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https://api.github.com/repos/huggingface/transformers/issues/709 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/709/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/709/comments | https://api.github.com/repos/huggingface/transformers/issues/709/events | https://github.com/huggingface/transformers/issues/709 | 459,154,612 | MDU6SXNzdWU0NTkxNTQ2MTI= | 709 | layer_norm_eps | {
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"Because some people wanted to configure this: https://github.com/huggingface/pytorch-pretrained-BERT/pull/585",
"So I have to add `config.layer_norm_eps = 1e-12` if I am taking the config from the link above ?",
"You don't need to, it's the default value when instantiating a `BertConfig` class.",
"When I pri... | 1,561 | 1,561 | 1,561 | NONE | null | In [modeling.py](https://github.com/huggingface/pytorch-pretrained-BERT/blob/c304593d8fa93f25febe1458c63497a846749c89/pytorch_pretrained_bert/modeling.py#L303) why `self.layer_norm_eps` is written even config don't have these parameter. Check [here](https://github.com/google-research/bert#pre-trained-models)
Or I am... | {
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"Hi Shubham,\r\nThis is a legacy from the original Tensorflow code (https://github.com/openai/finetune-transformer-lm/blob/master/train.py#L64-L69).",
"Thanks for the link to the original reference. "
] | 1,561 | 1,565 | 1,565 | NONE | null | Based on my understanding, [this](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_gpt2.py#L288) is the place where future attention masking for the causal model happens.
If this is the case
- Why is it called `bias`?
- Why is this in the core of `Attention` mo... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/707?src=pr&el=h1) Report\n> Merging [#707](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/707?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/c304593d8fa93f25febe1458c63497a8467... | 1,561 | 1,562 | 1,562 | NONE | null | model = BertForQuestionAnswering.from_pretrained(args.bert_model) is written twice.
I think the else part is redundant there | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/706?src=pr&el=h1) Report\n> Merging [#706](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/706?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/c304593d8fa93f25febe1458c63497a8467... | 1,561 | 1,561 | 1,561 | NONE | null | redundant else part, model = BertForQuestionAnswering.from_pretrained(args.bert_model) is already written in a different line | {
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"FYI @roholazandie we are currently working on XLNet with pytorch over here\r\nhttps://github.com/pingpong-ai/XLNet-pytorch/tree/dev/poc",
"I'll add it here also. I was working on a coming release this week anyway.\r\nIt's a mix of BERT/Transformer-XL and something I was also playing with (Two-Stream Self-Attenti... | 1,561 | 1,567 | 1,567 | NONE | null | Due to the new work of [XLNet](arxiv.org/abs/1906.08237 ) and the implementation of it in tensorflow maybe we need to add this one to the current repository.
https://github.com/zihangdai/xlnet | {
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"Yeah I've removed the memory-heavy tests",
"Thanks!"
] | 1,560 | 1,561 | 1,561 | NONE | null | I submitted a PR after modifying convert_gpt2_checkpoint_to_pytorch.py, and my build_py2 test passed, but I received a very vague error from build_py3 (as written in the title of this issue) that caused my build to fail. Does anyone have any ideas as to where the issue could be?
Edit: Attached image below
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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",
"Thanks I don't think we'll add this since converted models are already provided."
] | 1,560 | 1,567 | 1,567 | NONE | null | Add an argument --model_size to convert_gpt2_checkpoint_to_pytorch.py that lets the user specify whether they want to convert a checkpoint from the 117M model or from the 345M model. | {
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"@thomwolf \r\nHi, it seems there is something wrong with the training code in this repo.\r\nI used Google's official BERT training code and I could get decent results even with a small batch size: \r\n`{'EM': 73.80717341230668, 'F1': 77.11048422305339, 'AvNA': 80.78315235274762}`\r\n\r\nHere's the settings I used ... | 1,560 | 1,567 | 1,567 | NONE | null | Hi,
I used the default settings and run_squad.py script (with the exception of a batch size of 4 since my GPU has low memory) to train for 3 epochs. Turns out I got an EM & F1 score of 43% and 48% respectively. AvNA looks decent at 65%. Is this due to a small number of epochs or the small batch size? Note that train... | {
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"Seems like this is now possible with last week's [merged PR](https://github.com/huggingface/pytorch-pretrained-BERT/pull/597), but I'm curious to see what the core devs say about this as well (btw, keep up the great work!)",
"I have the same question :)\r\nI have tried the codes for BERT finetuning which is in l... | 1,560 | 1,571 | 1,571 | NONE | null | I'm looking for finetuning GPT-2 parameters for a custom piece of text, so that the weights are tuned for this piece of text, building from the initial model.
The script here does it for the original tensorflow implementation: https://github.com/nshepperd/gpt-2 , could you please give me suggestions on how to do th... | {
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"Hey, I tried doing the same and was successful(in running the script, at least).\r\nWhat do you specify as your --gpt2_checkpoint_path as? (hope you have copied your finetuned checkpoints to preloaded 117M)?\r\n\r\nUpdate: Currently the output just stores a `config.json` and `pytorch_model.bin`. Suprisingly I don... | 1,560 | 1,598 | 1,560 | NONE | null | I finetuned a GPT-2 model using TensorFlow (using https://github.com/nshepperd/gpt-2), and I tried to run the TF to PyTorch conversion script, but I got this error:
`Traceback (most recent call last):
File "convert_gpt2_checkpoint_to_pytorch.py", line 72, in <module>
args.pytorch_dump_folder_path)
File "c... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/697?src=pr&el=h1) Report\n> Merging [#697](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/697?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/3763f8944dc3fef8afb0c525a2ced8a0488... | 1,560 | 1,566 | 1,561 | MEMBER | null | This PR check that the examples are working well (fix learning rate bug in distributed settings)
Also:
- prepare 2 fine-tuned models on SQuAD (BERT Whole Word Masking) so people can also use fine-tuned models (nice performances: "exact_match": 86.9, "f1": 93.2, better than the original Google AI values)
- add a bert... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/696?src=pr&el=h1) Report\n> Merging [#696](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/696?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/a6f2511811f08c24184f8162f226f252cb6... | 1,560 | 1,566 | 1,560 | MEMBER | null | Split config and weights files for Bert also (was only done for GPT/GPT-2/Transformer-XL.
This will:
- make the Bert model instantiation faster (no need to untar an archive)
- simplify the distributed training (no need to have one archive for each process). | {
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"As with all the other issues about Bert being not deterministic (#403, #679, #432, #475, #265, #278), it's likely because you didn't set the model in eval mode to desactivate the DropOut modules: `model.eval()`.\r\n\r\nI will try to emphasize this more in the examples of the readme because this issue keeps being r... | 1,560 | 1,589 | 1,566 | NONE | null | BERT output is not deterministic.
I expect the output values are deterministic when I put a same input, but my bert model the values are changing. Sounds awkwardly, the same value is returned twice, once. That is, once another value comes out, the same value comes out and it repeats.
How I can make the output determi... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/694?src=pr&el=h1) Report\n> Merging [#694](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/694?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/80684f6f86c13a89fc1e4feac248ef96b01... | 1,560 | 1,566 | 1,560 | MEMBER | null | Preparing release 0.6.3
- adding Bert whole word masking models
- BERTology:
- add head masking, head pruning and optional output of multi-head attention output gradients
- output all layers hidden states in GPT/GPT-2
- PyTorch Hub: adding and checking all the models
- various clean-ups and doc/test improveme... | {
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"[You can train a tensorflow model using google colab for free](https://github.com/google-research/bert#using-bert-in-colab). After training it, you can [convert your tf model to pytorch](https://github.com/huggingface/pytorch-pretrained-BERT#command-line-interface). ",
"Or use 300 usd credit for google cloud, th... | 1,560 | 1,560 | 1,560 | NONE | null | Sorry I open this issue, is not issue of this repository.
I very appreciate what the authors created this repository, help us to more understand how BERT works and implement on several tasks.
So I have a problem with training because I have not GPU to train language modelling, I have Indonesian dataset (about 2G... | {
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"Ah, thank you very much for this! I'll read over the paper and include it as a reference soon.",
"Finally read it once I had some free time at the weekend and added PR #715. Thank you!",
"Thank you @Rocketknight1 !",
"This issue has been automatically marked as stale because it has not had recent activity. I... | 1,560 | 1,567 | 1,567 | NONE | null | From https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning#introduction
> As such, it's hard to predict what effect this step will have on final model performance, but it's reasonable to conjecture that this approach can improve the final classification performance, especially whe... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/691?src=pr&el=h1) Report\n> Merging [#691](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/691?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/b3f9e9451b3f999118f2299229bb13f2f69... | 1,560 | 1,560 | 1,560 | CONTRIBUTOR | null | {
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"Perfect, thanks @shashwath94!"
] | 1,560 | 1,560 | 1,560 | CONTRIBUTOR | null | Fixes the return value of `ProjectedAdaptiveLogSoftmax` layer for Transformer XL when it is a standard softmax without cutoffs (n_clusters=0). | {
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"Your server probably can't reach AWS to download the models.\r\nI need to make these error messages more clear, they currently gather several failure cases.\r\nWill do that in the coming release of next week.",
"What should I do if I have downloaded the package manually?",
"If you download them manually, you w... | 1,560 | 1,568 | 1,568 | CONTRIBUTOR | null | Hi,
I would like to fine tune BERT using my own data.
```
readonly model=bert-base-multilingual-cased
export PYTORCH_PRETRAINED_BERT_CACHE=.
#[https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning](BERT Model Finetuning using Masked Language Modeling objective)
... | {
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"This looks great @Timoeller – do you have an estimate for the compute power you used to train your model?\r\n\r\nUPDATE. Ok the answer is in the blogpost: https://deepset.ai/german-bert\r\n> We trained using Google's Tensorflow code on a single cloud TPU v2 with standard settings. \r\n> We trained 840k steps with ... | 1,560 | 1,567 | 1,560 | CONTRIBUTOR | null | We have been training a German BERT model from scratch on some 12 GB of clean text. It outperforms the multilingual BERT (cased + uncased) in 4 out of 5 German NLP tasks.
Furthermore we evalua... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/687?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@cad88e1`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage i... | 1,560 | 1,566 | 1,560 | MEMBER | null | - Fix GPT-2 test
- Update the documentation | {
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"You would need an insertion-based transformer model like Google's recent KERMIT (http://arxiv.org/abs/1906.01604). But unfortunately, we currently don't have this model in the library.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further act... | 1,560 | 1,565 | 1,565 | NONE | null | Hi, I'd like to know if I can use GPT2 to decorate a simple sentence such as "Peter was sad because his sister had eaten all his candy." to get sth like "Tuesday morning the ten years old Peter was sitting in his room and was sad because his mean sister Clara had eaten all his tasty candy with her friends."
Using BE... | {
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"Hi, I'm not convinced we need this additional option, see my [comment](https://github.com/huggingface/pytorch-pretrained-BERT/issues/676#issuecomment-502252962) in the associated issue thread.",
"As mentioned in, https://github.com/huggingface/pytorch-pretrained-BERT/issues/676#issuecomment-506134506, I recognis... | 1,560 | 1,561 | 1,561 | CONTRIBUTOR | null | ## Summary
In this PR, I changed some documentation, and added `from_tf_ckpt()` method to `BertPreTrainedModel`.
This method allows users to directly load TensorFlow checkpoints (e.g. `model.ckpt-XXXX` files) for a task specific Bert model like `BertForTokenClassification` or `BertForSequenceClassification`.
**F... | {
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"It should be fine. A bit of randomness in the pre-processing of the inputs is never bad when training a deep learning model.",
"> It should be fine. A bit of randomness in the pre-processing of the inputs is never bad when training a deep learning model.\r\n\r\nI found the same problem that the implementation is... | 1,560 | 1,568 | 1,568 | NONE | null | In the BERT paper, they randomly mask 15% words for pretraining, and that's exactly what they do in the TF version.
https://github.com/google-research/bert/blob/0fce551b55caabcfba52c61e18f34b541aef186a/create_pretraining_data.py#L342
However, the implementation here is a little bit different, instead of randomly... | {
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https://api.github.com/repos/huggingface/transformers/issues/682 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/682/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/682/comments | https://api.github.com/repos/huggingface/transformers/issues/682/events | https://github.com/huggingface/transformers/issues/682 | 455,859,694 | MDU6SXNzdWU0NTU4NTk2OTQ= | 682 | Can't find gpt2 vocab file. | {
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"I got the solution.\r\n"
] | 1,560 | 1,560 | 1,560 | NONE | null | When I run this
```
tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path='gpt2',cache_dir=None)
```
I am getting this
```
Model name 'gpt2' was not found in model name list (gpt2). We assumed 'gpt2' was a path or url but couldn't find files https://s3.amazonaws.com/models.huggingface.co/bert/... | {
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"What do yuu get when you multiply the probabilities for words in these 2 places. Probability for b times probability for \"##oa\".\r\n['[CLS]', 'This', 'is', 'a', 'picture', 'of', 'a', '[MASK]', '[MASK]', '.']\r\n['[CLS]', 'This', 'is', 'a', 'picture', 'of', 'a', '[MASK]', '##oa', '.']\r\n\r\nBut there is also who... | 1,560 | 1,565 | 1,565 | NONE | null | Hi,
I have this text:
```
[CLS] This is a picture of a boa.
```
And would like to have the predictions of the `BertForMaskedLM` model for the word `boa`, without masking this word.
However, when I tokenize the text to give it to the network, I get:
```
['[CLS]', 'This', 'is', 'a', 'picture', 'of', 'a'... | {
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"Yes, 512 tokens for Bert.",
"Thank you :) ",
"Is there a way to bypass this limit? To increase the number of words?"
] | 1,560 | 1,560 | 1,560 | NONE | null | Hi,
I often get this error:
```
File "/miniconda3/envs/brightwater/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 268, in forward
position_embeddings = self.position_embeddings(position_ids)
File "/miniconda3/envs/brightwater/lib/python3.6/site-packages/torch/nn/modules/module.py"... | {
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"They won't be able to help you if you don't provide a code for reproducing your issue, as this is not an expected behaviour.",
"Thanks a lot for reminding. The issue is renewed with the code.",
"That's true! I can reproduce it also on my computer... Really weird!",
"You should use `model.eval()` to desacti... | 1,560 | 1,560 | 1,560 | NONE | null | I tried to get word representations using the full-retrained bert model for several times, whereas the outputs of model are different for a same word in each time. Did I neglect something? Not knowing the reason and asking for help sincerely.
The code is:
`from pytorch_pretrained_bert import BertTokenizer, BertMode... | {
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"Yes! We don't see that when we use the pre-trained model because the number of clusters is greater than zero anyway. Will fix.",
"Thank you. I created a PR since it was a small bug. #690 "
] | 1,560 | 1,560 | 1,560 | CONTRIBUTOR | null | In <a href="https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling_transfo_xl_utilities.py#L120">this line</a>, shouldn't the output be assigned to `out` when `n_clusters` is 0? Otherwise we run into `UnboundLocalError: local variable 'out' referenced before assignment` | {
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"Is this what you want?\r\n\r\n```python\r\nPRETRAINED_MODEL_ARCHIVE_MAP = {\r\n 'bert-base-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz\",\r\n 'bert-large-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz\",\r\n 'bert-base... | 1,560 | 1,560 | 1,560 | NONE | null | Hi,
Is there a command to download a model (e.g. BertForMaskedLM) without having to execute a Python script?
For example, in Spacy, we can do `python -m spacy download en`. | {
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"Hello everyone,\r\n\r\nI have temporarily come up with a workaround for this. Not sure if it's the best solution but it works. What I did was I essentially merged what `load_tf_weights_in_bert()` and what part of `BertPreTrainedModel.from_pretrained()` was doing. `BertPreTrainedModel` is the parent class of `BertF... | 1,560 | 1,561 | 1,561 | CONTRIBUTOR | null | Hello Everyone,
I've been stuck with trying to load TensorFlow checkpoints to be used by `pytorch-pretrained-bert` as `BertForTokenClassification`.
**pytorch-pretrained-BERT Version:** Installed from latest master branch.
**What works:**
```python
config = BertConfig.from_json_file(CONFIG_FILE)
model = Bert... | {
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"Thanks @meetshah1995 "
] | 1,560 | 1,560 | 1,560 | CONTRIBUTOR | null | Hotfix for #461 | {
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"I've tried a bit to play with these training schemes on a deep transformer for our [tutorial on Transfer Learning in Natural Language Processing](https://naacl2019.org/program/tutorials/#t4-transfer-learning-in-natural-language-processing) held at NAACL last week but I couldn't get gradual unfreezing and discrimin... | 1,560 | 1,566 | 1,566 | NONE | null | Three of the tips for fine-tuning proposed in ULMFIT are slanted triangular learning rates, gradual unfreezing, and discriminative fine-tuning.
I understand that BERT's default learning rate scheduler does something similar to STLR, but I was wondering if gradual unfreezing and discriminative fine-tuning are conside... | {
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"To your first question, the inputs will be almost identical, but the token_type_ids argument will be unused, as this is the vector that indicates the split between the two 'sentences' for the NextSentence objective. I'm not familiar with that part of the code - you might be able to just pass `None` for that argume... | 1,560 | 1,642 | 1,566 | NONE | null | Hello,
I'm thinking about fine-tuning a BERT model using only the Masked LM pre-training objective, and I'd appreciate a bit of guidance. The most straightforward way is probably to modify the simple_lm_finetuning.py script to only do LM fine-tuning. Besides importing BertForMaskedLM instead of BertForPretraining (w... | {
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"Nice indeed, thanks @oliverguhr!"
] | 1,560 | 1,560 | 1,560 | CONTRIBUTOR | null | If you want to use your fine-tuned model to train a classifier you will need the configuration file and the vocabulary file. This PR adds them to both pre-training scripts. | {
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:param step: which of t_total steps we're on
def get_lr(self, step, nowarn=False):
"""
:param step: which of t_total steps we're on
:param nowarn: set to True to suppress warning regarding training beyond specified '... | {
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"Because we don't use BertAdam in fp16 mode but the optimizer of NVIDIA's apex library.",
"OK thank you!"
] | 1,560 | 1,560 | 1,560 | NONE | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/ee0308f79ded65dac82c53dfb03e9ff7f06aeee4/examples/run_classifier.py#L860
BertAdam() can update learning rate by itself.
Why update learning rate manually here? | {
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"Perhaps it's the problem of tokenizer...After stripping the lengths of two sequences changed, so `orig_text` is returned...",
"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"
] | 1,560 | 1,565 | 1,565 | NONE | null | Hi,
I set `max_answer_length` to `30`, but I still got really long answers, so I print the `tok_text`, `orig_text` and `final_text` in function `write_predictions`.
```
tok_text: 权 健 公 司 可 能 涉 及 的 刑 事 罪 名 是 否 仅 仅 是 [UNK] 虚 假 广 告 罪
orig_text: 根据相关法律，权健公司可能涉及的刑事罪名是否仅仅是“虚假广告罪”“组织、领导传销活动罪”两个罪名，应该说仍有不少需要进一步深入调查的空间
... | {
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"Nice, thanks @jeonsworld "
] | 1,560 | 1,560 | 1,560 | CONTRIBUTOR | null | apply Whole Word Masking technique.
referred to [link](https://github.com/google-research/bert/blob/master/create_pretraining_data.py) | {
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"Yes, probably overflow. Try a smaller batch size?",
"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"
] | 1,560 | 1,566 | 1,566 | NONE | null | using BertModel.from_pretrained( path of bert-large-uncased) caused error
RuntimeError: $ Torch: invalid memory size -- maybe an overflow? at ..\aten\src\TH\THGeneral.cpp:188
But using BertModel.from_pretrained( path of bert-case-uncased) can work
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"Have you tried the provided GPT-2 generation example? It's here: https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.py",
"> Have you tried the provided GPT-2 generation example? It's here: https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.py\r\... | 1,559 | 1,605 | 1,568 | NONE | null | I was trying to use the pretrained GPT2LMHeadModel for generating texts by feeding some initial English words. But it is always generating repetitive texts.
Input: All
Output: All All the same, the same, the same, the same, the same, the same, the same, the same, the same, the same, the same, the same,
Here is... | {
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"Take a look at the `attention` branch @g-karthik:\r\n\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/blob/attention/pytorch_pretrained_bert/modeling_gpt2.py#L42-L45",
"Thanks @julien-c, I had not looked at the file in the `attention` branch!",
"What is the recommended hardware setup for fine-tuning ... | 1,559 | 1,560 | 1,560 | NONE | null | I presume the below model is GPT-2 small.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/ee0308f79ded65dac82c53dfb03e9ff7f06aeee4/pytorch_pretrained_bert/modeling_gpt2.py#L42
When do you plan on supporting the medium (already released by OpenAI) and large versions (not released by OpenAI) of GPT-2?
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"No padding implemented in GPT-2, you have to add implement your-self if you want e.g. by adding a special token but note that:\r\n- GPT-2 doesn't like left side padding (doesn't mix well with a causal transformer having absolute positions)\r\n- right-side padding is often not necessary (the causal mask means that ... | 1,559 | 1,666 | 1,567 | NONE | null | How do I add padding in GTP2?
I get something like this when I add zero in front to pad the sequences, but then I found out that 0 is actually not "[PAD]" but "!".
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1639, 481]
The zeros change the result quite a lot, not that it totally ruins it, but it makes the results less precise a... | {
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"Update: specify device works for at least 1 GPU\r\n\r\nexport CUDA_VISIBLE_DEVICES=0\r\npython run_classifier.py \\\r\n\r\n\r\nmore than 1 GPU still not working:\r\n\r\nexport CUDA_VISIBLE_DEVICES=0,1\r\npython run_classifier.py \\\r\n",
"@AndreasFdev Your distributed training setting is False.",
"Problem:\r\n... | 1,559 | 1,560 | 1,560 | NONE | null | Hi there!
I am stuck since days.
ubuntu 19.04 (tried 18.04 also)
NVIDIA-SMI 418.74 Driver Version: 418.74
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130
>>> impor... | {
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"Follow the instructions in the readme?",
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"@okgrammer Larger batch size often means lower accuracy but faster epochs. You can try it by doing several runs of varying batch size ... | 1,559 | 1,592 | 1,565 | NONE | null | In the original paper, BERT model is fine-tuned on downstream NLP tasks, where the number of instances for each task is in the order of thousands to hundreds of thousands. In my case, I have about 5 million samples. I'm curious whether there are recommended batch size and epochs for such training size? I'm fine-tuning ... | {
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"It's not yet but thanks for the pointer, we can probably add it fairly easily. I'll have a look.",
"+10000 This would be very helpful!",
"Hi, \r\n\r\nI converted the cased and uncased whole-word-masking models using the command line tool. If you're interested in adding these to the repository, I've uploaded th... | 1,559 | 1,571 | 1,566 | NONE | null | Recently Google updated their TF implementation (`https://github.com/google-research/bert`) with Whole Word Masking Models that masks whole random word instead of just random wordpieces, which results in a performance gain.
Just wondering if this will be implemented here?
Thanks. | {
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"I took example code from https://github.com/huggingface/pytorch-pretrained-BERT#fine-tuning-bert-large-on-gpus (which has additional option `--loss_scale 128`). Still getting very low test scores:\r\n```\r\n$ python evaluate-v1.1.py dev-v1.1.json ../output/debug_squad_fp16/predictions.json \r\n{\"exact_match\": 0.... | 1,559 | 1,565 | 1,565 | NONE | null | I'm replicating SQuAD 1.1 https://github.com/huggingface/pytorch-pretrained-BERT/tree/v0.6.2#squad on latest release `v0.6.2`.
My setup:
* GeForce RTX 2080 Ti
* Driver Version: 418.43
* CUDA Version: 10.1
* Linux Ubuntu 18.10
* pytorch 1.1.0 (installed via conda: py3.7_cuda10.0.130_cudnn7.5.1_0)
* lates... | {
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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",
"Did anyone conduct different learning rate in different layers when fine-tuning BERT?\r\n\r\nThanks. ",
"[This paper](https://arxiv.o... | 1,559 | 1,610 | 1,564 | NONE | null | HI,
I am trying to use different learning rates for the bert and classifier. I am assuming that I can just say model.parameters and classifier.parameters like below.
`optimizer_grouped_parameters = [
{'params': model.bert.parameters(), 'lr': 0.001},
{'params': model.classifier.parameters(), 'lr': 0.01}
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] | 1,559 | 1,565 | 1,565 | NONE | null | Is there any way of using the openai-gpt module for multilingual language modelling? | {
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Fix some typos in docs.
@thomwolf could you have a look on the doc changes in `modeling_transfo_xl.py` more specifically?
Otherwise, I think it should be good. | {
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i am actually new to this field , I am trying to use gpt2 for sequence classification task in which i am adding "<|endoftext|>" after each sequence and using last hidden state f... | {
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https://api.github.com/repos/huggingface/transformers/issues/653 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/653/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/653/comments | https://api.github.com/repos/huggingface/transformers/issues/653/events | https://github.com/huggingface/transformers/issues/653 | 450,697,161 | MDU6SXNzdWU0NTA2OTcxNjE= | 653 | Different Results from version 0.4.0 to version 0.5.0 | {
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"Hi, no we didn't change the weights. Can you share a sample on which the results are different?",
"Hi @thomwolf , thanks for your quick reply. I found even version 0.4.0 is different to version 0.2.0 and 0.3.0. I trained the model on v0.4.0, and then I tried to load the model using v0.2.0, here is the mismatch o... | 1,559 | 1,561 | 1,559 | NONE | null | Hi, I found the results after training is different from version 0.4.0 to version 0.5.0. I have fixed all initialization to reproduce the results. And I also test version 0.2.0 and 0.3.0, the results are the same to version 0.4.0, but from version 0.5.0 +, the results is different. I am wondering that have you trained ... | {
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"Rerun with environmental variable `CUDA_LAUNCH_BLOCKING=1` and see what line it crashed on.\r\n\r\nThis is almost always an out-of-bounds error on some embeddings lookup. Usually positional embeddings, but it could be word embeddings or segment embeddings.",
"HI @stephenroller , I do set environmental variable `... | 1,559 | 1,706 | 1,564 | NONE | null | I got this error when using simple_lm_finetuning.py to continue to train a bert model. Could anyone can help? Thanks a lot.
Here is the cuda and python trace. I confirm that my input max_length don't over **max_position_embeddings**
```
/pytorch/aten/src/THC/THCTensorIndex.cu:362: void indexSelectLargeIndex(Tensor... | {
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"Amazing, thanks a lot @VictorSanh!"
] | 1,559 | 1,566 | 1,559 | MEMBER | null | I'll add GPT2 for torchhub later. | {
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https://api.github.com/repos/huggingface/transformers/issues/649 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/649/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/649/comments | https://api.github.com/repos/huggingface/transformers/issues/649/events | https://github.com/huggingface/transformers/issues/649 | 450,298,705 | MDU6SXNzdWU0NTAyOTg3MDU= | 649 | fine-tuning BERT, next sentence prediction loss is not decreasing | {
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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",
"same not working for me. NSP loss is not converging even though MLM loss is converging."
] | 1,559 | 1,595 | 1,565 | NONE | null | I run simple_lm_finetuning and monitor the loss change of next_sentence_loss and masked_lm_loss. The loss of masked token can converge but the next_sentence_loss is not decreasing.
For now I tried: tuning learning rate, change to optimizers from pytorch.optim, I checked the input_ids and the input looks good... I al... | {
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"@thomwolf Thanks!",
"line 604",
"> line 604\r\n\r\nThanks so much. my mistake.\r\n\r\nDo you know why there is no dropout for the dev and eval?\r\n",
"first of all, no one uses dropout at evaluation stage as it's a regularizer. The difference of implementation is due to the fact that a dropout layer in pyto... | 1,559 | 1,562 | 1,562 | NONE | null | I do not see any dropout layer after `get_pooled_output()` in the tf version referred to [here](https://github.com/google-research/bert/blob/master/run_classifier.py#L590). Why do you add a dropout layer in your implemention? | {
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https://api.github.com/repos/huggingface/transformers/issues/647 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/647/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/647/comments | https://api.github.com/repos/huggingface/transformers/issues/647/events | https://github.com/huggingface/transformers/issues/647 | 450,256,737 | MDU6SXNzdWU0NTAyNTY3Mzc= | 647 | No softmax activation in BertForTokenClassification | {
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"It's because `nn.CrossEntropyLoss` already has a Softmax integrated in the module:\r\nhttps://pytorch.org/docs/stable/nn.html?highlight=crossentropy#torch.nn.CrossEntropyLoss",
"I see it now. Thanks!"
] | 1,559 | 1,559 | 1,559 | NONE | null | The BertForTokenClassification class has the `classifier` member, which is a linear layer.
In the `forward` function, it treats it's out as probabilities (Cross Entropy for loss) but there's no softmax. Is there a reason for that? | {
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"Thanks!"
] | 1,559 | 1,560 | 1,560 | CONTRIBUTOR | null | Link was not working. Fixed. | {
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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"
] | 1,559 | 1,564 | 1,564 | NONE | null | when the initial values of `BertAdam`'s `params` have `requires_grad=False` Parameter, it will just continue the loop in `step()` function line 251, after step() function, when I want to use `get_lr()` to get the current learning rate, the state of this Parameter is a empty dict, so the function just return `[0]` in li... | {
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"Python version is 3.6, the cuda version is 10.1.105, cudnn version is 7.51, and pytorch version is 1.0.1, platform is ubuntu 14.04. And GPU is Titan GTX 1080Ti with 11g memory.\r\nThanks all of you!",
"I am running into almost exactly the same issue. python3.6 cuda 10.0 ubuntu 18.04 and a 1080ti as well. Not su... | 1,559 | 1,573 | 1,565 | NONE | null | I implemented my model referring to the implementation of the examples, when my model running several batches, the error shown in the title occurs.
The whole trace is as followed:
Traceback (most recent call last):
File "train.py", line 549, in <module>
train()
File "/media/***/***/***/***/***.py", line 12... | {
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"I got the same problem. Did you solve the problem?",
"> I got the same problem. Did you solve the problem?\r\n\r\nYes. Actually the project folder of this implementation does not contain the `pytorch_model.bin` file. For loading the actual pretrained model, you have to use `BertModel.from_pretrained('bert-base-u... | 1,559 | 1,560 | 1,560 | NONE | null | I was just trying to get familiar with the pytorch implementation of BERT. I tried with the examles mentioned in the README file. The statement : **tokenizer = BertTokenizer.from_pretrained(BERT_PRETRAINED_PATH,do_lower_case=True)** works perfectly but when I try the same with **model = BertForMaskedLM.from_pretrained(... | {
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"Oh yes you are right, we removed that, I'll update the readme",
"Thank you for your reply!\r\nBecause Adam's averages (i.e., `next_m` and `next_v`) are large, it is very helpful to support performing optimization step on CPU to reduce GPU memory.\r\nTherefore, I would like to know how you implemented this.\r\nDi... | 1,559 | 1,568 | 1,568 | CONTRIBUTOR | null | In README.md, it is explained that the optimization step was performed on CPU to train a SQuAD model.
> perform the optimization step on CPU to store Adam's averages in RAM.
https://github.com/huggingface/pytorch-pretrained-BERT#fine-tuning-bert-large-on-gpus
Is it still supported in `run_squad.py`?
If I und... | {
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I run the pretrained model BertForPreTraining and test it on my own text data. Because BERT has knowledge about language so I expect it to be able to predict the masked tokens with a reasonable accuracy,... | {
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"Great, thanks!"
] | 1,558 | 1,560 | 1,560 | CONTRIBUTOR | null | **Affected function**: fine tune example file
**Update summary**:
- Fix issue of bert-base-multilingual not found by fixing uncased version name in argument dict
- Add support for cased version by adding the right name into argument dict | {
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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"
] | 1,558 | 1,564 | 1,564 | NONE | null | Hello, I have a question. In BertLayer part of model we see, that in BertAttention module we do attention (nonlinear action) and selfOutput (linear transformation as it is dense + BN). Then we do BertIntermediate, starting with linear transformation(which means, we have dense, BN, dense transformations going one by one... | {
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"Maybe you can find the solution at #537 ",
"> Maybe you can find the solution at #537\r\n\r\nThank for your support! It works for me!"
] | 1,558 | 1,559 | 1,559 | CONTRIBUTOR | null | I tried to use GPT-2 to encode with `text = "This story gets more ridiculous by the hour! And, I love that people are sending these guys dildos in the mail now. But… if they really think there's a happy ending in this for any of them, I think they're even more deluded than all of the jokes about them assume."` but it e... | {
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"I also can't get the same result on my squad dataset",
"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"
] | 1,558 | 1,564 | 1,564 | NONE | null | I use squad1.1 data from https://www.kaggle.com/stanfordu/stanford-question-answering-dataset
I ran convert_tf_checkpoint_to_pytorch.py (using google's uncased_L-12_H-768_A-12 model) and run_squad.py. result was
```
{"exact_match": 73.30179754020814, "f1": 82.10116863001393}
```
Alse I ran run_squad_hvd.py fro... | {
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"The pretrained model is trained on the WebText dataset. It's a collection of documents from outgoing Reddit links that have above 3 karma (ensures quality).",
"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 ... | 1,558 | 1,566 | 1,566 | CONTRIBUTOR | null | What is the training dataset for the pre-trained GPT-2 Model? | {
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"Hi, we won't be able to add these feature as they would render the PyTorch model not compatible with the pretrained model open-sourced by OpenAI.",
"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 contri... | 1,558 | 1,565 | 1,565 | NONE | null | Hi,
Could the team add support for floating data types for the position embedding? Currently, it only allows torch.LongTensor between 0 - config.n_positions - 1 and must be of the same shape as the input. I see this a restriction as one may use floating values between e.g. 0-2 to represent the sequence.
What woul... | {
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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"
] | 1,558 | 1,564 | 1,564 | NONE | null | I'm using this repo to do Chinese NER on MSRA dataset, when I use the pretrained model `bert-base-chinese ` , the result is very good, it can reach 0.93+ f1 on test set in first epoch. But when I used `convert_tf_checkpoint_to_pytorch` to convert the original bert released checkpoint `chinese_L-12_H-768_A-12` to pytorc... | {
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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",
"@maeotaku were you ever able to figure this out? I'd be curious to see what numbers you were seeing when running in caffe2.\r\n\r\nIf y... | 1,558 | 1,570 | 1,564 | NONE | null | So really not sure if i should post this here but im having this problem with the pretrained bert for seq classification in particular when i try to consume the ONNX version of the model with Caffe2, I get this output:
File "/usr/local/lib/python3.6/dist-packages/caffe2/python/onnx/workspace.py", line 63, in f
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"I ran into the same error and fixed it by using this instead:\r\n\r\n[Pull Request](https://github.com/huggingface/pytorch-pretrained-BERT/pull/604)\r\n\r\nHaven't tried my full dataset yet but on a slice, it worked well!\r\n\r\nEdit: On the full dataset I still get the error.\r\n\r\nEdit2: \r\nChange the train da... | 1,558 | 1,567 | 1,567 | NONE | null | when I use my dataset， forward() missing 1 required positional argument: 'input_ids' ,but I can get "input_ids, input_mask, segment_ids, label_ids" in batch. | {
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https://api.github.com/repos/huggingface/transformers/issues/631 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/631/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/631/comments | https://api.github.com/repos/huggingface/transformers/issues/631/events | https://github.com/huggingface/transformers/issues/631 | 447,079,535 | MDU6SXNzdWU0NDcwNzk1MzU= | 631 | from_pretrained | {
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"Did you try to load the model following the best-practices indicated here: https://github.com/huggingface/pytorch-pretrained-BERT#serialization-best-practices",
"All but load the tokenizer from the vocab file. Think that would make a large difference?",
"This issue has been automatically marked as stale becaus... | 1,558 | 1,564 | 1,564 | NONE | null | I have a question regarding the from_pretrained method since I experienced a bit unexpected behaviour. It's regarding how to save a BERT classifier model as a whole.
I am experimenting with a classifier on top of BERT on the stack overflow question / tags dataset which contains 20 classes of 40.000 text samples. I t... | {
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https://api.github.com/repos/huggingface/transformers/issues/630 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/630/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/630/comments | https://api.github.com/repos/huggingface/transformers/issues/630/events | https://github.com/huggingface/transformers/pull/630 | 447,029,680 | MDExOlB1bGxSZXF1ZXN0MjgxMTA4NTIx | 630 | Update run_squad.py | {
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https://api.github.com/repos/huggingface/transformers/issues/629 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/629/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/629/comments | https://api.github.com/repos/huggingface/transformers/issues/629/events | https://github.com/huggingface/transformers/issues/629 | 446,952,695 | MDU6SXNzdWU0NDY5NTI2OTU= | 629 | Is loss.mean() needed? | {
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"This line is used when people use multi-gpu in a single python process (parallel instead of distributed). This is not the recommended setting (distributed is usually faster).\r\n\r\nI wrote a blog post on this (parallel/distributed and the like) a few months ago: https://medium.com/huggingface/training-larger-batc... | 1,558 | 1,564 | 1,564 | CONTRIBUTOR | null | In `run_classifier.py`, there is a:
https://github.com/huggingface/pytorch-pretrained-BERT/blob/3fc63f126ddf883ba9659f13ec046c3639db7b7e/examples/run_classifier.py#L841-L842
However, a couple of lines higher, the logits are already flattened
https://github.com/huggingface/pytorch-pretrained-BERT/blob/3fc63f126ddf8... | {
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https://api.github.com/repos/huggingface/transformers/issues/628 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/628/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/628/comments | https://api.github.com/repos/huggingface/transformers/issues/628/events | https://github.com/huggingface/transformers/issues/628 | 446,726,036 | MDU6SXNzdWU0NDY3MjYwMzY= | 628 | IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1) | {
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"Oops... I forgot to \"batch\" the input...\r\n\r\nHere is a working sample:\r\n\r\n```\r\nfrom pytorch_pretrained_bert.modeling import BertModel\r\nfrom pytorch_pretrained_bert.tokenization import BertTokenizer\r\nimport torch\r\n\r\nembed = BertModel.from_pretrained('bert-base-uncased')\r\ntokenizer = BertTokeniz... | 1,558 | 1,573 | 1,558 | CONTRIBUTOR | null | A simple call to BertModel does not work well here.
Here is a minimal code example:
```
from pytorch_pretrained_bert.modeling import BertModel
from pytorch_pretrained_bert.tokenization import BertTokenizer
import torch
embed = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pre... | {
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https://api.github.com/repos/huggingface/transformers/issues/627 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/627/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/627/comments | https://api.github.com/repos/huggingface/transformers/issues/627/events | https://github.com/huggingface/transformers/issues/627 | 446,551,658 | MDU6SXNzdWU0NDY1NTE2NTg= | 627 | BERT QnA is not matching correct answer when document is in QnA format | {
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"have you tried using your own data to train the model rather than using the squad1.1 or squad 2.0 data?\r\nI am doing QnA system as well, I have my own data and I split them into train, dev and test data, then use the train and dev data to train the model, eventually it works ok on the test data.\r\nbecause I am b... | 1,558 | 1,566 | 1,566 | NONE | null | I have BERT trained on Squad (and without trained as well) . My documents contains question and big answers. When we try to ask question as it is and BERT to find that question within document, then it gives some arbitrary answer from some other page of document.
What can be wrong? | {
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https://api.github.com/repos/huggingface/transformers/issues/626 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/626/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/626/comments | https://api.github.com/repos/huggingface/transformers/issues/626/events | https://github.com/huggingface/transformers/issues/626 | 446,529,444 | MDU6SXNzdWU0NDY1Mjk0NDQ= | 626 | How to use run_squad.py to produce multiple answers for a question? | {
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"soloved",
"Can you share how did you solve that problem?",
"@armheb \r\nSure, some questions in my dataset have multiple answers, some have one answer, some no answer.\r\n\r\nFirstly, I add a for loop in the \"read_squad_example\" method to allow the code to read all answers for each question and build N Squad... | 1,558 | 1,569 | 1,569 | NONE | null | Hello,
I am using run_squad.py to build my own question answering system, the problem is that, I want the system can output multiple answers for a question.
The number of answers can be 0, or one, or multiple if possible, how can I do to the code to achieve this? Thank you | {
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https://api.github.com/repos/huggingface/transformers/issues/625 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/625/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/625/comments | https://api.github.com/repos/huggingface/transformers/issues/625/events | https://github.com/huggingface/transformers/issues/625 | 446,203,941 | MDU6SXNzdWU0NDYyMDM5NDE= | 625 | Tried to visualize the CLS Token embeddings after fine-tuning on SST-2 using t-SNE, but no clear clustered visualizations of positive and negative sentences ! | {
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"Hi @rsc90, \r\nThe `BertForSequenceClassification` model use a [linear layer](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L994) on top of Bert's `pooled_output` which is a [small feed-forward layer with a tanh activation](https://github.com/huggingface/pyt... | 1,558 | 1,606 | 1,564 | NONE | null | I have used run_classifier.py to finetune the model on SST-2 data, and used this model in the extract_features.py to extract the embeddings of some sentences(fed only sentences-input.txt). Later used these features from .jsonl file and used the vectors of layer -2, corresponding to CLS token and tried to visualize usin... | {
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https://api.github.com/repos/huggingface/transformers/issues/624 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/624/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/624/comments | https://api.github.com/repos/huggingface/transformers/issues/624/events | https://github.com/huggingface/transformers/issues/624 | 446,112,582 | MDU6SXNzdWU0NDYxMTI1ODI= | 624 | tokenization_gpt2.py - on python 2 you can use backports.functools_lru_cache package from pypi | {
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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"
] | 1,558 | 1,564 | 1,564 | NONE | null | See https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization_gpt2.py#L28. Instead of not using `lru_cache` you can use this package. | {
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https://api.github.com/repos/huggingface/transformers/issues/623 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/623/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/623/comments | https://api.github.com/repos/huggingface/transformers/issues/623/events | https://github.com/huggingface/transformers/issues/623 | 445,914,304 | MDU6SXNzdWU0NDU5MTQzMDQ= | 623 | Integration with a retriever Model | {
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"Have a look at the ParlAI library and in particular [these great models based on BERT](https://github.com/facebookresearch/ParlAI/pull/1331) by @samhumeau.",
"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 y... | 1,558 | 1,579 | 1,564 | NONE | null | How can I integrate BERT to a retriever model? | {
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https://api.github.com/repos/huggingface/transformers/issues/622 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/622/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/622/comments | https://api.github.com/repos/huggingface/transformers/issues/622/events | https://github.com/huggingface/transformers/issues/622 | 445,884,076 | MDU6SXNzdWU0NDU4ODQwNzY= | 622 | In run_classifier.py, is "warmup_proportion" a fraction or percentage? | {
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"Found the same problem. `0.1` means `10%` in [Google's TensorFlow implementation](https://github.com/google-research/bert/blob/d66a146741588fb208450bde15aa7db143baaa69/run_classifier.py#L92).",
"It's a fraction of total training like indicated in the help doc: `0.1 = 10% of training`",
"This issue has been aut... | 1,558 | 1,564 | 1,564 | NONE | null | In `run_classifier.py`, the arg parameter `--warmup_proportion` [help doc](https://github.com/huggingface/pytorch-pretrained-BERT/blob/3fc63f126ddf883ba9659f13ec046c3639db7b7e/examples/run_classifier.py#L628) says, "E.g., 0.1 = 10%% of training.". Is it actually a percentage such that `0.1` => `0.1%` => `0.001`, which... | {
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https://api.github.com/repos/huggingface/transformers/issues/621 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/621/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/621/comments | https://api.github.com/repos/huggingface/transformers/issues/621/events | https://github.com/huggingface/transformers/issues/621 | 445,784,159 | MDU6SXNzdWU0NDU3ODQxNTk= | 621 | Question on duplicated sentence | {
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"Looks like a minor bug. However, it seems that simply removing this `else` statement may cause some problems according to the previous code logic.",
"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 contr... | 1,558 | 1,564 | 1,564 | NONE | null | Hi. I wonder whether they are unnecessary duplicated sentence or not.
When I run in "test mode", similar sentences are called twice.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_squad.py#L908
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_squad.... | {
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https://api.github.com/repos/huggingface/transformers/issues/620 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/620/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/620/comments | https://api.github.com/repos/huggingface/transformers/issues/620/events | https://github.com/huggingface/transformers/pull/620 | 445,755,371 | MDExOlB1bGxSZXF1ZXN0MjgwMTI3MjAy | 620 | Convert pytorch models back to tensorflow | {
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"Changed filename from convert_hf_checkpoint_to_tf.py to convert_pytorch_checkpoint_to_tf.py for consistency.",
"I use this to convert the fine-tuned pytorch model to TF and convert this converted TF back to pytorch model. The prediction result seems incorrect with the converted-converted pytorch model. ",
"I ... | 1,558 | 1,570 | 1,562 | CONTRIBUTOR | null | Added a file that converts pytorch models that have been trained/finetuned back to tensorflow. This currently supports the base BERT models (uncased/cased); conversion for other BERT models will be added in the future. | {
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https://api.github.com/repos/huggingface/transformers/issues/619 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/619/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/619/comments | https://api.github.com/repos/huggingface/transformers/issues/619/events | https://github.com/huggingface/transformers/issues/619 | 445,747,651 | MDU6SXNzdWU0NDU3NDc2NTE= | 619 | Custom data, gradient explosion, accuracy is 0 | {
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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"
] | 1,558 | 1,563 | 1,563 | NONE | null | Hi,
I have 16000+ labels to predict using sequence classifier. I tried running the code with BertAdam (no gradient clipping) and low LR of 1e-5. But my loss doesn't not improve and the accuracy stays at zero. Gradient clipping doesn't help either. I've check my inputs and they are correct. Any help is welcome.
mo... | {
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https://api.github.com/repos/huggingface/transformers/issues/618 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/618/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/618/comments | https://api.github.com/repos/huggingface/transformers/issues/618/events | https://github.com/huggingface/transformers/issues/618 | 445,703,262 | MDU6SXNzdWU0NDU3MDMyNjI= | 618 | Loss function of run_classifier.py takes in 2 inputs of different dimensions. | {
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"Closing issue, because I pass in the num_labels as 1 instead of 2 for the QNLI task. I was thinking that giving 1 label is enough because the 2nd label can be inferred from the 1st one. "
] | 1,558 | 1,558 | 1,558 | NONE | null | I am having an error here https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py#L836
In this line, `loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))`, suppose we have 2 labels (entailment vs. not_entailment like QNLI task), then,
`logits` is already in dime... | {
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https://api.github.com/repos/huggingface/transformers/issues/617 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/617/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/617/comments | https://api.github.com/repos/huggingface/transformers/issues/617/events | https://github.com/huggingface/transformers/issues/617 | 445,383,313 | MDU6SXNzdWU0NDUzODMzMTM= | 617 | How to get the softmax probabilities from the TransfoXLLMModel | {
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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"
] | 1,558 | 1,563 | 1,563 | NONE | null | ```
A tuple of (last_hidden_state, new_mems)
`softmax_output`: output of the (adaptive) softmax:
if target is None:
Negative log likelihood of shape [batch_size, sequence_length]
else:
log probabilities of tokens, shape [batch_size, sequence_length,... | {
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https://api.github.com/repos/huggingface/transformers/issues/616 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/616/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/616/comments | https://api.github.com/repos/huggingface/transformers/issues/616/events | https://github.com/huggingface/transformers/issues/616 | 445,354,741 | MDU6SXNzdWU0NDUzNTQ3NDE= | 616 | TransfoXLModel and TransforXLLMModel have the same example | {
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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"
] | 1,558 | 1,563 | 1,563 | NONE | null | can someone help me understand how the outputs would wary and if someone could give an example for the latter? | {
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https://api.github.com/repos/huggingface/transformers/issues/615 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/615/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/615/comments | https://api.github.com/repos/huggingface/transformers/issues/615/events | https://github.com/huggingface/transformers/issues/615 | 445,325,779 | MDU6SXNzdWU0NDUzMjU3Nzk= | 615 | Couldn't import '''BertPreTrainedModel''' | {
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"I guess you have to clone the repository. You just need to add all the classes you want to import in this line:\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/blob/3fc63f126ddf883ba9659f13ec046c3639db7b7e/pytorch_pretrained_bert/__init__.py#L12\r\nThen you can install from source:\r\n`pip install --edit... | 1,558 | 1,564 | 1,564 | NONE | null | I installed this lib by '''pip install pytorch-pretrained-bert''', and there are no problems when run the examples. However, when I import '''BertPreTrainedModel''', I use '''from pytorch_pretrained_bert import BertPreTrainedModel''', error occurs.
I want to write a new class like '''BertForSequenceClassification''', ... | {
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https://api.github.com/repos/huggingface/transformers/issues/614 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/614/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/614/comments | https://api.github.com/repos/huggingface/transformers/issues/614/events | https://github.com/huggingface/transformers/pull/614 | 444,547,131 | MDExOlB1bGxSZXF1ZXN0Mjc5MTg1MzA4 | 614 | global grad norm clipping (#581) | {
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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"
] | 1,557 | 1,563 | 1,563 | CONTRIBUTOR | null | (see #581 )
- norm-based gradient clipping was being done per param group
- when more than one param group, this is different from global norm grad clipping | {
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https://api.github.com/repos/huggingface/transformers/issues/613 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/613/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/613/comments | https://api.github.com/repos/huggingface/transformers/issues/613/events | https://github.com/huggingface/transformers/issues/613 | 444,501,264 | MDU6SXNzdWU0NDQ1MDEyNjQ= | 613 | Learning from scratch not working | {
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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"
] | 1,557 | 1,563 | 1,563 | NONE | null | I'm using simple_lm_learning as it was, except I didn't use the model from pretrained, but a new BertForPreTraining model with the same config as bert-base, how come it's not learning anything and predicting the same token 1996 ("the") for every output? | {
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https://api.github.com/repos/huggingface/transformers/issues/612 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/612/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/612/comments | https://api.github.com/repos/huggingface/transformers/issues/612/events | https://github.com/huggingface/transformers/issues/612 | 444,466,021 | MDU6SXNzdWU0NDQ0NjYwMjE= | 612 | How to use the fine tuned model for classification (CoLa) task? | {
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"Can you please confirm that the \"/examples/run_classifier.py\" file is indeed an example for simple sentence classification?\r\nIt looks like the code here uses the \"BertForSequenceClassification\" model where the tf model uses the \"BertModel\" (line 577 here https://github.com/google-research/bert/blob/master/... | 1,557 | 1,565 | 1,565 | CONTRIBUTOR | null | How to use the fine-tuned model for classification (CoLa) task?
I do not see the argument `--do_predict`, in `/examples/run_classifier.py`.
However, `--do_predict` exists in the original implementation of the Bert.
The fine-tuned model is getting saving in the BERT_OUTPUT_DIR as `pytorch_model.bin`, but is ... | {
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https://api.github.com/repos/huggingface/transformers/issues/611 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/611/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/611/comments | https://api.github.com/repos/huggingface/transformers/issues/611/events | https://github.com/huggingface/transformers/issues/611 | 444,413,399 | MDU6SXNzdWU0NDQ0MTMzOTk= | 611 | extract_features | {
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"Were you able to fix this issue? If yes, can you please share your solution?",
"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"
] | 1,557 | 1,564 | 1,564 | NONE | null | Traceback (most recent call last):
File "extract_features.py", line 297, in <module>
main()
File "extract_features.py", line 230, in main
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
File "/home/py36/lib/python3.6/site-packages/pytorch_pretrained_bert/t... | {
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https://api.github.com/repos/huggingface/transformers/issues/610 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/610/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/610/comments | https://api.github.com/repos/huggingface/transformers/issues/610/events | https://github.com/huggingface/transformers/issues/610 | 444,315,994 | MDU6SXNzdWU0NDQzMTU5OTQ= | 610 | t_total | {
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"\r\nI found the reason. When the data is relatively small, this happens. After I added the data, it is normal now."
] | 1,557 | 1,557 | 1,557 | NONE | null | Traceback (most recent call last):
File "finetune_on_pregenerated.py", line 333, in <module>
main()
File "finetune_on_pregenerated.py", line 321, in main
optimizer.step()
File "/home/py36/lib/python3.6/site-packages/pytorch_pretrained_bert/optimization.py", line 290, in step
lr_scheduled *= grou... | {
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