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"You could fix it with something like this\r\n```\r\nbatch_size = torch.tensor(data.shape[1]).to(device)\r\ndist.all_reduce(batch_size, op=dist.ReduceOp.SUM)\r\ndist.all_reduce(loss, op=dist.ReduceOp.SUM)\r\nmean_loss = loss/batch_size\r\n```",
"Thanks for your solution. I don't think it could fix it, because the... | 1,557 | 1,563 | 1,563 | NONE | null | The problem is that, when the input is distributed to multiple GPUs, the input on each GPU may have different `batch_size`.
For example, if you have 2 GPUs and the total batch_size is 13, then the `batch_size` for each GPU will be 7 and 6 respectively, `loss.mean()` will not give the exact loss. Although it may hav... | {
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] | 1,557 | 1,563 | 1,563 | NONE | null | I notice that the author only shows us how to use uncased model, could anyone show me how to import cased model in both BertModel and BertTokenClassifier model? Thanks | {
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] | 1,557 | 1,563 | 1,563 | NONE | null | self.apply(self.init_bert_weights) is already used in BertModel class, why do we still need to use self.apply(self.init_bert_weights) in all inhiritance model such as BertTokenClassificaiton model? | {
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"Looks good.",
"Ok merging thanks, sorry for the delay!"
] | 1,557 | 1,560 | 1,560 | CONTRIBUTOR | null | Fixing the issues reported in https://github.com/huggingface/pytorch-pretrained-BERT/issues/556
Reason for issue was that num_optimzation_steps was computed from example size, which is different from actual size of dataloader when an example is chunked into multiple instances.
Solution in this pull request is to ... | {
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"i have same question ",
"How best to fine-tune or pool BERT is an open question in bertology :p \r\n\r\n[\"How to Fine-Tune BERT for Text Classification?\"](https://arxiv.org/pdf/1905.05583.pdf) has a comprehensive overview. Look at table 3 specifically which found that taking the max of the last 4 layers achiev... | 1,557 | 1,568 | 1,568 | NONE | null | Following file extract_features.py, I use bert-large-uncased and it outputs 4 layer outputs for each token(word). Since I want to use it as feature extractor of entire sentence, which values should I use? Or is there any other processing we should do(like concatenate output for last token)? | {
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"@AdamDanielKing did you manage to fix the divergence somehow? ",
"Was this finally an issue? It seems important and it was closed due... | 1,557 | 1,591 | 1,567 | NONE | null | When using GPT-2 with mixed precision, the generated text is different from that produced by running it normally. This is true for both conditional and unconditional generation, and for top_k=1 (deterministic) and top_k=40. Typically the mixed precision and single precision outputs agree for a number of tokens and then... | {
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"You need to retrain it with your embeddings replacing `BertModel.embeddings.word_embeddings` and the model size being your embeddings 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 contribution... | 1,557 | 1,568 | 1,568 | NONE | null | I am using simple_lm_finetuning.py to fine tune BERT. However I want to get smaller embeddings. Where can I change this? | {
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] | 1,557 | 1,563 | 1,563 | NONE | null | Hi,
I am using simple_lm_finetuning.py to fine tune the model. I wanted to freeze all parameters from the very beginning to the beginning of the 12th transformer layer, I looked into parameters by name and used a counter, took the value of the counter corresponding to the beginning of the 12th layer, and used this ... | {
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"Hi Adrian, BERT already has a few unused tokens that can be used similarly to the `special_tokens` of GPT/GPT-2.\r\nFor more details see https://github.com/google-research/bert/issues/9#issuecomment-434796704 and issue #405 for instance.",
"In case we use an unused special token from the vocabulary, is it enough... | 1,557 | 1,564 | 1,564 | NONE | null | Hi,
I was wondering whether the team could expand BERT so that fine-tuning with newly defined special tokens would be possible - just like the GPT allows.
@thomwolf Could you share your thought with me on that?
Regards,
Adrian. | {
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https://api.github.com/repos/huggingface/transformers/issues/598 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/598/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/598/comments | https://api.github.com/repos/huggingface/transformers/issues/598/events | https://github.com/huggingface/transformers/pull/598 | 442,291,837 | MDExOlB1bGxSZXF1ZXN0Mjc3NDM2MTQz | 598 | Updating learning rate with special warm up in examples | {
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"Oh great thanks Burc!"
] | 1,557 | 1,557 | 1,557 | CONTRIBUTOR | null | Updating examples by removing division to num_train_optimization_steps for new WarmupLinearSchedule.
Fixes #566 | {
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https://api.github.com/repos/huggingface/transformers/issues/597 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/597/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/597/comments | https://api.github.com/repos/huggingface/transformers/issues/597/events | https://github.com/huggingface/transformers/pull/597 | 441,921,438 | MDExOlB1bGxSZXF1ZXN0Mjc3MTQ1ODk3 | 597 | GPT-2 (medium size model, special_tokens, fine-tuning, attention) + repo code coverage metric | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/597?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@f9cde97`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage i... | 1,557 | 1,566 | 1,560 | MEMBER | null | Superseded #560.
Improvements to GPT-2:
- add special tokens
- tested fine-tuning
- add medium size model
Improvements to GPT/GPT-2:
- option to extract attention weights.
Add code coverage | {
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https://api.github.com/repos/huggingface/transformers/issues/596 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/596/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/596/comments | https://api.github.com/repos/huggingface/transformers/issues/596/events | https://github.com/huggingface/transformers/issues/596 | 441,715,398 | MDU6SXNzdWU0NDE3MTUzOTg= | 596 | [Question] Cross-lingual sentence representations | {
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"Hi @shoegazerstella well XLM is already pretty much as powerful as BERT and focused on cross-lingual sentence representations so I would go directly for it instead of BERT.",
"Thanks @thomwolf, \r\nAre you considering integrating something for cross-lingual representations in the `pytorch-pretrained-BERT` librar... | 1,557 | 1,557 | 1,557 | NONE | null | Hi,
Would it be possible to integrate also a BERT model for cross-lingual sentence representations?
Something like, for example, the `XNLI-15` model in [https://github.com/facebookresearch/XLM](https://github.com/facebookresearch/XLM).
Thanks! | {
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https://api.github.com/repos/huggingface/transformers/issues/595 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/595/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/595/comments | https://api.github.com/repos/huggingface/transformers/issues/595/events | https://github.com/huggingface/transformers/issues/595 | 441,198,290 | MDU6SXNzdWU0NDExOTgyOTA= | 595 | Unclear error message when unable to cache the model | {
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"Yes, this error message hides several potential sources, I'll see if I can disentangle the error messages :) ",
"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",
"This is still an iss... | 1,557 | 1,571 | 1,571 | NONE | null | I encountered the following error:
```
[2019-05-07 11:06:51,904: ERROR/ForkPoolWorker-1] Model name 'bert-base-uncased'
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 a... | {
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https://api.github.com/repos/huggingface/transformers/issues/594 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/594/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/594/comments | https://api.github.com/repos/huggingface/transformers/issues/594/events | https://github.com/huggingface/transformers/issues/594 | 441,149,570 | MDU6SXNzdWU0NDExNDk1NzA= | 594 | size mismatch for lm_head.decoder.weight | {
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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",
"```Traceback (most recent call last):\r\n File \"question-generation/interact.py\", line 238, in <module>\r\n run()\r\n File \"que... | 1,557 | 1,674 | 1,563 | NONE | null | Hi i'm new to this,
first i started a finetune job
```
export ROC_STORIES_DIR=roc/
python run_openai_gpt.py \
--model_name openai-gpt \
--do_train \
--do_eval \
--train_dataset $ROC_STORIES_DIR/cloze_test_val__spring2016\ -\ cloze_test_ALL_val.csv \
--eval_dataset $ROC_STORIES_DIR/cloze_test_test... | {
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https://api.github.com/repos/huggingface/transformers/issues/593 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/593/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/593/comments | https://api.github.com/repos/huggingface/transformers/issues/593/events | https://github.com/huggingface/transformers/issues/593 | 441,132,791 | MDU6SXNzdWU0NDExMzI3OTE= | 593 | Embedding' object has no attribute 'shape' | {
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"@Dhanachandra Same issue with another pre-trained BERT model.\r\nHave you managed to solve that?",
"> @Dhanachandra Same issue with another pre-trained BERT model.\r\n> Have you managed to solve that?\r\n\r\n@Dhanachandra I've just found the solution: this exception occurs at \"modeling.py\" module. It's because... | 1,557 | 1,665 | 1,564 | NONE | null | While running the script to convert the Tensorflow checkpoints to Pytorch Model.
Model path: https://github.com/naver/biobert-pretrained/releases/download/v1.0-pubmed-pmc/biobert_pubmed_pmc.tar.gz
python pytorch_pretrained_BERT/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py \
--tf_checkpoint_path p... | {
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https://api.github.com/repos/huggingface/transformers/issues/592 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/592/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/592/comments | https://api.github.com/repos/huggingface/transformers/issues/592/events | https://github.com/huggingface/transformers/issues/592 | 441,030,704 | MDU6SXNzdWU0NDEwMzA3MDQ= | 592 | Can the use of [SEP] reduce the information extraction between the sentences? | {
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"I think so. Ultimately you should have s1 and s2 in input, your [CLS] + s1 + s2 + [SEP] will be equivalent to `[CLS] + s1 + [SEP]` in `[CLS] + s1 + [SEP] + s2 + [SEP]` where `s1` now is the concatenation of `s1` and `s2`. I don't think that's what you want to do."
] | 1,557 | 1,558 | 1,558 | NONE | null | Hello. I know that [CLS] means the start of a sentence and [SEP] makes BERT know the second sentence has begun. [SEP] can’t stop one sentence from extracting information from another sentence. However, I have a question.
If I have 2 sentences, which are s1 and s2, and our fine-tuning task is the same. In one way, I ad... | {
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"@RomanShen What is your observation on your question"
] | 1,557 | 1,558 | 1,557 | NONE | null | Hello. I know that [CLS] means the start of a sentence and [SEP] makes BERT know the second sentence has begun. [SEP] can’t stop one sentence from extracting information from another sentence. However, I have a question.
If I have 2 sentences, which are s1 and s2., and our fine-tuning task is the same. In one way, I a... | {
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"Closed in favour of #604 "
] | 1,557 | 1,557 | 1,557 | CONTRIBUTOR | null | Examples had wrongly computed t_total, resulting in warning messages (Issue #556 )
Added fixes in several examples but:
- only tested MRPC in `run_classifier.py` so far
- `finetune_on_pregenerated.py` still needs fixing (not sure why lines 221-227 are as they are) | {
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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 | 1,562 | 1,562 | NONE | null | Thank you for creating the pytorch version of BERT. But there is a problem when I use the convert_tf_checkpoint_to_pytorch script, I can't find any files created under the pytorch_dumpy_path. | {
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https://api.github.com/repos/huggingface/transformers/issues/588 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/588/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/588/comments | https://api.github.com/repos/huggingface/transformers/issues/588/events | https://github.com/huggingface/transformers/issues/588 | 440,677,033 | MDU6SXNzdWU0NDA2NzcwMzM= | 588 | installation error | {
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"Which commands are you running?",
"I am so sorry that I took your time, I accidentally posted this here. "
] | 1,557 | 1,557 | 1,557 | NONE | null | Hi, i am getting an error after following the installation orders you stated at read me.
My output's error message is here:
> error: command '/usr/bin/nvcc' failed with exit status 1
> error
> Cleaning up...
> Removing source in /tmp/pip-req-build-837wsq53
> Removed build tracker '/tmp/pip-req-tracker-txkm... | {
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"When BERT is fine-tuned, all layers are trained - this is quite different from fine-tuning in a lot of other ML models, but it matches what was described in the paper and works quite well (as long as you only fine-tune for a few epochs - it's very easy to overfit if you fine-tune the whole model for a long time on... | 1,557 | 1,568 | 1,563 | NONE | null | Hi, I looked at the code but couldn't manage to understand the layer from which BERT is being fine tuned. I am using simple_lm_finetuning.py function. | {
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https://api.github.com/repos/huggingface/transformers/issues/586 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/586/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/586/comments | https://api.github.com/repos/huggingface/transformers/issues/586/events | https://github.com/huggingface/transformers/issues/586 | 440,562,056 | MDU6SXNzdWU0NDA1NjIwNTY= | 586 | Padding Token in Transformer XL | {
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"For these causal models that consider the left-context only, it's ok not to worry too much about padding since the attention modules only look to the previous tokens. Just be careful when you compute the loss to ignore the out-of-sentence-tokens (using loss functions `ignore_index` for instance).",
"This issue h... | 1,557 | 1,588 | 1,562 | NONE | null | I have sentences of varying lengths and I was wondering how to handle that as I could not see any padding token present. The index 0 refers to <eos> in the vocab, so any help on addition of padding would be appreciated | {
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https://api.github.com/repos/huggingface/transformers/issues/585 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/585/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/585/comments | https://api.github.com/repos/huggingface/transformers/issues/585/events | https://github.com/huggingface/transformers/pull/585 | 440,463,287 | MDExOlB1bGxSZXF1ZXN0Mjc2MDA4ODEy | 585 | Make the epsilon of LayerNorm configurable. | {
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"Ok, good to go, thanks @huntzhan!"
] | 1,557 | 1,557 | 1,557 | CONTRIBUTOR | null | It would be great if we could configure `eps` in layer normalization since model like ERNIE uses `eps=1e-5` instead of `1e-12`.
#514 related | {
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https://api.github.com/repos/huggingface/transformers/issues/584 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/584/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/584/comments | https://api.github.com/repos/huggingface/transformers/issues/584/events | https://github.com/huggingface/transformers/issues/584 | 440,378,162 | MDU6SXNzdWU0NDAzNzgxNjI= | 584 | The number of train examples in STS-B is only 5749 | {
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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 | 1,562 | 1,562 | NONE | null | Hi,
Thanks a lot for the amazing work!
Here's my issue:
When I run the './example/run_classification.py' with task STS-B, I found the train example number is only 5749, less than 7k which was reported in the paper ([paper link](https://www.nyu.edu/projects/bowman/glue.pdf)).
Thanks again!
Best,
Dong | {
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https://api.github.com/repos/huggingface/transformers/issues/583 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/583/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/583/comments | https://api.github.com/repos/huggingface/transformers/issues/583/events | https://github.com/huggingface/transformers/issues/583 | 440,288,169 | MDU6SXNzdWU0NDAyODgxNjk= | 583 | BERT + PyTorch + XLA | {
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"Do u mean this one? [link](https://news.developer.nvidia.com/nvidia-achieves-4x-speedup-on-bert-neural-network/)",
"No, I mean this repo\nhttps://github.com/pytorch/xla/tree/master\n\nLooks like Facebook and Google want to make pytorch on TPU\n\n\nOn May 6, 2019 9:14:25 AM GMT+03:00, chunbo dai <notifications@gi... | 1,556 | 1,562 | 1,562 | NONE | null | Hi,
Many thanks for your amazing library!
Even though no models were shared for Russian, we used your interfaces with success when doing some [research](https://towardsdatascience.com/complexity-generalization-computational-cost-in-nlp-modeling-of-morphologically-rich-languages-7fa2c0b45909).
Anyway here is my q... | {
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https://api.github.com/repos/huggingface/transformers/issues/582 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/582/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/582/comments | https://api.github.com/repos/huggingface/transformers/issues/582/events | https://github.com/huggingface/transformers/issues/582 | 440,262,027 | MDU6SXNzdWU0NDAyNjIwMjc= | 582 | Add GPT-2 Bigger Model | {
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"For convenience to others, here's the config file for 345M:\r\n\r\n```\r\n{\r\n \"initializer_range\": 0.02,\r\n \"layer_norm_epsilon\": 1e-05,\r\n \"n_ctx\": 1024,\r\n \"n_embd\": 1024,\r\n \"n_head\": 16,\r\n \"n_layer\": 24,\r\n \"n_positions\": 1024,\r\n \"vocab_size\": 50257\r\n}\r\n```",
"Here are ... | 1,556 | 1,563 | 1,563 | NONE | null | OpenAI just release the next biggest version of their language model. I think to add the new model, one needs to use the conversion script from TF to Pytorch and then save the model as another option in PRETRAINED_MODEL_ARCHIVE_MAP. | {
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https://api.github.com/repos/huggingface/transformers/issues/581 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/581/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/581/comments | https://api.github.com/repos/huggingface/transformers/issues/581/events | https://github.com/huggingface/transformers/issues/581 | 440,218,813 | MDU6SXNzdWU0NDAyMTg4MTM= | 581 | BertAdam gradient clipping is not global | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | Just took a look at the gradient clipping algorithm used in: https://github.com/huggingface/pytorch-pretrained-BERT/blob/3ae8c8be1e3fc770968cd3fdb3b643e0b166e540/pytorch_pretrained_bert/optimization.py#L270
It's clipping gradients to a local norm of 1. It should be clipping gradients to a global norm of 1 as in http... | {
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https://api.github.com/repos/huggingface/transformers/issues/580 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/580/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/580/comments | https://api.github.com/repos/huggingface/transformers/issues/580/events | https://github.com/huggingface/transformers/issues/580 | 440,142,794 | MDU6SXNzdWU0NDAxNDI3OTQ= | 580 | Bert for passage reranking | {
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"The `convert_tf_checkpoint_to_pytorch` script is made to convert the Google pre-trained weights in `BertForPretraining` model, you have to modify it to convert another type model.\r\n\r\nIn your case, you want to load the passage re-ranking model in a `BertForSequenceClassification` model which has the same struct... | 1,556 | 1,634 | 1,557 | NONE | null | Hi I am currently trying to implement bert for passage reranking in pytorch. Here is the paper and github repo.
https://arxiv.org/abs/1901.04085
https://github.com/nyu-dl/dl4marco-bert
I've downloaded their bert large model checkpoint and bert config for the task the `convert_tf_checkpoint_to_pytorch` function see... | {
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https://api.github.com/repos/huggingface/transformers/issues/579 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/579/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/579/comments | https://api.github.com/repos/huggingface/transformers/issues/579/events | https://github.com/huggingface/transformers/issues/579 | 440,135,852 | MDU6SXNzdWU0NDAxMzU4NTI= | 579 | Resetting current_random_doc and current_doc | {
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"Hmm maybe @Rocketknight1 have an insight 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"
] | 1,556 | 1,562 | 1,562 | NONE | null | In the class BERTDataset the two variables `self.current_random_doc` and `self.current_doc` are never reset to 0, even when the corpus is closed and reopened. Is it supposed to work this way? I'd think it would run into issues on a small corpus where one counter gets to the same document but the counter is different be... | {
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"I join this issue.\r\n\r\nAlso I have a question related to the p.3\r\n> Optionally, modify run_classifier.py to allow loading of fine-tuned BERT language models from the lm_finetuning/ scripts\r\n\r\n`finetune_on_pregenerated.py` script uses `BertForPreTraining` with 2 heads and this is like vanilla training from... | 1,556 | 1,563 | 1,563 | MEMBER | null | I've noticed quite a few issues from people outside research who want to fine-tune a pre-trained BERT model to solve a task they're working on, but there's a steep learning curve. Right now, the workflow for someone who wants to use this repo for a custom task is something like this:
1) Understand how DataProcessors... | {
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"Maybe you get the error because of the position_ids that are most likely wrong. \r\n\r\nI believe positional ids are not needed - you can use this:\r\npredictions, past = model(tokens_tensor,position_ids=None token_type_ids=None, lm_labels=None, past=None)\r\n\r\nand the use the parameters you want at the place yo... | 1,556 | 1,566 | 1,566 | NONE | null | I am fine-tuning GPT2 model using the LMHead with a small number of special tokens.
GPT2 underlying transformer takes the whole input at once, thus, it's important to pad inputs of varying lengths to a fixed length. The GPT2 model library offers -1 to be used as the padding value:
> lm_labels: optional language mo... | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | Hi,
I am getting key error when using run_classifier.py in predict mode.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py
At prediction time we don't have labels hence it gives key error.
run_squad example is good as it was having is_training flag.
Could you pleas... | {
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] | 1,556 | 1,562 | 1,562 | NONE | null | the output of the TransfoXLModel has the size of [1, 3, 1024] if the Input has tree tokens.
`predictions, mems = model(tokens_tensor, mems=None)`
doc from code is
```
Outputs:
A tuple of (last_hidden_state, new_mems)
`last_hidden_state`: the encoded-hidden-states at the top of the mode... | {
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"@thomwolf I was wondering what are your thought on this issue?",
"The bug in the library causing the index out of range error comes from masking (-1) the LM labels.\r\n\r\n> lm_labels: optional language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ...,... | 1,556 | 1,571 | 1,571 | NONE | null | I have noted a very strange behaviour in GPT2 and I can't figure out why this happens. In one case when all of the inputs in the dataset have the same token length, the training works, however, when only one of the inputs has a different token length, the library throws an error. This is very strange since before I fee... | {
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https://api.github.com/repos/huggingface/transformers/issues/572 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/572/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/572/comments | https://api.github.com/repos/huggingface/transformers/issues/572/events | https://github.com/huggingface/transformers/issues/572 | 439,546,931 | MDU6SXNzdWU0Mzk1NDY5MzE= | 572 | BERT pre-training using only domain specific text | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | BERT is pre-trained using Wikipedia and other sources of normal text, but my problem domain has a very specific vocabulary & grammar. Is there an easy way to train BERT completely from domain specific data (preferably using Keras)?
The amount of pre-training data is not issue and we are not looking for the SOTA res... | {
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"Thanks!"
] | 1,556 | 1,557 | 1,557 | CONTRIBUTOR | null | Just fix some apparent documentation typos. | {
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https://api.github.com/repos/huggingface/transformers/issues/570 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/570/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/570/comments | https://api.github.com/repos/huggingface/transformers/issues/570/events | https://github.com/huggingface/transformers/pull/570 | 439,538,398 | MDExOlB1bGxSZXF1ZXN0Mjc1MzIxMzAx | 570 | Create optimizer only when args.do_train is True | {
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"Great, thanks @MottoX!"
] | 1,556 | 1,557 | 1,557 | CONTRIBUTOR | null | I am facing the same problem as #544 . When only setting args.do_eval to evaluate a trained model, there will be an error due to optimizer initialization. I think it is unnecessary to create an optimizer if args.do_train is False. Thanks for your review. | {
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https://api.github.com/repos/huggingface/transformers/issues/569 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/569/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/569/comments | https://api.github.com/repos/huggingface/transformers/issues/569/events | https://github.com/huggingface/transformers/issues/569 | 439,365,268 | MDU6SXNzdWU0MzkzNjUyNjg= | 569 | License of the pretrained models | {
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"Just found it's under Apache v2 in the Google bert repo. Closing."
] | 1,556 | 1,556 | 1,556 | CONTRIBUTOR | null | I noticed that once `from_pretrained` is called, the library automatically downloads a pretrained model from a URL. However, I found no license included in the downloaded pretrained model file. What is the license of the pretrained models? | {
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https://api.github.com/repos/huggingface/transformers/issues/568 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/568/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/568/comments | https://api.github.com/repos/huggingface/transformers/issues/568/events | https://github.com/huggingface/transformers/issues/568 | 439,228,906 | MDU6SXNzdWU0MzkyMjg5MDY= | 568 | Fine-tuning Bert | {
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"I was facing the same issue when finetuning using `finetune_on_pregenerated.py`. The problem was in the fact that I have some empty sentences in my dataset. Also there are some special characters, like `\\t` (tabulation) which can make a mess and should be cleared. \r\nI preprocess the text like this:\r\n```\r\nfo... | 1,556 | 1,565 | 1,565 | NONE | null | I want to fine-tune Bert's LM for a specific corpora. I converted the test into the format specified in the documentation and ran the fine-tuning codes given. I'm getting the following error:
File "simple_lm_finetuning.py", line 156, in random_sent
assert len(t2) > 0
AssertionError
I'm getting similar error in ... | {
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https://api.github.com/repos/huggingface/transformers/issues/567 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/567/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/567/comments | https://api.github.com/repos/huggingface/transformers/issues/567/events | https://github.com/huggingface/transformers/issues/567 | 439,115,855 | MDU6SXNzdWU0MzkxMTU4NTU= | 567 | about pytorch 1.1.0 rerlease | {
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"Hi,\r\n\r\nThe repo is compatible with PyTorch 1.1.0.\r\n\r\nBut, we probably won't switch to PyTorch Multi-headed-Attention module since this would mean refactoring all the models and adding complexity to the tensorflow conversion codes for unclear gains.",
"This issue has been automatically marked as stale bec... | 1,556 | 1,562 | 1,562 | NONE | null | Hi today pytorch 1.1.0 release(https://github.com/pytorch/pytorch/releases/tag/v1.1.0)
In version 1.1.0, added a new module implementing Multi-headed-Attention.
And various bugs have been modified.
Do you plan to update to suit that version?
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https://api.github.com/repos/huggingface/transformers/issues/566 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/566/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/566/comments | https://api.github.com/repos/huggingface/transformers/issues/566/events | https://github.com/huggingface/transformers/issues/566 | 439,085,421 | MDU6SXNzdWU0MzkwODU0MjE= | 566 | Bug in run_classifier.py fp16 learning rate | {
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"I'm asking the same question",
"I had been dealing with the issue of low and decreasing accuracy when I use fp16, as shown below,\r\n\r\n```\r\nEpoch 1 - Batch 1600/287417 - Training Acc. 0.106250 - Training Loss 2.295977\r\nEpoch 1 - Batch 3200/287417 - Training Acc. 0.098125 - Training Loss 2.299707\r\nEpoch 1... | 1,556 | 1,557 | 1,557 | NONE | null | After the latest update, my learning rate of fp16 in run_classifier.py keeps increasing.
https://github.com/huggingface/pytorch-pretrained-BERT/blob/2dee86319dbad575352358b8f2fb4129940e381a/examples/run_classifier.py#L857-L858
I think the right code is: lr_this_step = args.learning_rate * warmup_linear.get_lr(glo... | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | After I load the model with:
`
model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased",state_dict=model_state_dict)
model.eval()
`
The prediction results are not stable. They change dractically in every run.
It gets stable if I fix the seed but I dont understand why we need that. Isnt the mode... | {
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https://api.github.com/repos/huggingface/transformers/issues/564 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/564/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/564/comments | https://api.github.com/repos/huggingface/transformers/issues/564/events | https://github.com/huggingface/transformers/pull/564 | 439,051,911 | MDExOlB1bGxSZXF1ZXN0Mjc0OTQ1MzM4 | 564 | Fix #537 | {
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"Thanks a lot for that @8enmann!"
] | 1,556 | 1,556 | 1,556 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/563 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/563/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/563/comments | https://api.github.com/repos/huggingface/transformers/issues/563/events | https://github.com/huggingface/transformers/issues/563 | 438,999,408 | MDU6SXNzdWU0Mzg5OTk0MDg= | 563 | performance does not change but loss decrease | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | After training bert-lstm-crf model for 25 epoches, the performance on training set
here is the performance on train set, dev set and test set:
25th epoch:
tensor(10267.6279, device='cuda:0')
(0.42706720346856614, 0.4595134955014995, 0.4426966292134832)
(0.43147208121827413, 0.4271356783919598, 0.42929292929292... | {
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https://api.github.com/repos/huggingface/transformers/issues/562 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/562/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/562/comments | https://api.github.com/repos/huggingface/transformers/issues/562/events | https://github.com/huggingface/transformers/pull/562 | 438,974,141 | MDExOlB1bGxSZXF1ZXN0Mjc0ODg0OTE1 | 562 | Small fix to remove shifting of lm labels during pre process of RocStories. | {
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"Awesome, thanks!"
] | 1,556 | 1,556 | 1,556 | CONTRIBUTOR | null | In reference to https://github.com/huggingface/pytorch-pretrained-BERT/issues/473, remove the one shifting of lm labels since this shift happens internally during the model's forward pass.
@thomwolf | {
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https://api.github.com/repos/huggingface/transformers/issues/561 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/561/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/561/comments | https://api.github.com/repos/huggingface/transformers/issues/561/events | https://github.com/huggingface/transformers/issues/561 | 438,963,757 | MDU6SXNzdWU0Mzg5NjM3NTc= | 561 | Training Transformer XL from scratch | {
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"This looks good to me",
"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",
"@anshuman1992 could you share a code snippet/gist used for training TransformerXL model?\r\n",
"@anshuman1... | 1,556 | 1,566 | 1,562 | NONE | null | Hello,
I'm trying to train a transformer XL model from scratch by combining the architecture code from this library and training code from the official paper repo. But this yields to NaNs during training, just wanted to clarify the recommended way to initialize a new model.
Im doing it by,
```
architecture = Tr... | {
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https://api.github.com/repos/huggingface/transformers/issues/560 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/560/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/560/comments | https://api.github.com/repos/huggingface/transformers/issues/560/events | https://github.com/huggingface/transformers/pull/560 | 438,672,343 | MDExOlB1bGxSZXF1ZXN0Mjc0NjQ5OTg2 | 560 | Improvements to GPT-2 (special_tokens, fine-tuning, medium model) + repo code coverage metric | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/560?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@b832d5b`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage i... | 1,556 | 1,566 | 1,557 | MEMBER | null | - adding method to add special tokens to GPT-2 (like it's done for GPT).
- adding code coverage tracking for tests. | {
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https://api.github.com/repos/huggingface/transformers/issues/559 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/559/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/559/comments | https://api.github.com/repos/huggingface/transformers/issues/559/events | https://github.com/huggingface/transformers/issues/559 | 438,604,567 | MDU6SXNzdWU0Mzg2MDQ1Njc= | 559 | the size of words and the size of lables do not match | {
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"Can you give the exact log of (and before) the error message?",
"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"
] | 1,556 | 1,562 | 1,562 | NONE | null | When I run bert-large-cased model, it prints "the size of words and the size of lables do not match" but get no error message. What is this issue? Thanks | {
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"2 years late, but can anyone figure it out?"
] | 1,556 | 1,637 | 1,562 | NONE | null | looking through the new notes discussing GPT-2 I do not understand how one might run a squad fine tuning on a pretrained gpt-2 model
Any assistance would be greatly appreciated | {
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"@thomwolf Could you or someone from your team point me in the right direction to get the gtp2 model running with a small number of newly defined special tokens?\r\nAny help very appreciated as I really need to move on with my research project.",
"Hi @adigoryl, I'm adding this feature with PR #560\r\n\r\nYou can ... | 1,556 | 1,562 | 1,562 | NONE | null | About the aim:
I am trying to fine-tune a model on an English lyrics dataset in order to capture a style of a specific genre. To do this, at the fine-tuning input step, I wrap the lyrics with a "special token", e.g. <genre_type_tag> Lyrics text <genre_type_tag>. This means that I have to expand the vocab size by the ... | {
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"Actually, shouldn't the `int()` be a `ceiling()`? Because let's say `args.gradient_accumulation_steps` is 1, then it is `ceiling(len(train_examples) / args.train_batch_size)` that is the number of batches in an epoch.",
"I am having the same problem with my finetuned model for gpt2",
"I am having the same issu... | 1,556 | 1,568 | 1,564 | CONTRIBUTOR | null | I am seeing the above error in my training process. Is it a significant issue? Looks like it's related to `t_total`, which should be properly set here:
https://github.com/huggingface/pytorch-pretrained-BERT/blob/b832d5bb8a6dfc5965015b828e577677eace601e/examples/run_classifier.py#L742-L743
What could be potential ca... | {
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] | 1,556 | 1,562 | 1,562 | CONTRIBUTOR | null | Hello,
I have trained the original pytorch version of transformer xl, and I want to load it to get the hidden state and prediction.
However, it doesn't work. Apparently you only support to load a model from TensorFlow model checkpoints only.
Is there any hint or feature modification to make it work with model.... | {
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"Please give more information: the command used (arguments passed), traceback (the command line output), and version (can use `pip show pytorch_pretrained_bert`)\r\n\r\nI faced a similar problem with `read_squad_examples` when passing `input_file=dev.json` and `is_training=True`. ",
"I have this problem when trai... | 1,556 | 1,574 | 1,563 | NONE | null | Tried to run run_squad.py with the squad 2.0 dataset and came up with this error, ValueError: For training, each question should have exactly 1 answer. How do I solve this? | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | Once I am done fine tuning my `BertForSequenceClassification` model, I evaluate it on a validation set. I can see the loss and accuracy scores but I would also like to get the actual labels (as string) it predicted for each sentence (string) in the validation dataset. How could I do that? | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | I noticed that in the run_swag.py, the following code is included
if args.fp16 and args.loss_scale != 1.0:
# rescale loss for fp16 training
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
loss = loss * args.loss_scale
and in run_squad.py, this is ... | {
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] | 1,556 | 1,562 | 1,562 | MEMBER | null | Pad transformer's inputs to multiple of 8 to better use Tensorcores in fp16 mode.
@glample's [XLM](https://github.com/facebookresearch/XLM) does that and it seems still relevant with CUDA 10 (cc @yaroslavvb). | {
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https://api.github.com/repos/huggingface/transformers/issues/550 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/550/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/550/comments | https://api.github.com/repos/huggingface/transformers/issues/550/events | https://github.com/huggingface/transformers/pull/550 | 438,015,484 | MDExOlB1bGxSZXF1ZXN0Mjc0MTUzMTk5 | 550 | Fix GPT2 crash on special quotes in Python 3 | {
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"Thanks, this is closed now with #564"
] | 1,556 | 1,556 | 1,556 | NONE | null | In Python 3 the line
https://github.com/huggingface/pytorch-pretrained-BERT/blob/b832d5bb8a6dfc5965015b828e577677eace601e/pytorch_pretrained_bert/tokenization_gpt2.py#L224
splits `token` into full characters, not UTF-8 bytes, so for example the right single quote ’ gives `ord('’') == 8217`. That causes a crash since ... | {
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https://api.github.com/repos/huggingface/transformers/issues/549 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/549/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/549/comments | https://api.github.com/repos/huggingface/transformers/issues/549/events | https://github.com/huggingface/transformers/issues/549 | 438,005,556 | MDU6SXNzdWU0MzgwMDU1NTY= | 549 | CUDA out of memory issue when training | {
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"Try reducing the 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"
] | 1,556 | 1,606 | 1,562 | NONE | null | {
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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",
"@shawnkx Hi! Have you found a solution？",
"@all, any updates on this?"
] | 1,556 | 1,640 | 1,562 | NONE | null | I want to ensemble different checkpoints trained from the same parameter configuration but different seeds. Could you tell me how to ensemble these checkpoints? | {
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https://api.github.com/repos/huggingface/transformers/issues/547 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/547/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/547/comments | https://api.github.com/repos/huggingface/transformers/issues/547/events | https://github.com/huggingface/transformers/issues/547 | 437,991,834 | MDU6SXNzdWU0Mzc5OTE4MzQ= | 547 | How to get masked word prediction probabilities | {
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"I'm interested in an answer, too. A score/probability would help to select the best word for a masked token.",
"You are looking for the softmax function: https://pytorch.org/docs/stable/nn.html?highlight=softmax#torch.nn.functional.softmax",
"Thanks Thomas, I'll give it a try.",
"Thanks,\r\nSo you say that f... | 1,556 | 1,651 | 1,570 | NONE | null | Original sentence: i love apples. there are a lot of fruits in the world that i like, but apples would be my favorite fruit.
Masked sentence: i love apples . there are a lot of fruits in the world that i [MASK] , but apples would be my favorite fruit .
When I run through the pytorch version of bert, I get the follo... | {
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https://api.github.com/repos/huggingface/transformers/issues/546 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/546/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/546/comments | https://api.github.com/repos/huggingface/transformers/issues/546/events | https://github.com/huggingface/transformers/issues/546 | 437,986,848 | MDU6SXNzdWU0Mzc5ODY4NDg= | 546 | Import Error | {
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"This should be fixed with the new release (0.6.2).",
"Unfortunately, I still get this error with the new release. Could that be because I had installed the package before some time ago (and removed it afterwards)? \r\n\r\nNever mind, got it running by cleaning up the environments/paths.",
"This issue has been ... | 1,556 | 1,563 | 1,563 | NONE | null | I'm getting error " ImportError: cannot import name 'WEIGHTS_NAME' from 'pytorch_pretrained_bert.file_utils' " on running run_squad.py. I've already tried building from source but the problem persists. | {
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https://api.github.com/repos/huggingface/transformers/issues/545 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/545/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/545/comments | https://api.github.com/repos/huggingface/transformers/issues/545/events | https://github.com/huggingface/transformers/pull/545 | 437,968,723 | MDExOlB1bGxSZXF1ZXN0Mjc0MTIyOTkw | 545 | move pytroch_pretrained_bert cache folder under same path as torch | {
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"Ok, looks good, thanks @ailzhang!"
] | 1,556 | 1,557 | 1,557 | NONE | null | This PR does two things:
* Envs available:
PYTORCH_PRETRAINED_BERT_CACHE > TORCH_HOME > XDG_CACHE_HOME > `~/.cache`
* If no env is set, the default path is
`~/.cache/torch/pytorch_pretrained_bert` where `pytorch_pretrained_bert` is visible instead of hidden `.pytorch_pretrained_bert`. (since this is the cache ... | {
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https://api.github.com/repos/huggingface/transformers/issues/544 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/544/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/544/comments | https://api.github.com/repos/huggingface/transformers/issues/544/events | https://github.com/huggingface/transformers/issues/544 | 437,774,086 | MDU6SXNzdWU0Mzc3NzQwODY= | 544 | TypeError: '<' not supported between instances of 'NoneType' and 'int' | {
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"I also get this problem when predicting，Did you solved the problem？",
"Here is the problem during initialization of the optimizer:\r\n` t_total=num_train_optimization_steps)`\r\n\r\nThis var is initialized with `None` for the first time `num_train_optimization_steps = None`\r\nand it's initialized correctly onl... | 1,556 | 1,562 | 1,562 | NONE | null | Hi, I am trying to do classification fine tuning using bert-base-uncased. I am using examples from master and pytorch_pretrained_bert==0.6.2. Here are my repro steps:
1. I create a train.tsv and dev.tsv file with my own domain data. The files contain sentences and labels separated by a tab. I put these files in... | {
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https://api.github.com/repos/huggingface/transformers/issues/543 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/543/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/543/comments | https://api.github.com/repos/huggingface/transformers/issues/543/events | https://github.com/huggingface/transformers/issues/543 | 437,741,208 | MDU6SXNzdWU0Mzc3NDEyMDg= | 543 | How to train our own domain-specific data instead of using pre-training models? | {
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"I also have this question whenever someone gets to it, but I think that this isn't doable with this package. There's got to be a way to hack it, but you'd probably have to take away some of the code at the beginning of the pipeline. @yiranxijie ",
"Is there any news on this? Training one of these models from scr... | 1,556 | 1,563 | 1,563 | NONE | null | How to train our own domain-specific data instead of using pre-training models? | {
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https://api.github.com/repos/huggingface/transformers/issues/542 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/542/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/542/comments | https://api.github.com/repos/huggingface/transformers/issues/542/events | https://github.com/huggingface/transformers/issues/542 | 437,702,121 | MDU6SXNzdWU0Mzc3MDIxMjE= | 542 | Clarifying attention mask | {
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"The reason a classic binary attention mask won't work here is that the Softmax activation includes an exponential, and so an input of 0 can still yield quite a large softmax weight (since e^0 = 1).\r\n\r\nThe mask can't be applied after the softmax, because then the resulting values will not sum to 1. So the best ... | 1,556 | 1,616 | 1,556 | NONE | null | I don't quite understand the attention mask in the way that it's implemented.
Here is the relevant line: https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L312 :
```python
...
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply th... | {
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https://api.github.com/repos/huggingface/transformers/issues/541 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/541/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/541/comments | https://api.github.com/repos/huggingface/transformers/issues/541/events | https://github.com/huggingface/transformers/issues/541 | 437,555,026 | MDU6SXNzdWU0Mzc1NTUwMjY= | 541 | Any way to reduce the model size to <250mb? | {
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"Probably not - it would certainly be possible to make a smaller BERT model that would fit into this size, but all of the available pre-trained models have too many parameters, so you'd have to train it from scratch (which is very slow, and isn't something this repo supports yet).",
"This issue has been automatic... | 1,556 | 1,562 | 1,562 | NONE | null | Google Cloud's online prediction service has a 250mb limit for uploaded models. I don't think I have ever seen a BERT model that small. Casting all tensors to half precision reduces the model to ~350mb, is there any way to go even further than that? | {
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https://api.github.com/repos/huggingface/transformers/issues/540 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/540/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/540/comments | https://api.github.com/repos/huggingface/transformers/issues/540/events | https://github.com/huggingface/transformers/issues/540 | 437,549,824 | MDU6SXNzdWU0Mzc1NDk4MjQ= | 540 | no to_json_file(file) in BERT | {
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"Are you using the latest release (0.6.2) ?",
"Yes I am.",
"Strange, `to_json_file` should be provided in 0.6.2 (cf code [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/e6cf62d49945e6277b5e4dc855f9186b3f789e35/pytorch_pretrained_bert/modeling.py#L222) and the associated test [here](https://gi... | 1,556 | 1,556 | 1,556 | NONE | null | Hi, https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_squad.py#L1035
in the line 1035, I cannot use config.to_json_file(output_config_file) because there is no such function.
Instead I use
`file = model_to_save.config.to_json_string()`
`with open(file_path, "w") as f:`
` f.write(fi... | {
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https://api.github.com/repos/huggingface/transformers/issues/539 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/539/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/539/comments | https://api.github.com/repos/huggingface/transformers/issues/539/events | https://github.com/huggingface/transformers/issues/539 | 437,532,185 | MDU6SXNzdWU0Mzc1MzIxODU= | 539 | Can we use 'bert-base-uncased' to question_answer just for start, rather rather than run_squad pretraining? | {
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"Hi, no you need to fine tune the model on a question answering task like SQuAD before you can use it"
] | 1,556 | 1,556 | 1,556 | NONE | null | Hi,
Can we use 'bert-base-uncased' to question_answer just for start, rather rather than run_squad pretraining?
model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')
Thanks
Mahesh | {
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https://api.github.com/repos/huggingface/transformers/issues/538 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/538/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/538/comments | https://api.github.com/repos/huggingface/transformers/issues/538/events | https://github.com/huggingface/transformers/issues/538 | 437,526,651 | MDU6SXNzdWU0Mzc1MjY2NTE= | 538 | key error in BertQuestionAsnwering predict? | {
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"You found a solution?",
"@thomwolf , I made mistake in code, Repo code works just fine. Hence closed issue.\r\nThanks for this amazing repo :thumbsup:\r\n",
"How to solve this problem?\r\n",
"what was the solution ? Im seeing the same problem ",
"Hello! Do you mind opening a new issue with your problem?",
... | 1,556 | 1,625 | 1,556 | NONE | null | Hi,
I am getting key error while using BertQuestionAsnwering predict?
I am breaking following loop after 10 iterations
for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating", disable=local_rank not in [-1, 0]):
Thanks
Mahesh
Error:
KeyError ... | {
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https://api.github.com/repos/huggingface/transformers/issues/537 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/537/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/537/comments | https://api.github.com/repos/huggingface/transformers/issues/537/events | https://github.com/huggingface/transformers/issues/537 | 437,503,822 | MDU6SXNzdWU0Mzc1MDM4MjI= | 537 | New GPT2 tokenizer no longer encodes Unicode characters properly in Python 3 | {
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"Just ran into this problem. This seems to be a regression from an earlier version of Huggingface.\r\n\r\nFor instance it fails when encoding the following wikipedia snippet\r\n> The dismemberment of the French socialist movement into many groups and—following the suppression\r\n\r\nThe dash here is \"long dash\" w... | 1,556 | 1,571 | 1,571 | NONE | null | In commit 5afa497cbfc53c679a9b22997b6312fad57ee2f8, you changed `token.encode('utf-8')` to simply `token`.
This would make the code compatible with Python 2, but now it breaks in Python 3. You'll get a KeyError when you try to encode a Unicode character that requires more than 1 byte in UTF-8 encoding. For example, ... | {
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https://api.github.com/repos/huggingface/transformers/issues/536 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/536/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/536/comments | https://api.github.com/repos/huggingface/transformers/issues/536/events | https://github.com/huggingface/transformers/pull/536 | 437,348,950 | MDExOlB1bGxSZXF1ZXN0MjczNjQzNjM2 | 536 | Fix missing warmup_linear in run_classifier.py example | {
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"I see there is already a PR to fix this, I will close this."
] | 1,556 | 1,556 | 1,556 | NONE | null | Replaced warmup_linear function call with WarmupLinearSchedule | {
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https://api.github.com/repos/huggingface/transformers/issues/535 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/535/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/535/comments | https://api.github.com/repos/huggingface/transformers/issues/535/events | https://github.com/huggingface/transformers/issues/535 | 437,334,088 | MDU6SXNzdWU0MzczMzQwODg= | 535 | gpt2 fine tuning sources | {
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"I encountered the same issue",
"Also looking for how to finetune the GPT2 model, thanks.",
"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"
] | 1,556 | 1,571 | 1,571 | NONE | null | hi. I'm looking to fine tune the gpt2 model. I missed the part where that sort of fine tuning is taking place. Can someone point out where that code is (...or maybe where an example might be elsewhere on line)? | {
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https://api.github.com/repos/huggingface/transformers/issues/534 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/534/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/534/comments | https://api.github.com/repos/huggingface/transformers/issues/534/events | https://github.com/huggingface/transformers/issues/534 | 437,285,235 | MDU6SXNzdWU0MzcyODUyMzU= | 534 | How many datasets does Bert use in pretraining process? | {
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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"
] | 1,556 | 1,562 | 1,562 | NONE | null | Hi all,
I try to generate the pretraining corpus for BERT with pregenerate_training_data.py. In the BERT paper, it reports about 6M+ instances(segment A+segmentB, less than 512 tokens). But I get 18M instances, which is almost 3 time than BERT uses. Does anyone have any idea on the result and does anyone know if I nee... | {
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https://api.github.com/repos/huggingface/transformers/issues/533 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/533/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/533/comments | https://api.github.com/repos/huggingface/transformers/issues/533/events | https://github.com/huggingface/transformers/pull/533 | 437,224,702 | MDExOlB1bGxSZXF1ZXN0MjczNTQ1Mjcx | 533 | Docs for new learning rate code | {
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"Great thanks!",
"The curves plot in the README are beautiful (and perfect size), awesome!"
] | 1,556 | 1,556 | 1,556 | CONTRIBUTOR | null | - Added documentation for learning rate schedules in main README
- added some pictures for the README in docs/imgs/ (not sure if it's the best place)
- updated some docs in code for optimization | {
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https://api.github.com/repos/huggingface/transformers/issues/532 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/532/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/532/comments | https://api.github.com/repos/huggingface/transformers/issues/532/events | https://github.com/huggingface/transformers/issues/532 | 437,219,614 | MDU6SXNzdWU0MzcyMTk2MTQ= | 532 | [Feature request] Support configurable BertLayerNorm epsilon | {
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"Hi, I'm closing this in favor of #514 to gather all the discussion on ERNIE."
] | 1,556 | 1,556 | 1,556 | CONTRIBUTOR | null | It would be great if we could configure `eps` in layer normalization since model like ERNIE uses `eps=1e-5` instead of `1e-12`. | {
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https://api.github.com/repos/huggingface/transformers/issues/531 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/531/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/531/comments | https://api.github.com/repos/huggingface/transformers/issues/531/events | https://github.com/huggingface/transformers/pull/531 | 437,178,066 | MDExOlB1bGxSZXF1ZXN0MjczNTA3OTY3 | 531 | fixed new LR API in examples | {
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"The default text generation example in the codebase will generate unlimited length.\r\n\r\nHowever, each prediction is only influenced by current context (1024 tokens long). Something like [transformer-xl](https://github.com/kimiyoung/transformer-xl/tree/master/pytorch) is needed to depend on things outside of cur... | 1,556 | 1,667 | 1,561 | CONTRIBUTOR | null | Hello,
First, thanks so much for all of the open source work here! This has been super useful to build off of.
I noticed that the size of the pretrained positional embedding set for GPT2 is 1024, and was wondering if there were standard methods or suggestions for (a) running the language model head over text long... | {
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https://api.github.com/repos/huggingface/transformers/issues/529 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/529/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/529/comments | https://api.github.com/repos/huggingface/transformers/issues/529/events | https://github.com/huggingface/transformers/issues/529 | 436,691,723 | MDU6SXNzdWU0MzY2OTE3MjM= | 529 | Why classifier fine-tuning don't save best model based on the evaluation on dev dataset | {
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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",
"were you able to fix this problem. If yes can you please tell how"
] | 1,556 | 1,588 | 1,561 | CONTRIBUTOR | null | I want to use bert to train a classify model, I use the example [run_classifier.py].
But I find that the model will continue to train on train dataset until the max_epoch, without doing the evaluation on the dev dataset and save the best model according to the metric on the dev dataset.
So, the final saved model, it ... | {
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"Which version of pytorch-pretrained-bert are you using?\r\nCan you give the full error message to see which call to `__init__()` is failing?\r\nWe should have the keyword argument [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/3d78e226e68a5c5d0ef612132b601024c3534e38/pytorch_pretrained_bert/tok... | 1,556 | 1,561 | 1,561 | NONE | null | In the README, this line is written:
```
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True, do_basic_tokenize=True)
```
But when I execute it, I get this error:
```
__init__() got an unexpected keyword argument 'do_basic_tokenize'
```
| {
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"Hi @Mathieu-Prouveur, thanks for that.\r\nIndeed I think using `tr_loss/global_step` would be more easy to read.\r\nCan you update this? ",
"Sure, I've just done the update ",
"Great, thanks!"
] | 1,556 | 1,556 | 1,556 | NONE | null | Hi developpers!
Fix training loss value :
* if gradient_accumulation_steps > 1 then the batch loss value(which is a mean) is scaled by a factor 1/args.gradient_accumulation_steps.
To compare it to evaluation loss it is thus necessary to scale it back by multiplying by args.gradient_accumulation_steps (as done... | {
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https://api.github.com/repos/huggingface/transformers/issues/526 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/526/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/526/comments | https://api.github.com/repos/huggingface/transformers/issues/526/events | https://github.com/huggingface/transformers/issues/526 | 436,561,267 | MDU6SXNzdWU0MzY1NjEyNjc= | 526 | Will BERT weights for SQuAD be released? | {
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"Hi Lucas, probably not.\r\n\r\nThe goal of this repository is to provide easy access to pretrained model for transfer learning research. \r\n\r\nProviding downstream task models will make us handle a combinatory explosion of combinations to provide the various pretrained BERT models fine-tuned on each GLUE/SQuAD t... | 1,556 | 1,575 | 1,556 | NONE | null | Hi,
Are you going to release the weights after training on SQuAD 2.0?
Thank you for your great work.
Best,
Lucas Willems | {
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"According to the instructions [module-torch.optim](https://pytorch.org/docs/stable/optim.html?highlight=torch%20optim#module-torch.optim) from PyTorch API and [fused_adam.py](https://github.com/NVIDIA/apex/blob/master/apex/optimizers/fused_adam.py) from apex repo, I think `weight_decay` and `weight_decay_rate` are... | 1,556 | 1,561 | 1,561 | NONE | null | Thanks for the awesome work.
Just as line [simple_lm_finetuning.py#L540](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/lm_finetuning/simple_lm_finetuning.py#L540), When I use bert for downstream tasks, should I use `weight_decay` or `weight_decay_rate` when I add a decay operation to th... | {
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"Hi, why should it be the other way around?",
"I think I mixed up the meaning of 0 and 1 in this context and maybe wrote this post a bit too quickly before looking deeper into the code and documentation.. (sorry!). On second glance, the documentation for the BertForPreTraining is rather clear: \r\n\r\nhttps://git... | 1,556 | 1,564 | 1,556 | NONE | null | I'm wondering if the isNextsentence "label" in the below function is correct? Shouldn't the label be 1 in the case that t1,t2 are taken from self.get_corpus_line(index) (i.e., the first condition on line 150), and 0 if it is random (line 153)?
https://github.com/huggingface/pytorch-pretrained-BERT/blob/c36cca075a32... | {
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"Same is happening for `run_classifier.py` ",
"Yes the examples currently require to install from source (see the section in the readme).\r\nI'll release a new version tomorrow so the pip release will be in sync with `master` examples again.",
"Okay, thank you :)",
"Waiting for this; installing from source gi... | 1,556 | 1,556 | 1,556 | NONE | null | I just tried to run `run_squad.py` example and I got this error:
```
Traceback (most recent call last):
File "run_squad.py", line 37, in <module>
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
ImportError: cannot import name 'WEIGHTS_NAME' from 'pyto... | {
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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"
] | 1,556 | 1,561 | 1,561 | NONE | null | Hello community,
I am looking for an example which could help me to extend the Transformer XL to a model similar to bert-as-service model [1]. I would like to know how to set up new layers on the pretrained Tranformer XL and train the last new layers or the whole model. Could anyone give me an advice regarding this ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/521?src=pr&el=h1) Report\n> Merging [#521](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/521?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/80684f6f86c13a89fc1e4feac248ef96b01... | 1,556 | 1,570 | 1,570 | NONE | null | Issue #438 still exists if you choose to use something else rather then BertForTokenClassification. Furthermore, you still need to edit the code before running the convertor. Lastly, BertForTokenClassification is not the same as BertForQuestionAnswering, since the latter omits the dropout before the output layer.
M... | {
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"Ok, this should be fixed in the new release v0.6.2. See #523."
] | 1,556 | 1,556 | 1,556 | NONE | null | No such file or directory: 'LM_Trained/bert_config.json'
I think bert_config is not saved when finetuning a LM | {
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"Do you have a working internet connection?\r\nWe should probably improve the error messages here, 2 different error are bundled in this error (no internet connection and wrong model name)",
"Yes, I have an internet connection. I am able to download the other models.",
"Oh wait, you are mixing two models here.\... | 1,556 | 1,556 | 1,556 | NONE | null | I tried to load the `gpt2` model listed in the README.md, but I got this error:
```
Model name 'gpt2' 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 'gpt2' was a ... | {
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"@lukovnikov do you want to give this PR a look and confirm it's fine?\r\n\r\nAlso, we should document a bit the new optimizer API in the README. Do you want to use this PR to copy a few docstring in the README (we currently don't have auto-generated doc)?",
"Hi. Sorry, forgot about the examples.\r\nDid a couple ... | 1,556 | 1,556 | 1,556 | MEMBER | null | Re #445:
- update examples to work with the new optimizer API | {
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"Hi, only two segment labels are pre-trained in BERT.\r\nYou could fine-tune a new vocabulary token but we don't have a script to do that currently so you would have to modify the vocabulary and model.\r\nGPT and GPT-2 have option to do that where you can take inspiration from.\r\nI'm happy to welcome a PR on this ... | 1,555 | 1,561 | 1,561 | NONE | null | I want to segment input sentences in more segments, like [CLS]S1[SEP]S2[SEP]S3[SEP]. Therefore, when I convert example to features, I do the following.
`segment_ids = [0] * len(tokens_s1)`
`segment_ids += [1] * len(tokens_s2)`
`segment_ids += [2] * len(tokens_s2)`
but I got the following error when I run the `sel... | {
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"I have never tried Int8 in PyTorch.\r\nCan you share some code so we can have a look?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,555 | 1,561 | 1,561 | NONE | null | I am experimenting with low-precision on the pre-trained BERT for SQuAD scenario.
I am seeing a strange issue: the loss value when fine-tuning the model with FP16 is very similar to the loss value when fine-tuning the model at Int8. However, the eval results are are quite different -- with Int8, the results are quite... | {
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"Good catch!"
] | 1,555 | 1,556 | 1,556 | MEMBER | null | On reviewing the code I realized the --reduce_memory code path in `finetune_on_pregenerated.py` had a bug, but also wasn't getting used because the relevant argument wasn't getting passed correctly. The bugs have been fixed and the argument is now passed correctly. Performance still seems good, so now it should be poss... | {
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"Hi @nghuyong, I won't convert ERNIE but I'm open to welcome a PR if somebody want to give it a try.\r\n\r\nAlso, note that unlike examples, a PR with a new model should have a configuration class, tests, a conversion script and be documented like the other models in the library.\r\n",
"I do implement that conver... | 1,555 | 1,557 | 1,557 | CONTRIBUTOR | null | Can we add a new model ERNIE?
ERNIE is based on the Bert model and has better performance on Chinese NLP tasks.
Github address: https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE
paper: https://arxiv.org/abs/1904.09223
Thanks | {
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"I have tried to finetune GPT rather than BERT. An appropriate running epochs is **3** in the generation setting, including learning on embedding of some custom special tokens. Hope it help you :)",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no ... | 1,555 | 1,561 | 1,561 | NONE | null | Hi,
Could somebody provide some insights on how many epochs are necessary for finetuning bert model?
Google BERT has 100000 steps.(total_data/batch_size)
flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
Thanks
Mahesh | {
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"Thanks @cynthia!"
] | 1,555 | 1,556 | 1,556 | CONTRIBUTOR | null | Minor patch, not sure how it originally managed to sneak in in the first place. | {
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"I made changes in the code pregenerate_training_data.py\r\nfrom \r\n```\r\nparser.add_argument(\"--bert_model\", type=str, required=True,\r\n choices=[\"bert-base-uncased\", \"bert-large-uncased\", \"bert-base-cased\",\r\n \"bert-base-multilingual\", \"bert-ba... | 1,555 | 1,568 | 1,568 | NONE | null | I wanted to use fine tuning for hindi language data. For that I tried to give bert-base-mutlilingual model but I am getting the following error
> python pregenerate_training_data.py --train_corpus=./hindi_pytorch_bert_data_1.txt --bert_model=bert-base-multilingual --output_dir=./hindi_train_data_1_3epochs/ --epochs_... | {
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"We could update that indeed, that's just a relic of the Tensorflow conversion.\r\nDo you want to submit a PR? Otherwise I'll do it when I work on the next release.",
"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 y... | 1,555 | 1,561 | 1,561 | CONTRIBUTOR | null | Both [BertAdam](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/optimization.py) and [OpenAIAdam](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/optimization_openai.py) don't follow the pytroch convetion to define the `betas` paramet... | {
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