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https://api.github.com/repos/huggingface/transformers/issues/409 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/409/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/409/comments | https://api.github.com/repos/huggingface/transformers/issues/409/events | https://github.com/huggingface/transformers/pull/409 | 425,405,711 | MDExOlB1bGxSZXF1ZXN0MjY0NTA5NzI5 | 409 | Remove padding_idx from position_embeddings and token_type_embeddings | {
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"Indeed, thanks @ikuyamada!"
] | 1,553 | 1,553 | 1,553 | CONTRIBUTOR | null | Because embedding vectors at 0th position of `position_embeddings` and `token_type_embeddings` have roles in the model (i.e., representing the first token and the token in the first sentence), these vectors should not be treated as padding vectors. | {
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https://api.github.com/repos/huggingface/transformers/issues/408 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/408/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/408/comments | https://api.github.com/repos/huggingface/transformers/issues/408/events | https://github.com/huggingface/transformers/issues/408 | 425,298,071 | MDU6SXNzdWU0MjUyOTgwNzE= | 408 | slow training speed even 20 steps | {
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"We have new lm_fintetuning scripts thanks to @Rocketknight1 PR #392.\r\nMaybe you can try these ones.\r\nThey are in the `./examples/lm_finetuning/` folder.",
"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 ... | 1,553 | 1,559 | 1,559 | NONE | null | Hi,
I am running run_lm_finetuning.py code on a 1 million sentences .
It's taking more than 20 hours even for each epoch.
where as run_pretraining.py code from google-research/bert takes very less time.
What can be the reason?
How to resolve this? | {
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"Maybe you should ask in the AllenNLP repo as well?",
"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"
] | 1,553 | 1,559 | 1,559 | NONE | null | Has anyone done any work on wrapping up TransformerXL for AllenNLP? | {
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"I realised that the way I was loading model was wrong.\r\n\r\nI used this code\r\n\r\n ```\r\n# Save a trained model \r\n model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self \r\n output_model_file = os.path.join(args.output_dir, \"pytorch_model.bin\") \r\n torch.save(... | 1,553 | 1,553 | 1,553 | NONE | null | I have used run_lm_finetuning.py code on my domain specific corpus.
Now I want to use the fine tuned model to get better embeddings.
>>> import torch
>>> config = modeling.BertConfig(attention_probs_dropout_prob=0.1, hidden_dropout_prob=0.1, hidden_size=768, initializer_range=0.02, intermediate_size=3072, max_po... | {
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https://api.github.com/repos/huggingface/transformers/issues/405 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/405/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/405/comments | https://api.github.com/repos/huggingface/transformers/issues/405/events | https://github.com/huggingface/transformers/issues/405 | 425,034,809 | MDU6SXNzdWU0MjUwMzQ4MDk= | 405 | embeddings after fine tuning | {
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"I used this code and it worked.\r\n\r\n```\r\n output_model_file = /path/to/pytorch_mode.bin/\r\n model_state_dict = torch.load(output_model_file) \r\n model = BertModel.from_pretrained(bert_model, state_dict=model_state_dict)\r\n```",
"Hi @KavyaGujjala \r\n\r\nI was fine-tuning the 'Bert base uncased' model as ... | 1,553 | 1,563 | 1,563 | NONE | null | Hi,
I have fine tune 'bert base uncased' using run_lm_finetuning.py script on my domain specific text corpus.
I have got pytorch_model.bin file after fine tuning.
Now how to load that model and get embeddings.
And if I can do that, does the embeddings get changed because I have fine tuned?
Please can some... | {
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https://api.github.com/repos/huggingface/transformers/issues/404 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/404/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/404/comments | https://api.github.com/repos/huggingface/transformers/issues/404/events | https://github.com/huggingface/transformers/pull/404 | 424,661,498 | MDExOlB1bGxSZXF1ZXN0MjYzOTM3NDM2 | 404 | Fix Language Modeling Loss | {
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"I fixed the test failure above in `472857c`. Note that this occurred because I was running torch > 1.0. They fixed the contiguous view issue in https://github.com/pytorch/pytorch/issues/3653. In my view, it would probably make sense to update torch to >1.0 and remove `contiguous()` calls where possible throughout ... | 1,553 | 1,563 | 1,554 | CONTRIBUTOR | null | This fixes the language modeling loss setup for GPT and GPT-2. Minimizing the loss would previously destroy the language model within a few steps. I believe that both loss computations were incorrect, since they computed the cross-entropy without shifting the logits. Given the masking setup here, we want the ith logits... | {
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https://api.github.com/repos/huggingface/transformers/issues/403 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/403/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/403/comments | https://api.github.com/repos/huggingface/transformers/issues/403/events | https://github.com/huggingface/transformers/issues/403 | 424,630,249 | MDU6SXNzdWU0MjQ2MzAyNDk= | 403 | [Question]Embedding Generate Problem | {
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"Hi,\r\n\r\nHow did you get the english embeddings using the bert-base-uncase model.\r\n\r\nI have fine tuned the model on domain specific corpus. I am trying to get embeddings now using the pytorch_model.bin I got after fine tuning.\r\n\r\nAny idea on how to do this?",
"Are your models in evaluation mode (`model... | 1,553 | 1,564 | 1,564 | NONE | null | When i use 'bert-base-uncase' model to get english embeddings,I can get accurate and unchangeable tensor.But when I do same things on 'bert-base-chinese',I input same sequence every times,but I get different tensors.My input senquece like ''[CLS]+sequence+[SEP]" | {
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https://api.github.com/repos/huggingface/transformers/issues/402 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/402/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/402/comments | https://api.github.com/repos/huggingface/transformers/issues/402/events | https://github.com/huggingface/transformers/issues/402 | 424,585,953 | MDU6SXNzdWU0MjQ1ODU5NTM= | 402 | gpt2 tokenizer issue with ValueError: chr() arg not in range(256) in Python 2.X | {
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"Indeed, I have added backward compatibility to python 2 for GPT-2.\r\nDo you want to submit a PR on 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"
] | 1,553 | 1,559 | 1,559 | NONE | null | See [the code](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization_gpt2.py#L50)
Here's a solution for python 2.X
```python
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversi... | {
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"Also interested in this! ",
"Hi,\r\nIt's quite difficult to use BERT to generate text as BERT is not a causal language model per se.\r\nHere is an example: https://github.com/nyu-dl/bert-gen by @W4ngatang and @kyunghyuncho.",
"Bert was not trained for text generation since it's not trained in the classical lm ... | 1,553 | 1,560 | 1,560 | NONE | null | Hey,
Once I've fine-tuned the Language Model, how can I get it to generate new text ? Is there any example available ?
Thanks ! | {
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"Hi the BERT models are regular PyTorch models, you can just use the usual way we freeze layers in PyTorch. For example you can have a look at the [Transfer Learning tutorial of PyTorch](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#convnet-as-fixed-feature-extractor).\r\n\r\nIn our case fr... | 1,553 | 1,685 | 1,553 | NONE | null | How to freeze all layers of bert and just train task based classifier? | {
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"Hi Aly, GPT-2 is pretrained on an English only corpus.",
"Hi @thomwolf , thank you for the clarification."
] | 1,553 | 1,553 | 1,553 | NONE | null | I'm trying to build a language model that trains on a Polish corpus. And I'm wondering if the GPT-2 pretrained model you present supports that, or if it's English only.
Thank You. | {
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https://api.github.com/repos/huggingface/transformers/issues/398 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/398/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/398/comments | https://api.github.com/repos/huggingface/transformers/issues/398/events | https://github.com/huggingface/transformers/pull/398 | 424,000,240 | MDExOlB1bGxSZXF1ZXN0MjYzNDUwODM5 | 398 | Multi GPU | {
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"Hi @dirkgr, thanks for this PR.\r\n\r\nI think we will keep all the GPU/multi-GPU logic outside of the main library for now. It makes it easier to integrate the module in downstream libraries and integrating such modifications at the current stage would cause too many breaking changes for the users unfortunately.\... | 1,553 | 1,553 | 1,553 | CONTRIBUTOR | null | This is an incomplete proof-of-concept of how to run BERT across multiple GPUs. It will take advantage of multiple GPUs' memory, but not of their compute cores.
Do not merge | {
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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",
"@thomwolf Please re-open. If I have the time, I can try to work on this.",
"This issue has been automatically marked as stale because... | 1,553 | 1,592 | 1,592 | COLLABORATOR | null | It would make sense to me that you could set `do_lower_case=True` even when `do_basic_tokenize=False`. If your input has already been tokenized (but not lower-cased), you still want to lowercase it. As the code is currently written, that does not seem possible as [the BasicTokenizer is responsible for the lowercasing](... | {
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https://api.github.com/repos/huggingface/transformers/issues/396 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/396/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/396/comments | https://api.github.com/repos/huggingface/transformers/issues/396/events | https://github.com/huggingface/transformers/pull/396 | 423,718,783 | MDExOlB1bGxSZXF1ZXN0MjYzMjI2Nzk2 | 396 | add tqdm to the process of eval in examples/run_swag.py | {
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"tests/tokenization_openai_test.py::OpenAIGPTTokenizationTest::test_full_tokenizer FAILED",
"Ok, thanks!"
] | 1,553 | 1,553 | 1,553 | CONTRIBUTOR | null | Maybe better. | {
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https://api.github.com/repos/huggingface/transformers/issues/395 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/395/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/395/comments | https://api.github.com/repos/huggingface/transformers/issues/395/events | https://github.com/huggingface/transformers/issues/395 | 423,581,764 | MDU6SXNzdWU0MjM1ODE3NjQ= | 395 | AttributeError: 'BertOnlyMLMHead' object has no attribute 'seq_relationship' | {
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"Hi,\r\nWhat command are you running to load the model?\r\nAre you loading your own model or one or our pre-trained one?\r\n\r\nIt's normal that there is no `seq_relationship` attribute in a `BertOnlyMLMHead` but our pre-trained model should load without error.",
"\r\nI was loading my own pre-trained model by \"c... | 1,553 | 1,559 | 1,559 | NONE | null | Is there way to fix it?
```bash
Skipping cls/predictions/transform/dense/kernel/adam_m
Skipping cls/predictions/transform/dense/kernel/adam_v
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-11-5... | {
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https://api.github.com/repos/huggingface/transformers/issues/394 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/394/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/394/comments | https://api.github.com/repos/huggingface/transformers/issues/394/events | https://github.com/huggingface/transformers/pull/394 | 423,573,705 | MDExOlB1bGxSZXF1ZXN0MjYzMTE3Mzk4 | 394 | Minor change in README | {
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"Thanks!"
] | 1,553 | 1,553 | 1,553 | CONTRIBUTOR | null | Spelling fix of: weigths to weights | {
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"Hi,\r\nIs it a model trained from the original Google BERT Tensorflow implementation?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"I get the same error as @leejason. I used th... | 1,553 | 1,665 | 1,571 | NONE | null | Is there any suggestion for fixing the following? I was trying "convert_tf_checkpoint_to_pytorch.py" to convert a model trained from scratch but the conversion didn't work out....
```bash
Skipping cls/seq_relationship/output_weights/adam_v
Traceback (most recent call last):
File "pytorch_pretrained_bert/convert_t... | {
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https://api.github.com/repos/huggingface/transformers/issues/392 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/392/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/392/comments | https://api.github.com/repos/huggingface/transformers/issues/392/events | https://github.com/huggingface/transformers/pull/392 | 423,390,699 | MDExOlB1bGxSZXF1ZXN0MjYyOTcyNjkw | 392 | Add full language model fine-tuning | {
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"Also, build_py2 has failed because I deliberately did not include Py2 compatibility - it's only a few months from end-of-life now. If you really want me to, I can go back and include it, but we should be trying to let it go by now!",
"This is really great @Rocketknight1!\r\nThanks for taking the time to make a v... | 1,553 | 1,553 | 1,553 | MEMBER | null | These scripts add language model fine-tuning that closely mirrors the training process in the original BERT repo. The old fine-tuning example has been renamed `simple_lm_finetuning.py`. The key difference is the old script did not merge sentences when creating training examples, and so tended to create short training e... | {
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"Cola is probably one of the most unstable tasks for BERT. For us it mostly boiled down to running many seeds. If all you care about is a good pre-trained model checkpoint, we have a 65 / 61 run at https://github.com/zphang/bert_on_stilts ",
"This issue has been automatically marked as stale because it has not ha... | 1,553 | 1,563 | 1,560 | CONTRIBUTOR | null | I try to reproduce the CoLA results reported in the BERT paper but the numbers are far from the reported one. My best mcc (BERT large) for dev is 64.79% and the test result is 56.9% while the reported test result is 60.5%. The learning rate is 2e-5 and the total number of epochs is 5. For BERT base，the result is also l... | {
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"Me too.It is probably Network connection problem.",
"The network connection check has been relaxed in the now merged #500.\r\nIt will be included in the next PyPI release (probably next week).\r\nIn the meantime you can install from `master`.",
"@thomwolf Thank you.",
"The new release is on pypi!"
] | 1,553 | 1,556 | 1,556 | NONE | null | I run the code below and often get 'NoneType' object. (I usually run multiprocessing)
```python
model = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/389 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/389/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/389/comments | https://api.github.com/repos/huggingface/transformers/issues/389/events | https://github.com/huggingface/transformers/pull/389 | 422,242,965 | MDExOlB1bGxSZXF1ZXN0MjYyMDc1MDc5 | 389 | Fix cosine schedule | {
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"Hi @lukovnikov, yes I think the various schedules you have added to your fork are very nice!\r\nDo you want to add them in this PR as well?\r\nOtherwise, I'll merge it.",
"Merging it for now. Thanks @lukovnikov ",
"Hi, sorry, lost track of this, will make a new PR soon."
] | 1,552 | 1,554 | 1,554 | CONTRIBUTOR | null | Fixing similar problem to #327 and #324 in cosine schedule.
Btw, do you think it would make sense to have [something like this](https://github.com/lukovnikov/pytorch-pretrained-BERT/blob/optim/pytorch_pretrained_bert/optimization.py) for both of your `optimization.py`'s? | {
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"Hi @ananyahjha93,\r\nThanks for this PR.\r\nDo you have some results from fine-tuning BERT on the other tasks?\r\nAlso, I think we should add some details on the available tasks in the readme as well.",
"@thomwolf I have added results on GLUE dev set in the README and details on how to run any GLUE task. But, I ... | 1,552 | 1,553 | 1,553 | CONTRIBUTOR | null | Also added metrics used in the GLUE paper for each task. | {
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"It is better to imitate original Bert repo to add separate argument `args.vocab_file`, and during prediction, argument `bert_model` is the directory containing the fine-tuned model. ",
"Make sense indeed. Would you like to submit a PR on that?",
"This issue has been automatically marked as stale because it ... | 1,552 | 1,558 | 1,558 | NONE | null | Existing code cannot load fine-tuned model properly.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/f3e5404880902a1bdfed2b1d47d10a6c672dc430/examples/run_squad.py#L1011-L1025 | {
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"Hi Catalin, thanks for that!\r\nYes backward compatibility is an important things to keep in mind here.\r\nI think the changes look nice.\r\nWe should also:\r\n- document them in the readme, and\r\n- add associated tests in the relevant test files.",
"Just before this gets merged - I've noticed that the GPT(1) t... | 1,552 | 1,563 | 1,561 | CONTRIBUTOR | null | Up to this point, `tokenize()` and `encode()` mean different places. In GPT-land, `tokenize` doesn't get us all the way to token IDs. Idteally, he tokenizers would share a common interface so that they can be plugged in and out of places just like the models.
I don't know if you want breaking changes, so I just crea... | {
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"We can't now. The code is still incomplete. Is it possible recently? I really want to help but not familiar with tensorflow.",
"A related issue is #376.\r\n\r\nHowever, pytorch-pretraned-BERT was mostly designed to provide easy and fast access to pretrained models.\r\n\r\nIf you want to train a BERT model from s... | 1,552 | 1,612 | 1,569 | NONE | null | I am wondering whether I can train a new BERT from scratch with this pytorch BERT. | {
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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",
"Wondering Noone is replying\n\nOn Sat, 25 May, 2019, 4:09 PM stale[bot], <notifications@github.com> wrote:\n\n> Closed #384\n> <https:/... | 1,552 | 1,558 | 1,558 | NONE | null | This question is posted in stackexchange too, but is pointing to BERT group:
https://datascience.stackexchange.com/questions/47406/incrementally-train-bert-with-minimum-qna-records
Question is after training on my data on some new questions and answers, new checkpoints are generated. With new checkpoints, when as... | {
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"Hi @andrewPoulton, yes indeed we could update that for GPT-2, would be happy to get a PR.\r\nCan you check the generations are identical for a few seeds (it should be)?",
"Yeah, sure - what generations do you mean?",
"Fixed with #495"
] | 1,552 | 1,555 | 1,555 | NONE | null | When trying to train in mixed precision, after casting model weights to fp16 overflow is bound to occur since multiplication by 1e10 is used to mask the attention weights.
I noticed BERT multiplies by 1e4 (within fp16 range) instead, and the overflow problem doesn't occur and now it's training happily :)
I'm happ... | {
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"Thanks @tseretelitornike!"
] | 1,552 | 1,552 | 1,552 | CONTRIBUTOR | null | {
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Fix #374 | {
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"Good catch! Do you want to submit a PR? Otherwise, I'll fix it in the next release.",
"Hi @thomwolf \r\nThe issue still persists, there were two extra indentations and you removed only one to move the line out of inner if-else but, one more indentation should be removed to bring [L620](https://github.com/hugging... | 1,552 | 1,559 | 1,552 | NONE | null | This is due to extra indentation on line 623 in run_squad.py
It should be outside of the "if else" loop. | {
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"Sorry, I just realized this was mentioned in the [original PR](https://github.com/huggingface/pytorch-pretrained-BERT/pull/124).",
"Indeed. Happy to welcome a PR if you want to improve this example!",
"Working on it now! One question, though: It seems likely that I'll have to make significant changes. The reas... | 1,552 | 1,553 | 1,553 | MEMBER | null | In the original Tensorflow BERT repo, training cases for the Next Sentence task are generated by [concatenating multiple sentences](https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L219) up to the maximum sequence length. In other words the "sentences" used are actually longer chunks of te... | {
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"maybe you can take a look at the \"cache_dir\" argument. For the run_classifier.py file, it is located at line 498",
"I tried with --cache_dir , giving the fine-tunings output directory as cache_dir. \r\nI added these 2 files to the directory: bert_config.json and vocab.txt from the original bert_basic_uncased\r... | 1,552 | 1,560 | 1,560 | NONE | null | I run the finetuning as instructed in the example "LM Fine-tuning"
python run_lm_finetuning.py \
--bert_model bert-base-uncased \ .
--output_dir models \
...
As a result the fine-tuned model is now in models/pytorch_model.bin
But how do I use it to classify? The example doesn't mention that.
I don't fi... | {
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"Just doing `from pytorch_pretrained_bert import BertTokenizer, BertModel` in your project doesn't work?\r\n\r\nThat's what they do in [AllenNLP](https://github.com/allenai/allennlp/blob/3f0953d19de3676ea82e642659fc96d90690e34d/allennlp/modules/token_embedders/bert_token_embedder.py#L14) or [flair](https://github.c... | 1,552 | 1,552 | 1,552 | NONE | null | The relative path that starts with . does not work when a file is used from an outside project. I added a safe code to handle the ImportError exception in this case, so I can use the source file without having to make local changes to it. | {
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"Similar problem. My token ids is something like \"[cls]qqqq[sep]0000cccccccc[sep]00000\". Have you solve it? or is there anyone met the similar problem?",
"> Similar problem. My token ids is something like \"[cls]qqqq[sep]0000cccccccc[sep]00000\". Have you solve it? or is there anyone met the similar problem?\r\... | 1,552 | 1,554 | 1,554 | NONE | null | I just want to ask this here and see whether other people encountered the same situation.
I am doing modifications on the run_squad.py example.
So for the original training feature, the input ids are [cls]qqqqq[sep]cccccc000000. The attention mask is just something like 111111100000 where first k inputs were mask... | {
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"Why should it be limited to half of 512?",
"> Why should it be limited to half of 512?\r\n\r\ncause when do train, we have sentence embedding 0 and 1, but in a single sentence classification task ,we just embedding 0, if this get bad influence",
"You can just set the whole sequence to sentence 0. Create a Dat... | 1,552 | 1,562 | 1,562 | NONE | null | hi, if i have a single sentence classification task, should the max length of sentence limited to half of 512, that is to say 256? | {
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"wouldn't this cause some kind of indentation error? (I don't have time to test the change sorry)"
] | 1,552 | 1,552 | 1,552 | CONTRIBUTOR | null | delete redundancy line 597 `if args.train` which is the same function to line 547, in order to simplify code. | {
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"Check Jacob Devlin slides starting from slide 26 [here](https://nlp.stanford.edu/seminar/details/jdevlin.pdf?fbclid=IwAR2TBFCJOeZ9cGhxB-z5cJJ17vHN4W25oWsjI8NqJoTEmlYIYEKG7oh4tlY)",
"@thomwolf thanks, the slides were helpful. Do you know if there is a recording of the talk publicly available somewhere?",
"I don... | 1,552 | 1,563 | 1,559 | NONE | null | The current best performing model on[ SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) is BERT + N-Gram Masking + Synthetic Self-Training (ensemble):
What is Synthetic Self-Training?
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"If you want only the context you can find the index from the segment vector by finding the last first 1 in the vector and splitting the query_context on that index. Then the context will be everything after the 1 index and everything before will be the question.",
"This issue has been automatically marked as sta... | 1,552 | 1,560 | 1,560 | NONE | null | I've made several attempts to, but all seem to fail. Do you have a good way to do this? Right now, passing what i thought to just be the context hidden state to the final output layer in run_squad.py drops my scores (F1) by 10 points. | {
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"I had the same issue. I'm getting seg fault on an aws deep learning ami with a tesla v100 gpu instance. I get the error with or with out fp16",
"I also have the same problem. Did you guys figure out any solution? \r\nI am able to load the data, however at the first epoch 0, I see the error segmentation fault. ",... | 1,552 | 1,590 | 1,563 | NONE | null | I have done some modification on BertForSequenceClassification to apply a mutilabel prediction task, but
when i run my code on my serve, it always says that Segmentation fault when it runs to
"loss = model(input_ids, segment_ids, input_mask,label_ids)", and even if i don't use GPU and fp16, it comes the same fault.
... | {
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"@Liangtaiwan and @abeljim were the contributors of the `run_squad.py` example.\r\nMaybe they can help.",
"Hi @elephantomkk,\r\n\r\nThe non-answerable solving is using [CLS] token as the ground truth.\r\nAs a result, the start login = and end logit = -1.\r\n\r\nYou can find it out in this repo code or official Be... | 1,552 | 1,567 | 1,552 | NONE | null | Hi,
I want to do a similar reading comprehension task with non-answerable questions but i didn't figure out how you deal with it from the codes. Did you add additional token on this? Or only outputs the no-answer when the start logit = end logit = -1? Thanks! | {
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"Indeed, this example could be improved. I would happy to welcome a PR on that.",
"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"
] | 1,552 | 1,558 | 1,558 | NONE | null | There appears to be a bug in the way the vocabulary file is handled.
For example, if we execute [`run_squad.py`](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_squad.py) with `--do_train`, and set the `--output_dir` to `/tmp/debug_squad/`, we successfully build a model and the result... | {
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"Can you give a simple self-contained script to reproduce your issue?",
"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"
] | 1,552 | 1,558 | 1,558 | NONE | null | I have trained Google BERT with a custom training.
I have included the exact question and answer along with the context from the input document in the training file and trained BERT.
With new generated checkpoints (ckpt) I am still getting the same wrong answer as obtained before training. However it is observed th... | {
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"I agree with you, this part of the API could be improved.\r\n\r\nThe BERT model is now used in several third-party libraries like AllenNLP and FLAIR so we have to be careful not to make any breaking change on this model.\r\n\r\nWe could add a flag to get full output maybe.",
"Mind if I submit a PR adding a flag ... | 1,552 | 1,558 | 1,558 | CONTRIBUTOR | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/7cc35c31040d8bdfcadc274c087d6a73c2036210/examples/run_classifier.py#L641-L642
Here we are calling the model twice. I understand that the model returns different things depending on the presence of `label_ids`, but this could actually be quite expensive. I t... | {
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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"
] | 1,552 | 1,558 | 1,558 | NONE | null | Hello!
I'm trying to extract features for a QA task where the document is composed of multiple disparate paragraphs. So my input is:
question ||| document
where document is {para1 SEP para2 SEP para3 SEP}, so overall, it's something like:
question ||| para1 SEP para2 SEP para3 SEP
My question is: Is it okay to u... | {
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"Thanks!"
] | 1,552 | 1,552 | 1,552 | CONTRIBUTOR | null | In the case of the ImportError in modeling.py [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/7cc35c31040d8bdfcadc274c087d6a73c2036210/pytorch_pretrained_bert/modeling.py#L219), make the hyperlink to NVIDIA Apex redirect properly by spacing the '.' | {
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https://api.github.com/repos/huggingface/transformers/issues/361 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/361/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/361/comments | https://api.github.com/repos/huggingface/transformers/issues/361/events | https://github.com/huggingface/transformers/pull/361 | 419,008,830 | MDExOlB1bGxSZXF1ZXN0MjU5NjQ3NjM0 | 361 | Correct line number in README for classes | {
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"Thanks @junjieqian!"
] | 1,552 | 1,553 | 1,552 | CONTRIBUTOR | null | Correct the linked line number in README for classes | {
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"I am also interested in this, it looks like we would have to append the prediction probability to the `all_predictions` JSON output.",
"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"
] | 1,552 | 1,558 | 1,558 | NONE | null | I am using `BertForQuestionAnswering`
I am trying to make a prediction from the same question asked on different paragraphs. It outputs an `OrderedDict` of tuples with format `(paragraphID, answer)`. How can I rank those predictions to get the most probable answer across all paragraphs?
Thanks for great repo! | {
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https://api.github.com/repos/huggingface/transformers/issues/359 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/359/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/359/comments | https://api.github.com/repos/huggingface/transformers/issues/359/events | https://github.com/huggingface/transformers/pull/359 | 418,872,236 | MDExOlB1bGxSZXF1ZXN0MjU5NTQwOTU0 | 359 | Update run_gpt2.py | {
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"Thanks Elon"
] | 1,552 | 1,561 | 1,552 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/358 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/358/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/358/comments | https://api.github.com/repos/huggingface/transformers/issues/358/events | https://github.com/huggingface/transformers/pull/358 | 418,274,901 | MDExOlB1bGxSZXF1ZXN0MjU5MDg1NzYx | 358 | add 'padding_idx=0' for BertEmbeddings | {
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"Thanks @cdjhz "
] | 1,551 | 1,552 | 1,552 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/357 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/357/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/357/comments | https://api.github.com/repos/huggingface/transformers/issues/357/events | https://github.com/huggingface/transformers/pull/357 | 418,202,612 | MDExOlB1bGxSZXF1ZXN0MjU5MDI5Mjg5 | 357 | Use Dropout Layer in OpenAIGPTMultipleChoiceHead | {
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"Seems good to me, thanks for that.\r\nLet me just check why we don't have Circle-CI tests on the PR anymore and I'll merge it."
] | 1,551 | 1,552 | 1,552 | CONTRIBUTOR | null | closes #354 | {
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"GPT is a causal model so each tokens only attend to the left context and masking is not really needed.\r\nJust mask the output according to your lengths (and be such that each input sample start at the very first left token)."
] | 1,551 | 1,551 | 1,551 | NONE | null | I use `attention_mask` when I do `bert.forward(input, attention_mask)`. But in GPT, when I try to pass a batch of input to `OpenAIGPTModel` to extract a batch of features, and the lengths of sentences in a batch are different, I have no idea how to do it. Or maybe it doesn't need the mask to be given? If so, is zero th... | {
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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"
] | 1,551 | 1,557 | 1,557 | NONE | null | I'm trying to develop a model that will do "word level extractive summarization" e.g. that it will delete unimportant words or tokens and summarize a document. This is also known as "Sentence Compression" in the NLP community.
I'm thinking using the BertforTokenClassification module. Will it work with a large datas... | {
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"Strange error.\r\n\r\nCan you try:\r\n```python\r\nimport pytorch_pretrained_bert as ppb\r\nassert 'bert-large-cased' in ppb.modeling.PRETRAINED_MODEL_ARCHIVE_MAP\r\n```\r\nDo you have an open internet connection on the server that run the script?",
"@thomwolf Is there a way to point to a model on disk? This que... | 1,551 | 1,656 | 1,555 | NONE | null | >>> model = BertModel.from_pretrained('bert-large-cased')
Model name 'bert-large-cased' was not found in model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese). We assumed 'https://s3.amazonaws.com/mode... | {
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"I think my answer here can help you: https://github.com/huggingface/pytorch-pretrained-BERT/issues/332",
"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",
"Hey @shuvadibp, did you fig... | 1,551 | 1,560 | 1,558 | NONE | null | I am using Bert for Question Answering. After fine tuning with Squad data set, I want to further train new questions of my own domain.
Please suggest how can I use newly generated pytorch_model.bin file and then increment it with my own training weights to get my own pytorch_suqad_plus_my_model.bin ? | {
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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"
] | 1,551 | 1,558 | 1,558 | NONE | null | When tried to enter few training data in trainxx.json (few questions and few answers) and ran the training, then new pytorch_model.bin file got generated ( = uncased + squad training + few my questions).
However, when same question was put in devxx.json the answer is not same which was put in training.
Why is... | {
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- do_lower_case=True
- max_answer_length=30
- max_answer_length=30
- n_best_size=20
- verbose_logging=False
- bert_model="bert-large-uncased"
- max_seq_length=384
- doc_stride=128
- max_query_length=192
- local_rank=-1
- train_batch_size=12
- predict_batch_size=12
- num_train_epochs=2.0
... | {
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https://api.github.com/repos/huggingface/transformers/issues/349 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/349/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/349/comments | https://api.github.com/repos/huggingface/transformers/issues/349/events | https://github.com/huggingface/transformers/issues/349 | 417,596,167 | MDU6SXNzdWU0MTc1OTYxNjc= | 349 | Unable to train (fine-tuning) BERT with small training set | {
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"Probably an issue with `t_total` and the number of training optimization steps similarly to #329.\r\nCould you check the number of total training step sent to the optimizer? Which example script are you using?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be... | 1,551 | 1,557 | 1,557 | NONE | null | I am trying to train BERT with 1 context and 1 answer in the train.json, I am getting the below error.
_lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
ZeroDivisionError: division by zero_
After training with 1 context and 5 answers, the error is avoided, but I do not s... | {
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https://api.github.com/repos/huggingface/transformers/issues/348 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/348/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/348/comments | https://api.github.com/repos/huggingface/transformers/issues/348/events | https://github.com/huggingface/transformers/pull/348 | 417,472,951 | MDExOlB1bGxSZXF1ZXN0MjU4NDYyNzI3 | 348 | output data | {
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"Wrong upstream I guess. Closing."
] | 1,551 | 1,551 | 1,551 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/347 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/347/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/347/comments | https://api.github.com/repos/huggingface/transformers/issues/347/events | https://github.com/huggingface/transformers/pull/347 | 417,468,974 | MDExOlB1bGxSZXF1ZXN0MjU4NDU5NjA3 | 347 | Processor for SST-2 task | {
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"Thanks @jplehmann!"
] | 1,551 | 1,551 | 1,551 | CONTRIBUTOR | null | Added a processor for SST-2 to the `run_classifier` script.
| {
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"Argh, just realized I was on `0.3.0` which is what pip installed due to some dependencies. Upgrading to `0.6.1` and now I'm getting expected scores:\r\n```\r\neval_accuracy = 0.8529411764705882\r\neval_loss = 0.39120761538837473\r\nglobal_step = 345\r\nloss = 0.17308216924252717\r\n\r\neval_accuracy = 0.843137254... | 1,551 | 1,551 | 1,551 | CONTRIBUTOR | null | I expect to see MRPC scores between 84-88% as advertised. What I am seeing with different seeds is 79-84% consistently. (I thought perhaps the weight initialization was the issue but seems not to be the case #339.)
I am running with the provided command and fp16, using a GCE instance with a Tesla T4.
> time py... | {
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"how do you fix this issue?",
"> how do you fix this issue?\r\n\r\ntry to update your torch version，i found it didn't work in torch 4.0.0, try \"torch >=4.0.1\""
] | 1,551 | 1,552 | 1,551 | NONE | null | Not able to import RandomSampler, Getting error "ImportError: cannot import name 'RandomSampler'"? Did I get a wrong torch version? | {
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https://api.github.com/repos/huggingface/transformers/issues/344 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/344/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/344/comments | https://api.github.com/repos/huggingface/transformers/issues/344/events | https://github.com/huggingface/transformers/issues/344 | 417,137,321 | MDU6SXNzdWU0MTcxMzczMjE= | 344 | BertEmbedding not initialized with `padding_idx=0` | {
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"Could be, do you want to submit a PR to update this?",
"Closed by #358, thanks @cdjhz!"
] | 1,551 | 1,552 | 1,552 | CONTRIBUTOR | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/2152bfeae82439600dc5b5deab057a3c4331c62d/pytorch_pretrained_bert/modeling.py#L696
The bert-embeddings are not initialized with `padding_idx=0`, which may potentially result in none zero embeddings for zeros paddings in some early version of pytorch. | {
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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"
] | 1,551 | 1,557 | 1,557 | NONE | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/2152bfeae82439600dc5b5deab057a3c4331c62d/pytorch_pretrained_bert/tokenization.py#L77
A more clear behavior would be to use whether or not 'uncased' is in bert_model, and set the default behavior of do_lower_case accordingly. | {
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"Argh, I just realized that due to dependency conflicts, pip had installed an old version `0.3.0`.\r\n\r\nWas fixed here:\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/issues/303\r\n"
] | 1,551 | 1,551 | 1,551 | CONTRIBUTOR | null | Since probably #176, the usage example results in the special tokens getting normalized in a bad way and the assertion clearly fails.
```
['[',
'cl',
'##s',
']',
'who',
'was',
'jim',
'henson',
'[MASK]',
'[',
'sep',
']',
'jim',
'henson',
'was',
'a',
'puppet',
'##eer',
'[',
'sep',
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https://api.github.com/repos/huggingface/transformers/issues/341 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/341/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/341/comments | https://api.github.com/repos/huggingface/transformers/issues/341/events | https://github.com/huggingface/transformers/pull/341 | 417,023,120 | MDExOlB1bGxSZXF1ZXN0MjU4MTEzNTQ3 | 341 | catch exception if pathlib not install | {
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"Thanks!"
] | 1,551 | 1,551 | 1,551 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/340 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/340/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/340/comments | https://api.github.com/repos/huggingface/transformers/issues/340/events | https://github.com/huggingface/transformers/issues/340 | 416,582,484 | MDU6SXNzdWU0MTY1ODI0ODQ= | 340 | optimizer.zero_grad() in run_openai_gpt.py? | {
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"Oh that's a mistake indeed, thanks for pointing out.\r\nFixed on master."
] | 1,551 | 1,551 | 1,551 | NONE | null | In `run_openai_gpt.py`, should there be a call to `optimizer.zero_grad()` after updating parameters so that we zero out the gradients between minibatches?
https://github.com/huggingface/pytorch-pretrained-BERT/blob/2152bfeae82439600dc5b5deab057a3c4331c62d/examples/run_openai_gpt.py#L212 | {
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"I was wondering this myself. It looks like there's some configuration mismatch -- some parameters found which aren't used, and a few expected that aren't found.\r\n\r\nI'm not sure if this is expected, since the top-level task-specific classifier is correctly NOT pre-trained... or if it's something more.\r\n\r\n(... | 1,551 | 1,592 | 1,551 | NONE | null | 03/03/2019 14:13:01 - INFO - pytorch_pretrained_bert.modeling - Weights of BertForMultiLabelSequenceClassification not initialized from pretrained model: ['classifier.weight', 'classifier.bias']
03/03/2019 14:13:01 - INFO - pytorch_pretrained_bert.modeling - Weights from pretrained model not used in BertForMultiLa... | {
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"Thanks @CatalinVoss!"
] | 1,551 | 1,563 | 1,551 | CONTRIBUTOR | null | Seems like the shapes didn't line up for the comparison. Logits are `(batch_size, values)`. The minima had shape `(batchsize)` and couldn't be directly compared | {
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https://api.github.com/repos/huggingface/transformers/issues/337 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/337/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/337/comments | https://api.github.com/repos/huggingface/transformers/issues/337/events | https://github.com/huggingface/transformers/pull/337 | 416,458,040 | MDExOlB1bGxSZXF1ZXN0MjU3NzAyNTA0 | 337 | Allow tokenization of sequences > 512 for caching | {
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"OK, done. Thanks!",
"Nice, thanks @CatalinVoss (and @rodgzilla)!"
] | 1,551 | 1,563 | 1,551 | CONTRIBUTOR | null | For many applications requiring randomized data access, it's easier to cache the tokenized representations than the words. So why not turn this into a warning? | {
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"Use the Squad python scripts available on their website",
"Is that run_squad.py? I used that one but didn’t see output scores, having\nthe output predictions files though. Thanks!\n\nabeljim <notifications@github.com>于2019年3月3日 周日上午3:03写道：\n\n> Use the Squad python scripts available on their website\n>\n> —\n> Y... | 1,551 | 1,552 | 1,552 | NONE | null | Hi,
I was doing prediction after fine-tuning the bert-base model and I was wondering whether the f1 and em scores will show automatically since I only saw the following two log outputs
03/02/2019 22:20:05 - INFO - __main__ - Writing predictions to: /tmp/debug_squad/predictions.json
03/02/2019 22:20:05 - INFO - ... | {
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"Yes, feel free to open a PR if you want.\r\nIt's just a regular PyTorch model so all the standard ways of training a PyTorch model work.",
"Is is possible to fine-tune GPT2 on downstream tasks currently?",
"same questions"
] | 1,551 | 1,562 | 1,551 | CONTRIBUTOR | null | {
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"Did you run python install --editable .",
"There's a way to install cloned repositories with pip, but the easiest way is to use plain python for this:\r\n\r\nAfter cloning and changing into the pytorch-pretrained-BERT directory, run `python setup.py develop`.",
"Yes, please follow the installation instructions... | 1,551 | 1,577 | 1,551 | NONE | null | Hi, when using "pip install [--editable] . ", after cloned the git.
I'm getting this error:
Exception:
Traceback (most recent call last):
File "/venv/lib/python3.5/site-packages/pip/_vendor/packaging/requirements.py", line 93, in __init__
req = REQUIREMENT.parseString(requirement_string)
File "/venv/lib/p... | {
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"Yes, feel free to submit a PR for that."
] | 1,551 | 1,551 | 1,551 | NONE | null | {
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"1. You can put the `pytorch_model.bin` file that was output from your finetuning on squad in some other folder and set that folder as the bert_model='path/to/this/folder'. The folder needs to have the files `bert_config.json` and `vocab.txt` from the first pretrained model you used though.\r\n2. I think you can fi... | 1,551 | 1,576 | 1,576 | NONE | null | Hi all
I have trained bert question answering on squad v 1 data set. As I was using colab which was slow . so I used 5000 examples from squad and trained the model which took 2 hrs and gave accuracy of 51%. My question is that
1) As i saved pytorch_bin file after trainining. Can i use this new bin file and again t... | {
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Thank you very much! | {
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"You should probably use the `bert-base-chinese` model to start from.\r\nPlease refer to the original bert tensorflow implementation from Google.\r\nThere are a lot of discussion about chinese models in the issues of this repo."
] | 1,551 | 1,551 | 1,551 | NONE | null | Is this pre-trained BERT good for NER or classification on Chinese corpus?
Thanks. | {
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https://api.github.com/repos/huggingface/transformers/issues/329 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/329/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/329/comments | https://api.github.com/repos/huggingface/transformers/issues/329/events | https://github.com/huggingface/transformers/issues/329 | 415,449,361 | MDU6SXNzdWU0MTU0NDkzNjE= | 329 | run_lm_finetuning - ZeroDivisionError | {
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"Seems like an error with `t_total`. `t_total` is the number of training optimization steps of the optimizer defined [here](num_train_optimization_steps) in the `run_lm_finetuning` example.\r\nCan you make sure it's not zero?",
"Your `batch_size` of 32 is too big for such a small `train_file`, i.e. sample_text.tx... | 1,551 | 1,557 | 1,557 | NONE | null | Trying to get run_lm_finetunning example on working on below GPU machine but finding getting zeroDivisonError.Any idea what could be causing this error?
python /home/ec2-user/SageMaker/bert_pytorch/pyt... | {
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"I've found a fix to get around this.\r\nRunning the same code with pytorch-pretrained-bert==0.4.0 solves the issue and the performance is restored to normal.\r\nThere's something messing with the model performance in BERT Tokenizer or BERTForTokenClassification in the new update which is affecting the model perfor... | 1,551 | 1,585 | 1,563 | NONE | null | I have been using your PyTorch implementation of Google’s [BERT][1] by [HuggingFace][2] for the MADE 1.0 dataset for quite some time now. Up until last time (11-Feb), I had been using the library and getting an **F-Score** of **0.81** for my Named Entity Recognition task by Fine Tuning the model. But this week when I r... | {
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https://api.github.com/repos/huggingface/transformers/issues/327 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/327/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/327/comments | https://api.github.com/repos/huggingface/transformers/issues/327/events | https://github.com/huggingface/transformers/pull/327 | 415,258,178 | MDExOlB1bGxSZXF1ZXN0MjU2Nzg1NjI3 | 327 | Issue#324: warmup linear fixes | {
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"Great, thanks @lukovnikov!"
] | 1,551 | 1,551 | 1,551 | CONTRIBUTOR | null | Fixes for [Issue#324](https://github.com/huggingface/pytorch-pretrained-BERT/issues/324).
- Using the same schedule functions in BertAdam and OpenAIAdam, fixing `warmup_linear` of OpenAIAdam
- fix for negative learning rate after t_total for `warmup_linear`
- some more docstrings
- warning when t_total is exceede... | {
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" evalonly works last ver, need to mv some code out of train...",
"> evalonly works last ver, need to mv some code out of train...\r\n\r\nyup I agree. I shall do the eval loss and do it\r\n",
"Seems fixed in master, right? Feel free to re-open the issue if it's not the case."
] | 1,551 | 1,551 | 1,551 | NONE | null | Thanks for giving such awesome project.
However, I have encountered some problem.
After training the model, I just want to do eval on another dataset with the trained model. Therefore I only open do_eval.
However, it gives me this error:
Traceback (most recent call last):
File "run_classifier_torch.py", line 6... | {
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https://api.github.com/repos/huggingface/transformers/issues/325 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/325/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/325/comments | https://api.github.com/repos/huggingface/transformers/issues/325/events | https://github.com/huggingface/transformers/pull/325 | 414,937,998 | MDExOlB1bGxSZXF1ZXN0MjU2NTM0ODg4 | 325 | add BertTokenizer flag to skip basic tokenization | {
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"Thanks for the PR (and documenting this), I added a note",
"Ok this is great, thanks @john-hewitt, thanks!"
] | 1,551 | 1,553 | 1,551 | CONTRIBUTOR | null | When tokenization is done before text hits this package (e.g., when tokenization is specified as part of the dataset) there exists a use case for skipping the `BasicTokenizer` step, going right to `WordpieceTokenizer`.
When one still wants to use the `BertTokenizer.from_pretrained` helper function, they have been ab... | {
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"I just ran into this problem while running BERT on large samples from `run_squad.py`. I think a fix would be welcome because this is a really disturbing and hard to catch issue. \r\n\r\nIt would probably be enough to move the optimizer creation + computing of `num_train_optimization_steps` inside the train loop.",... | 1,551 | 1,567 | 1,551 | CONTRIBUTOR | null | 1. OpenAIAdam version of `warmup_linear` does not linearly increase lr, instead it looks like this:
This is different from BertAdam version of `warmup_linear`. Should they not be the same (Bert version)?
... | {
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"I have a similar problem. I labeled the tokens as \"X\" and then got an error relating to NUM_LABELS. BERT appears to have thought the X was a third label, and I only specified there to be two labels.",
"You do not need to introduce an additional tag. This is explained here:\r\n\r\nhttps://github.com/huggingface... | 1,551 | 1,626 | 1,551 | NONE | null | Hi,
I'm trying to use BERT for a token-level tagging problem such as NER in German.
This is what I've done so far for input preparation:
```
from pytorch_pretrained_bert.tokenization import BertTokenizer, WordpieceTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased", do_lower... | {
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https://api.github.com/repos/huggingface/transformers/issues/322 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/322/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/322/comments | https://api.github.com/repos/huggingface/transformers/issues/322/events | https://github.com/huggingface/transformers/issues/322 | 414,596,654 | MDU6SXNzdWU0MTQ1OTY2NTQ= | 322 | Single sentence corpus in run_lm_finetuning? | {
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"https://github.com/huggingface/pytorch-pretrained-BERT/issues/272\r\n\r\nI had the same issue and but apparently this cant be done in BERT",
"Yes, can't be done currently. Feel free to submit a PR to extend the `run_lm_finetuning` example @vebits!"
] | 1,551 | 1,551 | 1,551 | NONE | null | Hi,
I am trying to pre-train using `BertForPreTraning` in `run_lm_finetuning.py`. My target corpus is based on very many tweets and I am unsure how the model will tackle that since they are mostly only one sentence. Will it affect the IsNextSentence task?
Should my .txt input file consist of one tweet on each li... | {
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"Should work. Without more information I can't really help you."
] | 1,551 | 1,551 | 1,551 | NONE | null | i use my output dir as bert_model, but cannot find the model | {
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https://api.github.com/repos/huggingface/transformers/issues/320 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/320/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/320/comments | https://api.github.com/repos/huggingface/transformers/issues/320/events | https://github.com/huggingface/transformers/issues/320 | 414,497,924 | MDU6SXNzdWU0MTQ0OTc5MjQ= | 320 | what is the batch size we can use for SQUAD task? | {
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"solved"
] | 1,551 | 1,551 | 1,551 | NONE | null | I am running the squad example.
I have a Tesla M60 GPU which has about 8GB of memory. For bert-large-uncased model, I can only take batch size as 2, even after I used --fp16. Is it normal?
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"Hi @VarnithChordia \r\n\r\nWere you able to fix this issue? Because I run into a similar issue.\r\n\r\nI would appreciate any guidance on this issue. \r\n\r\nThank you.\r\n",
"same. bump. Latest master does not handle cache_dir or mode ",
"@PetreanuAndi You are correct that in the latest master this issue occu... | 1,551 | 1,594 | 1,551 | NONE | null | python3 run_classifier.py
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir $GLUE_DIR/MRPC/
--bert_model bert-base-uncased
--max_seq_length 128
--train_batch_size 32
--learning_rate 2e-5
--num_train_epochs 3.0
--output_dir /tmp/mrpc_outputunexpected
02/25/2019 15:50:51 - INFO ... | {
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"Hi,\r\n1/ it's the usual language modeling probabilities: each token probability given the previous tokens\r\n2/ thanks, fixed."
] | 1,550 | 1,551 | 1,551 | NONE | null | TransfoXLLMHeadModel gives an output of log probabilities of shape [batch_size, sequence_length, n_tokens]. What do these probabilities represent? For example, what distribution is output at the first sequence position? Is it the conditional distribution given the first word? If so, how can the probability of a complet... | {
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https://api.github.com/repos/huggingface/transformers/issues/317 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/317/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/317/comments | https://api.github.com/repos/huggingface/transformers/issues/317/events | https://github.com/huggingface/transformers/issues/317 | 413,719,230 | MDU6SXNzdWU0MTM3MTkyMzA= | 317 | anyone notice large difference of using fp16 ? | {
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"followers_url": "https://api.github.com/users/... | [] | closed | false | null | [] | [] | 1,550 | 1,550 | 1,550 | CONTRIBUTOR | null | I recently noticed that using fp16 dropped the performance of BERT on my own dataset but improved on another (it works fine on examples like MPRC). It's about 4% so unlikely to be random noise.
I'm trying to see the reason and noticed examples from apex:
https://github.com/NVIDIA/apex/tree/master/examples
actually... | {
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"Hi @joelgrus, you are right, the docstring were lagging a lot.\r\nAll the information is in the `README.py`, more specifically [these sections detailing the API of the GPT2 models](https://github.com/huggingface/pytorch-pretrained-BERT#14-gpt2model) but I forgot to update the docstrings. Do you want to have a look... | 1,550 | 1,550 | 1,550 | CONTRIBUTOR | null | fixes a few incorrect details in the gpt-2 documentation.
one remaining thing, all of the models return an extra `presents` variable that I'm not quite sure what it is, so there's a ... in the doc. if you tell me what to put there I can put it there, or you can do it yourself. | {
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https://api.github.com/repos/huggingface/transformers/issues/315 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/315/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/315/comments | https://api.github.com/repos/huggingface/transformers/issues/315/events | https://github.com/huggingface/transformers/issues/315 | 413,590,083 | MDU6SXNzdWU0MTM1OTAwODM= | 315 | run_classifier.py : TypeError: join() argument must be str or bytes, not 'PosixPath' | {
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"https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/file_utils.py#L30\r\n`PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',\r\n Path.home() / '.pytorch_pretrained_bert'))` --> \r\n`PYTORCH_PRETRAI... | 1,550 | 1,551 | 1,551 | CONTRIBUTOR | null | when trying the MRPC example :
python3.5 run_classifier.py \
--task_name MRPC \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--bert_model bert-base-uncased \
--max_seq_length 128 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--o... | {
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https://api.github.com/repos/huggingface/transformers/issues/314 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/314/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/314/comments | https://api.github.com/repos/huggingface/transformers/issues/314/events | https://github.com/huggingface/transformers/issues/314 | 413,272,916 | MDU6SXNzdWU0MTMyNzI5MTY= | 314 | Issue with apex import on MAC | {
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"Unfortunately, apex (and fp16 in general) only work on GPU. So you can't use it on MacOS :/"
] | 1,550 | 1,550 | 1,550 | NONE | null | Python 3.7
MacOS High Sierra 10.13.6
```
Traceback (most recent call last):
File "examples/classifier.py", line 1, in <module>
from pytorch_pretrained_bert.tokenization import BertTokenizer, WordpieceTokenizer
File "/Users/Bhoomit/work/robin/nlp/pytorch-pretrained-BERT/env/lib/python3.7/site-packages/py... | {
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"What tr_loss are you exactly printing here? Is it possible that you just print this one here? https://github.com/huggingface/pytorch-pretrained-BERT/blob/2152bfeae82439600dc5b5deab057a3c4331c62d/examples/run_lm_finetuning.py#L600 If yes, you should divide it by the number of training steps (nb_tr_steps) first to g... | 1,550 | 1,557 | 1,557 | NONE | null | When I run_lm_finetuning with the exemplary training corpus (small_wiki_sentence_corpus.txt), I printed the tr_loss every 20 steps. I found that the tr_loss increases very fast. I wonder what the reason is.
... | {
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https://api.github.com/repos/huggingface/transformers/issues/312 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/312/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/312/comments | https://api.github.com/repos/huggingface/transformers/issues/312/events | https://github.com/huggingface/transformers/issues/312 | 413,204,487 | MDU6SXNzdWU0MTMyMDQ0ODc= | 312 | Problems converting TF BioBERT model to PyTorch | {
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"I have solved my issue. All my code was correctly written. The error was an corrupted/improperly saved model.bin file.",
"I'm trying to convert BioBert to Pytorch also, so just wondering if you could share a bit more details on how you are doing the conversion. Thanks!",
"First, I downloaded the BioBERT TF che... | 1,550 | 1,553 | 1,551 | NONE | null | My goal is to convert and train on the [BioBERT pretrained checkpoints](https://github.com/naver/biobert-pretrained) in pytorch and train on the [SQuAD v2.0 Dataset](https://rajpurkar.github.io/SQuAD-explorer/).
I have (seemingly) successfully transfered the checkpoint using the `./pytorch_pretrained_bert/convert_... | {
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"Maybe this would break pre-trained weights loading? Interested to understand if that's the only reason?",
"Possibility, feel free to test the modification and submit a PR @spolu!",
"Hi guys,\r\nI also wondered whether anyone modified the gpt2 model to have nn.Linear instead of Conv1D layers (using the pre-trai... | 1,550 | 1,705 | 1,551 | NONE | null | Conv1D seems to be inherited from GPT but does not seem to serve any special purpose in GPT2 (BERT uses Linear).
Should GPT2's model be moved to using Linear (which is easier to grasp obvioulsy)? | {
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https://api.github.com/repos/huggingface/transformers/issues/310 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/310/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/310/comments | https://api.github.com/repos/huggingface/transformers/issues/310/events | https://github.com/huggingface/transformers/pull/310 | 412,821,213 | MDExOlB1bGxSZXF1ZXN0MjU0OTQyMDc3 | 310 | Few small nits in GPT-2's README code examples | {
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"They were just basic typos :) Thanks Stanislas"
] | 1,550 | 1,550 | 1,550 | NONE | null | (unless these were on purpose as a responsible disclosure mechanism :p) | {
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