1,544 | NONE | null | Hi,
I found that you initilized all weights twice:
The first one is in BertModel class:
https://github.com/huggingface/pytorch-pretrained-BERT/blob/3ba5470eb85464df62f324bea88e20da234c423f/pytorch_pretrained_bert/modeling.py#L586
And the second one is in classes of each tasks such as in BertForSequenceClass... | {
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https://api.github.com/repos/huggingface/transformers/issues/104 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/104/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/104/comments | https://api.github.com/repos/huggingface/transformers/issues/104/events | https://github.com/huggingface/transformers/issues/104 | 388,930,579 | MDU6SXNzdWUzODg5MzA1Nzk= | 104 | BERT for classification example training files | {
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"Please read the [example section in the readme](https://github.com/huggingface/pytorch-pretrained-BERT#fine-tuning-with-bert-running-the-examples)"
] | 1,544 | 1,544 | 1,544 | NONE | null | Are there any example training files for `run_classifier.py`? | {
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https://api.github.com/repos/huggingface/transformers/issues/103 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/103/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/103/comments | https://api.github.com/repos/huggingface/transformers/issues/103/events | https://github.com/huggingface/transformers/issues/103 | 388,915,407 | MDU6SXNzdWUzODg5MTU0MDc= | 103 | Words after tokenization replaced with # | {
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"Because it uses WordPiece tokenization, and will introduce the `#` token.\r\nCheck: https://github.com/google-research/bert#tokenization",
"@ymcui okay sweet, thank you. Will use the relevant one. ",
"@ymcui How do I change this ? or is not possible to do so?",
"1. If you are training completely from scratch... | 1,544 | 1,544 | 1,544 | NONE | null | Hello,
When training the bert-base-multilingual-cased model for Question and Answering, I see that the tokens look like this :
```tokens: [CLS] what is the ins ##ured _ name ? [SEP] versi ##cherung ##ss ##che ##in erg ##o hau ##srat ##versi ##cherung hr - sv 927 ##26 ##49 ##2 ```
Any idea why words are gettin... | {
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https://api.github.com/repos/huggingface/transformers/issues/102 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/102/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/102/comments | https://api.github.com/repos/huggingface/transformers/issues/102/events | https://github.com/huggingface/transformers/issues/102 | 388,901,365 | MDU6SXNzdWUzODg5MDEzNjU= | 102 | How to modify the model config? | {
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"The problem is because of the `max_position_embeddings` default size is 512 and it is exceeding in the case of my input as I mentioned. For now I have just made hack by hard coding it directly in the [modelling.py](url) file directly 😅. Yet need to know, where to find the bert_config.json file and changing it th... | 1,544 | 1,627 | 1,544 | NONE | null | Well I am trying to generate embedding for a large sentence. I get this error
> Traceback (most recent call last):
all_encoder_layers, _ = model(input_ids, token_type_ids=None, attention_mask=input_mask)
File "/Users/venv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
re... | {
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https://api.github.com/repos/huggingface/transformers/issues/101 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/101/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/101/comments | https://api.github.com/repos/huggingface/transformers/issues/101/events | https://github.com/huggingface/transformers/pull/101 | 388,788,249 | MDExOlB1bGxSZXF1ZXN0MjM2OTczMzA0 | 101 | Adding --do_lower_case for all uncased BERTs examples | {
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"Indeed, thanks for that!"
] | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | I had missed those, it should make sense to use them | {
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https://api.github.com/repos/huggingface/transformers/issues/100 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/100/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/100/comments | https://api.github.com/repos/huggingface/transformers/issues/100/events | https://github.com/huggingface/transformers/issues/100 | 388,713,951 | MDU6SXNzdWUzODg3MTM5NTE= | 100 | Squad dataset has multiple answers to a question. | {
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"Hi,\r\nIn `train-v2.0.json`, there is only one answer for the question.\r\nIn `dev-v2.0.json` and hidden `test-v2.0.json`, there are several answers for a given question.\r\nI think the code that you mentioned is designed for not mistakenly using `dev-v2.0.json` for training. If you are going to use your own data ... | 1,544 | 1,544 | 1,544 | NONE | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/3ba5470eb85464df62f324bea88e20da234c423f/examples/run_squad.py#L143
The confusing part here is that in line 146, only the first answer is considered, so I am wondering why is there a check for multiple answers before.
Also, SQuad dataset has multiple answe... | {
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https://api.github.com/repos/huggingface/transformers/issues/99 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/99/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/99/comments | https://api.github.com/repos/huggingface/transformers/issues/99/events | https://github.com/huggingface/transformers/issues/99 | 388,660,542 | MDU6SXNzdWUzODg2NjA1NDI= | 99 | run_squad.py stuck on batch size greater than 1 | {
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"Please copy paste the command you are using to run this example.",
"Here you go\r\n\r\n```\r\npython ./run_squad.py \r\n --bert_model bert-base-uncased \\\r\n --do_train \\\r\n --do_predict \\\r\n --train_file $SQUAD_DIR/train-v1.1.json \\\r\n --predict_file $SQUAD_DIR/dev-v1.1.json \\\r\n --learning_rate ... | 1,544 | 1,550 | 1,544 | NONE | null | Thanks a lot for the code! I need help figuring out why the script is not working so long the batch_size is set to be above 1. Specifically, it seems to be stuck at Line 908: loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions). I am using 4 k80. Thanks! | {
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https://api.github.com/repos/huggingface/transformers/issues/98 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/98/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/98/comments | https://api.github.com/repos/huggingface/transformers/issues/98/events | https://github.com/huggingface/transformers/issues/98 | 388,660,132 | MDU6SXNzdWUzODg2NjAxMzI= | 98 | Problem about convert TF model and pretraining | {
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"Hi @zhezhaoa, I see, I will fix this in the next release.\r\n\r\nFor now you should be able to fix that by installing the repo from source (git clone the repo and `pip install -e .` and changing [line 53 of convert_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pyto... | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | First of all, Thank you for this great job. I use the official tensorflow implementation to pretrain on my corpus and then save the model. I want to convert this model to pytorch format and use it, but I got the error:
Traceback (most recent call last):
File "convert_tf_checkpoint_to_pytorch.py", line 105, in <mo... | {
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https://api.github.com/repos/huggingface/transformers/issues/97 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/97/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/97/comments | https://api.github.com/repos/huggingface/transformers/issues/97/events | https://github.com/huggingface/transformers/issues/97 | 388,470,290 | MDU6SXNzdWUzODg0NzAyOTA= | 97 | RuntimeError: cuda runtime error (59) : device-side assert triggered | {
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"And here is the trace when running in cpu.\r\n```\r\n File \"/data/home/liuyang/dlab/dlab/embedder/stack_embedder.py\", line 23, in embed\r\n present, _ = embedder(batch_sentence)\r\n File \"/data/home/liuyang/pyenv/bert-pyt-p3/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 477, in __call__\r\... | 1,544 | 1,654 | 1,544 | NONE | null | I got this error when using bert model to get the present as a feature for training. Could anyone can help? Thanks a lot.
Here is the cuda and python trace.
```
/pytorch/aten/src/THC/THCTensorIndex.cu:362: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, ... | {
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https://api.github.com/repos/huggingface/transformers/issues/96 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/96/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/96/comments | https://api.github.com/repos/huggingface/transformers/issues/96/events | https://github.com/huggingface/transformers/pull/96 | 388,342,497 | MDExOlB1bGxSZXF1ZXN0MjM2NjI5NTA3 | 96 | BertForMultipleChoice and Swag dataset example. | {
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"Hi Gregory, I will take some time to review and test that this week.\r\n\r\nJust a word on additional dependencies, I would like to keep the package as light as possible (currently it's aligned with the dependencies of AllenNLP) so if you can manage to avoid adding any additional dependency it would be better.",
... | 1,544 | 1,606 | 1,544 | CONTRIBUTOR | null | Hi!
This is the code that enables Bert models to be used for Multiple Choice problems (such as [Swag](https://github.com/rowanz/swagaf) and [ROCStories](http://cs.rochester.edu/nlp/rocstories/).
For my implementation, I use the algorithm described in #90 and issue [#38](https://github.com/google-research/bert/is... | {
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https://api.github.com/repos/huggingface/transformers/issues/95 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/95/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/95/comments | https://api.github.com/repos/huggingface/transformers/issues/95/events | https://github.com/huggingface/transformers/issues/95 | 388,242,901 | MDU6SXNzdWUzODgyNDI5MDE= | 95 | Not updating the BERT embeddings during the fine tuning process | {
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"You can do it by setting the `requires_grad` attribute of the embedding layer in `BertModel`. That will look something like this: \r\n\r\n```\r\n model = BertForQuestionAnswering.from_pretrained(args.bert_model,\r\n cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank... | 1,544 | 1,642 | 1,544 | NONE | null | Is there any way of not updating the BERT embeddings during the fine tuning process? For example while running on SQUAD, I want to see the effect of not updating the parameters associated with the BERT embeddings. I saw that `required_grad` is set to True for cpu and fp16. Which makes me think that it's assuming `do_gr... | {
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https://api.github.com/repos/huggingface/transformers/issues/94 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/94/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/94/comments | https://api.github.com/repos/huggingface/transformers/issues/94/events | https://github.com/huggingface/transformers/pull/94 | 388,188,357 | MDExOlB1bGxSZXF1ZXN0MjM2NTA5NjIw | 94 | Fixing the commentary of the `SquadExample` class. | {
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https://api.github.com/repos/huggingface/transformers/issues/93 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/93/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/93/comments | https://api.github.com/repos/huggingface/transformers/issues/93/events | https://github.com/huggingface/transformers/pull/93 | 388,026,951 | MDExOlB1bGxSZXF1ZXN0MjM2Mzg2MDI5 | 93 | Zoeliao/dev | {
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https://api.github.com/repos/huggingface/transformers/issues/92 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/92/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/92/comments | https://api.github.com/repos/huggingface/transformers/issues/92/events | https://github.com/huggingface/transformers/issues/92 | 387,903,721 | MDU6SXNzdWUzODc5MDM3MjE= | 92 | Bert uncased and Bert large giving much lower results than Bert cased base | {
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"Any specific example that we could investigate?",
"I've implemented a version of SQuAD 2.0 on top of the current SQuAD that is similar to the way Google implemented their's on the official Bert repo. The base cased model works fine, but I noticed that uncased models tend to give worse results, even the large mod... | 1,544 | 1,640 | 1,544 | NONE | null | Is there a reason why the Bert uncased model and the Bert large model give lower results that the cased model on downstream tasks? | {
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https://api.github.com/repos/huggingface/transformers/issues/91 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/91/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/91/comments | https://api.github.com/repos/huggingface/transformers/issues/91/events | https://github.com/huggingface/transformers/pull/91 | 387,862,855 | MDExOlB1bGxSZXF1ZXN0MjM2MjU4MzE3 | 91 | run_classifier.py improvements | {
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"Neat!"
] | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | Hi !
This PR contains multiple improvements to the `run_classifier.py` file.
The changes are:
- removing trailing whitespaces ([PEP 8](https://www.python.org/dev/peps/pep-0008/)),
- simplifying a bit the data processing code, in particular tensor formatting,
- fixing issue #83 by adapting the value of the `nu... | {
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https://api.github.com/repos/huggingface/transformers/issues/90 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/90/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/90/comments | https://api.github.com/repos/huggingface/transformers/issues/90/events | https://github.com/huggingface/transformers/issues/90 | 387,770,421 | MDU6SXNzdWUzODc3NzA0MjE= | 90 | Fine tuned to Multi-choice dataset? | {
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"Yes it is, the code is not written yet but I'm planning to work on it. The idea is to format the input data the same way the authors of [Improving Language Understanding with Unsupervised Learning](https://blog.openai.com/language-unsupervised/)\r\n\r\n\r\n\r\... | 1,544 | 1,602 | 1,544 | NONE | null | Is it posible to fine tuned to the multi choices problems , which usually has one passage, question and ABCD four options? | {
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https://api.github.com/repos/huggingface/transformers/issues/89 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/89/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/89/comments | https://api.github.com/repos/huggingface/transformers/issues/89/events | https://github.com/huggingface/transformers/issues/89 | 387,683,054 | MDU6SXNzdWUzODc2ODMwNTQ= | 89 | bert-base-multilingual-cased - Text bigger than 512 | {
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"Hello,\r\n\r\nI do not think that it is possible out of the box. The article states the following:\r\n\r\n> We use learned positional embeddings with supported sequence lengths up to 512 tokens.\r\n\r\nThe positional embeddings are therefore limited to 512 tokens. You may be able to add positional embeddings for p... | 1,544 | 1,544 | 1,544 | CONTRIBUTOR | null | Hello,
I am trying to extract features from German text using bert-base-multilingual-cased. However, my text is bigger than 512 words.
Is there any way to use the pertained Bert for text greater than 512 words | {
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https://api.github.com/repos/huggingface/transformers/issues/88 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/88/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/88/comments | https://api.github.com/repos/huggingface/transformers/issues/88/events | https://github.com/huggingface/transformers/issues/88 | 387,286,653 | MDU6SXNzdWUzODcyODY2NTM= | 88 | Error when calculating loss and running backward | {
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"I probably know the bug. The final output layer is for binary classification but I use it for 4-class classification. I thought BERT can automatically decide between sigmoid and soft max. I will replace it with my own classifier tomorrow and see how it goes.",
"The mismatched output size between BERT and our dat... | 1,543 | 1,543 | 1,543 | NONE | null | I'm using the sentence classification example. I used my own dataset for emotionclassification (4 classes).
The hyper-parameters are as follows:
<pre>
args.max_seq_length = 100
args.do_train = True
args.do_eval = True
args.do_lower_case = True
args.train_batch_size = 32
args.eval_batch_size = 8
args.learning... | {
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https://api.github.com/repos/huggingface/transformers/issues/87 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/87/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/87/comments | https://api.github.com/repos/huggingface/transformers/issues/87/events | https://github.com/huggingface/transformers/pull/87 | 387,269,400 | MDExOlB1bGxSZXF1ZXN0MjM1Nzk1ODUx | 87 | Readme file links | {
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"Thanks Grégory!"
] | 1,543 | 1,544 | 1,544 | CONTRIBUTOR | null | Adding links to examples files in `README.md`. | {
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https://api.github.com/repos/huggingface/transformers/issues/86 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/86/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/86/comments | https://api.github.com/repos/huggingface/transformers/issues/86/events | https://github.com/huggingface/transformers/issues/86 | 387,233,714 | MDU6SXNzdWUzODcyMzM3MTQ= | 86 | code in run_squad.py line 263 | {
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"\r\n",
"Hi, what is your question?",
"Strictly speaking, the zero-padding in segment_ids leads to ambiguous tensor entries, because 0 can mean both \"first sentence\" (or query in another task?) and \"... | 1,543 | 1,544 | 1,544 | NONE | null | # Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
in segment_ids array,1 indicates token from passage and 0 indicate token form query.
when padding,why segment_ids filled with 0,which represents que... | {
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https://api.github.com/repos/huggingface/transformers/issues/85 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/85/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/85/comments | https://api.github.com/repos/huggingface/transformers/issues/85/events | https://github.com/huggingface/transformers/issues/85 | 387,100,844 | MDU6SXNzdWUzODcxMDA4NDQ= | 85 | How to use pre-trained SQUAD model? | {
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"Hi there are now examples on how you can save and reload the models in the examples (`run_classifier`, `run_squad` and `run_swag`)"
] | 1,543 | 1,544 | 1,544 | CONTRIBUTOR | null | After training squad, I have a model file in a local folder:
```
-rw-rw-r-- 1 khashab2 cs_danr 4.7M Nov 21 19:20 dev-v1.1.json
-rw-rw-r-- 1 khashab2 cs_danr 3.4K Nov 29 22:52 evaluate-v1.1.py
drwxrwsr-x 2 khashab2 cs_danr 10 Nov 30 14:57 out2
-rw-rw-r-- 1 khashab2 cs_danr 29M Nov 21 19:20 train-v1.1.json... | {
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https://api.github.com/repos/huggingface/transformers/issues/84 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/84/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/84/comments | https://api.github.com/repos/huggingface/transformers/issues/84/events | https://github.com/huggingface/transformers/pull/84 | 387,059,110 | MDExOlB1bGxSZXF1ZXN0MjM1NjM0MDky | 84 | elementwise_mean -> mean (thinking ahead to pytorch 1.0) | {
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"oops, doesn't work under current pytorch, never mind"
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | under the pytorch 1.0 nightly this test generates
```
UserWarning: reduction='elementwise_mean' is deprecated, please use reduction='mean' instead.
```
so this PR fixes that. | {
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https://api.github.com/repos/huggingface/transformers/issues/83 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/83/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/83/comments | https://api.github.com/repos/huggingface/transformers/issues/83/events | https://github.com/huggingface/transformers/issues/83 | 386,988,878 | MDU6SXNzdWUzODY5ODg4Nzg= | 83 | Error while runing example | {
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"Hi!\r\n\r\nIn case you haven't already, modifying the source at https://github.com/huggingface/pytorch-pretrained-BERT/blob/e60e8a606837ff7f49e583de8492e55575155eb6/examples/run_classifier.py#L491 and turning it into\r\n\r\n`cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank), num_la... | 1,543 | 1,543 | 1,543 | NONE | null | Hi!
I have a problem when running the example, could you please give me a hint on what may I be doing wrong?
I use:
`PYTHONPATH=. python examples/run_classifier.py --task_name MNLI --do_train --do_eval --do_lower_case --data_dir ../GLUE-baselines/glue_data/MNLI/ --bert_model bert-base-uncased --max_seq_len 40 -... | {
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https://api.github.com/repos/huggingface/transformers/issues/82 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/82/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/82/comments | https://api.github.com/repos/huggingface/transformers/issues/82/events | https://github.com/huggingface/transformers/issues/82 | 386,887,965 | MDU6SXNzdWUzODY4ODc5NjU= | 82 | AttributeError: 'tuple' object has no attribute 'backward' | {
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"Looks like there was a code change which changed the forward method of the model involved here from returning a tensor to returning a tuple of tensors and the example hasn't been updated yet to reflect that change. There's probably a line in run_classifier.py like\r\n```Python\r\nloss = model(input...)\r\n```\r\nw... | 1,543 | 1,543 | 1,543 | NONE | null | Traceback (most recent call last): | 0/11 [00:00<?, ?it/s]
File "examples/run_classifier.py", line 637, in <module>
main()
File "examples/run_classifier.py", line 558, in main
... | {
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https://api.github.com/repos/huggingface/transformers/issues/81 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/81/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/81/comments | https://api.github.com/repos/huggingface/transformers/issues/81/events | https://github.com/huggingface/transformers/issues/81 | 386,786,079 | MDU6SXNzdWUzODY3ODYwNzk= | 81 | There is some problem in supporting continuously training | {
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"Hi @ZacharyWaseda, continuous training is an open-research problem. You should rather seek some solution in the papers/workshop/conference discussing researches in this field. This is not my personal field of expertise so I can only direct you to google and other search engine for more information."
] | 1,543 | 1,544 | 1,544 | NONE | null | I change the run_classfifier.py in order to support continuously training. i save the model.state_dict() and the BertAdam optimizer.state_dict(), and I load them when start continuously training. However, After some epochs, the loss will increase little by little and finally end with a large loss value. I do not know t... | {
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https://api.github.com/repos/huggingface/transformers/issues/80 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/80/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/80/comments | https://api.github.com/repos/huggingface/transformers/issues/80/events | https://github.com/huggingface/transformers/issues/80 | 386,763,906 | MDU6SXNzdWUzODY3NjM5MDY= | 80 | How can I apply BERT to a cloze task? | {
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"I think that you best option would be to use the masked language modeling head and restrict the output of the softmax layer to your candidates.\r\n\r\nI think the following code does the job:\r\n\r\n```\r\nimport torch\r\nfrom pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM\r\n\r\ntokenize... | 1,543 | 1,586 | 1,544 | NONE | null | Hi, I have a dataset like :
From Monday to Friday most people are busy working or studying, but in the evenings and weekends they are free and _ themselves.
And there are four candidates for the missing blank area:
["love", "work", "enjoy", "play"], here "enjoy" is the correct answer, it is a cloze-style t... | {
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https://api.github.com/repos/huggingface/transformers/issues/79 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/79/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/79/comments | https://api.github.com/repos/huggingface/transformers/issues/79/events | https://github.com/huggingface/transformers/issues/79 | 386,698,511 | MDU6SXNzdWUzODY2OTg1MTE= | 79 | numpy.core._internal.AxisError: axis 1 is out of bounds for array of dimension 1 | {
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"Hi, just update the repo to the current master, this should have been fixed this weekend (re-open the issue of it's not)."
] | 1,543 | 1,543 | 1,543 | NONE | null | hello, when I am running run_classifier.py with MRPC dataset, there seems to be an mistake. the mistake is as following:
<img width="752" alt="default" src="https://user-images.githubusercontent.com/29532760/49360256-9de0e100-f713-11e8-9a5c-d9f2bc5331e6.PNG">
the mistake is happening when training is over and the mod... | {
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https://api.github.com/repos/huggingface/transformers/issues/78 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/78/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/78/comments | https://api.github.com/repos/huggingface/transformers/issues/78/events | https://github.com/huggingface/transformers/issues/78 | 386,553,265 | MDU6SXNzdWUzODY1NTMyNjU= | 78 | TypeError: object of type 'WindowsPath' has no len() | {
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"Can you post a more detailed log?",
"I install your PyTorch pretrained bert with pip like \"pip install pytorch-pretrained-bert\", then I run the code in Usage section like:\r\n\r\n`import torch`\r\n`from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM`\r\n\r\n`# Load pre-trained model t... | 1,543 | 1,543 | 1,543 | NONE | null | Hi, when I run "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')", the error "TypeError: object of type 'WindowsPath' has no len()" occurs, what is the problem? Thank you for your excellent code! | {
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https://api.github.com/repos/huggingface/transformers/issues/77 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/77/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/77/comments | https://api.github.com/repos/huggingface/transformers/issues/77/events | https://github.com/huggingface/transformers/pull/77 | 386,551,555 | MDExOlB1bGxSZXF1ZXN0MjM1MjUzNTk5 | 77 | Correct assignement for logits in classifier example | {
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"Ok thanks, that should work for now. I simplified the output of the classes indeed (only send back loss when a label is provided) so this example broke."
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | I tried to address https://github.com/huggingface/pytorch-pretrained-BERT/issues/76
should be correct, but there's likely a more efficient way. | {
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"You are right, I also encountered this small error.",
"Thanks for noticing, fixed in #77."
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | I think that
https://github.com/huggingface/pytorch-pretrained-BERT/blob/063be09b714bf4d2fbbc3de7f52c45b8bc6817eb/examples/run_classifier.py#L608
may well have a problem, as it's not consistent with
https://github.com/huggingface/pytorch-pretrained-BERT/blob/063be09b714bf4d2fbbc3de7f52c45b8bc6817eb/examples/run_cl... | {
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https://api.github.com/repos/huggingface/transformers/issues/75 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/75/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/75/comments | https://api.github.com/repos/huggingface/transformers/issues/75/events | https://github.com/huggingface/transformers/pull/75 | 386,394,416 | MDExOlB1bGxSZXF1ZXN0MjM1MTUwMDc2 | 75 | Point typo fix | {
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https://api.github.com/repos/huggingface/transformers/issues/74 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/74/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/74/comments | https://api.github.com/repos/huggingface/transformers/issues/74/events | https://github.com/huggingface/transformers/pull/74 | 386,394,375 | MDExOlB1bGxSZXF1ZXN0MjM1MTUwMDQ0 | 74 | Update finetuning example in README adding --do_lower_case | {
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"Indeed"
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | Should be consistent with the fact that an uncased model is used | {
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https://api.github.com/repos/huggingface/transformers/issues/73 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/73/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/73/comments | https://api.github.com/repos/huggingface/transformers/issues/73/events | https://github.com/huggingface/transformers/pull/73 | 386,369,366 | MDExOlB1bGxSZXF1ZXN0MjM1MTMwMzgw | 73 | Third release | {
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- added the two new pre-trained model from Google: `bert-large-cased` and `bert-multilingual-cased`,
- added a model for token-level classification: `BertForTokenClassification`,
- added tests for every model class, with and without labels,
- fixed tokenizer loadin... | {
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https://api.github.com/repos/huggingface/transformers/issues/72 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/72/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/72/comments | https://api.github.com/repos/huggingface/transformers/issues/72/events | https://github.com/huggingface/transformers/pull/72 | 386,352,239 | MDExOlB1bGxSZXF1ZXN0MjM1MTE2ODY5 | 72 | Fix internal hyperlink typo | {
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https://api.github.com/repos/huggingface/transformers/issues/71 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/71/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/71/comments | https://api.github.com/repos/huggingface/transformers/issues/71/events | https://github.com/huggingface/transformers/issues/71 | 386,303,565 | MDU6SXNzdWUzODYzMDM1NjU= | 71 | run_squad script gets stuck | {
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"Never mind, it just needed time to process the examples. It might be good to have the progress bar inside convert_examples_to_features.",
"Maybe try distributed training? I don't think PyTorch `DataParallel` will be very efficient on 8 GPUs due to the python GIL.",
"Thanks for the suggestion. I will try that. ... | 1,543 | 1,543 | 1,543 | NONE | null | Hello,
I am trying to run the squad fine tuning script, but it hangs after printing out a few predictions. I am attaching the log. Can you help take a look?
I am running the script on a machine with 8 M40s.
[bert_squad.log](https://github.com/huggingface/pytorch-pretrained-BERT/files/2634588/bert_squad.log)
... | {
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https://api.github.com/repos/huggingface/transformers/issues/70 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/70/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/70/comments | https://api.github.com/repos/huggingface/transformers/issues/70/events | https://github.com/huggingface/transformers/pull/70 | 386,245,181 | MDExOlB1bGxSZXF1ZXN0MjM1MDMzMDI3 | 70 | fix typo in input for masked lm loss function | {
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"thanks"
] | 1,543 | 1,543 | 1,543 | NONE | null | Fixing #55 . There was still a typo. | {
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https://api.github.com/repos/huggingface/transformers/issues/69 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/69/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/69/comments | https://api.github.com/repos/huggingface/transformers/issues/69/events | https://github.com/huggingface/transformers/issues/69 | 386,197,836 | MDU6SXNzdWUzODYxOTc4MzY= | 69 | cannot access to pretrained vocab file on S3 | {
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"I have the same issue. \r\n\r\n> OSError: HEAD request failed for url https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt with status code 404\r\n\r\n\r\nIt would be nice to be able to cache the vocab files as well as the model weights out of the box.",
"I found temporary solution for... | 1,543 | 1,620 | 1,543 | NONE | null | Hi, thanks for develop well-made pytorch version of BERT.
Unfortunately, pretrained vocab files are not reachable.
error traceback is below.
> File "/usr/local/lib/python3.6/dist-packages/pytorch_pretrained_bert/tokenization.py", line 124, in from_pretrained
resolved_vocab_file = cached_path(vocab_file)
Fi... | {
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https://api.github.com/repos/huggingface/transformers/issues/68 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/68/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/68/comments | https://api.github.com/repos/huggingface/transformers/issues/68/events | https://github.com/huggingface/transformers/issues/68 | 386,055,987 | MDU6SXNzdWUzODYwNTU5ODc= | 68 | Accuracy on classification task is lower than the official tensorflow version | {
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"Hi!\r\nCould it be different seeds?\r\nSee e.g. https://github.com/huggingface/pytorch-pretrained-BERT/issues/53#issuecomment-441565229",
"Hi @ejld, yes BERT has a large variance on many fine-tuning tasks (see also the discussion in #64).\r\nYou should try a bunch of different seeds (like 10 seeds for example) a... | 1,543 | 1,543 | 1,543 | NONE | null | Hi, I am running the same task with the same hyper parameters as the official Google Tensorflow implementation of BERT, however, I am getting around 1.5% lower accuracy. Can you please give any hint about the possible cause?
Thanks！ | {
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https://api.github.com/repos/huggingface/transformers/issues/67 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/67/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/67/comments | https://api.github.com/repos/huggingface/transformers/issues/67/events | https://github.com/huggingface/transformers/issues/67 | 386,047,173 | MDU6SXNzdWUzODYwNDcxNzM= | 67 | `TypeError: object of type 'NoneType' has no len()` when tuning on squad | {
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"Oh I see, this should be fixed in `master` by 257a35134a1bd378b16aa985ee76675289ff439c just update your repo please."
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | When running the following command for tuning on squad, I am getting a petty error inside logger `TypeError: object of type 'NoneType' has no len()`. Any thoughts what could be the main cause of the problem?
Full log:
```
python3.6 examples/run_squad.py \
> --bert_model bert-base-uncased \
> --do_train ... | {
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https://api.github.com/repos/huggingface/transformers/issues/66 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/66/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/66/comments | https://api.github.com/repos/huggingface/transformers/issues/66/events | https://github.com/huggingface/transformers/pull/66 | 385,774,972 | MDExOlB1bGxSZXF1ZXN0MjM0NjY4MDQy | 66 | speedup by truncating unused part | {
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"Hi Mathis,\r\nThanks for that. I think it's better for the user to send inputs that they truncated themselves rather than doing that hidden inside the model.\r\nBest,\r\nThomas"
] | 1,543 | 1,543 | 1,543 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/65 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/65/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/65/comments | https://api.github.com/repos/huggingface/transformers/issues/65/events | https://github.com/huggingface/transformers/issues/65 | 385,638,595 | MDU6SXNzdWUzODU2Mzg1OTU= | 65 | 3 sentences as input for BertForSequenceClassification? | {
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"Technically it is possible but BERT was not pretrained to handle multiple SEP tokens between sentences and does not have a third token_type, so I think it won't be easy to make it work. You may also want to use a new token for the second separation.",
"> Technically it is possible but BERT was not pretrained to ... | 1,543 | 1,631 | 1,543 | NONE | null | Hi there,
Thanks for releasing this awesome repo, it does lots people like me a great favor.
So far I've tried sentence-pair BertForSequenceClassification task, and it indeed work. I'd like to know if it is possible to use BertForSequenceClassification to model triple sentences classification problem and its inpu... | {
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https://api.github.com/repos/huggingface/transformers/issues/64 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/64/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/64/comments | https://api.github.com/repos/huggingface/transformers/issues/64/events | https://github.com/huggingface/transformers/issues/64 | 385,555,095 | MDU6SXNzdWUzODU1NTUwOTU= | 64 | Feature extraction for sequential labelling | {
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"Well that seems like a good approach. Maybe you can find some inspiration in the code of the `BertForQuestionAnswering` model? It is not exactly what you are doing but maybe it can help.",
"Thanks. It worked. However, a interesting issue about BERT is that it's highly sensitive to learning rate, which makes it v... | 1,543 | 1,647 | 1,579 | NONE | null | Hi, I have a question in terms of using BERT for sequential labeling task.
Please correct me if I'm wrong.
My understanding is:
1. Use BertModel loaded with pretrained weights instead of MaskedBertModel.
2. In such case, take a sequence of tokens as input, BertModel would output a list of hidden states, I only use ... | {
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https://api.github.com/repos/huggingface/transformers/issues/63 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/63/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/63/comments | https://api.github.com/repos/huggingface/transformers/issues/63/events | https://github.com/huggingface/transformers/issues/63 | 385,487,365 | MDU6SXNzdWUzODU0ODczNjU= | 63 | Unseen Vocab | {
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"If you tokenize properly the input (tokenize before convert_tokens), it automatically 'fallbacks' to subword/character-level(-like) embedding.\r\nYou can add new words in the vocabulary but you'll have to train the corresponding embeddings.",
"Hi @siddsach,\r\nThanks for your kind words!\r\n@artemisart is right,... | 1,543 | 1,571 | 1,543 | NONE | null | Thank you so much for this well-documented and easy-to-understand implementation! I remember meeting you at WeCNLP and am so happy to see you push out usable implementations of the SOA in pytorch for the community!!!!!
I have a question: The convert_tokens_to_ids method in the BertTokenizer that provides input to th... | {
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https://api.github.com/repos/huggingface/transformers/issues/62 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/62/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/62/comments | https://api.github.com/repos/huggingface/transformers/issues/62/events | https://github.com/huggingface/transformers/issues/62 | 385,368,286 | MDU6SXNzdWUzODUzNjgyODY= | 62 | Specify a model from a specific directory for extract_features.py | {
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"The last update broke this, but you can fix this in tokenization.py, you have to add this after `vocab_file = pretrained_model_name`:\r\n```\r\nif os.path.isdir(vocab_file):\r\n vocab_file = os.path.join(vocab_file, \"vocab.txt\")\r\n```\r\n",
"Thank you, is it fair to assume that this will get accepted as an... | 1,543 | 1,543 | 1,543 | NONE | null | I have downloaded the model and vocab files into a specific location, using their original file names, so my directory for bert-base-cased contains:
```
bert-base-cased-vocab.txt
bert_config.json
pytorch_model.bin
```
But when I try to specify the directory which contains these files for the `--bert_model` par... | {
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https://api.github.com/repos/huggingface/transformers/issues/61 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/61/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/61/comments | https://api.github.com/repos/huggingface/transformers/issues/61/events | https://github.com/huggingface/transformers/issues/61 | 385,304,675 | MDU6SXNzdWUzODUzMDQ2NzU= | 61 | BERTConfigs in example usages in `modeling.py` are not OK (?) | {
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"Hi @davidefiocco, you are right, I updated the docstrings in the new release 0.3.0."
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | Hi!
In the `config` definition https://github.com/huggingface/pytorch-pretrained-BERT/blob/21f0196412115876da1c38652d22d1f7a14b36ff/pytorch_pretrained_bert/modeling.py#L848
in the Example usage of `BertForSequenceClassification` in `modeling.py`, there's things I don't understand:
- `vocab_size` in not an accept... | {
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https://api.github.com/repos/huggingface/transformers/issues/60 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/60/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/60/comments | https://api.github.com/repos/huggingface/transformers/issues/60/events | https://github.com/huggingface/transformers/pull/60 | 385,278,339 | MDExOlB1bGxSZXF1ZXN0MjM0Mjg0NDg3 | 60 | Updated quick-start example with `BertForMaskedLM` | {
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"Nice, thanks @davidefiocco "
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | As `convert_ids_to_tokens` returns a list, the code in the README currently throws an `AssertionError`, so I propose a quick fix. | {
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https://api.github.com/repos/huggingface/transformers/issues/59 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/59/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/59/comments | https://api.github.com/repos/huggingface/transformers/issues/59/events | https://github.com/huggingface/transformers/issues/59 | 385,158,595 | MDU6SXNzdWUzODUxNTg1OTU= | 59 | not good when I use BERT for seq2seq model in keyphrase generation | {
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"have u tried transformer decoder ?instead of rnn decoder. ",
"not yet, I will try. But I think rnn decoder should not be such bad. ",
"> not yet, I will try. But I think rnn decoder should not be such bad.\r\n\r\nemmm，maybe u should used mean of last layer to initialize decoder, not the last token representa... | 1,543 | 1,563 | 1,543 | NONE | null | Hi,
recently, I am researching about Keyphrase generation. Usually, people use seq2seq with attention model to deal with such problem. Specifically I use the framework: https://github.com/memray/seq2seq-keyphrase-pytorch, which is implementation of http://memray.me/uploads/acl17-keyphrase-generation.pdf .
Now I ... | {
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https://api.github.com/repos/huggingface/transformers/issues/58 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/58/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/58/comments | https://api.github.com/repos/huggingface/transformers/issues/58/events | https://github.com/huggingface/transformers/pull/58 | 384,691,312 | MDExOlB1bGxSZXF1ZXN0MjMzODMwMzc1 | 58 | Bug fix in examples;correct t_total for distributed training;run pred… | {
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"Thanks @lliimsft!"
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | Bug fix in examples;
correct t_total for distributed training;
run prediction for full dataset | {
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https://api.github.com/repos/huggingface/transformers/issues/57 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/57/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/57/comments | https://api.github.com/repos/huggingface/transformers/issues/57/events | https://github.com/huggingface/transformers/issues/57 | 384,525,339 | MDU6SXNzdWUzODQ1MjUzMzk= | 57 | Missing function convert_to_unicode in tokenization.py | {
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"Fixed in master, thanks!"
] | 1,543 | 1,543 | 1,543 | NONE | null | The function _convert_to_unicode_ is not in tokenization.py but used to be there in v0.1.2. When fine tuning with run_classifier.py, you get an ImportError: cannot import name 'convert_to_unicode'.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/ce37b8e4819142171b61558e64f7dcb0286e9937/examples/run_class... | {
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https://api.github.com/repos/huggingface/transformers/issues/56 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/56/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/56/comments | https://api.github.com/repos/huggingface/transformers/issues/56/events | https://github.com/huggingface/transformers/issues/56 | 384,276,059 | MDU6SXNzdWUzODQyNzYwNTk= | 56 | [Feature request ] Add support for the new cased version of the multilingual model | {
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"Hi @elyase, this model is now added in the new release 0.3.0.\r\nI also added the other new model by Google (`bert-large-cased`)"
] | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | https://github.com/google-research/bert/commit/332a68723c34062b8f58e5fec3e430db4563320a | {
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https://api.github.com/repos/huggingface/transformers/issues/55 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/55/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/55/comments | https://api.github.com/repos/huggingface/transformers/issues/55/events | https://github.com/huggingface/transformers/issues/55 | 384,044,666 | MDU6SXNzdWUzODQwNDQ2NjY= | 55 | Loss calculation error | {
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"Hi Jian, can you give me a small (self-contained) example showing how to get this error?",
"Hi Thomas! I modified the code in your `README.md` for an example:\r\n\r\n```python\r\nfrom pytorch_pretrained_bert.modeling import BertForMaskedLM, BertConfig\r\nfrom pytorch_pretrained_bert import BertTokenizer\r\nimpor... | 1,543 | 1,543 | 1,543 | NONE | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/982339d82984466fde3b1466f657a03200aa2ffb/pytorch_pretrained_bert/modeling.py#L744
Got `ValueError: Expected target size (1, 30522), got torch.Size([1, 11])` at line 744 of `modeling.py`. I think the line should be changed to `masked_lm_loss = loss_fct(predi... | {
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https://api.github.com/repos/huggingface/transformers/issues/54 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/54/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/54/comments | https://api.github.com/repos/huggingface/transformers/issues/54/events | https://github.com/huggingface/transformers/issues/54 | 383,967,106 | MDU6SXNzdWUzODM5NjcxMDY= | 54 | example in BertForSequenceClassification() conflicts with the api | {
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"Hi,\r\n(1) is solved on master. I will release a new release soon with the fixes on pip. In the mean time you can install from sources if you want.\r\nI fixed the typo in the docstring you mention in (2), thanks, it should be a `1` instead of a `2`."
] | 1,543 | 1,543 | 1,543 | NONE | null | Hi, firstly, admire u for the great job. but I encounter 2 problems when i use it:
**1**. `UnicodeDecodeError: 'gbk' codec can't decode byte 0x85 in position 4527: illegal multibyte sequence`,
same problem as ISSUE 52 when I excute the `BertTokenizer.from_pretrained('bert-base-uncased')`, but I successfully excute `... | {
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https://api.github.com/repos/huggingface/transformers/issues/53 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/53/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/53/comments | https://api.github.com/repos/huggingface/transformers/issues/53/events | https://github.com/huggingface/transformers/issues/53 | 383,946,736 | MDU6SXNzdWUzODM5NDY3MzY= | 53 | Multi-GPU training vs Distributed training | {
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"Hi,\r\n\r\nThanks for the feedback, it's always interesting to compare the various possible ways to train the model indeed.\r\n\r\nThe most likely cause for (2) is that MRPC is a small dataset and the model shows a high variance in the results depending on the initialization of the weights for example (see the ori... | 1,543 | 1,543 | 1,543 | CONTRIBUTOR | null | Hi,
I have a question about Multi-GPU vs Distributed training, probably unrelated to BERT itself.
I have a 4-GPU server, and was trying to run `run_classifier.py` in two ways:
(a) run single-node distributed training with 4 processes and minibatch of 32 each
(b) run Multi-GPU training with minibatch of 128, a... | {
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https://api.github.com/repos/huggingface/transformers/issues/52 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/52/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/52/comments | https://api.github.com/repos/huggingface/transformers/issues/52/events | https://github.com/huggingface/transformers/issues/52 | 383,586,156 | MDU6SXNzdWUzODM1ODYxNTY= | 52 | UnicodeDecodeError: 'charmap' codec can't decode byte 0x90 in position 3920: character maps to <undefined> | {
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"I am facing the same problem.\r\n\r\nFixed it with \"with open(vocab_file, \"r\"**, encoding=\"utf-8\"**) as reader:\" in line 68 of tokenization.py",
"Thanks, it's fixed on master and will be included in the next release."
] | 1,542 | 1,542 | 1,542 | NONE | null | Installed pytorch-pretrained-BERT from source, Python 3.7, Windows 10
When I run the following snippet:
import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
... | {
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https://api.github.com/repos/huggingface/transformers/issues/51 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/51/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/51/comments | https://api.github.com/repos/huggingface/transformers/issues/51/events | https://github.com/huggingface/transformers/issues/51 | 383,162,319 | MDU6SXNzdWUzODMxNjIzMTk= | 51 | Missing options/arguments in run_squad.py for BERT Large | {
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"Yes, the readme example was for an older version. I have updated them with the simplified parameters used in the current release. Thanks."
] | 1,542 | 1,543 | 1,543 | NONE | null | Thanks for the great code..However, the `run_squad.py` for BERT Large seems to not have the `vocab_file` and `bert_config_file` (or other) options/arguments. Did you push the latest version?
Also, it is looking for a pytorch model file (a bin file). Does it need to be there?
I also had to add this line to the file... | {
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https://api.github.com/repos/huggingface/transformers/issues/50 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/50/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/50/comments | https://api.github.com/repos/huggingface/transformers/issues/50/events | https://github.com/huggingface/transformers/issues/50 | 383,055,235 | MDU6SXNzdWUzODMwNTUyMzU= | 50 | pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py error | {
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"Maybe some additional information could help me help you?",
"Initialize PyTorch weight ['cls', 'seq_relationship', 'output_weights']\r\nSkipping cls/seq_relationship/output_weights/adam_m\r\nSkipping cls/seq_relationship/output_weights/adam_v\r\nTraceback (most recent call last):\r\n File \"/home/tiandan.cxj/py... | 1,542 | 1,554 | 1,542 | NONE | null | attributeError: 'BertForPreTraining' object has no attribute 'global_step' | {
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https://api.github.com/repos/huggingface/transformers/issues/49 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/49/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/49/comments | https://api.github.com/repos/huggingface/transformers/issues/49/events | https://github.com/huggingface/transformers/issues/49 | 383,028,844 | MDU6SXNzdWUzODMwMjg4NDQ= | 49 | Multilingual Issue | {
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"Hi, you can use the multilingual model as [indicated in the readme](https://github.com/huggingface/pytorch-pretrained-BERT#loading-google-ais-pre-trained-weigths-and-pytorch-dump) with the commands:\r\n```python\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-multilingual')\r\nmodel = BertModel.from_pretra... | 1,542 | 1,542 | 1,542 | NONE | null | Dear authors,
I have two questions.
First, how can I use multilingual pre-trained BERT in pytorch?
Is it all download model to $BERT_BASE_DIR?
Second is tokenization issue.
For Chinese and Japanese, tokenizer may works, however, for Korean, it shows different result that I expected
```
import torch
from p... | {
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https://api.github.com/repos/huggingface/transformers/issues/48 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/48/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/48/comments | https://api.github.com/repos/huggingface/transformers/issues/48/events | https://github.com/huggingface/transformers/issues/48 | 382,937,718 | MDU6SXNzdWUzODI5Mzc3MTg= | 48 | example for is next sentence | {
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"I think it should work. You should get a [1, 2] tensor of logits where `predictions[0, 0]` is the score of Next sentence being `True` and `predictions[0, 1]` is the score of Next sentence being `False`. So just take the max of the two (or use a `SoftMax` to get probabilities).\r\nDid you try it?\r\nThe model behav... | 1,542 | 1,574 | 1,542 | NONE | null | Can you make up a working example for 'is next sentence'
Is this expected to work properly ?
```
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenized input
text = "Who was Jim Morrison ? Jim Morrison was a puppeteer"
tokenized_text = tok... | {
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https://api.github.com/repos/huggingface/transformers/issues/47 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/47/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/47/comments | https://api.github.com/repos/huggingface/transformers/issues/47/events | https://github.com/huggingface/transformers/issues/47 | 382,761,771 | MDU6SXNzdWUzODI3NjE3NzE= | 47 | Fine-Tuned BERT-base on Squad v1. | {
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"Thanks for the details.\r\nThis PyTorch repo is starting to be used by a larger community so we would have to be a little more precise than just rough numbers if we want to include such pre-trained weights.\r\nIf you want to add your weights to the repo, you should convert the weights in the PyTorch repo model and... | 1,542 | 1,555 | 1,542 | NONE | null | I have fine-tuned the TF model on SQuAD v1 and I've made the weights available at: https://s3.eu-west-2.amazonaws.com/nlpfiles/squad_bert_base.tgz
I get 88.5 FM using these weights on SQuAD dev. (If I recall correctly I get roughly 82 EM).
I think it may be beneficial to have these weights here, so that people c... | {
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https://api.github.com/repos/huggingface/transformers/issues/46 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/46/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/46/comments | https://api.github.com/repos/huggingface/transformers/issues/46/events | https://github.com/huggingface/transformers/issues/46 | 382,649,103 | MDU6SXNzdWUzODI2NDkxMDM= | 46 | Assertion `srcIndex < srcSelectDimSize` failed. | {
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"Your log is very hard to read. Can you format it cleanly?",
"I'm so sorry\r\nThe first error log is as follows:\r\n```bash\r\n/opt/conda/conda-bld/pytorch_1532584813488/work/aten/src/THC/THCTensorIndex.cu:362: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexTy... | 1,542 | 1,591 | 1,542 | NONE | null | Sorry to bother you
I recently have used your extract_features.py to extract features of some data set but failed. The error information is as follows:
`/opt/conda/conda-bld/pytorch_1532584813488/work/aten/src/THC/THCTensorIndex.cu:362: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, Te... | {
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https://api.github.com/repos/huggingface/transformers/issues/45 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/45/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/45/comments | https://api.github.com/repos/huggingface/transformers/issues/45/events | https://github.com/huggingface/transformers/issues/45 | 382,579,717 | MDU6SXNzdWUzODI1Nzk3MTc= | 45 | Issue of `bert_model` arg in `run_classify.py` | {
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"Hi, please read [this section](https://github.com/huggingface/pytorch-pretrained-BERT#loading-google-ais-pre-trained-weigths-and-pytorch-dump) of the readme."
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | Hi,
I am trying to understand the `bert_model` arg in `run_classify.py`. In the file, I can see
```
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
```
where `bert_model` is expected to be the vocab text file of the model
However, I also see
```
model = BertForSequenceClassification.from_pretr... | {
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https://api.github.com/repos/huggingface/transformers/issues/44 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/44/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/44/comments | https://api.github.com/repos/huggingface/transformers/issues/44/events | https://github.com/huggingface/transformers/issues/44 | 382,576,559 | MDU6SXNzdWUzODI1NzY1NTk= | 44 | Race condition when prepare pretrained model in distributed training | {
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"My current workaround is to set the env var `PYTORCH_PRETRAINED_BERT_CACHE` to a different path per process before import `pytorch_pretrained_bert`. But I think the module itself should handle this properly",
"I see, thanks for the feedback. I will find a way to make that better in the next release. Not sure we ... | 1,542 | 1,543 | 1,543 | CONTRIBUTOR | null | Hi,
I launched two processes per node to run distributed run_classifier.py. However, I am occasionally get below error:
```
11/20/2018 09:31:48 - INFO - pytorch_pretrained_bert.file_utils - copying /tmp/tmpa25_y4es to cache at /root/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6... | {
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https://api.github.com/repos/huggingface/transformers/issues/43 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/43/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/43/comments | https://api.github.com/repos/huggingface/transformers/issues/43/events | https://github.com/huggingface/transformers/issues/43 | 382,553,589 | MDU6SXNzdWUzODI1NTM1ODk= | 43 | grad is None in squad example | {
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"Oh you're right. I've just fixed that. you can try to pull the current master and test again.",
"@thomwolf it works, thanks"
] | 1,542 | 1,542 | 1,542 | NONE | null | Hi, guys, I try the `run_squad` example with
```
Traceback (most recent call last): | 0/7331 [00:00<?, ?it/s]
File "examples/run_squad.py", line 973, in <m... | {
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https://api.github.com/repos/huggingface/transformers/issues/42 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/42/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/42/comments | https://api.github.com/repos/huggingface/transformers/issues/42/events | https://github.com/huggingface/transformers/pull/42 | 382,492,723 | MDExOlB1bGxSZXF1ZXN0MjMyMTg2NjE0 | 42 | Fixed UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 | {
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"Thanks!"
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | I encountered `UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 3793: ordinal not in range(128)` when running the starter example shown under the Usage section. It turned out to be related to the `load_vocab` function in `tokenization.py`. Forcing `open` to use encoding `utf8` solved this issue on ... | {
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https://api.github.com/repos/huggingface/transformers/issues/41 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/41/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/41/comments | https://api.github.com/repos/huggingface/transformers/issues/41/events | https://github.com/huggingface/transformers/issues/41 | 382,489,751 | MDU6SXNzdWUzODI0ODk3NTE= | 41 | Typo in README | {
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"Yes"
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | I think I spotted a typo in the README file under the Usage header. There is a piece of code that uses `BertTokenizer` and the typo is on this line:
`tokenized_text = "Who was Jim Henson ? Jim Henson was a puppeteer"`
I think `tokenized_text` should be replaced with `text`, since the next line is
`tokenized_text =... | {
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https://api.github.com/repos/huggingface/transformers/issues/40 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/40/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/40/comments | https://api.github.com/repos/huggingface/transformers/issues/40/events | https://github.com/huggingface/transformers/pull/40 | 382,327,249 | MDExOlB1bGxSZXF1ZXN0MjMyMDYwODU1 | 40 | update pip package name | {
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https://api.github.com/repos/huggingface/transformers/issues/39 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/39/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/39/comments | https://api.github.com/repos/huggingface/transformers/issues/39/events | https://github.com/huggingface/transformers/issues/39 | 382,300,869 | MDU6SXNzdWUzODIzMDA4Njk= | 39 | Command-line interface Document Bug | {
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"Thanks!"
] | 1,542 | 1,542 | 1,542 | NONE | null | There is a bug in README.md about Command-line interface:
`export BERT_BASE_DIR=chinese_L-12_H-768_A-12`
**Wrong:**
```
pytorch_pretrained_bert convert_tf_checkpoint_to_pytorch \
--tf_checkpoint_path $BERT_BASE_DIR/bert_model.ckpt.index \
--bert_config_file $BERT_BASE_DIR/bert_config.json \
--pytorch_... | {
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https://api.github.com/repos/huggingface/transformers/issues/38 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38/comments | https://api.github.com/repos/huggingface/transformers/issues/38/events | https://github.com/huggingface/transformers/issues/38 | 382,297,444 | MDU6SXNzdWUzODIyOTc0NDQ= | 38 | truncated normal initializer | {
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"We could try that. Not sure how important it is though. Did you try it?",
"Ok I think we will stick to the normal_initializer for now. Thanks for indicating this option!"
] | 1,542 | 1,543 | 1,543 | NONE | null | I have a reasonable truncated normal approximation. (Actually that is what tf does).
https://discuss.pytorch.org/t/implementing-truncated-normal-initializer/4778/16?u=ruotianluo
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https://api.github.com/repos/huggingface/transformers/issues/37 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/37/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/37/comments | https://api.github.com/repos/huggingface/transformers/issues/37/events | https://github.com/huggingface/transformers/issues/37 | 382,265,174 | MDU6SXNzdWUzODIyNjUxNzQ= | 37 | using BERT as a language Model | {
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"I don't think you can do that with Bert. The masked LM loss is not a Language Modeling loss, it doesn't work nicely with the [chain rule](https://en.wikipedia.org/wiki/Chain_rule_%28probability%29) like the usual Language Modeling loss.\r\nPlease see the discussion on the TensorFlow repo on that [here](https://git... | 1,542 | 1,627 | 1,542 | NONE | null | I was trying to use BERT as a language model to assign a score(could be PPL score) of a given sentence. Something like
P("He is go to school")=0.008
P("He is going to school")=0.08
Which is indicating that the probability of second sentence is higher than first sentence. Is there a way to get a score like this?
... | {
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https://api.github.com/repos/huggingface/transformers/issues/36 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/36/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/36/comments | https://api.github.com/repos/huggingface/transformers/issues/36/events | https://github.com/huggingface/transformers/issues/36 | 382,054,626 | MDU6SXNzdWUzODIwNTQ2MjY= | 36 | How to detokenize a BertTokenizer output? | {
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"You can remove ' ##' but you cannot know if there was a space around punctuations tokens or uppercase words.",
"Yes. I don't plan to include a reverse conversion of tokens in the tokenizer.\r\nFor an example on how to keep track of the original characters position, please read the `run_squad.py` example.",
"In... | 1,542 | 1,576 | 1,542 | NONE | null | I was wondering if there's a proper way of detokenizing the output tokens, i.e., constructing the sentence back from the tokens? Considering the fact that the word-piece tokenisation introduces lots of `#`s. | {
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https://api.github.com/repos/huggingface/transformers/issues/35 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/35/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/35/comments | https://api.github.com/repos/huggingface/transformers/issues/35/events | https://github.com/huggingface/transformers/issues/35 | 381,998,040 | MDU6SXNzdWUzODE5OTgwNDA= | 35 | issues with accents on convert_ids_to_tokens() | {
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"This is expected behaviour and is how the multilingual and the uncased models were trained. From the [original repo](https://github.com/google-research/bert/blob/master/README.md):\r\n\r\n> We are releasing the BERT-Base and BERT-Large models from the paper. Uncased means that the text has been lowercased before W... | 1,542 | 1,542 | 1,542 | NONE | null | Hello, the BertTokenizer seems loose accents when convert_ids_to_tokens() is used :
Example:
- original sentence: "great breakfasts in a nice furnished cafè, slightly bohemian."
- corresponding list of token produced : ['great', 'breakfast', '##s', 'in', 'a', 'nice', 'fur', '##nis', '##hed', 'cafe', ',', 'slightly... | {
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https://api.github.com/repos/huggingface/transformers/issues/34 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/34/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/34/comments | https://api.github.com/repos/huggingface/transformers/issues/34/events | https://github.com/huggingface/transformers/issues/34 | 381,965,833 | MDU6SXNzdWUzODE5NjU4MzM= | 34 | Can not find vocabulary file for Chinese model | {
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"need to specify the path of vocab.txt for:\r\ntokenizer = BertTokenizer.from_pretrained(args.bert_model)",
"@zlinao ，i try to load the vocab using the following code：\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-chinese//vocab.txt\"\r\n\r\nhowever，get errors\r\n11/19/2018 15:33:13 - INFO - pytorch_pr... | 1,542 | 1,542 | 1,542 | NONE | null | After I convert the TF model to pytorch model, I run a classification task on a new Chinese dataset, but get this:
CUDA_VISIBLE_DEVICES=3 python run_classifier.py --task_name weibo --do_eval --do_train --bert_model chinese_L-12_H-768_A-12 --max_seq_length 128 --train_batch_size 32 --learning_rate 2e-5 --num_... | {
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https://api.github.com/repos/huggingface/transformers/issues/33 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/33/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/33/comments | https://api.github.com/repos/huggingface/transformers/issues/33/events | https://github.com/huggingface/transformers/issues/33 | 381,939,792 | MDU6SXNzdWUzODE5Mzk3OTI= | 33 | [Bug report] Ineffective no_decay when using BERTAdam | {
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"You're right, thanks!"
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py#L505-L508
With this code, all parameters are decayed because the condition "parameter_name in no_decay" will never be satisfied.
I've made a PR #32 to fix it. | {
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https://api.github.com/repos/huggingface/transformers/issues/32 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/32/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/32/comments | https://api.github.com/repos/huggingface/transformers/issues/32/events | https://github.com/huggingface/transformers/pull/32 | 381,939,230 | MDExOlB1bGxSZXF1ZXN0MjMxNzc1MTI1 | 32 | Fix ineffective no_decay bug when using BERTAdam | {
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"thanks!",
"Question - wouldn't `.named_parameters()` for the model return a tuple `(name, param_tensor)`, where name looks similar to these\r\n```\r\n['bert.embeddings.word_embeddings.weight',\r\n 'bert.embeddings.position_embeddings.weight',\r\n 'bert.embeddings.token_type_embeddings.weight',\r\n 'bert.embeddin... | 1,542 | 1,557 | 1,542 | CONTRIBUTOR | null | With the original code, all parameters are decayed because the condition "parameter_name in no_decay" will never be satisfied. | {
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https://api.github.com/repos/huggingface/transformers/issues/31 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/31/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/31/comments | https://api.github.com/repos/huggingface/transformers/issues/31/events | https://github.com/huggingface/transformers/issues/31 | 381,920,522 | MDU6SXNzdWUzODE5MjA1MjI= | 31 | BERT model for Machine Translation | {
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"Hi Kerem, I don't think so. Have a look at the fairsep repo maybe.",
"@thomwolf hi there, I couldn't find out anything about the fairsep repo. Could you post a link? Thanks!",
"Hi, I am talking about this repo: https://github.com/pytorch/fairseq.\r\nHave a look at their Transformer's models for machine transla... | 1,542 | 1,636 | 1,542 | NONE | null | Is there a way to use any of the provided pre-trained models in the repository for machine translation task?
Thanks | {
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https://api.github.com/repos/huggingface/transformers/issues/30 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/30/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/30/comments | https://api.github.com/repos/huggingface/transformers/issues/30/events | https://github.com/huggingface/transformers/issues/30 | 381,872,071 | MDU6SXNzdWUzODE4NzIwNzE= | 30 | [Feature request] Add example of finetuning the pretrained models on custom corpus | {
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"Hi I don't plan to add that in the near future but feel free to open a PR if you would like to share an additional example.",
"Necrobumping this for reference, as this is addressed in https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_lm_finetuning.py"
] | 1,542 | 1,547 | 1,542 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/29 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/29/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/29/comments | https://api.github.com/repos/huggingface/transformers/issues/29/events | https://github.com/huggingface/transformers/pull/29 | 381,853,838 | MDExOlB1bGxSZXF1ZXN0MjMxNzIyMjU4 | 29 | First release | {
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https://api.github.com/repos/huggingface/transformers/issues/28 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/28/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/28/comments | https://api.github.com/repos/huggingface/transformers/issues/28/events | https://github.com/huggingface/transformers/issues/28 | 381,835,436 | MDU6SXNzdWUzODE4MzU0MzY= | 28 | speed is very slow | {
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"Running on a GPU, I find that dumping extracted features takes up most time. So you may optimize it yourself. ",
"Hi, these examples are provided as starting point to write your own training scripts using the package modules. I don't plan to update them any further."
] | 1,542 | 1,542 | 1,542 | NONE | null | convert samples to features, is very slow | {
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https://api.github.com/repos/huggingface/transformers/issues/27 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/27/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/27/comments | https://api.github.com/repos/huggingface/transformers/issues/27/events | https://github.com/huggingface/transformers/issues/27 | 381,833,694 | MDU6SXNzdWUzODE4MzM2OTQ= | 27 | how to load checkpoint? | {
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"Converting TensorFlow checkpoint from ../dataset/bert/uncased_L-12_H-768_A-12/bert_model\r\nTraceback (most recent call last):\r\n File \"convert_tf_checkpoint_to_pytorch.py\", line 111, in <module>\r\n convert()\r\n File \"convert_tf_checkpoint_to_pytorch.py\", line 60, in convert\r\n init_vars = tf.train... | 1,542 | 1,591 | 1,542 | NONE | null | i download the model from bert, it only has model.ckpt.data，model.ckpt.meta and model.ckpt.index, i donnot which to load, what is checkpoint file for convert.py? | {
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https://api.github.com/repos/huggingface/transformers/issues/26 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/26/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/26/comments | https://api.github.com/repos/huggingface/transformers/issues/26/events | https://github.com/huggingface/transformers/issues/26 | 381,718,424 | MDU6SXNzdWUzODE3MTg0MjQ= | 26 | Checkpoints not saved | {
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"In the `run_squad.py`script, I added the following lines after the training loop:\r\n\r\n```\r\nlogger.info(***** Saving fine-tuned model *****)\r\noutput_model_file = os.path.join(args.output_dir, \"pytorch_model.bin\")\r\nif n_gpu > 1:\r\n torch.save(model.module.bert.state_dict(), output_model_file)\r\nelse:... | 1,542 | 1,551 | 1,542 | NONE | null | There is an option `save_checkpoints_steps` that seems to control checkpointing. However, there is no actual saving operation in the `run_*` scripts. So, should we add that functionality or remove this argument? | {
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https://api.github.com/repos/huggingface/transformers/issues/25 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/25/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/25/comments | https://api.github.com/repos/huggingface/transformers/issues/25/events | https://github.com/huggingface/transformers/issues/25 | 381,490,584 | MDU6SXNzdWUzODE0OTA1ODQ= | 25 | can you push the run-pretraining and create_pretraining_data codes? | {
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"Hi, I don't have plan for that in the near future."
] | 1,542 | 1,542 | 1,542 | NONE | null | just want to study codes, don't need to have same pre-train performance. | {
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https://api.github.com/repos/huggingface/transformers/issues/24 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/24/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/24/comments | https://api.github.com/repos/huggingface/transformers/issues/24/events | https://github.com/huggingface/transformers/issues/24 | 381,387,717 | MDU6SXNzdWUzODEzODc3MTc= | 24 | [Feature request] Port SQuAD 2.0 support | {
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"Hi, I don't have plan for that in the near future but feel free to open a PR."
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | Recently the Google team added support for Squad 2.0:
https://github.com/google-research/bert/commit/60454702590a6c69bd45c5d4258c7e17b8a3e1da
Would be great to also have it available in the Pytorch version. | {
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https://api.github.com/repos/huggingface/transformers/issues/23 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/23/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/23/comments | https://api.github.com/repos/huggingface/transformers/issues/23/events | https://github.com/huggingface/transformers/issues/23 | 381,250,921 | MDU6SXNzdWUzODEyNTA5MjE= | 23 | ValueError while using --optimize_on_cpu | {
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"Thanks! I pushed a fix for that, you can try it again. You should be able to increase a bit the batch size.\r\n\r\nBy the way, the real batch size that is used on the gpu is `train_batch_size / gradient_accumulation_steps` so `2` in your case. I think you should be able to go to `3` with `--optimize_on_cpu`\r\n\r\... | 1,542 | 1,542 | 1,542 | NONE | null | > Traceback (most recent call last): | 1/87970 [00:00<8:35:35, 2.84it/s]
File "./run_squad.py", line 990, in <module>
main()
File "./run_squad.py", line 922, in main
is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True)
File "./run_squad.py", line 691, in set_optimizer_params... | {
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https://api.github.com/repos/huggingface/transformers/issues/22 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/22/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/22/comments | https://api.github.com/repos/huggingface/transformers/issues/22/events | https://github.com/huggingface/transformers/pull/22 | 381,097,721 | MDExOlB1bGxSZXF1ZXN0MjMxMTQ0MTAx | 22 | adding `no_cuda` flag | {
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"Thanks, I've added that manually (the library organization has changed a bit with the first pip release)."
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | The `--no_cuda` flag is missing from the flagset in `extract_features.py`. On running the current code, the following error occurs.
```
(py3.5) [rahul pytorch-pretrained-BERT]$ python extract_features.py \
> --input_file=./input.txt \
> --output_file=./output.jsonl \
> --vocab_file=$BERT_BASE_DIR/vocab.txt... | {
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https://api.github.com/repos/huggingface/transformers/issues/21 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/21/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/21/comments | https://api.github.com/repos/huggingface/transformers/issues/21/events | https://github.com/huggingface/transformers/pull/21 | 381,038,724 | MDExOlB1bGxSZXF1ZXN0MjMxMDk4MjMy | 21 | Fix some glitches in extract_features.py | {
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"Thanks, I've pushed these fixes in the first release (the organization of the library changed quite a bit)."
] | 1,542 | 1,542 | 1,542 | NONE | null | Do the following fixing to make the extract_features.py runnable:
1. Add no_cuda argument
2. Fix the "not all arguments converted during string formatting" error thrown at line 230 | {
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https://api.github.com/repos/huggingface/transformers/issues/20 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/20/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/20/comments | https://api.github.com/repos/huggingface/transformers/issues/20/events | https://github.com/huggingface/transformers/issues/20 | 380,581,495 | MDU6SXNzdWUzODA1ODE0OTU= | 20 | model loading the checkpoint error | {
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"But I print the model.embeddings.token_type_embeddings it was Embedding(16,768) .",
"which model are you loading?",
"> which model are you loading?\r\n\r\nthe pre-trained model chinese_L-12_H-768_A-12",
"mycode:\r\nbert_config = BertConfig.from_json_file('bert_config.json')\r\nmodel=BertModel(bert_conf... | 1,542 | 1,684 | 1,542 | NONE | null | RuntimeError: Error(s) in loading state_dict for BertModel:
size mismatch for embeddings.token_type_embeddings.weight: copying a param of torch.Size([16, 768]) from checkpoint, where the shape is torch.Size([2, 768]) in current model. | {
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https://api.github.com/repos/huggingface/transformers/issues/19 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/19/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/19/comments | https://api.github.com/repos/huggingface/transformers/issues/19/events | https://github.com/huggingface/transformers/issues/19 | 380,555,132 | MDU6SXNzdWUzODA1NTUxMzI= | 19 | will you push the pytorch code for the pre-training process? | {
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"Hi, I don't have plan for that in the near future."
] | 1,542 | 1,542 | 1,542 | NONE | null | Can you push the pytorch code for the pre-training process,such as MLM task, please?
I really want to study, but I can't understand tensorflow, it's so complex.
thanks!!! | {
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https://api.github.com/repos/huggingface/transformers/issues/18 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/18/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/18/comments | https://api.github.com/repos/huggingface/transformers/issues/18/events | https://github.com/huggingface/transformers/pull/18 | 380,305,486 | MDExOlB1bGxSZXF1ZXN0MjMwNTM2Mzg4 | 18 | include the output layer in the model using the pretrained weights | {
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"Thanks for that. I've ended up taking a more modular approach in the first pip release of the library."
] | 1,542 | 1,542 | 1,542 | NONE | null | This is to be able to load the final output layer (bert.output_layer) from the TensorFlow pre-trained model.
In particular, it is a fully connected layer that is used to map the final hidden layer to the vocabulary size, to then apply the softmax, as follows:
logits = bert.output_layer(sequence_output)
log_softmax... | {
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https://api.github.com/repos/huggingface/transformers/issues/17 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/17/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/17/comments | https://api.github.com/repos/huggingface/transformers/issues/17/events | https://github.com/huggingface/transformers/pull/17 | 380,292,054 | MDExOlB1bGxSZXF1ZXN0MjMwNTI2MTY0 | 17 | activation function in BERTIntermediate | {
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"Looks good, thanks for that!"
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | Was previously hardcoded to gelu because pretrained BERT models use gelu.
Changed to make BERTIntermediate use functions and "gelu", "relu" or "swish" from `config`. | {
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https://api.github.com/repos/huggingface/transformers/issues/16 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/16/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/16/comments | https://api.github.com/repos/huggingface/transformers/issues/16/events | https://github.com/huggingface/transformers/pull/16 | 380,272,853 | MDExOlB1bGxSZXF1ZXN0MjMwNTEwOTY4 | 16 | Excluding AdamWeightDecayOptimizer internal variables from restoring | {
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"Is your pre-trained model a TensorFlow model?",
"Yes",
"Nice, thanks for that!"
] | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | I tried to use convert_tf_checkpoint_to_pytorch.py script to convert my pretrained model, but in order to do so, I had to make some minor tweaks. I thought I would share in case you find it useful. | {
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https://api.github.com/repos/huggingface/transformers/issues/15 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/15/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/15/comments | https://api.github.com/repos/huggingface/transformers/issues/15/events | https://github.com/huggingface/transformers/issues/15 | 380,271,134 | MDU6SXNzdWUzODAyNzExMzQ= | 15 | activation function in BERTIntermediate | {
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"Yes, I hard coded that since the pre-trained models are all trained with gelu anyway.",
"ok. but since config is there anyway, isn't it cleaner to use it (to avoid errors for people using configs that use a different activation for some reason) ?",
"Yes we can, I'll change that in the coming first release (unl... | 1,542 | 1,542 | 1,542 | CONTRIBUTOR | null | BERTConfig is not used for `BERTIntermediate`'s activation function. `intermediate_act_fn` is always `gelu`. Is this normal?
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/modeling.py#L240 | {
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https://api.github.com/repos/huggingface/transformers/issues/14 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/14/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/14/comments | https://api.github.com/repos/huggingface/transformers/issues/14/events | https://github.com/huggingface/transformers/pull/14 | 379,587,417 | MDExOlB1bGxSZXF1ZXN0MjI5OTk0NDY2 | 14 | fixed typo | {
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"Hi,\r\nThanks for the PR, we don't want to add a shell script to the repo.\r\nI will correct the typo,\r\nBest,\r\nThom"
] | 1,541 | 1,542 | 1,542 | NONE | null | When test with SQuAD | {
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https://api.github.com/repos/huggingface/transformers/issues/13 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/13/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/13/comments | https://api.github.com/repos/huggingface/transformers/issues/13/events | https://github.com/huggingface/transformers/issues/13 | 379,440,759 | MDU6SXNzdWUzNzk0NDA3NTk= | 13 | Bug in run_classifier.py | {
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https://api.github.com/repos/huggingface/transformers/issues/12 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/12/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/12/comments | https://api.github.com/repos/huggingface/transformers/issues/12/events | https://github.com/huggingface/transformers/issues/12 | 379,422,090 | MDU6SXNzdWUzNzk0MjIwOTA= | 12 | py2 code | {
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"Hi, we won't provide a python 2 version but if you want to do a python 2/3 compatible version feel free to open a PR."
] | 1,541 | 1,541 | 1,541 | NONE | null | if I convert code to python2 version of code, it can't converage ; Would you present py2 code? | {
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https://api.github.com/repos/huggingface/transformers/issues/11 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/11/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/11/comments | https://api.github.com/repos/huggingface/transformers/issues/11/events | https://github.com/huggingface/transformers/issues/11 | 379,036,394 | MDU6SXNzdWUzNzkwMzYzOTQ= | 11 | Swapped to_seq_len/from_seq_len in comment | {
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"Yes! fixed the comment"
] | 1,541 | 1,541 | 1,541 | NONE | null | I'm pretty sure this comment:
https://github.com/huggingface/pytorch-pretrained-BERT/blob/2c5d993ba48841575d9c58f0754bca00b288431c/modeling.py#L339-L343
should instead say:
```
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
```
... | {
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https://api.github.com/repos/huggingface/transformers/issues/10 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/10/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/10/comments | https://api.github.com/repos/huggingface/transformers/issues/10/events | https://github.com/huggingface/transformers/issues/10 | 378,996,831 | MDU6SXNzdWUzNzg5OTY4MzE= | 10 | Is there a plan to have a FP16 for GPU so to have larger batch size or longer text documents support ? | {
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"Yes probably. I am testing fp16 right now. If it works well I will push it to the repo.",
"Ok I've added FP16 support (see updated readme)",
"Thanks for this quick updates.",
"I'm not able to work with FP16 for pytorch BERT code. Particularly for BertForSequenceClassification, which I tried and got the issue... | 1,541 | 1,545 | 1,542 | CONTRIBUTOR | null | Is there a plan to have an FP16 for GPU so to have a larger batch size or longer text documents support? | {
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https://api.github.com/repos/huggingface/transformers/issues/9 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/9/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/9/comments | https://api.github.com/repos/huggingface/transformers/issues/9/events | https://github.com/huggingface/transformers/issues/9 | 378,935,595 | MDU6SXNzdWUzNzg5MzU1OTU= | 9 | Crash at the end of training | {
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"Here's the specific command I ran for more context: \r\n```\r\npython3.6 code/run_squad.py \\\r\n --bert_config_file bert/bert_config.json \\\r\n --vocab_file bert/vocab.txt \\\r\n --output_dir output \\\r\n --train_file data/original/train.json \\\r\n --predict_file data/original/dev.json \\\r\n --init_chec... | 1,541 | 1,541 | 1,541 | NONE | null | Hi, I tried running the Squad model this morning (on a single GPU with gradient accumulation over 3 steps) but after 3 hours of training, my job failed with the following output:
I was running the code, unmodified, from commit 3bfbc21376af691b912f3b6256bbeaf8e0046ba8
Is this an issue you know about?
```
11/08/2... | {
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