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"Hi, what fine-tuning script and model are you referring to?",
"I would like to know how to restart / continue runs as well.\r\nI would like to fine tune on half data first, checkpoint it. Then restart and continue on the other half of the data.\r\n\r\nLike the `main` function in this finetuning script:\r\nhttps:... | 1,555 | 1,662 | 1,571 | NONE | null | I wanted to experiment with longer training schedules. How do I re-start a run from it’s fine-tuned checkpoint? | {
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"Thanks for the PR. This is fixed now."
] | 1,555 | 1,556 | 1,556 | CONTRIBUTOR | null | As the title, we will never reach the code from line 115 to 131 because the space before `if args.unconditional` is not enough. | {
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"Hi rohuns, I was wondering what padding value have you used for the lm_labels, since the -1 specified in the docs doesn't work for me on GPT2LMHead model. See #577. ",
"> Hi rohuns, I was wondering what padding value have you used for the lm_labels, since the -1 specified in the docs doesn't work for me on GPT2L... | 1,555 | 1,556 | 1,556 | NONE | null | Hi, wrote some code to finetune GPT2 on rocstories using the DoubleHeads model mirroring the GPT1 code. However, I'm only getting performance of 68% on the eval. Was wondering if anyone else had tried it and seen this drop in performance. Thanks | {
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"Hi @ailzhang,\r\nThis is great! I went through it and it looks good to me.\r\n\r\nI guess we should update the `from_pretrained` method of the other models as well (like [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/19666dcb3bee3e379f1458e295869957aac8590c/pytorch_pretrained_bert/modeling_open... | 1,555 | 1,556 | 1,556 | NONE | null | fixes #504
Also add hubconf for bert related tokenizer & models.
There're a few GPT models and transformer models, but would like to send this out to get a review first.
Also there's possibility to unify the cache dir with pytorch one. | {
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"Here's an example of text generation, picks second most likely word at each step\r\n\r\n```\r\ntokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')\r\nmodel = TransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')\r\nline = \"Cars were invented in\"\r\nline_tokenized = tokenizer.tokenize(line)\r\nl... | 1,555 | 1,595 | 1,555 | CONTRIBUTOR | null | Hi everyone,
I am trying to generate text with the pre-trained transformer XL model in a similar way to how we do with the GPT-2 model. But I guess there is a bug in the `sample_sequence` function after I adjusted to the transformer XL architecture. But the generated text is completely random in general and with res... | {
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"actually this is related to my current work, I will send a fix along with my PR."
] | 1,555 | 1,556 | 1,556 | NONE | null | ```
model = BertForTokenClassification.from_pretrained('bert-base-uncased', 2)
```
will complain about missing positional arg for `num_labels`.
The root cause is here the function signature should actually be
https://github.com/huggingface/pytorch-pretrained-BERT/blob/19666dcb3bee3e379f1458e295869957aac8590c/pyt... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,555 | 1,560 | 1,560 | CONTRIBUTOR | null | Hi developers !
When I use the openai tokenizer, I find it hard to handle the `special tokens` correctly (my library version is v0.6.1) , even though I have already defined them and told the tokenizer NEVER SPLIT them. It is because all tokens, including the special ones will be processed by BPE. So I add one line f... | {
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"Not really.\r\nYou should build a new sub-class of `BertPreTrainedModel` which is identical to `BertModel`but send back self-attention values in addition to the hidden states.\r\n",
"I see. Thank you! ",
"Hi, \r\n\r\nJust to add on. If this is what I would be doing, would it be advisable to fine-tune the weigh... | 1,555 | 1,561 | 1,561 | NONE | null | Hi all,
Please correct me if I am wrong.
From my understanding, The encoded values for each layer (12 of them for base model) would be returned when we run our results through the pre-trained model.
However, I would like to examine the self-attention values for each layer. Is there a way I can extract that ou... | {
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"I noticed the following snippet in the code. (which I have edited to solve my problem)\r\n\r\n if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):\r\n # Save a trained model, configuration and tokenizer\r\n model_to_save = model.module if hasattr(model, 'module') els... | 1,555 | 1,569 | 1,555 | NONE | null | I have fine-tuned a BERT-QA model on SQuAD and it produced a `pytorch_model.bin` file. Now, I want to load this fine-tuned model and evaluate on SQuAD. How can I do that? I am using the `run_squad.py` script. | {
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- a bunch of tests for the models and tokenizers stored on S3 with `--runslow` (download and load one model/tokenizer for each type of model BERT, GPT, GPT-2, Transformer-XL)
- relax network connection checking (fallback on the last downloaded model in the cache when we can't get the last eTag from s3) | {
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"Did you install pytorch-pretrained-bert as indicated in the README?\r\n`pip install pytorch_pretrained_bert`\r\n\r\nYou don't have to convert the checkpoints yourself, there are already converted.\r\n\r\nTry reading the installation and usage sections of the README.",
"Of cause I installed,\r\nMore precisely, er... | 1,555 | 1,580 | 1,556 | NONE | null | Hello,
I am newbie of pytorch-pretrained-Bert.
After successfully converted from init-checkpoint of tensorflow to pytorch bin,
I found an error when I do run_squad.
Guessing I should've included some configuration ahead, could anyone can help?
See below.
```bash
File "run_squad.py", line 37, in <module>
fro... | {
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- adding tests on GPT-2 Tokenizer (at last)
- fixing GPT-2 tokenization to work on python 2 as well
- adding `special_tokens` handling logic in GPT-2 tokenizer
- fixing GPT and GPT-2 serialization logic to save special tokens | {
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https://api.github.com/repos/huggingface/transformers/issues/497 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/497/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/497/comments | https://api.github.com/repos/huggingface/transformers/issues/497/events | https://github.com/huggingface/transformers/issues/497 | 434,028,654 | MDU6SXNzdWU0MzQwMjg2NTQ= | 497 | UnboundLocalError: local variable 'special_tokens_file' referenced before assignment | {
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"Yes, this should be fixed by #498."
] | 1,555 | 1,555 | 1,555 | CONTRIBUTOR | null | Happens during this
```enc = GPT2Tokenizer.from_pretrained('gpt2')```
```
File "example_lambada_prediction_difference.py", line 23, in <module>
enc = GPT2Tokenizer.from_pretrained(model_name)
File "/bflm/pytorch-pretrained-BERT/pytorch_pretrained_bert/tokenization_gpt2.py", line 134, in from_pretrained
... | {
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"Indeed, thanks @8enmann!"
] | 1,555 | 1,555 | 1,555 | CONTRIBUTOR | null | {
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"Ok, great, thanks @SudoSharma!",
"While the outputs are the same between 1e10 and 1e4, I shouldn't expect the outputs between fp32 and fp16 to be the same, should I? I get different outputs between the two when doing unconditional/conditional generation with top_k=40 but even with top_k=1. Usually they're the sa... | 1,555 | 1,557 | 1,555 | CONTRIBUTOR | null | This fix is in reference to issue #382. GPT2 can now be trained in mixed precision, which I've confirmed with testing. I also tested unconditional generation on multiple seeds before and after changing 1e10 to 1e4 and there was no difference. Please let me know if there is anything else I can do to make this pull reque... | {
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"Thanks!"
] | 1,555 | 1,555 | 1,555 | CONTRIBUTOR | null | Hey guys, there was an issue with the example file for generating unconditional samples. I just fixed the indentation. Let me know if there is anything else I need to do! Thanks for the great work on this repo. | {
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"Without fine-tuning, BERT features are usually less useful than plain GloVe or wrd2vec indeed.\r\nThey start to be interesting when you fine-tune a classifier on top of BERT.\r\n\r\nSee the recent study by Matthew Peters, Sebastian Ruder, Noah A. Smith ([To Tune or Not to Tune? Adapting Pretrained Representations ... | 1,555 | 1,576 | 1,562 | NONE | null | I extract features like examples in extarct_features.py. But went I used these features (the last encoded_layers) as word embeddings in a text classification task, I got a worse result than using 300D Glove(any other parameters are the same). I also used these features to compute the cos similarity for each word in sen... | {
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"Yes. We are reproducing the behavior of the original optimizer, see [here](https://github.com/google-research/bert/blob/master/optimization.py#L65).",
"thanks~",
"but why?",
"I have the same question, but did this prove to be better? Or is it just to speed up calculations?"
] | 1,555 | 1,613 | 1,555 | NONE | null | what does this means? Whay these three kind no decay? | {
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"Accuracy goes to 31% if I use [stop-word filter](https://github.com/cybertronai/bflm/blob/51908bdd15477a0cedfbd010d489f8d355443b6a/eval_lambada_slow.py#L62), still seems lower than expected ([predictions](https://s3.amazonaws.com/yaroslavvb2/data/lambada_predictions_stopword_filter.txt))\r\n",
"Hi, I doubt it's... | 1,555 | 1,559 | 1,559 | CONTRIBUTOR | null | For some reason I only see 26% accuracy when evaluating on Lambada for GPT-2 checkpoint instead of expected 45.99%
Here's a file of [predictions](https://s3.amazonaws.com/yaroslavvb2/data/lambada_predictions.txt) with sets of 3 lines of the form:
ground truth
predicted last_word
is_counted_as_error
Generated... | {
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Also fix an issue with special adding tokens. | {
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Also add a serialization method to all the Configuration classes: `Config.to_json_file(file_path)`
Added clean examples for serialization best practices in README and... | {
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"Indeed, it looks better.\r\nDo you want to have a look and confirm @rodgzilla?",
"@thomwolf any word on this?",
"Oh yes sorry. Looking at it and reading Alec Radford's paper on GPT (section 3.3) again, I think @rodgzilla was actually right in the original implementation.\r\n\r\nSo I guess we should close this ... | 1,555 | 1,556 | 1,556 | CONTRIBUTOR | null | the number of choices is not respected because you've hardcoded '1' into the classifier layer. also `token_type_ids` and `attention_mask` will cause an error if `None` because `None` does not have a `view` method. | {
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https://api.github.com/repos/huggingface/transformers/issues/487 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/487/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/487/comments | https://api.github.com/repos/huggingface/transformers/issues/487/events | https://github.com/huggingface/transformers/issues/487 | 432,963,792 | MDU6SXNzdWU0MzI5NjM3OTI= | 487 | BERT multilingual for zero-shot classification | {
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"UPD. I tried it with bert-multilingual-cased, but the results are still bad. A number of very simple (text, translated text) give very different probability distributions (the translated versions almost always fall into one major category).\r\n\r\nSpecifiically, **I fine-tune pre-trained bert-multilingual-cased on... | 1,555 | 1,561 | 1,561 | NONE | null | Hi!
I'm interested in solving a classification problem in which I train the model on one language and make the predictions for another one (zero-shot classification).
It is said in the README for the multilingual BERT model (https://github.com/google-research/bert/blob/master/multilingual.md) that:
> For tokeniz... | {
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"Yes there are some optimizations. Please take a look here:\r\n\r\nhttps://hanxiao.github.io/2019/01/02/Serving-Google-BERT-in-Production-using-Tensorflow-and-ZeroMQ/#engineering-building-a-scalable-service",
"Hi, there is no specific relation between the present repo (which provides PyTorch implementations of se... | 1,555 | 1,561 | 1,561 | NONE | null | Hi,
I wondered if anybody knows the difference between the `BertModel` of this repo and [bert-as-service](https://github.com/hanxiao/bert-as-service).
1. I cannot get the same result between these two even if I use the same checkpoint. pytorch-pretrained-BERT yield a lower acc and slower convergence.
2. The m... | {
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"I found out the issue. The text corpus I was using is just one document. So the code is for two or more documents only?",
"Yes only for multiple documents.\r\nWe have a test now to check that since #478 thanks to @Rocketknight1."
] | 1,555 | 1,555 | 1,555 | NONE | null | Hi @thomwolf
I am using the lm_finetuning codes. Generated training data using generate_pretraining_data.py
When running finetune_on_pregenerated.py . I am getting this error.
logs
python finetune_on_pregenerated.py --pregenerated_data training_1/ --bert_model bert-base-uncased --do_lower_case --output_dir ... | {
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https://api.github.com/repos/huggingface/transformers/issues/484 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/484/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/484/comments | https://api.github.com/repos/huggingface/transformers/issues/484/events | https://github.com/huggingface/transformers/issues/484 | 432,826,407 | MDU6SXNzdWU0MzI4MjY0MDc= | 484 | KeyError: in convert_tokens_to_ids() | {
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"Hi @wasiahmad,\r\nThis should actually already been taken care of by the WordPieceTokenizer ([here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/19666dcb3bee3e379f1458e295869957aac8590c/pytorch_pretrained_bert/tokenization.py#L357)).\r\nDo you have a simple example to share so I can try to reproduce... | 1,555 | 1,555 | 1,555 | NONE | null | In BertTokenizer's, [convert_tokens_to_ids](https://github.com/huggingface/pytorch-pretrained-BERT/blob/19666dcb3bee3e379f1458e295869957aac8590c/pytorch_pretrained_bert/tokenization.py#L117) function gives KeyError. So, I suggest to modify the **for loop** in the function as follows.
```
for token in tokens:
i... | {
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https://api.github.com/repos/huggingface/transformers/issues/483 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/483/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/483/comments | https://api.github.com/repos/huggingface/transformers/issues/483/events | https://github.com/huggingface/transformers/issues/483 | 432,747,117 | MDU6SXNzdWU0MzI3NDcxMTc= | 483 | Perplexity number of wikitext-103 on gpt-2 don't match the paper | {
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"Are you using context length 256 by chance?\r\n\r\nOn WikiText-2 I see\r\n\r\nI see 29.17378 using context length 1024 and 43.3393707 using context length 256\r\nThis is close to OpenAI report result of 29.41\r\n(although I don't get why it doesn't match it exactly)\r\n",
"@yaroslavvb \r\nNo, I'm using a contex... | 1,555 | 1,592 | 1,567 | NONE | null | Hi,
The reported perplexity number of gpt-2 (117M) on wikitext-103 is 37.5.
However when I use the pre-trained tokenizer for gpt-2 `GPT2Tokenizer` using:
`tokenizer = GPT2Tokenizer.from_pretrained('gpt2')` to tokenize wikitext-103, and then evaluate it using the pre-trained 117M gpt-2 model, I get a ppl of 48.4
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"Hi Sungho,\r\nThis should already be taken care of by the BertTokenizer which defaults on the `unk` token when a sub-word is not in the vocabulary (see [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization.py#L372)).\r\nWhat BERT model are you using?\r\nDo y... | 1,555 | 1,573 | 1,555 | NONE | null | Dear concern,
I appreciate your favor for public implementation.
As you know, all NLP people have an interest in applying your gorgeous model to every NLP problem.
I am writing this to suggest to add exception handling or warning message about out-of-vocabulary when a pretrained model is used.
I had been suff... | {
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https://api.github.com/repos/huggingface/transformers/issues/481 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/481/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/481/comments | https://api.github.com/repos/huggingface/transformers/issues/481/events | https://github.com/huggingface/transformers/issues/481 | 432,700,310 | MDU6SXNzdWU0MzI3MDAzMTA= | 481 | BERT does mask-answering or sequence prediction or both??? | {
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"I'm not sure I understand your question.\r\n\r\nBut one thing BERT can do is mask-answering indeed (guessing a word in the middle of a sentence).\r\n\r\nBERT is quite bad at doing sequence prediction at the end of an input because it's not trained on partial sentences.",
"When BERT fills-in a MASK, does it alway... | 1,555 | 1,561 | 1,561 | NONE | null | I'm working on a exciting project but need to know something fast.
I seen BERT adds a word at the end of your input, like sequence prediction, elongating your input text.
But I read that BERT has been trained at (and is a pro at) filling in the blank word mask, and in fact CANNOT do sequence prediction at the end... | {
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https://api.github.com/repos/huggingface/transformers/issues/480 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/480/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/480/comments | https://api.github.com/repos/huggingface/transformers/issues/480/events | https://github.com/huggingface/transformers/pull/480 | 432,692,428 | MDExOlB1bGxSZXF1ZXN0MjcwMTExMzc3 | 480 | Extend the BertForSequenceClassification docs to mention the special CLS token. | {
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"Ok, let's go for that @mboyanov!",
"Great!"
] | 1,555 | 1,555 | 1,555 | CONTRIBUTOR | null | {
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"the allen institute tool may help you....it has probabilities of next 10 words for when adding a next word....i think the prorbablities are shown....may just be the 10 words but the git code is available i think so may be what you want.",
"The output of `GPT2LMHeadModel` are logits so you can just apply a softma... | 1,555 | 1,561 | 1,561 | NONE | null | How can we use this GPT2 model to create the basic functionality of the GLTR tool?
Like getting probabilities for each word in a sequence.? | {
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"Looks good to me, thanks @Rocketknight1 "
] | 1,555 | 1,555 | 1,555 | MEMBER | null | This adds the helpful error message suggested in #452 for users trying to do language model fine-tuning with one long document as a corpus, and replaces some of the `randint()` calls with equivalent cleaner `randrange()` ones. | {
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"So p_M(S) is just the output of the model right?\r\n\r\nFor p_u(S), I think the easiest is probably to use the empirical probabilities.\r\n`TransfoXLTokenizer` has a counter to store words frequencies [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization_tra... | 1,555 | 1,687 | 1,561 | NONE | null | So I was trying to implement the SLOR score, using the transformer-XL, mostly avoiding training. But given the XL model and the sentence, how could i go about getting the sentence level log probability?
Attached is the formula I'm trying to implement.
![image](https://user-images.githubusercontent.com/18056781/56... | {
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"What hardware are you using?\r\nWhat is your software configuration (versions of python, pytorch, pytorh-pretrained-bert, etc...)?",
"I'm not so sure that this is the same problem that I experienced.\r\n\r\nIf you installed apex, the problem may be related to BertLayerNorm, because BertLayerNorm will use FusedLa... | 1,555 | 1,567 | 1,567 | NONE | null | (g-torch) [bipkg@SVR16173HP380 examples]$ python run_squad.py --bert_model bert-base-uncased --train_file squad/train-v1.1.json --do_train --output_dir exps2
04/12/2019 11:32:03 - INFO - __main__ - device: cuda n_gpu: 1, distributed training: False, 16-bits training: False
04/12/2019 11:32:03 - WARNING - pytorch_... | {
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"There is some non-determinism in cuDNN. Try setting `torch.backends.cudnn.deterministic = True` in your code: with that plus the RNG seeding, you should be able to get deterministic results.",
"Yes go with @Rocketknight1 suggestion.\r\nAlso check that you set model in eval mode to disable the DropOut modules bef... | 1,555 | 1,556 | 1,556 | NONE | null | I modified the example file `run_classifier.py` a little bit, so that the model could evaluate after each training epoch and save each evaluation results on file. This is good for someone who wants to see how the training epoch number influence the result. It is good to simply set the train_epoch = 50, save a checkpoin... | {
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https://api.github.com/repos/huggingface/transformers/issues/474 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/474/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/474/comments | https://api.github.com/repos/huggingface/transformers/issues/474/events | https://github.com/huggingface/transformers/pull/474 | 432,218,030 | MDExOlB1bGxSZXF1ZXN0MjY5NzMyMTY1 | 474 | Fix tsv read error in Windows | {
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"Ok, thanks @jiesutd!"
] | 1,555 | 1,555 | 1,555 | CONTRIBUTOR | null | The initial version suffers from the error of `UnicodeDecodeError: 'charmap' codec can't decode byte 0x90 in position` when loading the `.tsv` filein **Windows** System, as indicated in https://github.com/huggingface/pytorch-pretrained-BERT/issues/52
It is solved by adding `encoding='utf-8'` when reading the `.tsv` ... | {
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https://api.github.com/repos/huggingface/transformers/issues/473 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/473/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/473/comments | https://api.github.com/repos/huggingface/transformers/issues/473/events | https://github.com/huggingface/transformers/issues/473 | 432,162,932 | MDU6SXNzdWU0MzIxNjI5MzI= | 473 | GPT as a Language Model | {
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"Looks good to me. It's perplexity so lower is better.\r\nYou can do a `math.exp(loss.item())` and call you model in a `with torch.no_grad()` context to be a little cleaner.",
"Oh no wait, you need to compare to the shifted inputs:\r\n`loss=model(tensor_input[:-1], lm_labels=tensor_input[1:])`\r\nIt's a causal mo... | 1,555 | 1,623 | 1,563 | NONE | null | I am interested to use GPT as Language Model to assign Language modeling score (Perplexity score) of a sentence. Here is what I am using
```import torch
import math
from pytorch_pretrained_bert import OpenAIGPTTokenizer, OpenAIGPTModel, OpenAIGPTLMHeadModel
# Load pre-trained model (weights)
model = OpenAIGPTLMH... | {
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"managed to install it by running \r\n\r\n> sudo apt-get install python3 python-dev"
] | 1,554 | 1,554 | 1,554 | NONE | null | Hi,
I cannot pip install the package. I have
> regex_3/_regex.c:48:10: fatal error: Python.h: No such file or directory
#include "Python.h"
^~~~~~~~~~
compilation terminated.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 1
Might have something to do with CI failure for ... | {
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"Good catch, I'll fix this."
] | 1,554 | 1,554 | 1,554 | NONE | null | line 651 has a potential bug
``` # Copy word and positional embeddings from the previous weights
self.tokens_embed.weight.data[: self.config.vocab_size, :] = old_embed.weight.data[: self.config.vocab_size, :]
self.tokens_embed.weight.data[-self.config.n_positions :, :] = old_embed.weight.dat... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"I'm having a similar issue where I run a BERT tutorial by Chris McCormick and receive 65-78% accuracy on training data and 0.0% for te... | 1,554 | 1,568 | 1,560 | NONE | null | when I do the classifying with task cola, the result is like this?
eval_loss = 0.0
global_step = 49173
loss = 0.0
mcc = 0.0
what is my prediction result?
and how should I use the output model????
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"First, yes you can. You should be able to download the model(whether use shell command or python downloader) into a specified directory and modify the loading lines. That works for me. \r\nSecond, please be appreciated to these fantastic works huggingface team pulled off. It's not their fault that you don't have s... | 1,554 | 1,581 | 1,561 | NONE | null | Sorry for my Impolite words. I am very appreciate huggingface team for the excellent repo and
my original intention is to provide some suggestion to great repo to help more people like me, forgive me for being rude. | {
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"What kind of task are you fine-tuning on? I If it's something like ROCstories task, you need the extra tokens. I think people are doing BERT for up-stream tasks because birectional context gives better results than left-to-right",
"Hi @yaroslavvb, I am mostly focusing on classification tasks(like ROCstories as y... | 1,554 | 1,561 | 1,561 | NONE | null | I wonder if GPT-2 model has some examples of how to do fine tuning like GPT. The DoubleHeadsModel interface of GPT-2 looks similar to GPT. But there's no special token handler for GPT-2 tokenizer. Is that necessary? | {
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"Thanks Yaroslav!"
] | 1,554 | 1,555 | 1,555 | CONTRIBUTOR | null | Fix for
```> > > > 04/09/2019 21:39:38 - INFO - __main__ - device: cuda n_gpu: 1, distributed training: False, 16-bits training: False
Traceback (most recent call last):
File "/home/ubuntu/pytorch-pretrained-BERT/examples/lm_finetuning/simple_lm_finetuning.py", line 642, in <module>
main()
File "/home/... | {
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"You are right, thanks for catching this.\r\n\r\nThis behavior is inherited from the BERT Tokenizer but the TransformerXL Tokenizer should behave differently ([this line](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization_transfo_xl.py#L316) which split on punctu... | 1,554 | 1,637 | 1,555 | NONE | null | In `examples/run_transfo_xl.py`, the pre-processed wikitext-103 corpus is loaded using:
`corpus` = TransfoXLCorpus.from_pretrained(args.model_name)
`
Example of pre-processed batch converted to tokens:
> ['<eos>', '=', 'Homarus', 'gammarus', '=', '<eos>', '<eos>', 'Homarus', 'gammarus', ',', 'known', 'as', 'the... | {
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"Can you try to run it with `CUDA_LAUNCH_BLOCKING=1` so we can see which exact CUDA call fails?\r\nAlso, do you have a simple way for me to try to reproduce this error?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Tha... | 1,554 | 1,560 | 1,560 | CONTRIBUTOR | null | Using `pytorch-pretrained-BERT` with `apex` installed breaks with the errors below. I am using it through allennlp, and my environment settings are:
```
ubuntu 16.04
nvidia driver 410.48
4 Titan V gpus
python 3.6.8
cuda 9
pytorch 1.0.1.post2
pytorch-pretrained-bert 0.6.1
```
I also tried python 3.7, cuda 10... | {
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"You can get `pytorch_model.bin` and `config.json` just as indicated in the examples: https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py#L861-L866\r\n\r\nThe vocabulary stays the same, just load the tokenizer as you did for the training (`BertTokenizer.from_pretrained(...)... | 1,554 | 1,554 | 1,554 | NONE | null | Hi,
I am fine tuning bert on custom data.
As output I am getting only pytorch_model.bin but how to get updated vocab.txt and bert_config.json .
Please suggest.
Thanks
Mahesh | {
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"Hi,\r\nAlso it should produce vocab.txt and bert_config.json along with pytorch_model.bin.\r\nHow you are getting those?",
"We did this for [SciBERT](https://github.com/allenai/scibert), and you might find this discussion useful https://github.com/allenai/scibert/issues/29",
"lm_finetuning produce pytorch_mode... | 1,554 | 1,596 | 1,570 | NONE | null | I want to use lm_finetuning for BERT. A potential issue is vocab_size. Since I'm using Hinglish data (Hindi text written using English Alphabets) there can be new words which are not present in English vocabulary. According to BERT doc...
> If using your own vocabulary, make sure to change vocab_size in bert_config.... | {
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"Fixes #412 ",
"Ok, looks good to me, thanks!"
] | 1,554 | 1,555 | 1,555 | CONTRIBUTOR | null | Before this PR, unconditional sample generation fails silently. Fixing the loop reveals a reference before assignment error. | {
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https://api.github.com/repos/huggingface/transformers/issues/461 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/461/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/461/comments | https://api.github.com/repos/huggingface/transformers/issues/461/events | https://github.com/huggingface/transformers/issues/461 | 430,686,503 | MDU6SXNzdWU0MzA2ODY1MDM= | 461 | Pooler weights not being updated for Multiple Choice models? | {
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"Indeed this looks like a bug in the `run_swag.py` example.\r\nWhat do you think @rodgzilla?\r\nIsn't the exclusion of the pooler parameters from optimization ([line 392 of `run_swag.py`](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_swag.py#L392)) a typo?",
"This issue has been ... | 1,554 | 1,560 | 1,560 | CONTRIBUTOR | null | I'm trying use pretrained BERT to finetune on a multiple choice dataset.
The parameters from `pooler` are excluded from the optimizer params [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L1044-L1047), however, the MutlipleChoice model does indeed use `p... | {
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"My guess is that the inputs are either larger than the maximum input size of the model (512) or outside the vocabulary (larger than the vocabulary size).\r\n\r\nDo you think you can try to check this?",
"@ananyahjha93 did the implementation of the additional GLUE tasks. Maybe he has some additional insights on t... | 1,554 | 1,561 | 1,561 | NONE | null | I am trying to run the run_classifier.py script against the CoLA task as a smoke test to make sure I have everything installed correctly. However, when I run
`CUDA_LAUNCH_BLOCKING=1 python run_classifier.py --task_name CoLA --do_train --do_eval --do_lower_case --data_dir /workspace/glue/CoLA/ --bert_model bert-base-... | {
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"I understand the issue but I'm not sure this would be very easy to implement as we would like to keep the model and tokenizer separated one from the other.\r\n\r\nDo you have a solution in mind?\r\n\r\nOtherwise, I'll guess people will have to continue to read the paper before using the model... 😉",
"I totally ... | 1,554 | 1,555 | 1,555 | CONTRIBUTOR | null | Thank you for the awesome package!
As I understand it right now, it is the user's responsibility to add the special `CLS` and `SEP` tokens. People who haven't read the paper might miss this detail.
It would be nice to issue a warning in the tokenizer or the model itself if the input is missing these tokens. An alt... | {
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"Have you tried the solutions discussed in the other issues on this topic:\r\n- https://github.com/huggingface/pytorch-pretrained-BERT/issues/312\r\n- https://github.com/huggingface/pytorch-pretrained-BERT/issues/239",
"This issue has been automatically marked as stale because it has not had recent activity. It w... | 1,554 | 1,591 | 1,560 | NONE | null | These are the steps I followed to get Biobert working with the existing Bert hugging face pytorch code.
1. I downloaded the pre-trained weights 'biobert_pubmed_pmc.tar.gz' from [the Releases page](https://github.com/naver/biobert-pretrained/releases).
2. I ran this command to convert the tf checkpoint to pytorch ... | {
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"Then you're doomed for this answer. There are a possibility to do a sliding window approach but we didn't implemented it in the examples of pytorch-pretrained-bert.\r\nCheck this issue (and the linked TensorFlow issue) for a discussion on this: https://github.com/huggingface/pytorch-pretrained-BERT/issues/89"
] | 1,554 | 1,559 | 1,554 | CONTRIBUTOR | null | The example script for squad sets `--max_seq_length` at 384 as default. However it seems that many paragraphs in squad exceed this length. Then what if the answer to some question lies in the truncated part of the paragraph? | {
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https://api.github.com/repos/huggingface/transformers/issues/455 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/455/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/455/comments | https://api.github.com/repos/huggingface/transformers/issues/455/events | https://github.com/huggingface/transformers/issues/455 | 430,116,432 | MDU6SXNzdWU0MzAxMTY0MzI= | 455 | LM fine tuning on top of a custom model | {
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"Not really, I guess we could remove this restriction and just let people point to an arbitrary folder (as we do in the other scripts).\r\nWhat do you think @Rocketknight1?",
"No, that's entirely my bad. We should allow an arbitrary folder!",
"Should I just remove the `choices` argument and allow any string the... | 1,554 | 1,573 | 1,554 | CONTRIBUTOR | null | Currently the `finetune_on_pregenerated.py` script allows only fine-tuning on top of one of the five pretrained bert models. However I don't understand why there is such a restriction. I am trying to finetune a LM on top of a custom bert model (mt-dnn). Of course I can just remove the `choices`, but I am wondering if t... | {
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"Hi, yes."
] | 1,554 | 1,554 | 1,554 | NONE | null | given [cls] word1 word2 word3 [sep] word1 word2 [sep]
If i get sequence embeddings, will word1 of sentence 1 will have context of word1 of sentence2? | {
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"Hi, it means that the computation graphs of the Tensorflow and PyTorch versions are identical."
] | 1,554 | 1,554 | 1,554 | NONE | null | {
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"Hi, I wrote that script! That's a tricky issue, though - what behaviour do you expect when your data is one long text? \r\n\r\nIn the original BERT repo, they used the document breaks to control sampling for the NextSentence task - 'random' next sentences were selected from a different document. I'm not sure there... | 1,554 | 1,560 | 1,560 | NONE | null | The script for pregenerating language modelling data assumes that the training corpus consists of multiple documents (i.e. a single training corpus file where empty lines separate documents). If the training corpus is made up of only one long text, the pregen script produces empty output.
I have a small fix for this... | {
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"It is likely due to the script is not able to find the vocabulary file. So you should download the vocab first and copy it over. Then when you load the tokenizer you need to specify the path to the vocab. So if you vocab is in \"/tmp/transformer_xl/\" you do:\r\n```python\r\ntokenizer = TransfoXLTokenizer.from_pre... | 1,554 | 1,583 | 1,562 | NONE | null | I need to run the package on a machine without internet. Copied over the ".pytorch_pretrained_bert" folder from one machine to another.
Installed anaconda3 and tried to run `tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')`.
Got this error:
`Model name 'transfo-xl-wt103' was not found in model na... | {
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"Maybe a good introduction on the topic are the writings of @sebastianruder:\r\n- http://ruder.io/transfer-learning/\r\n- https://thegradient.pub/nlp-imagenet/\r\n- the ULMFiT paper: http://nlp.fast.ai/classification/2018/05/15/introducting-ulmfit.html\r\n\r\nRegarding your specific question of training a Bert mode... | 1,554 | 1,560 | 1,560 | COLLABORATOR | null | I am confused about what these two steps actually do to the model. I would have assumed that pre-training is unsupervised (i.e. no labels) and, thus, the only thing that can be 'learned' is the embedding representations of all tokens. You can then use this pre-trained model (which is an 'empty' model but with pretraine... | {
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"I also encountered a similar problem AttributeError: 'BertForPreTraining' object has no attribute 'crf_loss'\r\n\r\n@thomwolf \r\n\r\nLooking forward to your reply",
"Hi, from my reading of the [Natural Questions model](https://github.com/google-research/language/blob/master/language/question_answering/bert_jo... | 1,554 | 1,562 | 1,560 | NONE | null | Hello,
I know that the format of Squad and Google NQ is different, but is there a way to convert the bert joint model for Natural Questions (https://github.com/google-research/language/tree/master/language/question_answering/bert_joint) to pytorch? I get this error
'BertForPreTraining' object has no attribute 'answ... | {
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"Hi, you should probably turn to the TensorFlow version for pre-training.\r\nThis package is mostly intended to be used for fine-tuning pre-trained models.\r\nAnother option for BERT-like pre-training is to use Facebook's [XLM](https://github.com/facebookresearch/XLM)"
] | 1,554 | 1,557 | 1,554 | NONE | null | Hi , I want to pretrain my model for chinese dataset . can i use my own vocab.txt ?
and what is the format for vocab.txt ? thanks a lot | {
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"Hi @zijwang , the package doesn't implement any specific batching logic, only tokenizers and models.\r\nYou are supposed to take care of this yourself in your scripts."
] | 1,554 | 1,554 | 1,554 | NONE | null | Does the package support dynamic `max_seq_length`, e.g., if it's None, it will automatically be the maximum length in the mini-batch? | {
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"Hi @yexing99,\r\nWhat you need is the `hidden_state`, not the `weights`of the model.\r\nDon't use `model.named_parameters()` but just use the output of the model.\r\nHere is an example: https://github.com/huggingface/pytorch-pretrained-BERT#bert\r\nAnd more details here: https://github.com/huggingface/pytorch-pret... | 1,554 | 1,554 | 1,554 | NONE | null | My understanding from the paper that each token is represented by a 768-dim vector from the last hidden layer. Is it correct? If so, how can I get the second-to-the last layer's parameter as token representation?
model = BertForTokenClassification.from_pretrained("bert-base-uncased", num_labels=len(tag2idx))
list... | {
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"Sorry for the delay in reviewing this.\r\nThis is a great PR and it looks good to me.\r\nThanks for adding some tests also.\r\nI agree with the (mostly cosmetic) comments from @marpaia.\r\nDo you think you can fix them and then we can merge?",
"Fixed @marpaia 's comments.",
"Awesome @lukovnikov, I think it loo... | 1,554 | 1,556 | 1,556 | CONTRIBUTOR | null | re: [PR#389](https://github.com/huggingface/pytorch-pretrained-BERT/pull/389)
- refactored learning rate schedules into objects
- added `WarmupCosineWithHardRestartsSchedule` for cosine schedule with hard restarts
- added `WarmupCosineWithWarmupRestartsSchedule` for cosine schedule with restarts where each restart u... | {
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"A CRF gives better NER F1 scores in some cases, but not necessarily in all cases. In the BERT paper, no CRF is used and hence also no CRF in this repository. I'd presume the BERT authors tested both with and without CRF and found that a CRF layer gives no improvement, since using a CRF is kind of the default setti... | 1,554 | 1,652 | 1,560 | NONE | null | If crf needed when do ner? In BertForTokenClassification, just Linear is used to predict tag. If not, why? | {
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"Maybe it's a problem of not setting `do_lower_case=False` and using a `cased` model like in #436.\r\nWhich pretrained model are you using?",
"@thomwolf \r\nsorry but this is completely not what I am asking for.\r\nSuppose I have medical data and the names of the medicines and diseases are not in the bert pre-tra... | 1,554 | 1,563 | 1,560 | NONE | null | I am using a domain specific dataset for text classification.
But major of my data points are treated with [UNK] token in Bert.
Can I please get help on how to keep my custom corpus tokens? | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,554 | 1,560 | 1,560 | NONE | null | I have trained BERT with custom small training. I am unable to train the same on QQP and then with custom train. Any discussion will be appreciated. | {
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https://api.github.com/repos/huggingface/transformers/issues/441 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/441/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/441/comments | https://api.github.com/repos/huggingface/transformers/issues/441/events | https://github.com/huggingface/transformers/pull/441 | 428,369,581 | MDExOlB1bGxSZXF1ZXN0MjY2NzUzMzE2 | 441 | Fix bug in run_squad.py | {
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"Hi @MottoX, sorry for the delay on reviewing this.\r\nIt seems to make sense to me.\r\nWhat kind of testing are you referring to? Could you share a bit more about them?",
"Hi, @thomwolf \r\nI was trying to train a BERT-based model on NewsQA, a document-level QA dataset, using `run_squad.py`. I used 384 for max_s... | 1,554 | 1,556 | 1,556 | CONTRIBUTOR | null | I fired an issue in Google's repo, https://github.com/google-research/bert/issues/540#issue-428344784
After testing, I found it is indeed a bug.
We should not put these chunks with `start_position==0` and `end_position==0` into training set.
Thanks for code review. | {
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"I extract features like examples in extarct_features.py. But went I used these features(the last encoded_layers) as word embeddings, I got a worse result than using 300D Glove. I also used these features to compute the cos similarity for each word in sentences, I found that all values were around 0.6",
"This is... | 1,554 | 1,567 | 1,561 | NONE | null | Hi all,
Can I use pre-trained BERT for finding fixed sized word embeddings, like 300D Glove or Word2vec word embeddings?.
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"Hi @moinnadeem,\r\nWhat is the hardware you are using and what is the exact command you are using to run DistributedDataParallel with the PyTorch launch module? ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you... | 1,554 | 1,560 | 1,560 | NONE | null | Hi, I've been stuck on this for days, so I decided to make an issue.
When I run DistributedDataParallel with the PyTorch launch module, I see that one machine will start training without waiting for the other one to start; this is different than if I run it without the launch module. WIthout the launch module, I am ... | {
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"Hi @SandeepBhutani,\r\nCan you point me to the script you use for finetuning in Tensorflow?",
"Hi @thomwolf : Thanks for reply. \r\nFine Tuning is done by mentioning do_train=True on run_squad.py (From google bert release github page: [https://github.com/google-research/bert](https://github.com/google-research/... | 1,554 | 1,589 | 1,554 | NONE | null | Trying to convert BERT checkpoints to pytorch checkpoints. It worked for default uncased bert_model.ckpt. However, after we did a custom training of tensorflow version and then tried to convert TF checkpoints to pytorch, it is giving error: 'BertPreTrainingHeads' object has no attribute 'squad'
When printed
``... | {
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https://api.github.com/repos/huggingface/transformers/issues/436 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/436/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/436/comments | https://api.github.com/repos/huggingface/transformers/issues/436/events | https://github.com/huggingface/transformers/issues/436 | 427,944,845 | MDU6SXNzdWU0Mjc5NDQ4NDU= | 436 | BertTokenizer.from_pretrained('bert-base-multilingual-cased') does not recognize Korean | {
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"Hi @icewing1996,\r\nThis comes from the fact that your tokenizer has `do_lower_case=True` but you load an uncased model.\r\n\r\nTry loading the tokenizer like this `tok = BertTokenizer.from_pretrained('bert-base-multilingual-uncased', do_lower_case=False)`.\r\n\r\nThis is actually a common issue and I see Jacob ha... | 1,554 | 1,582 | 1,554 | NONE | null | I'm using the pre-trained multilingual tokenizer to tokenize some Korean texts, but it seems like this tokenizer is unable to recognize any Korean text at all.
For example, when running on all the Universal Dependency Korean treebanks, this tokenizer fails to tokenize (it produces '[UNK]') the following characters. ... | {
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"Yes! Thanks @marpaia!"
] | 1,554 | 1,554 | 1,554 | NONE | null | This PR contains a small fix to the script which converts TensorFlow weights to PyTorch weights. The related issues are #50, #306, etc.
Thanks for all of the open source code you've been putting out in this domain, it has been incredibly helpful to me and my team. | {
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"Try train_batch_size =1. Alternatively I propose to finetune with the tensorflow model using colabs TPUs as these have far more memory.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n... | 1,554 | 1,560 | 1,560 | NONE | null | Hi! Thanks for your help.
I am initiating training in the following way in a Colab notebook with GPU acceleration (with very small train batch size and max seq length to prove I'm not getting out of memory problems!):
!pip install pytorch-pretrained-bert
!rm -rf bert_output
!mkdir bert_output
!... | {
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"```\r\npython3 examples/lm_finetuning/simple_lm_finetuning.py \r\n--train_corpus sample_text.txt \r\n--bert_model bert-base-uncased \r\n--do_lower_case \r\n--output_dir finetuned_lm/\r\n```\r\nIn addition, you can refer to #385 ",
"Yes let's keep a single issue on this. Closing in favor of #385."
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"You probably forgot to deactivate the DropOut modules with `model.eval()`",
"Oh ok, I get it. That's helpful. Thanks!",
"I tried that and now I seem to be getting the same predictions for any input. In other words, the logits don't change with change in input. Not sure why this is happening?",
"Maybe a bug i... | 1,554 | 1,614 | 1,560 | NONE | null | I used the code in run_classifier.py to train a model for intent detection which is a multi-class classification problem. After training the model, when I used it for prediction, I found the predictions to be changing from one run to another. I'm trying to understand the reason for the same and how I can avoid this beh... | {
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"Hi,\r\nYou can tokenize your data with the Transformer-XL tokenizer and use it to train your model.",
"I understand it. But I want to fine tune pretrained model",
"What is the difference?\r\n\r\nCan you start by trying to do what you would do normally to fine-tune a pytorch model and then if you encounter an i... | 1,554 | 1,563 | 1,563 | NONE | null | I have my own dataset. What format of data I need to fine tune TransformerXL for text generation? | {
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https://api.github.com/repos/huggingface/transformers/issues/429 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/429/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/429/comments | https://api.github.com/repos/huggingface/transformers/issues/429/events | https://github.com/huggingface/transformers/issues/429 | 427,290,740 | MDU6SXNzdWU0MjcyOTA3NDA= | 429 | GPT2Tokenizer <|endoftext|> | {
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"`\"<|endoftext|>\"` is a *special token* that is not intended to be feed through the tokenizer but added to the indices list after the tokenization process (see for example the way it is used in the short example [`run_gpt2.py`](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.p... | 1,553 | 1,694 | 1,554 | NONE | null | I am confused as to how the GPT2Tokenizer is intended to be used. It looks like `GPT2Tokenizer.encode` doesn't always take the byte pair encoding into account -- is this intentional?
```
from pytorch_pretrained_bert import GPT2Tokenizer
tok = GPT2Tokenizer.from_pretrained("gpt2")
print(tok.encode("<|endoftex... | {
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"Hi, indeed there is no Synthetic self-training in this repository, and the SQuAD leaderboard website actually refers to the Tensorflow repository so I'll close this issue.",
"Will you be adding synthetic self training though?"
] | 1,553 | 1,554 | 1,554 | NONE | null | The SQuAD leader board's (https://rajpurkar.github.io/SQuAD-explorer/) 3rd highest scored model uses 'synthetic self-training'.
There is a PDF explaining it: https://nlp.stanford.edu/seminar/details/jdevlin.pdf?fbclid=IwAR2TBFCJOeZ9cGhxB-z5cJJ17vHN4W25oWsjI8NqJoTEmlYIYEKG7oh4tlY but I have found no such model within t... | {
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"Good catch, thanks!",
"Gah. I meant to use `randrange()`, but this fix is equivalent!"
] | 1,553 | 1,555 | 1,554 | CONTRIBUTOR | null | If the value of rand_end is returned from the randint function, the value of sampled_doc_index that matches current_idx is returned from searchsorted.
example:
cumsum_max = {int64} 30
doc_cumsum = {ndarray} [ 5 7 11 19 30]
doc_lengths = {list} <class 'list'>: [5, 2, 4, 8, 11]
if current_idx = 1,
rand_start = ... | {
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https://api.github.com/repos/huggingface/transformers/issues/426 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/426/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/426/comments | https://api.github.com/repos/huggingface/transformers/issues/426/events | https://github.com/huggingface/transformers/pull/426 | 426,985,414 | MDExOlB1bGxSZXF1ZXN0MjY1NzE0ODgz | 426 | instantiate loss_fct once | {
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"Thanks for the PR but I think it's fine like it is now (slightly easier to read and debug)."
] | 1,553 | 1,554 | 1,554 | CONTRIBUTOR | null | {
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"Thanks!"
] | 1,553 | 1,553 | 1,553 | CONTRIBUTOR | null | {
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"I haven't looked in the details of the vocabularies for each model.\r\nIf you investigate this question, be sure to share the results here, it may interest others as well!",
"I did a diff on the two vocabulary files and there is no difference. As long as you use the uncased version at least. I haven't investigat... | 1,553 | 1,560 | 1,560 | CONTRIBUTOR | null | I understand that a cased tokenizer and an uncased one are surely different because their vocabs are different in casing, but how does a base tokenizer different from a large tokenizer? Does a large tokenizer have a larger vocab? | {
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"Hi thanks for the PR.\r\nI think we still need to clean up the example a little more indeed.\r\nThese lines should be taken care off:\r\n```python\r\nwhile not args.unconditional:\r\n if not args.unconditional:\r\n```\r\nI will see if I can find time to refactor it next week or you can update your PR if you want... | 1,553 | 1,555 | 1,555 | CONTRIBUTOR | null | The unconditional generation works now but if the seed is fixed, the sample is the same every time.
n_samples > 1 will give different samples though.
I am giving the start token as '<|endoftext|>' for the unconditional generation. | {
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"Sequence tagging is explained here: https://github.com/huggingface/pytorch-pretrained-BERT/issues/64#issuecomment-443703063",
"Yes, this is the relevant issue on this topic. I'll close this issue in favor of #64."
] | 1,553 | 1,554 | 1,554 | NONE | null | I'm trying to do Named Entity Recognition with BertForTokenClassification.
Say I have 10 words with 10 labels, after WordPiece tokenization I get 15 tokens and I assign them labels, "X" for pieces of words like (##ing).
In the original paper https://arxiv.org/pdf/1810.04805.pdf in 4.3 section is said that we don't ma... | {
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"Hi, there is no script to do that currently. I don't plan to add this feature in the short term but I would be happy to welcome a PR on that.",
"A PR for this would be great. It would allow a simple deployment via Han's bert-as-service\r\n https://github.com/hanxiao/bert-as-service/",
"This issue has been aut... | 1,553 | 1,560 | 1,560 | NONE | null | How to convert a pytorch_model.bin to tensorflow checkpoint? | {
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"Some explanation is given here: https://github.com/huggingface/pytorch-pretrained-BERT/blob/694e2117f33d752ae89542e70b84533c52cb9142/README.md#optimizers\r\n\r\n`BertAdam` is a `torch.optimizer` adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. The differences with PyTorch `Adam`... | 1,553 | 1,594 | 1,554 | COLLABORATOR | null | I have implemented BERT, taking the output of [CLS] and feeding that to a linear layer on top to do regression. I froze the embedding layers of BERT, though. I was using the standard Adam optimizer and did not run into any issues.
When and/or why should one use BERTAdam? And, in a set-up like mine, would you use BER... | {
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https://api.github.com/repos/huggingface/transformers/issues/419 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/419/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/419/comments | https://api.github.com/repos/huggingface/transformers/issues/419/events | https://github.com/huggingface/transformers/issues/419 | 426,376,347 | MDU6SXNzdWU0MjYzNzYzNDc= | 419 | bug in examples/run_squad.py line 88 & 90 | {
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"Indeed, thanks"
] | 1,553 | 1,554 | 1,554 | NONE | null |
if self.start_position:
s += ", end_position: %d" % (self.end_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
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https://api.github.com/repos/huggingface/transformers/issues/418 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/418/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/418/comments | https://api.github.com/repos/huggingface/transformers/issues/418/events | https://github.com/huggingface/transformers/issues/418 | 426,349,075 | MDU6SXNzdWU0MjYzNDkwNzU= | 418 | can I fine-tuning pretrained gpt2 model on my corpus? | {
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https://api.github.com/repos/huggingface/transformers/issues/417 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/417/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/417/comments | https://api.github.com/repos/huggingface/transformers/issues/417/events | https://github.com/huggingface/transformers/issues/417 | 426,327,845 | MDU6SXNzdWU0MjYzMjc4NDU= | 417 | Is there any pre-training example code? | {
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"Hi @tmchojo there are now several detailed examples [here](https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning) thanks to @Rocketknight1 and #392 ",
"Thanks.\r\nBut it still needs pre-trained model.\r\nMy data is another domain (not English or Chinese), so I can't use pre-tr... | 1,553 | 1,640 | 1,553 | NONE | null | I want to pre-train BERT with my own data.
[#124](https://github.com/huggingface/pytorch-pretrained-BERT/pull/124), [#170](https://github.com/huggingface/pytorch-pretrained-BERT/issues/170) say the model can pre-training.
But I can't find pre-training example code.
Is there any pre-training example code?
If there i... | {
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https://api.github.com/repos/huggingface/transformers/issues/416 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/416/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/416/comments | https://api.github.com/repos/huggingface/transformers/issues/416/events | https://github.com/huggingface/transformers/issues/416 | 426,242,746 | MDU6SXNzdWU0MjYyNDI3NDY= | 416 | Distributed Training Gets Stuck | {
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"Hi,\r\nOne possible cause of this behavior may be the way you are freezing your parameters.\r\n\r\nPyTorch's `DistributedDataParallel` is a rather sensitive beast as you can juge by the number of warnings in [its doc](https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel).\r\n\r\nTwo fe... | 1,553 | 1,560 | 1,560 | NONE | null | Hi,
When I freeze the BERT layers, distributed training works just fine. However, when I unfreeze the BERT layers, the first node continues training, and all other nodes wait on the training step with 100% GPU utilization on the first GPU. Is this expected behavior, or am I doing something wrong?
I'm using PyTorc... | {
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https://api.github.com/repos/huggingface/transformers/issues/415 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/415/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/415/comments | https://api.github.com/repos/huggingface/transformers/issues/415/events | https://github.com/huggingface/transformers/issues/415 | 426,146,199 | MDU6SXNzdWU0MjYxNDYxOTk= | 415 | For sequence classification, is this model using the wrong token? | {
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"Never mind, I think I got confused. Is it that the pooler already takes care of that and encoded_layers represents the pooler output for each attention layer?",
"Hi Catalin, the content of the outputs (`encoded_layers` and `pooled_output`) is detailed in the readme [here](https://github.com/huggingface/pytorch-p... | 1,553 | 1,553 | 1,553 | CONTRIBUTOR | null | According to the BERT paper, we want to use the weights for the `[CLS]` token, which – as far as I understand – would be the first hidden output here, not the last?
i.e. shouldn't this be `encoded_layers[0]` below?
https://github.com/huggingface/pytorch-pretrained-BERT/blob/f7c9dc8c998395d2ad9edbf0fd6fa072f03cc66... | {
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https://api.github.com/repos/huggingface/transformers/issues/414 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/414/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/414/comments | https://api.github.com/repos/huggingface/transformers/issues/414/events | https://github.com/huggingface/transformers/issues/414 | 425,788,102 | MDU6SXNzdWU0MjU3ODgxMDI= | 414 | Help with implementing strides into features for multi-label classifier | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"@alvin-leong did this make any progress? I am interested in doing something similar for the sequence classifier. ",
"@alvin-leong @ma... | 1,553 | 1,678 | 1,559 | NONE | null | As you might know, BERT has a maximum wordpiece token sequence length of 512.
The SQuAD example actually uses strides to account for this: https://github.com/google-research/bert/issues/27
I want to implement something like what Jacob Devlin described in that post for Kaushal's multi-label BERT classifier: https://m... | {
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https://api.github.com/repos/huggingface/transformers/issues/413 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/413/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/413/comments | https://api.github.com/repos/huggingface/transformers/issues/413/events | https://github.com/huggingface/transformers/issues/413 | 425,665,342 | MDU6SXNzdWU0MjU2NjUzNDI= | 413 | Bert Pretrained model has no modules nor parameters | {
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"followers_url": "https://api.github.com/use... | [] | closed | false | null | [] | [] | 1,553 | 1,553 | 1,553 | NONE | null | "from pytorch_pretrained_bert.modeling import BertPreTrainedModel
bert_model = BertPreTrainedModel.from_pretrained(pretrained_model_name_or_path='bert-base-uncased')
bert_model.to(device)"
returns:
2019-03-26 21:38:07,404 pytorch_pretrained_bert.modeling INFO loading archive file https://s3.amazonaws.com/mode... | {
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https://api.github.com/repos/huggingface/transformers/issues/412 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/412/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/412/comments | https://api.github.com/repos/huggingface/transformers/issues/412/events | https://github.com/huggingface/transformers/issues/412 | 425,663,361 | MDU6SXNzdWU0MjU2NjMzNjE= | 412 | Possible error in "pytorch-pretrained-BERT/examples/run_gpt2.py" unconditional | {
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"This should be fixed by #462."
] | 1,553 | 1,555 | 1,555 | NONE | null | Hello,
First of all thanks for offering us those great NLP implementations.
I think there may be an error in the file pytorch-pretrained-BERT/examples/run_gpt2.py
The way it is implemented if we do unco... | {
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"Multi-GPU loss returns a tuple of losses with one loss for each GPU.\r\nWe average them to get the full loss. See [this blog post](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) for more details."
] | 1,553 | 1,553 | 1,553 | NONE | null | Could anyone help to explain the motivation for this operation?
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py#L573-L574 | {
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"Do you have the latest `pytorch-pretrained-bert` ?\r\n```python\r\nimport pytorch_pretrained_bert\r\npytorch_pretrained_bert.__version__\r\n```",
"Thank you so much!!!!!!!",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occur... | 1,553 | 1,559 | 1,559 | NONE | null |
the segmentation is wrong。。 | {
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