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https://api.github.com/repos/huggingface/transformers/issues/1009 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1009/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1009/comments | https://api.github.com/repos/huggingface/transformers/issues/1009/events | https://github.com/huggingface/transformers/issues/1009 | 479,508,305 | MDU6SXNzdWU0Nzk1MDgzMDU= | 1,009 | GPT2 Sentence Probability: Necessary to Prepend "<|endoftext|>"? | {
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"Dig into this a little, and it looks like the answer is yes:\r\n\r\n```\r\ntext = \"the book is on the desk.\"\r\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\r\nmodel = GPT2LMHeadModel.from_pretrained('gpt2')\r\ninput_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0) # Batch size 1\r\ntokenize_input =... | 1,565 | 1,667 | 1,565 | NONE | null | When computing sentence probability, do we need to prepend the sentence with a dummy start token (e.g. <|endoftext|>) to get the full sentence probability? I am currently using the following implemention (from https://github.com/huggingface/pytorch-transformers/issues/473):
```
model = GPT2LMHeadModel.from_pretrain... | {
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"This repository is especially useful if you're looking to use a pre-trained transformer of the same architecture than that of BERT, GPT, GPT-2, XLM, XLNet or TransfoXL.\r\n\r\nIf you're looking at using a simple transformer of your own making, how about using the newly released [torch.nn.Transformer](https://pytor... | 1,565 | 1,565 | 1,565 | NONE | null | ## ❓ Questions & Help
I want to use only one layer transformer on the head of some backbone model. Can I use this repository in a simple way? | {
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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"
] | 1,565 | 1,571 | 1,571 | NONE | null | ## ❓ Questions & Help
I have huge text corpus without label and few data points with label.
can somebody guide on how to use GPT2 model for multi class classification problem with fine tuned Language model ? | {
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"You're right, it should! Thanks for pointing it out!"
] | 1,565 | 1,565 | 1,565 | CONTRIBUTOR | null | I assume that it should test the `re-load` functionality after testing the `save` functionality, however I'm also surprised that nobody points this out after such a long time, so maybe I've misunderstood the purpose. This PR is just in case :) | {
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https://api.github.com/repos/huggingface/transformers/issues/1005 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1005/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1005/comments | https://api.github.com/repos/huggingface/transformers/issues/1005/events | https://github.com/huggingface/transformers/issues/1005 | 479,358,726 | MDU6SXNzdWU0NzkzNTg3MjY= | 1,005 | Can't get attribute 'Corpus' on <module '__main__' from 'convert_transfo_xl_checkpoint_to_pytorch.py'> | {
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error occurs：
AttributeError: Can't get attribute 'Corpus' on <module '__main__' from 'convert_transfo_xl_checkpoint_to_pytorch.py'>
to use my data, What code do I want to chan... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1004?src=pr&el=h1) Report\n> Merging [#1004](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1004?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/e768f2322abd2a2f60a3a6d64a6a94c2d957fe89?... | 1,565 | 1,573 | 1,568 | CONTRIBUTOR | null | Pytorch-transformers! Nice work!
Refactoring old run_swag.py.
## Motivation:
I have seen the swag PR1 #951 and related issues #931
According to @thomwolf 's comments on PR1, I think it's necessary to adopt code styles of [run_squad.py](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_... | {
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https://api.github.com/repos/huggingface/transformers/issues/1003 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1003/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1003/comments | https://api.github.com/repos/huggingface/transformers/issues/1003/events | https://github.com/huggingface/transformers/issues/1003 | 479,271,279 | MDU6SXNzdWU0NzkyNzEyNzk= | 1,003 | Can't GPT-2 set special_tokens? (or unk tokens) | {
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"I also saw #468. It will be probably added soon.\r\nBut, If someone informs new information about this, I'll thank for that.",
"Passing them like so works for me:\r\n\r\n`GPT2Tokenizer.from_pretrained(args.model_name, unk_token=\"<|endoftext|>\")`\r\n\r\nYou can all pass a list to `tokenizer.add_tokens`, then ca... | 1,565 | 1,572 | 1,572 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
In GPT, we can set special tokens. (I also did it branch 0.6.2)
https://github.com/huggingface/pytorch-transformers/blob/v1.0.0/pytorch_transformers/modeling_openai.py
But, in GPT-2, It seems like no way to add special tokens.
http... | {
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https://api.github.com/repos/huggingface/transformers/issues/1002 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1002/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1002/comments | https://api.github.com/repos/huggingface/transformers/issues/1002/events | https://github.com/huggingface/transformers/issues/1002 | 479,268,507 | MDU6SXNzdWU0NzkyNjg1MDc= | 1,002 | How to make a new line when using gpt2 to generate lyrics? | {
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"Did you pre-train your model while keeping all the line returns or did you remove them? You can keep them during the training so that the model learns to predict them.\r\n\r\nIf you remove them during training and wish to apply them later on, I guess you can always just create the long string of lyrics and split t... | 1,565 | 1,571 | 1,571 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I use pre-trained gpt2 to generate lyrics (text generation). I can generate a long string of lyrics. While how to break the line, I try to add "\n" into it. But it seems that is not a good idea.
Thanks! | {
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"Sure, one way you could go about it would be to create a new class similar to `BertForSequenceClassification` and implement your own custom final classifier.\r\n\r\nThe lib is pretty modular so you can usually subclass/extend what you need.",
"You can also replace `self.classifier` with your own model.\r\n\r\n``... | 1,565 | 1,599 | 1,566 | NONE | null | Hi,
Thanks for providing an efficient and easy-to-use implementation of BERT and other models.
I am working on a project that requires me to do binary classification of sentences. I am using `BertForSequenceClassification` for that but I am not getting good results i.e. my loss function doesn't converge. I notice... | {
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"Hi!\r\n\r\nWhen you're talking about extracting the embeddings using a pre-trained model, what are you talking about exactly?\r\n\r\nAre you talking about using the tokenizer like :\r\n```python\r\ntokenizer.encode(text)\r\n```\r\n which returns the word ids?\r\n\r\nAre you talking about using the embedding layer ... | 1,565 | 1,589 | 1,565 | NONE | null | ## ❓ Questions & Help
Hello,
I have a straightforward question I think, which I am curious about.
When I load a pretrained model and use it to tokenise and extract embeddings, is the model running on a GPU or CPU? The reason why I am asking is that using bert is very slow. In particular approximately 100 times... | {
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"Hi!\r\n\r\nIn the forward pass of the BertSelfAttention model you’re getting the hidden state of the previous layer which is of size `(batch_size, sequence_length, 768)` (768 being the embedding dimension).\r\n\r\nThe first step of the attention is to obtain the `mixed_query_layer`, `mixed_key_layer` as well as th... | 1,565 | 1,693 | 1,565 | NONE | null | I have been digging through the code to understand the whole architecture of BERT (great job by the way, it's really easy to follow), and I noticed the way Multi-Headed Attention is implemented is different than from the original Transformer (unless I'm missing something). In particular, instead of using learnable weig... | {
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"More precisely it hangs on line 280:\r\n\r\n if args.local_rank == 0:\r\nHERE ---> torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache\r\n\r\n # Convert to Tensors and build dataset\r\n all_input_ids = torch.... | 1,565 | 1,572 | 1,572 | NONE | null | ## 🐛 Bug
Model I am using (Bert, XLNet....): BERT base uncased
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [x] the official example scripts: (give details) The glue distributed example from Readme
## To Reproduce
Steps to reproduce the behavior:
1... | {
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"Hi, there is no XLA/TPU support with the current library. Maybe in a future release!"
] | 1,565 | 1,565 | 1,565 | NONE | null | ## ❓ Questions & Help
I find https://news.developer.nvidia.com/nvidia-achieves-4x-speedup-on-bert-neural-network/ says tensorflow XLA has higher speed on bert, however, the pull request in this repo it mentioned https://github.com/huggingface/pytorch-pretrained-BERT/pull/116 didn't implement something like XLA. Is t... | {
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"Great, thanks !"
] | 1,565 | 1,565 | 1,565 | CONTRIBUTOR | null | I noticed two small typos when converting from Tensorflow checkpoints to PyTorch. | {
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"Hi! Yes, I think your understanding is correct. Your setup seems fine to me!",
"regarding the token_type_ids, shall we mark [PAD] token as 0 or 1? [PAD] by default does not belong to any of the two input sequences. Therefore it is ambiguous to determine whether it should be 0 or 1.",
"The default padding value... | 1,565 | 1,638 | 1,565 | NONE | null | ## ❓ Questions & Help
I am having trouble understanding how to setup BERT when doing a classification task like STS, for example, inputting two sentences and getting a classification of some sorts. I am using `BertForSequenceClassification` for this purpose. However, what boggles me is how to set up `attention_mask`... | {
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"Hi! GPT-2 does not have an unknown token because of its byte-level BPE. This is a warning so it should not affect your code, but maybe we should do something about this warning for models that do not have unknown tokens. cc @thomwolf.",
"However, it seems that having a defined _unk_ symbol is necessary to run ot... | 1,565 | 1,565 | 1,565 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Im using GPT2 (on pytorch-transformers 1.0.0) using the introductory tutorial but it seems that the tokenizer does not load the unk special symbol from the pretrained dictionary.
`
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gp... | {
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"Indeed, there seems to be a problem here. I'll look into it.",
"Thanks for the report, the error was in the docstring, we cannot use `-1` as the index for the last token, it has to be the positive index of the CLS token (in the case of the example `9`.",
"The fix seems to have led to other issues. I'm getting ... | 1,565 | 1,566 | 1,565 | NONE | null | ## 🐛 Bug
Model I am using (Bert, XLNet....): GPT2DoubleHeadsModel
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [x] the official example scripts: Trying out documentation
* [ ] my own modified scripts:
## To Reproduce
Steps to reproduce the behavio... | {
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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"
] | 1,565 | 1,571 | 1,571 | NONE | null | Thanks @huggingface for such a great library.
I am interested to use pytorch-transformers for entity linking. Any idea how to that?
Any help in this regard is highly appreciated.
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"This is simply a warning, it won't change your results. I think it's important we keep it for people that are unaware that sequences have a max length of 512, so there's currently no option to suppress that warning.",
"Just a note: if you want to avoid displaying the warning, you can raise the level of the logge... | 1,565 | 1,653 | 1,565 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
While tokenizing some sequences longer than 512 i get this error. I am aware that bert can't handle sequences longer than 512 so i split it later.
> Token indices sequence length is longer than the specified maximum sequence length... | {
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"H! Could you please specify on which OS you have this error? I cannot reproduce this on MacOS 10.15, nor on Ubuntu 18.04 with both Python 3.5 and 3.6.",
"Hi, @LysandreJik \r\n\r\nI tried these on Ubuntu 16.04.6 LTS.",
"@ntubertchen\r\nJust in case helpful for you -- I had exactly the same issue with release 1.... | 1,565 | 1,572 | 1,572 | NONE | null | ## 🐛 Bug
When I was checking out bert-base-multilingual-uncased vocabulary. I receive the warning "Saving vocabulary to ./vocab.txt: vocabulary indices are not consecutive. Please check that the vocabulary is not corrupted"
I ran the similar command on two different machine and got the same warning.
from pyto... | {
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"Hi, no, Bert doesn't have a cached hidden-states option.",
"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"
] | 1,565 | 1,572 | 1,572 | NONE | null | Hi,
In question and answer model using BERT, I will be querying context and around 50 questions on the same context. To reduce latency for obtaining results, I would like to cache hidden states of context before prediction.
For each question answer prediction on the same context, can the model use precomputed hidde... | {
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"Are you looking for LSTM/RNN-based seq2seq architectures or Transformer-based architectures? This repository does not host any LSTM/RNN architectures.\r\n\r\nYou can find information on all our (transformer) [models here](https://huggingface.co/pytorch-transformers/pretrained_models.html), and [examples using them... | 1,565 | 1,571 | 1,571 | NONE | null | Hi
I am urgently looking for a sequence to sequence model with transformer with script to
finetuning and training, I appreciate telling me which of the implementations in this repo could do a sequence to sequence model?
thanks
Best regards
Julia | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/987?src=pr&el=h1) Report\n> Merging [#987](https://codecov.io/gh/huggingface/pytorch-transformers/pull/987?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/3566d2791905269b75014e8ea9db322c86f980b2?src... | 1,565 | 1,578 | 1,567 | MEMBER | null | Example script for fine-tuning generative models such as GPT-2 using causal language modeling (CLM). Will eventually cover masked language modeling (MLM) for BERT and RoBERTa as well.
Edit (thom): Added `max_len_single_sentence` and `max_len_sentences_pair` properties to the tokenizer to easily access the max length... | {
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https://api.github.com/repos/huggingface/transformers/issues/986 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/986/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/986/comments | https://api.github.com/repos/huggingface/transformers/issues/986/events | https://github.com/huggingface/transformers/issues/986 | 477,982,585 | MDU6SXNzdWU0Nzc5ODI1ODU= | 986 | Potential bug with gradient clipping when using gradient accumulation in examples | {
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"Hi, indeed we could move the gradient clipping just before the call to the optimizer.\r\nDo you want to send a PR to fix that on `run_squad` and `run_glue`?",
"Hi, was this ever implemented? I think it makes the most sense to clip right before an optimizer step. Right now it's implemented in two different ways i... | 1,565 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
Hi developpers,
Thanks for the awesome package. I have a question related to the recent major from pytorch_pretrained_bert to pytorch_transformers.
Gradient clipping used to be done inside the optimizer BertAdam and is now done at the same time as gradient computation in `run_squad.py` : ... | {
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"Hi, you are trying to download one of your own models kept on a personal AWS s3 bucket or one of our models? What string do you pass to the `from_pretrained` method?",
"Thank you for your response, \r\nI am trying to use my own model kept on my personal AWS s3 bucket. \r\nThe string to from_pretrained method is ... | 1,565 | 1,572 | 1,572 | NONE | null | I am using pre-trained BERT model kept at AWS s3 bucket. When i am trying to read the model using BertModel.from_pretrained It return NONE object. Things are working offline when i download a folder on the same location as my code resides. | {
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https://api.github.com/repos/huggingface/transformers/issues/984 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/984/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/984/comments | https://api.github.com/repos/huggingface/transformers/issues/984/events | https://github.com/huggingface/transformers/pull/984 | 477,961,918 | MDExOlB1bGxSZXF1ZXN0MzA1MTc1NTQy | 984 | docs: correct number of layers for various xlm models | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/984?src=pr&el=h1) Report\n> Merging [#984](https://codecov.io/gh/huggingface/pytorch-transformers/pull/984?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/7729ef738161a0a182b172fcb7c351f6d2b9c50d?src... | 1,565 | 1,566 | 1,566 | COLLABORATOR | null | Hi,
during some NER experiments I found out, that the number of reported layers in the documentation is different compared to the model configuration for some XLM models.
This PR fixes the documentation :) | {
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https://api.github.com/repos/huggingface/transformers/issues/983 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/983/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/983/comments | https://api.github.com/repos/huggingface/transformers/issues/983/events | https://github.com/huggingface/transformers/issues/983 | 477,898,864 | MDU6SXNzdWU0Nzc4OTg4NjQ= | 983 | Worse performance of gpt2 than gpt | {
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"@Nealcly Could you try to use the `gpt2-medium` model? It has more layers :)",
"Also I'm not sure that your testing procedure is statistically representative :)",
"Any new updates on this issue? I am also facing the same question. ",
"Based on the paper, only the largest model is called gpt2. The smallest mo... | 1,565 | 1,579 | 1,579 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hi, I just want to compare the performance of gpt and gpt2 as Language Model to assign Language modeling score. Like #473 , I implement my model as follows:
```
def gpt_score(text, model, tokenizer):
input_ids = torch.tensor(toke... | {
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https://api.github.com/repos/huggingface/transformers/issues/982 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/982/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/982/comments | https://api.github.com/repos/huggingface/transformers/issues/982/events | https://github.com/huggingface/transformers/issues/982 | 477,860,801 | MDU6SXNzdWU0Nzc4NjA4MDE= | 982 | How to predict masked whole word which was tokenized as sub-words for bert-base-multilingual-cased | {
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"Hi I don't have any good solution for your use-case, unfortunately.\r\n\r\nThere are two \"Whole-Word_masking\" models for Bert (see the [list here](https://huggingface.co/pytorch-transformers/pretrained_models.html)) that would be better at guessing full words but they are only in English unfortunately.\r\n\r\nSp... | 1,565 | 1,589 | 1,571 | NONE | null | ## ❓ Questions & Help
Hello,
I have started working with pytorch-transformers and want to use it to predict masked words in polish. I use ' bert-base-multilingual-cased' pre-trained model and want to predict masked words which very often are tokenized into sub-word.
My question is how can I predict the whole word?
... | {
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https://api.github.com/repos/huggingface/transformers/issues/981 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/981/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/981/comments | https://api.github.com/repos/huggingface/transformers/issues/981/events | https://github.com/huggingface/transformers/issues/981 | 477,823,213 | MDU6SXNzdWU0Nzc4MjMyMTM= | 981 | The pre-trained model you are loading is a cased model but you have not set `do_lower_case` to False. | {
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"Hi! You seem to be loading a cased model (such as the `bert-base-multilingual-cased`), but you're specifying `do_lower_case` to your tokenizer, which strips accents and lowercases every character.\r\n\r\nThe model you specified has been trained with uppercase and lowercase characters as well as accent markers, so ... | 1,565 | 1,565 | 1,565 | CONTRIBUTOR | null | I initialized the tokenizer and the model like
```python
def load_bert_score_model(bert="bert-base-multilingual-cased", num_layers=8):
assert bert in bert_types
tokenizer = BertTokenizer.from_pretrained(bert, do_lower_case=True)
model = BertModel.from_pretrained(bert)
model.eval()
d... | {
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https://api.github.com/repos/huggingface/transformers/issues/980 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/980/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/980/comments | https://api.github.com/repos/huggingface/transformers/issues/980/events | https://github.com/huggingface/transformers/pull/980 | 477,662,980 | MDExOlB1bGxSZXF1ZXN0MzA0OTQ1NTE2 | 980 | n/a | {
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https://api.github.com/repos/huggingface/transformers/issues/979 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/979/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/979/comments | https://api.github.com/repos/huggingface/transformers/issues/979/events | https://github.com/huggingface/transformers/pull/979 | 477,625,615 | MDExOlB1bGxSZXF1ZXN0MzA0OTE1ODE5 | 979 | n/a | {
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"Hi @ibeltagy,\r\nDoes it train on TPU?",
"Not yet, it still has some issues. I will create another PR when it is in good shape. "
] | 1,565 | 1,565 | 1,565 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/978 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/978/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/978/comments | https://api.github.com/repos/huggingface/transformers/issues/978/events | https://github.com/huggingface/transformers/issues/978 | 477,557,501 | MDU6SXNzdWU0Nzc1NTc1MDE= | 978 | RuntimeError: bool value of Tensor with more than one value is ambiguous | {
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"Could you please provide more information, especially regarding the `sample_sequence` function and where it is coming from?",
"Thanks for the response!\r\n\r\nMy goal is to wrap the GPT2 model interface in a function so I can input a prompt and output generated text. I'm trying to adapt one of the examples, and... | 1,565 | 1,565 | 1,565 | NONE | null | ## ❓ Questions & Help
<!-- Using tokenizer to decode tensor is throwing this error: RuntimeError: bool value of Tensor with more than one value is ambiguous -->
Here's the code I'm trying to run, the tensor itself gets returned, but when I try to decode it I get the error above.
Any ideas? Thanks!
`if _... | {
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https://api.github.com/repos/huggingface/transformers/issues/977 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/977/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/977/comments | https://api.github.com/repos/huggingface/transformers/issues/977/events | https://github.com/huggingface/transformers/pull/977 | 477,526,470 | MDExOlB1bGxSZXF1ZXN0MzA0ODM0NjM1 | 977 | Fixed typo in migration guide | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/977?src=pr&el=h1) Report\n> Merging [#977](https://codecov.io/gh/huggingface/pytorch-transformers/pull/977?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/4fc9f9ef54e2ab250042c55b55a2e3c097858cb7?src... | 1,565 | 1,565 | 1,565 | CONTRIBUTOR | null | This PR fixes a minor typo in the migration guide. `weights` was misspelled as `weigths` | {
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https://api.github.com/repos/huggingface/transformers/issues/976 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/976/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/976/comments | https://api.github.com/repos/huggingface/transformers/issues/976/events | https://github.com/huggingface/transformers/issues/976 | 477,404,327 | MDU6SXNzdWU0Nzc0MDQzMjc= | 976 | Issue: Possibly wrong documentation about labels in BERT classifier | {
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"Indeed. @LysandreJik I think it should be `Indices should be in ``[0, ..., config.num_labels-1]`` for classification or torch.floats for regression`, what do you think? "
] | 1,565 | 1,565 | 1,565 | NONE | null | Possibly also elsewhere, but when discussing the proper format of labels for BERT classification, the documentation states the following:
https://github.com/huggingface/pytorch-transformers/blob/44dd941efb602433b7edc29612cbdd0a03bf14dc/pytorch_transformers/modeling_bert.py#L935
However, shouldn't it be `[0, ..., ... | {
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https://api.github.com/repos/huggingface/transformers/issues/975 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/975/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/975/comments | https://api.github.com/repos/huggingface/transformers/issues/975/events | https://github.com/huggingface/transformers/issues/975 | 477,291,576 | MDU6SXNzdWU0NzcyOTE1NzY= | 975 | Inconsistant output between pytorch-transformers and pytorch-pretrained-bert | {
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"See #954. I got bitten by the same documentation _bug_.",
"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"
] | 1,565 | 1,570 | 1,570 | NONE | null | ## 📚 Migration
<!-- Important information -->
Model I am using (GPT, GPT2, XLNet):
Language I am using the model on (English):
The problem arise when using:
* [ ] the official example scripts: (give details)
* [x] my own modified scripts: (give details)
```
def xlnet_score(text, model, tokenizer):
... | {
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https://api.github.com/repos/huggingface/transformers/issues/974 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/974/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/974/comments | https://api.github.com/repos/huggingface/transformers/issues/974/events | https://github.com/huggingface/transformers/issues/974 | 477,285,103 | MDU6SXNzdWU0NzcyODUxMDM= | 974 | Support longer sequences with BertForSequenceClassification | {
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"A little question because we are trying to organize the issues better:\r\n- what made you not use the issue templates we have added?",
"> A little question because we are trying to organize the issues better:\r\n> \r\n> * what made you not use the issue templates we have added?\r\n\r\nDidn't know about it...",
... | 1,565 | 1,584 | 1,574 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I am using `BertForSequenceClassification` for solving a regression task. I have a long sequence as an input and the model outputs a float in range [0,1].
Most of my sequences are longer than 512, which is the max sequence length i... | {
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https://api.github.com/repos/huggingface/transformers/issues/973 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/973/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/973/comments | https://api.github.com/repos/huggingface/transformers/issues/973/events | https://github.com/huggingface/transformers/pull/973 | 477,151,598 | MDExOlB1bGxSZXF1ZXN0MzA0NTM0MzQ1 | 973 | Fix examples of loading pretrained models in docstring | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/973?src=pr&el=h1) Report\n> Merging [#973](https://codecov.io/gh/huggingface/pytorch-transformers/pull/973?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/4fc9f9ef54e2ab250042c55b55a2e3c097858cb7?src... | 1,565 | 1,565 | 1,565 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/972 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/972/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/972/comments | https://api.github.com/repos/huggingface/transformers/issues/972/events | https://github.com/huggingface/transformers/issues/972 | 477,015,898 | MDU6SXNzdWU0NzcwMTU4OTg= | 972 | XLNetForQuestionAnswering - weight pruning | {
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"I'm interested in this as well. I've seen similar inference times of nearly 1.5 seconds running BERT for inference on a fine-tuned classification task on TF Serving and would like to improve it without paying for a GPU.\r\n\r\nI'm not associated with the following work, but found the paper interesting: \r\n\"tran... | 1,565 | 1,572 | 1,572 | NONE | null | ## 🚀 Feature
Hi guys, first of all, thank you a lot for the great API, I'm using a lot `pytorch-transformers`, you guys are really doing a good job!
I have recently fine-tuned a `XLNetForQuestionAnswering` on SQuAD1.10, results looks good, however the model is taking ~ 2.0 seconds (in a MacBook Pro) to do a forw... | {
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https://api.github.com/repos/huggingface/transformers/issues/971 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/971/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/971/comments | https://api.github.com/repos/huggingface/transformers/issues/971/events | https://github.com/huggingface/transformers/issues/971 | 476,984,580 | MDU6SXNzdWU0NzY5ODQ1ODA= | 971 | Brackets are not aligned in the DocString of Bert. | {
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"You're right, will fix! cc @LysandreJik "
] | 1,565 | 1,565 | 1,565 | NONE | null | The Brackets in the file https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/modeling_bert.py#L606 are not aligned, which will cause some highlight mistakes in some editers (i.e. VSCODE).
it should be fixed as : [0, config.max_position_embeddings - 1] | {
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https://api.github.com/repos/huggingface/transformers/issues/970 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/970/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/970/comments | https://api.github.com/repos/huggingface/transformers/issues/970/events | https://github.com/huggingface/transformers/issues/970 | 476,950,746 | MDU6SXNzdWU0NzY5NTA3NDY= | 970 | How to use GPT2LMHeadModel for conditional generation | {
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"Hi Rabeeh,\r\n\r\nPlease take a look at the [run_generation.py](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_generation.py) example which shows how to do conditional generation with the library's auto-regressive models (GPT/GPT-2/Transformer-XL/XLNet).",
"What's cracking Rabeeh, \... | 1,565 | 1,703 | 1,565 | NONE | null | Hi
could you please provide one single example on how to use GPT2LMHeadModel for conditional generation?
thanks
Rabeeh | {
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https://api.github.com/repos/huggingface/transformers/issues/969 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/969/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/969/comments | https://api.github.com/repos/huggingface/transformers/issues/969/events | https://github.com/huggingface/transformers/issues/969 | 476,940,820 | MDU6SXNzdWU0NzY5NDA4MjA= | 969 | Finetune GPT2 | {
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"Hi Rabeeh,\r\n\r\nWe are currently working on an updated example on fine-tuning generative models, especially GPT-2. The example should be up later this week, keep an eye out!",
"Any update on when this example will be available? Thanks!",
"Hope this issue won't be closed until the example is done.",
"The s... | 1,565 | 1,590 | 1,565 | NONE | null | Hi
According to pytorch-transformers/docs/source/index.rst
There was a run_gpt2.py example which also shows how to finetune GPT2 on the training data.
I was wondernig if you could add this example back, and proving sample script to finetune GPT2.
thanks.
Best regards,
Rabeeh | {
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https://api.github.com/repos/huggingface/transformers/issues/968 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/968/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/968/comments | https://api.github.com/repos/huggingface/transformers/issues/968/events | https://github.com/huggingface/transformers/issues/968 | 476,888,772 | MDU6SXNzdWU0NzY4ODg3NzI= | 968 | Error when running run_squad.py in colab | {
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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"
] | 1,565 | 1,570 | 1,570 | NONE | null | Hi I used the below code which was given as an example:
!python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
--model_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking \
--do_train \
--do_eval \
--do_lower_case \
--train_file SQUAD_DIR/tra... | {
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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",
"any updates on this?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed i... | 1,564 | 1,575 | 1,575 | NONE | null | The function ``` load_tf_weights_in_bert ``` in ``` modeling_bert.py ``` is buggy and throws a lot of attribute errors because of what seems as the pointer pointing to the entire model.
For instance for the variable ```bert/encoder/layer_0/attention/output/dense/kernel ``` it throws an attribute error along the lin... | {
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https://api.github.com/repos/huggingface/transformers/issues/966 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/966/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/966/comments | https://api.github.com/repos/huggingface/transformers/issues/966/events | https://github.com/huggingface/transformers/issues/966 | 476,716,934 | MDU6SXNzdWU0NzY3MTY5MzQ= | 966 | AttributeError: module 'tensorflow.python.training.training' has no attribute 'list_variables' | {
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"You just have to upgrade your tensorflow "
] | 1,564 | 1,576 | 1,570 | NONE | null | TF version 1.1.0:
convert_tf_checkpoint_to_pytorch("../biobert1.1/biobert_v1.1_pubmed/biobert_model.ckpt",
"../biobert1.1/biobert_v1.1_pubmed/bert_config.json",
"../biobert1.1/pytorch_model") | {
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"Hi, you can use the BertModel to give you the encoded representation of the word ids you have as input. The tensor output by the model’s last layer (of dimension `(batch_size, sequence_length, 768)` for the BertModel) can be considered as the BERT-encoded representation of your input and then be used as input for ... | 1,564 | 1,565 | 1,565 | NONE | null | How to use BertModel to output a word's vector which like a vector in word2vec? | {
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https://api.github.com/repos/huggingface/transformers/issues/964 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/964/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/964/comments | https://api.github.com/repos/huggingface/transformers/issues/964/events | https://github.com/huggingface/transformers/pull/964 | 476,624,895 | MDExOlB1bGxSZXF1ZXN0MzA0MTE3MTY4 | 964 | RoBERTa: model conversion, inference, tests 🔥 | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/964?src=pr&el=h1) Report\n> Merging [#964](https://codecov.io/gh/huggingface/pytorch-transformers/pull/964?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/7729ef738161a0a182b172fcb7c351f6d2b9c50d?src... | 1,564 | 1,567 | 1,565 | MEMBER | null | {
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"for win10 cpu",
"Ok!"
] | 1,564 | 1,565 | 1,565 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/962 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/962/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/962/comments | https://api.github.com/repos/huggingface/transformers/issues/962/events | https://github.com/huggingface/transformers/pull/962 | 476,614,899 | MDExOlB1bGxSZXF1ZXN0MzA0MTEwMDc5 | 962 | Update modeling_xlnet.py | {
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"for win10 cpu",
"LGTM!"
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https://api.github.com/repos/huggingface/transformers/issues/961 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/961/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/961/comments | https://api.github.com/repos/huggingface/transformers/issues/961/events | https://github.com/huggingface/transformers/issues/961 | 476,574,046 | MDU6SXNzdWU0NzY1NzQwNDY= | 961 | Deep learning NLP models for children's story understanding? | {
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"Hi! Your best bet is indeed to use the models that are state-of-the-art on question answering. It is currently a modified version of BERT (see SpanBERT). I cannot tell you what the accuracy would be on your dataset however, as unfortunately, these models are very sensitive to dataset changes. The SQuAD model (fine... | 1,564 | 1,575 | 1,575 | CONTRIBUTOR | null | I'm working on building NLP systems with common sense reasoning, starting with children's story understanding. I'm very interested in applying the latest pre-trained models here (and maybe Facebook's Roberta too) to a story (not one of the tested datasets like Squad 2.0 and GLUE) for QA, but am not sure how to approach... | {
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https://api.github.com/repos/huggingface/transformers/issues/960 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/960/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/960/comments | https://api.github.com/repos/huggingface/transformers/issues/960/events | https://github.com/huggingface/transformers/pull/960 | 476,570,664 | MDExOlB1bGxSZXF1ZXN0MzA0MDc5NTUw | 960 | Fixing unused weight_decay argument | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/960?src=pr&el=h1) Report\n> Merging [#960](https://codecov.io/gh/huggingface/pytorch-transformers/pull/960?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/44dd941efb602433b7edc29612cbdd0a03bf14dc?src... | 1,564 | 1,565 | 1,565 | CONTRIBUTOR | null | Currently the L2 regularization is hard-coded to "0.01", even though there is a --weight_decay flag implemented (that is unused). I'm making this flag control the weight decay used for fine-tuning in this script. | {
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https://api.github.com/repos/huggingface/transformers/issues/959 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/959/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/959/comments | https://api.github.com/repos/huggingface/transformers/issues/959/events | https://github.com/huggingface/transformers/issues/959 | 476,556,396 | MDU6SXNzdWU0NzY1NTYzOTY= | 959 | Use the fine-tuned model for another task | {
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"Hi!\r\n\r\nIf you saved the model `BertForMultipleChoice` to a directory, you can then load the weights for the `BertForMaskedLM` by simply using the `from_pretrained(dir_name)` method. The transformer weights will be re-used by the `BertForMaskedLM` and the weights corresponding to the multiple-choice classifier ... | 1,564 | 1,571 | 1,571 | NONE | null | Hi, I am currently using this code to research the transferability of those pre-trained models and I wonder how could I apply the fine-tuned parameter of a model to another model. For example, I fine-tuned the **BertForMultipleChoice** and got the **pytorch_model.bin**, and what if I want to use the parameters weight a... | {
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https://api.github.com/repos/huggingface/transformers/issues/958 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/958/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/958/comments | https://api.github.com/repos/huggingface/transformers/issues/958/events | https://github.com/huggingface/transformers/pull/958 | 476,519,030 | MDExOlB1bGxSZXF1ZXN0MzA0MDQ0ODIx | 958 | Fixed small typo | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/958?src=pr&el=h1) Report\n> Merging [#958](https://codecov.io/gh/huggingface/pytorch-transformers/pull/958?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/44dd941efb602433b7edc29612cbdd0a03bf14dc?src... | 1,564 | 1,565 | 1,565 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/957 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/957/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/957/comments | https://api.github.com/repos/huggingface/transformers/issues/957/events | https://github.com/huggingface/transformers/issues/957 | 476,473,596 | MDU6SXNzdWU0NzY0NzM1OTY= | 957 | total training steps and tokenization in run_glue | {
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In run_glue line 78, the total number of training steps is calculated using `t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs`.
I was wondering if we use gradient accumulation the num_train_epochs in the above code is not actual traini... | {
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"The framework has been updated to store all additional special tokens in `additional_special_tokens` list and custom tokens are no longer available through class attributes."
] | 1,564 | 1,565 | 1,565 | NONE | null | It might not be a bug but I think it would be useful and more consitent behaviour if tokenizers could maintain the added special tokens as attributes after saving and loading a tokenizer. See the following example.
```python
if 'added_tokens.json' in os.listdir('.'):
# loading the saved extended tokenizer
... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/955?src=pr&el=h1) Report\n> Merging [#955](https://codecov.io/gh/huggingface/pytorch-transformers/pull/955?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/44dd941efb602433b7edc29612cbdd0a03bf14dc?src... | 1,564 | 1,565 | 1,565 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/954 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/954/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/954/comments | https://api.github.com/repos/huggingface/transformers/issues/954/events | https://github.com/huggingface/transformers/issues/954 | 476,409,732 | MDU6SXNzdWU0NzY0MDk3MzI= | 954 | Bert model instantiated from BertForMaskedLM.from_pretrained('bert-base-uncased') and BertForMaskedLM(BertConfig.from_pretrained('bert-base-uncased')) give different results | {
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"Hi, not only they give different results, but also BertModel(BertConfig.from_pretrained('bert-base-uncased')) will give a different result each time you run it. **Other bert models also have this problem**; I think this is a bug. @thomwolf \r\n\r\nFollowing code works well and produce the same result each time you... | 1,564 | 1,570 | 1,570 | NONE | null | The two different methods for instantiating a model produce different losses.
`from pytorch_transformers import BertForMaskedLM, BertConfig, BertTokenizer
import torch
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) ... | {
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https://api.github.com/repos/huggingface/transformers/issues/953 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/953/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/953/comments | https://api.github.com/repos/huggingface/transformers/issues/953/events | https://github.com/huggingface/transformers/issues/953 | 476,278,948 | MDU6SXNzdWU0NzYyNzg5NDg= | 953 | How to add some parameters in gpt-2 (in attention layer) and initialize the original gpt-2 parameters with pre-trained model and the new introduced parameters randomly? | {
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"You should make a class deriving from `GPT2Model` in which:\r\n- the `__init__` method\r\n * calls its super class `__init__` method (to add the original GPT2 modules),\r\n * you then add the new modules (with names differents from GPT2 original attributes so you don't overwrite over them).\r\n * you call `s... | 1,564 | 1,573 | 1,573 | NONE | null | Hi,
I want to add some weight matrices inside attention layers of gpt-2 model. However, I want to initialize all original parameters with pre-trained gpt-2 and the newly added ones randomly.
Can someone guide me how that's possible or point me to the right direction?
Thanks | {
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https://api.github.com/repos/huggingface/transformers/issues/952 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/952/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/952/comments | https://api.github.com/repos/huggingface/transformers/issues/952/events | https://github.com/huggingface/transformers/pull/952 | 476,257,948 | MDExOlB1bGxSZXF1ZXN0MzAzODUwNjQ1 | 952 | Add 117M and 345M as aliases for pretrained models | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/952?src=pr&el=h1) Report\n> Merging [#952](https://codecov.io/gh/huggingface/pytorch-transformers/pull/952?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/44dd941efb602433b7edc29612cbdd0a03bf14dc?src... | 1,564 | 1,566 | 1,566 | CONTRIBUTOR | null | This keeps better with the convention in the tensorflow repository. | {
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https://api.github.com/repos/huggingface/transformers/issues/951 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/951/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/951/comments | https://api.github.com/repos/huggingface/transformers/issues/951/events | https://github.com/huggingface/transformers/pull/951 | 476,223,043 | MDExOlB1bGxSZXF1ZXN0MzAzODIyMzkz | 951 | run_swag.py should use AdamW | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/951?src=pr&el=h1) Report\n> Merging [#951](https://codecov.io/gh/huggingface/pytorch-transformers/pull/951?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/44dd941efb602433b7edc29612cbdd0a03bf14dc?src... | 1,564 | 1,567 | 1,567 | NONE | null | run_swag.py doesn't compile currently, BertAdam is removed (per readme). | {
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https://api.github.com/repos/huggingface/transformers/issues/950 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/950/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/950/comments | https://api.github.com/repos/huggingface/transformers/issues/950/events | https://github.com/huggingface/transformers/issues/950 | 476,194,359 | MDU6SXNzdWU0NzYxOTQzNTk= | 950 | CONFIG_NAME and WEIGHTS_NAME are missing in modeling_transfo_xl.py | {
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"Thanks!"
] | 1,564 | 1,565 | 1,565 | CONTRIBUTOR | null | When I run `convert_transfo_xl_checkpoint_to_pytorch.py`, the following error occurs.
```
Traceback (most recent call last):
File "convert_transfo_xl_checkpoint_to_pytorch.py", line 27, in <module>
from pytorch_transformers.modeling_transfo_xl import (CONFIG_NAME,
ImportError: cannot import name 'CONFIG_NAME... | {
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https://api.github.com/repos/huggingface/transformers/issues/949 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/949/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/949/comments | https://api.github.com/repos/huggingface/transformers/issues/949/events | https://github.com/huggingface/transformers/issues/949 | 476,174,085 | MDU6SXNzdWU0NzYxNzQwODU= | 949 | <model>ForQuestionAnswering loading non-deterministic weights | {
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"These weights are not pretrained, they are added for fine-tuning the model on a downstream question answering task. You have to train the `qa_output` weights.\r\nThey are initialized randomly and so will be different at each run."
] | 1,564 | 1,565 | 1,565 | NONE | null | I was comparing the weight and bias parameters of two different pre-trained-loaded BertForQuestionAnswering model, and they seem to differ. This causes every instantiation of pre-trained models to have slightly different results.
Compared to #695 where you set the model to eval mode to deactivate dropout layers, th... | {
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https://api.github.com/repos/huggingface/transformers/issues/948 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/948/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/948/comments | https://api.github.com/repos/huggingface/transformers/issues/948/events | https://github.com/huggingface/transformers/issues/948 | 476,036,376 | MDU6SXNzdWU0NzYwMzYzNzY= | 948 | How to train BertModel | {
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"Hi, there are examples in the \"examples\" folder on finetuning language models. Please take a look at [the scripts available here](https://github.com/huggingface/pytorch-transformers/tree/master/examples/lm_finetuning)."
] | 1,564 | 1,565 | 1,565 | NONE | null | Hi,
I am trying to train BertModel on my domain-based dataset. Please let me know how to train the BertModel. | {
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https://api.github.com/repos/huggingface/transformers/issues/947 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/947/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/947/comments | https://api.github.com/repos/huggingface/transformers/issues/947/events | https://github.com/huggingface/transformers/issues/947 | 476,001,056 | MDU6SXNzdWU0NzYwMDEwNTY= | 947 | [XLNet] Parameters to reproduce SQuAD scores | {
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"Maybe we can use the same issue so the people following #822 can learn from your experiments as well?",
"I'm using xlnet-large-cased.\r\nAt first I got \r\n{\r\n \"exact\": 75.91296121097446,\r\n \"f1\": 83.19559419987176,\r\n \"total\": 10570,\r\n \"HasAns_exact\": 75.91296121097446,\r\n \"HasAns_f1\": 83.... | 1,564 | 1,580 | 1,580 | CONTRIBUTOR | null | I'm trying to reproduce the results of XLNet-base on SQuAD 2.0.
From the [README of XLNet](https://github.com/zihangdai/xlnet#results) :
Model | [RACE accuracy](http://www.qizhexie.com/data/RACE_leaderboard.html) | SQuAD1.1 EM | SQuAD2.0 EM
--- | --- | --- | ---
BERT-Large | 72.0 | 84.1 | 78.98
XLNet-Base | |... | {
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https://api.github.com/repos/huggingface/transformers/issues/946 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/946/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/946/comments | https://api.github.com/repos/huggingface/transformers/issues/946/events | https://github.com/huggingface/transformers/issues/946 | 475,847,994 | MDU6SXNzdWU0NzU4NDc5OTQ= | 946 | Using memory states with XLNet / TransfoXL | {
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"Which command did you use to \"naively\" feed in the memory states?\r\nYou can just feed the mems that you get from the previous forward pass, but the inputs need to be the continuation of the previous input. So the batch_size, in particular, should stay the same.",
"I found this post that talks about how to org... | 1,564 | 1,570 | 1,570 | NONE | null | I would like to fine-tune XLNet / TransfoXL for a classification task where I classify each sentence in the context of a large document. Is there an example for how to use the memory states in XLNet and TransfoXL?
This example only uses memory states for inference but there is no example for training:
https://github... | {
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https://api.github.com/repos/huggingface/transformers/issues/945 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/945/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/945/comments | https://api.github.com/repos/huggingface/transformers/issues/945/events | https://github.com/huggingface/transformers/issues/945 | 475,844,593 | MDU6SXNzdWU0NzU4NDQ1OTM= | 945 | _convert_id_to_tokens for XLNet not working | {
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"Which command can we use to reproduce the behavior?",
"Upon further testing, looks like this tokenizer doesn't like numpy arrays, the other ones seem to be fine\r\n```\r\nimport numpy as np\r\nfrom pytorch_transformers import XLNetTokenizer, TransfoXLTokenizer, BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from... | 1,564 | 1,570 | 1,570 | NONE | null | ```
text = self.tokenizer.convert_ids_to_tokens(token_list)
File "/home/lambda/repos/pytorch-transformers/pytorch_transformers/tokenization_utils.py", line 444, in convert_ids_to_tokens
tokens.append(self._convert_id_to_token(index))
File "/home/lambda/repos/pytorch-transformers/pytorch_transformers/tokeniz... | {
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https://api.github.com/repos/huggingface/transformers/issues/944 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/944/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/944/comments | https://api.github.com/repos/huggingface/transformers/issues/944/events | https://github.com/huggingface/transformers/issues/944 | 475,744,982 | MDU6SXNzdWU0NzU3NDQ5ODI= | 944 | Missing lines in Readme examples? | {
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"Thanks",
"@thomwolf please, note: the first example hasn't been fixed by the commit.",
"Yes, doesn't look like a problem to me. Usually, people put the model in training mode inside the train function (and even inside the training loop I would recommend).",
"Ok, got it!"
] | 1,564 | 1,565 | 1,565 | NONE | null | 1. In the [example](https://github.com/huggingface/pytorch-transformers#serialization)
```
...
### Do some stuff to our model and tokenizer
# Ex: add new tokens to the vocabulary and embeddings of our model
tokenizer.add_tokens(['[SPECIAL_TOKEN_1]', '[SPECIAL_TOKEN_2]'])
model.resize_token_embeddings(len(tokenize... | {
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https://api.github.com/repos/huggingface/transformers/issues/943 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/943/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/943/comments | https://api.github.com/repos/huggingface/transformers/issues/943/events | https://github.com/huggingface/transformers/issues/943 | 475,684,471 | MDU6SXNzdWU0NzU2ODQ0NzE= | 943 | Is pytorch-transformers useful for training from scratch on a custom dataset? | {
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"This depends on the model you're interested in. For GPT2, for example, there's a class called `GPT2LMHeadModel` that you could use for pretraining with minimal modifications. For XLNet, the implementation in this repo is missing some key functionality (the permutation generation function and an analogue of the dat... | 1,564 | 1,573 | 1,572 | NONE | null | Hello,
I'm looking into the great repo, and I'm wondering if there is a feature that could allow me to train a, let's say, gpt2 model on a custom dataset of sequences.
Is it already provided in your codebase and features ? Otherwise I'll tinker with code on my own.
Thanks in advance and again, great job for... | {
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https://api.github.com/repos/huggingface/transformers/issues/942 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/942/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/942/comments | https://api.github.com/repos/huggingface/transformers/issues/942/events | https://github.com/huggingface/transformers/issues/942 | 475,597,223 | MDU6SXNzdWU0NzU1OTcyMjM= | 942 | Using BERT for predicting masked token | {
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"Hi, no need to mask, just input your sequence and keep the hidden-states of the top tokens that correspond to your ingredients.\r\n\r\nIf your ingredients are not in the vocabulary, they will be split by the tokenizer in sub-word units (totally fine). Then, just use as a representation the mean or the max of the r... | 1,564 | 1,579 | 1,570 | NONE | null | I have a task where I want to obtain better word embeddings for food ingredients. Since I am a bit new to the field of NLP, I have certain fundamental doubts as well which I would love to be corrected upon.
1. I want to get word embeddings so started with Word2Vec. Now, I want to get more contextual representation so ... | {
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https://api.github.com/repos/huggingface/transformers/issues/941 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/941/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/941/comments | https://api.github.com/repos/huggingface/transformers/issues/941/events | https://github.com/huggingface/transformers/pull/941 | 475,595,787 | MDExOlB1bGxSZXF1ZXN0MzAzMzE0Njg3 | 941 | Updated model token sizing to replace removed parameter `num_special_… | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/941?src=pr&el=h1) Report\n> Merging [#941](https://codecov.io/gh/huggingface/pytorch-transformers/pull/941?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/f2a3eb987e1fc2c85320fc3849c67811f5736b50?src... | 1,564 | 1,575 | 1,575 | NONE | null | …tokens`
`num_special_tokens` seems to no longer be implemented. Replaced with `model.resize_token_embeddings(new_num_tokens=len(tokenizer))` which resizes (non-destructively, I think) the embeddings to include the new tokens. | {
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https://api.github.com/repos/huggingface/transformers/issues/940 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/940/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/940/comments | https://api.github.com/repos/huggingface/transformers/issues/940/events | https://github.com/huggingface/transformers/issues/940 | 475,553,801 | MDU6SXNzdWU0NzU1NTM4MDE= | 940 | Unexpectedly preprocess when multi-gpu using | {
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"Why do you think the unique_id is not serial?\r\nEach process should convert ALL the dataset.\r\nOnly the PyTorch dataset should be split among processes.\r\n\r\nBy the way it would be cleaner if the other processes wait for the first process to pre-process the dataset before using the cache so the dataset is only... | 1,564 | 1,600 | 1,578 | CONTRIBUTOR | null | When run example/run_squad with more than one gpu, the preprocessor cannot work as expected. For example, the unique_id will not be a serial numbers, then keyerror occurs when writing the result to json file.
https://github.com/huggingface/pytorch-transformers/blob/f2a3eb987e1fc2c85320fc3849c67811f5736b50/examples/u... | {
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https://api.github.com/repos/huggingface/transformers/issues/939 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/939/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/939/comments | https://api.github.com/repos/huggingface/transformers/issues/939/events | https://github.com/huggingface/transformers/issues/939 | 475,551,085 | MDU6SXNzdWU0NzU1NTEwODU= | 939 | Chinese BERT broken | {
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"Yes you need to install from master for now. We have not yet done a new release with the fix of #860.",
"@thomwolf Not related to this specific issue here, but do you think it makes sense to add the following policy to the newly introduced issue templates: all bug reports should be filed against latest `master` ... | 1,564 | 1,570 | 1,570 | CONTRIBUTOR | null | There are still some bug after #860
The same issue is also mention in #903
I'm running on Chinese-Style SQuAD dataset (DRCD).
I can train Chinese-Bert successfully about half year ago.
However, I could not train the model successfully but I can train Multi-Bert successfully.
I'm not able to find out the reaso... | {
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https://api.github.com/repos/huggingface/transformers/issues/938 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/938/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/938/comments | https://api.github.com/repos/huggingface/transformers/issues/938/events | https://github.com/huggingface/transformers/issues/938 | 475,549,739 | MDU6SXNzdWU0NzU1NDk3Mzk= | 938 | Performance dramatically drops down after replacing pytorch-pretrained-bert with pytorch-transformers | {
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"Same here. I am finetuning language models on new dataset. Once I change from `pytorch-pretrained-bert` to `pytorch-transformers`, generation quality dramatically drops. ",
"I have the same problem. I refer to a example of Named Entity Recognition which used pytorch-pretrained-bert. I changed it to pytorch-tra... | 1,564 | 1,578 | 1,565 | NONE | null | I am trying to run a baseline model, whose encoder is the pretrained BERT ('bert-base-uncased'). I have tried both versions of this package and found that the performance of the pytorch-transformers-BERT is much worse than pytorch-pretrained-bert-BERT, i.e. the BLeU-4 has dropped from 8. to 2.
Below is my codes, I wan... | {
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https://api.github.com/repos/huggingface/transformers/issues/937 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/937/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/937/comments | https://api.github.com/repos/huggingface/transformers/issues/937/events | https://github.com/huggingface/transformers/issues/937 | 475,538,381 | MDU6SXNzdWU0NzU1MzgzODE= | 937 | Wrong refactoring of mandatory parameters for run_squad.py | {
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"indeed, we could remove this",
"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"
] | 1,564 | 1,570 | 1,570 | NONE | null | When only running evaluation on a squad dev set, it should *not* be mandatory to add a --train_file because only the --predict_file is necessary.
Current script invocation:
```
python run_squad \
--model_type bert \
--model_name_or_path xxx \
--output_dir xxx \
--train_file UNNECESSARY_BUT_MAND... | {
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https://api.github.com/repos/huggingface/transformers/issues/936 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/936/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/936/comments | https://api.github.com/repos/huggingface/transformers/issues/936/events | https://github.com/huggingface/transformers/issues/936 | 475,484,693 | MDU6SXNzdWU0NzU0ODQ2OTM= | 936 | XLNet large low accuracy | {
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"The first thought could be that the learning rate is too high and you overfit.\r\nYou probably should try changing the batch size too.\r\nYou can have a look at #795 where we discussed similar questions for SST-2. ",
"This issue has been automatically marked as stale because it has not had recent activity. It wi... | 1,564 | 1,570 | 1,570 | NONE | null | I was running run_glue.py on one of my classification problems. Using XLNet-base-cased, everything seems to be fine, the classification accuracy converge to around 92%. But using XLNet-large, the accuracy is 89% at the first checkpoint and then drop into 24.85% at the second checkpoint. The data should be ok because I ... | {
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https://api.github.com/repos/huggingface/transformers/issues/935 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/935/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/935/comments | https://api.github.com/repos/huggingface/transformers/issues/935/events | https://github.com/huggingface/transformers/issues/935 | 475,481,230 | MDU6SXNzdWU0NzU0ODEyMzA= | 935 | run_glue : Evaluating in every grad_accumulation_step if flag eval during training is true | {
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"```\r\nif (step + 1) % args.gradient_accumulation_steps == 0:\r\n scheduler.step() # Update learning rate schedule\r\n optimizer.step()\r\n model.zero_grad()\r\n global_step += 1\r\n\r\n if args.local_rank in [-1, 0] and args.logging_steps... | 1,564 | 1,564 | 1,564 | NONE | null | https://github.com/huggingface/pytorch-transformers/blob/f2a3eb987e1fc2c85320fc3849c67811f5736b50/examples/run_glue.py#L154 | {
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https://api.github.com/repos/huggingface/transformers/issues/934 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/934/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/934/comments | https://api.github.com/repos/huggingface/transformers/issues/934/events | https://github.com/huggingface/transformers/issues/934 | 475,441,055 | MDU6SXNzdWU0NzU0NDEwNTU= | 934 | Feature Request : run_swag with XLNet and XLM | {
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"Yes, don't have bandwith for that in the short term. If you want to give it a go feel free.\r\nClosing this issue in favor of the previous one #931 "
] | 1,564 | 1,565 | 1,565 | NONE | null | It would be great if the run_swag script too was updated with XLNet and XLM models. They should be similar to BertForMultipleChoice ? | {
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https://api.github.com/repos/huggingface/transformers/issues/933 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/933/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/933/comments | https://api.github.com/repos/huggingface/transformers/issues/933/events | https://github.com/huggingface/transformers/pull/933 | 475,422,182 | MDExOlB1bGxSZXF1ZXN0MzAzMTc5MDE2 | 933 | link to `swift-coreml-transformers` | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/933?src=pr&el=h1) Report\n> Merging [#933](https://codecov.io/gh/huggingface/pytorch-transformers/pull/933?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/f2a3eb987e1fc2c85320fc3849c67811f5736b50?src... | 1,564 | 1,564 | 1,564 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/932 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/932/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/932/comments | https://api.github.com/repos/huggingface/transformers/issues/932/events | https://github.com/huggingface/transformers/issues/932 | 475,410,078 | MDU6SXNzdWU0NzU0MTAwNzg= | 932 | pip install error: "regex_3/_regex.c:48:10: fatal error: Python.h: No such file or directory" | {
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"Hi @seyuboglu I think you need to install the Python Dev package on your distribution. For Ubuntu >= 18.04 this should be possible with `apt install python3.7-dev` :)",
"Thank you @stefan-it! That did the trick. Any idea why this is necessary to install pytorch-transformers in particular? ",
"@stefan-it do you... | 1,564 | 1,579 | 1,564 | NONE | null | When pip installing pytorch-pretrained-bert on ubuntu and getting the following error:
```
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 ... | {
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https://api.github.com/repos/huggingface/transformers/issues/931 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/931/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/931/comments | https://api.github.com/repos/huggingface/transformers/issues/931/events | https://github.com/huggingface/transformers/issues/931 | 475,377,720 | MDU6SXNzdWU0NzUzNzc3MjA= | 931 | Updating run_swag script for new pytorch_transformers setup | {
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"I found a few other issues: 1) the script uses the old BertAdam (instead of AdamW)\r\n\r\nhttps://github.com/huggingface/pytorch-transformers/blob/44dd941efb602433b7edc29612cbdd0a03bf14dc/examples/single_model_scripts/run_swag.py#L431\r\n\r\n2) the train and test loops still use the old version of forward, i.e., \... | 1,564 | 1,570 | 1,570 | NONE | null | https://github.com/huggingface/pytorch-transformers/blob/f2a3eb987e1fc2c85320fc3849c67811f5736b50/examples/single_model_scripts/run_swag.py#L35
It appears that WEIGHTS_NAME and CONFIG_NAME have moved out of {pytorch_transformers/pytorch_pretrained_bert}.file_utils (and instead can be imported directly from pytorch_t... | {
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https://api.github.com/repos/huggingface/transformers/issues/930 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/930/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/930/comments | https://api.github.com/repos/huggingface/transformers/issues/930/events | https://github.com/huggingface/transformers/pull/930 | 475,173,073 | MDExOlB1bGxSZXF1ZXN0MzAyOTc3Mzk1 | 930 | Fixing a broken link in the README.md | {
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"Thanks Gregory :)",
"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/930?src=pr&el=h1) Report\n> Merging [#930](https://codecov.io/gh/huggingface/pytorch-transformers/pull/930?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/6b763d04a930e070e4096... | 1,564 | 1,564 | 1,564 | CONTRIBUTOR | null | Fixing the `Quick tour` link. | {
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https://api.github.com/repos/huggingface/transformers/issues/929 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/929/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/929/comments | https://api.github.com/repos/huggingface/transformers/issues/929/events | https://github.com/huggingface/transformers/issues/929 | 475,041,766 | MDU6SXNzdWU0NzUwNDE3NjY= | 929 | AttributeError: 'NoneType' object has no attribute 'split' | {
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"conda 4.5.12\r\nPython 3.6.8 :: Anaconda, Inc.\r\ntorch 1.1.0",
"Is it possible you installed the CPU-only version of PyTorch? Which command did you use to install it? Did you do it via conda or pip?",
"Seems like a problem related to apex, you should open an issue on NVIDIA's repo.\r\nI'm closing this one for... | 1,564 | 1,565 | 1,565 | NONE | null | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1-4393ada473d4> in <module>
1 import torch
----> 2 from pytorch_transformers import *
~/anaconda3/envs/python/lib/python3.6/site-packages/py... | {
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https://api.github.com/repos/huggingface/transformers/issues/928 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/928/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/928/comments | https://api.github.com/repos/huggingface/transformers/issues/928/events | https://github.com/huggingface/transformers/issues/928 | 474,928,910 | MDU6SXNzdWU0NzQ5Mjg5MTA= | 928 | ERNIE 2.0 ? | {
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"This is relevant.\r\nhttps://medium.com/syncedreview/baidus-ernie-2-0-beats-bert-and-xlnet-on-nlp-benchmarks-51a8c21aa433",
"We don't have any plan to add ERNIE in the short-term but if someone wants to do a (clean) PR with this model, happy to have a look and add it to the library.",
"This issue has been auto... | 1,564 | 1,570 | 1,570 | NONE | null | Latest NLP Language Model.:)
[ERNIE 2.0](https://arxiv.org/pdf/1907.12412.pdf?source=post_page) | {
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https://api.github.com/repos/huggingface/transformers/issues/927 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/927/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/927/comments | https://api.github.com/repos/huggingface/transformers/issues/927/events | https://github.com/huggingface/transformers/issues/927 | 474,875,299 | MDU6SXNzdWU0NzQ4NzUyOTk= | 927 | `do_wordpiece_only` argument | {
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"Use _additional_special_tokens_ instead.\r\n\r\n```\r\n>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', additional_special_tokens=['[unused0]'])\r\n>>> tokenizer.tokenize('[CLS] [unused0] this is a [SEP] test')\r\n['[CLS]', '[unused0]', 'this', 'is', 'a', '[SEP]', 'test']\r\n```\r\n",
"This is... | 1,564 | 1,570 | 1,570 | CONTRIBUTOR | null | A `do_wordpiece_only` argument is referenced [here](https://github.com/huggingface/pytorch-transformers/blob/fec76a481d1ecfbf068d87735dd44ffc26158f6e/pytorch_transformers/tokenization_bert.py#L97) -- does that argument actually exist? I'm not able to find it in the repo anywhere.
Related, is this expected behavior?... | {
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https://api.github.com/repos/huggingface/transformers/issues/926 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/926/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/926/comments | https://api.github.com/repos/huggingface/transformers/issues/926/events | https://github.com/huggingface/transformers/issues/926 | 474,690,976 | MDU6SXNzdWU0NzQ2OTA5NzY= | 926 | Feature request: roBERTa | {
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"See #829 (and thanks for the kind words!)"
] | 1,564 | 1,564 | 1,564 | NONE | null | Hi, thanks for making a unified framework for all transformer-based models. Just out of curiosity, do you plan to add the roBERTa pre-trained models? Although FairSeq has provided the model, I still prefer using your framework. Thanks again, big fan. 🤗🤗🤗 | {
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https://api.github.com/repos/huggingface/transformers/issues/925 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/925/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/925/comments | https://api.github.com/repos/huggingface/transformers/issues/925/events | https://github.com/huggingface/transformers/issues/925 | 474,619,833 | MDU6SXNzdWU0NzQ2MTk4MzM= | 925 | Torchscipt mode for BertForPreTraining | {
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"I realised what was the problem. There is an error in documentation.\r\n```from pytorch_pretrained_bert import BertModel, BertTokenizer, BertConfig```\r\nshould be\r\n```from pytorch_transformers import BertModel, BertTokenizer, BertConfig```\r\n\r\nAnd also [there](https://github.com/huggingface/pytorch-transform... | 1,564 | 1,564 | 1,564 | NONE | null | Hello, i used code from this tutorial https://huggingface.co/pytorch-transformers/torchscript.html
pytorch-transformers==1.0.0
```
from pytorch_pretrained_bert import BertModel, BertTokenizer, BertConfig
import torch
enc = BertTokenizer.from_pretrained("bert-base-uncased")
# Tokenizing input text
text = "[... | {
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https://api.github.com/repos/huggingface/transformers/issues/924 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/924/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/924/comments | https://api.github.com/repos/huggingface/transformers/issues/924/events | https://github.com/huggingface/transformers/issues/924 | 474,521,857 | MDU6SXNzdWU0NzQ1MjE4NTc= | 924 | [RuntimeError: sizes must be non-negative] in run_squad.py using xlnet large model | {
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"I have encountered a similar problem:\r\nJust copy the code\r\n`\r\nimport torch\r\n#from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM\r\nfrom pytorch_transformers import XLNetLMHeadModel, XLNetTokenizer,XLNetConfig\r\nimport numpy as np\r\nimport math\r\n\r\nconfig = XLNetConfig.from_p... | 1,564 | 1,565 | 1,565 | NONE | null | [RuntimeError: sizes must be non-negative]
run_squad.py in main
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
run_squad.py in train
outputs = model(**inputs)
modeling_xlnet.py
mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask), in which
mlen = 0 resulting from "mem... | {
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https://api.github.com/repos/huggingface/transformers/issues/923 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/923/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/923/comments | https://api.github.com/repos/huggingface/transformers/issues/923/events | https://github.com/huggingface/transformers/pull/923 | 474,509,257 | MDExOlB1bGxSZXF1ZXN0MzAyNDM3Mzcy | 923 | Don't save model without training (example/run_squad.py bug) | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/923?src=pr&el=h1) Report\n> Merging [#923](https://codecov.io/gh/huggingface/pytorch-transformers/pull/923?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/a7b4cfe9194bf93c7044a42c9f1281260ce6279e?src... | 1,564 | 1,566 | 1,566 | CONTRIBUTOR | null | There is a mirror bug in run_squad.py.
The model should not be saved if only do_predict. | {
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https://api.github.com/repos/huggingface/transformers/issues/922 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/922/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/922/comments | https://api.github.com/repos/huggingface/transformers/issues/922/events | https://github.com/huggingface/transformers/issues/922 | 474,428,337 | MDU6SXNzdWU0NzQ0MjgzMzc= | 922 | TypeError: 'NoneType' object is not callable | {
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"What is the code you are running to get this error?",
"I assume it is when trying to run the \"quick tour\" from the readme. I'm getting the same error and found a similar issue in #712 where the feedback was \"usually, this comes from the library not being able to reach AWS S3 servers to download the pretrained... | 1,564 | 1,570 | 1,570 | NONE | null | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-d477193005ba> in <module>
12 output_attentions=True)
13 input_ids = torch.tensor([tokenizer.e... | {
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https://api.github.com/repos/huggingface/transformers/issues/921 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/921/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/921/comments | https://api.github.com/repos/huggingface/transformers/issues/921/events | https://github.com/huggingface/transformers/issues/921 | 474,385,458 | MDU6SXNzdWU0NzQzODU0NTg= | 921 | Issues in visualizing a fine tuned model | {
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"\"But every word is attentive to every other word.\" --> I don't think that's an error, that's the general way how attention mechanism works. But definitely weights of these attentions to a particular word would vary and based on these weighted attentions and other contextual info. the downstream tasks (entailment... | 1,564 | 1,570 | 1,570 | NONE | null | BertModel finetuned for a sequence classification task does not give expected results on visualisation.
Ideally, the pretrained model should be loaded into BertForSequenceClassification, but that model does not return attentions scores for visualisation.
When loaded into BertModel (0 to 11 layers), I assume the 11th ... | {
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https://api.github.com/repos/huggingface/transformers/issues/920 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/920/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/920/comments | https://api.github.com/repos/huggingface/transformers/issues/920/events | https://github.com/huggingface/transformers/issues/920 | 474,350,383 | MDU6SXNzdWU0NzQzNTAzODM= | 920 | Unigram frequencies in GPT-2 or XLnet? | {
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"XLnet tokenizer utilizes SentencePiece and you can use it's score for unigram as relative frequency with something like `math.exp(XLNetTokenizer.sp_model.GetScore(token_id))`.",
"Ah perfect. So the raw score gives the unigram log probability, and the exp of it gives the normalised frequency.\r\n\r\nHow about GPT... | 1,564 | 1,633 | 1,565 | NONE | null | Question: does the GPT2 or XLnet tokenizer contain unigram frequencies? From the discussion here (https://github.com/huggingface/pytorch-transformers/issues/477), it looks like the tokenizer from TransformerXL has it, but I'm not sure if the same applies for GPT2 and XLnet. If they do contain unigram frequencies, can y... | {
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https://api.github.com/repos/huggingface/transformers/issues/919 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/919/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/919/comments | https://api.github.com/repos/huggingface/transformers/issues/919/events | https://github.com/huggingface/transformers/issues/919 | 474,290,438 | MDU6SXNzdWU0NzQyOTA0Mzg= | 919 | Code snippet on docs page using old import | {
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"Thanks!"
] | 1,564 | 1,565 | 1,565 | NONE | null | This is a documentation issue. I couldn't find where to edit the website source https://huggingface.co/pytorch-transformers/torchscript.html
On that page the code snippet still uses `from pytorch_pretrained_bert import BertModel, BertTokenizer, BertConfig`
The documentation in this repo under [https://github.com/... | {
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https://api.github.com/repos/huggingface/transformers/issues/918 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/918/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/918/comments | https://api.github.com/repos/huggingface/transformers/issues/918/events | https://github.com/huggingface/transformers/issues/918 | 474,093,722 | MDU6SXNzdWU0NzQwOTM3MjI= | 918 | Export to Tensorflow not properly implemented | {
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"Thanks @dhpollack !"
] | 1,564 | 1,565 | 1,565 | CONTRIBUTOR | null | Apologies for going about this backwards. I created a pull request #907 to fix your implementation of converting pytorch weights to tensorflow weights. As explained in the PR, the current implementation puts the weights from the pytorch model into two places in the newly created tensorflow checkpoint. The fix not on... | {
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https://api.github.com/repos/huggingface/transformers/issues/917 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/917/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/917/comments | https://api.github.com/repos/huggingface/transformers/issues/917/events | https://github.com/huggingface/transformers/issues/917 | 473,863,588 | MDU6SXNzdWU0NzM4NjM1ODg= | 917 | XLNet: Sentence probability/perplexity | {
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"Hi, I want to ask that question too.\r\nBelow is my implementation\r\n```\r\ndef xlnet_score(text, model, tokenizer):\r\n #text = \"<cls>\" + text + \"<sep>\"\r\n # Tokenized input\r\n tokenized_text = tokenizer.tokenize(text)\r\n # text = \"[CLS] Stir the mixture until it is done [SEP]\"\r\n senten... | 1,564 | 1,588 | 1,566 | NONE | null | Based on my understanding, XLnet can compute sentence probability/perplexity. Is there a example that illustrates how we can do this? I saw one for GPT-2 (https://github.com/huggingface/pytorch-transformers/issues/473), but don't think it'll work exactly the same... | {
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https://api.github.com/repos/huggingface/transformers/issues/916 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/916/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/916/comments | https://api.github.com/repos/huggingface/transformers/issues/916/events | https://github.com/huggingface/transformers/issues/916 | 473,733,841 | MDU6SXNzdWU0NzM3MzM4NDE= | 916 | Avoid i/o in class __init__ methods | {
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"Make sense to me. I'll include that in a coming PR.",
"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"
] | 1,564 | 1,570 | 1,570 | NONE | null | Working with model serialization and configs is pretty painful, and we went through a lot of design iterations on this for spaCy.
I think one thing that's definitely unideal in `pytorch_transformers` is that the tokenizers often expect file names in the `__init__` methods. This means that if you're holding the data ... | {
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https://api.github.com/repos/huggingface/transformers/issues/915 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/915/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/915/comments | https://api.github.com/repos/huggingface/transformers/issues/915/events | https://github.com/huggingface/transformers/issues/915 | 473,727,096 | MDU6SXNzdWU0NzM3MjcwOTY= | 915 | Wrong layer names for selecting parameters groups (run_openai_gpt.py) | {
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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"
] | 1,564 | 1,570 | 1,570 | NONE | null | Hi,
In this script [run_openai_gpt.py](https://github.com/huggingface/pytorch-transformers/blob/master/examples/single_model_scripts/run_openai_gpt.py)
Parameter names for selecting param groups are wrong.
Should be:
`
no_decay = no_decay = ['bias', 'ln_1.bias', 'ln_1.weight', 'ln_2.bias', 'ln_2.weight']... | {
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https://api.github.com/repos/huggingface/transformers/issues/914 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/914/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/914/comments | https://api.github.com/repos/huggingface/transformers/issues/914/events | https://github.com/huggingface/transformers/issues/914 | 473,695,718 | MDU6SXNzdWU0NzM2OTU3MTg= | 914 | Using new pretrained model with it's own vocab.txt file. | {
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"Found the answer\r\n\r\nhttps://github.com/huggingface/pytorch-transformers/issues/69#issuecomment-443215315\r\n\r\nyou can just do a direct path to it",
"Can it work? I tried the solution but didn't work. I put the vocab.txt file under a certain path.",
"What error message did you get? Maybe try an absolute p... | 1,564 | 1,573 | 1,573 | CONTRIBUTOR | null | I am trying to use SciBert pretrained weights, which has its own vocab , so it's own 'vocab.txt', file.
I think it's fairly straight forward to point the `pytorch_model.bin` but I do not see any options to introduce a new vocab.txt file. | {
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https://api.github.com/repos/huggingface/transformers/issues/913 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/913/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/913/comments | https://api.github.com/repos/huggingface/transformers/issues/913/events | https://github.com/huggingface/transformers/issues/913 | 473,695,113 | MDU6SXNzdWU0NzM2OTUxMTM= | 913 | Best practices for combining large pretrained models with smaller models? | {
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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",
"@dchang56 Any updates? I am looking to do this as well. ",
"Hi! I see that you're also doing scientific/medical NLP :)\r\nI sent you ... | 1,564 | 1,572 | 1,570 | NONE | null | Hello,
If I were to try to combine a large model (like BERT) with a smaller model (some variation of fully connected, convolutional network with significantly less params and pretraining) by jointly training them and concatenating their outputs for a final classifier, what would be some things I should consider?
... | {
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https://api.github.com/repos/huggingface/transformers/issues/912 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/912/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/912/comments | https://api.github.com/repos/huggingface/transformers/issues/912/events | https://github.com/huggingface/transformers/issues/912 | 473,612,336 | MDU6SXNzdWU0NzM2MTIzMzY= | 912 | adding vocabulary in OpenAI GPT2 tokenizer issue | {
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"What specifically did you change in `tokenization_utils.py`? \r\n\r\n> it works fine at the training stage, but the index mapping went totally different in the\r\n> evaluation phase.\r\n\r\nCan you elaborate on what you mean? Perhaps post some output? Is it hanging? Or are you just getting wildly poor performance ... | 1,564 | 1,564 | 1,564 | NONE | null | Hi,
I am trying to add few vocabulary tokens to the gpt2 tokenizer
but there seems few problems in adding vocab.
Let's say I want to make sequence like
> "__bos__" + sequence A + "__seperator__" + sequence B + "__seperator__" + sequence C + "__eos__"
This means that I have to add "__bos__", "__seperator__"... | {
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https://api.github.com/repos/huggingface/transformers/issues/911 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/911/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/911/comments | https://api.github.com/repos/huggingface/transformers/issues/911/events | https://github.com/huggingface/transformers/pull/911 | 473,503,781 | MDExOlB1bGxSZXF1ZXN0MzAxNjU4OTE5 | 911 | Small fixes | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/911?src=pr&el=h1) Report\n> Merging [#911](https://codecov.io/gh/huggingface/pytorch-transformers/pull/911?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/c054b5ee64df1a180417c5e87816879c93f54e17?src... | 1,564 | 1,578 | 1,564 | MEMBER | null | Fix #908 and #901 | {
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https://api.github.com/repos/huggingface/transformers/issues/910 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/910/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/910/comments | https://api.github.com/repos/huggingface/transformers/issues/910/events | https://github.com/huggingface/transformers/pull/910 | 473,460,663 | MDExOlB1bGxSZXF1ZXN0MzAxNjIzNjQ5 | 910 | Adding AutoTokenizer and AutoModel classes that automatically detect architecture - Clean up tokenizers | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/910?src=pr&el=h1) Report\n> Merging [#910](https://codecov.io/gh/huggingface/pytorch-transformers/pull/910?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/46cc9dd2b51a152b2e262ec12e40dddd13235aba?src... | 1,564 | 1,566 | 1,565 | MEMBER | null | As discussed in #890
Classes that automatically detect the relevant model/config/tokenizer to instantiate based on the`pretrained_model_name_or_path` string provided to `AutoXXX.from_pretrained(pretrained_model_name_or_path)`.
Right now:
- `AutoConfig`
- `AutoTokenizer`
- `AutoModel` (bare models outputting h... | {
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