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https://api.github.com/repos/huggingface/transformers/issues/309 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/309/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/309/comments | https://api.github.com/repos/huggingface/transformers/issues/309/events | https://github.com/huggingface/transformers/issues/309 | 412,807,997 | MDU6SXNzdWU0MTI4MDc5OTc= | 309 | Tests error: Issue with python3 compatibility, on zope interface implementation | {
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"any solution here?",
"This looks like an incompatibility between apex and zope.\r\nHave you tried without installing apex?",
"> This looks like an incompatibility between apex and zope.\r\n> Have you tried without installing apex?\r\n\r\nI uninstalled apex, it works now!\r\nThank you so much!!!! "
] | 1,550 | 1,551 | 1,551 | NONE | null | Hi, I came across the following error after run **python -m pytest tests/modeling_test.py**
________________________________________________________________________________ ERROR collecting tests/modeling_test.py __________________________________________________________________________________
modeling_test.py:25:... | {
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https://api.github.com/repos/huggingface/transformers/issues/308 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/308/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/308/comments | https://api.github.com/repos/huggingface/transformers/issues/308/events | https://github.com/huggingface/transformers/issues/308 | 412,742,435 | MDU6SXNzdWU0MTI3NDI0MzU= | 308 | It seems the eval speed of transformer-xl is not faster than bert-base-uncased. | {
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The log:
```
Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.
02/21/2019 12:11:44 - INFO - __main__ - device: cpu n_gpu: 1, distributed training: False, 16-bits training: False
0... | {
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https://api.github.com/repos/huggingface/transformers/issues/306 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/306/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/306/comments | https://api.github.com/repos/huggingface/transformers/issues/306/events | https://github.com/huggingface/transformers/issues/306 | 412,720,358 | MDU6SXNzdWU0MTI3MjAzNTg= | 306 | Issue happens while using convert_tf_checkpoint_to_pytorch | {
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"I resolved this issue by adding the global_step to the skipping list. I think global_step is not required for using pretrained model. Please correct me if I am wrong.",
"Is Pytorch requires a TF check point converted? am finding hard to load the checkpoint I generated.BTW is it safe to convert TF checkpoint ?",
... | 1,550 | 1,563 | 1,563 | NONE | null | Hi,
We are using your brilliant project for working on the Japanese BERT model with Sentence Piece.
https://github.com/yoheikikuta/bert-japanese
We are trying to use the convert to to convert below TF BERT model to PyTorch.
https://drive.google.com/drive/folders/1Zsm9DD40lrUVu6iAnIuTH2ODIkh-WM-O
But we see err... | {
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https://api.github.com/repos/huggingface/transformers/issues/304 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/304/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/304/comments | https://api.github.com/repos/huggingface/transformers/issues/304/events | https://github.com/huggingface/transformers/issues/304 | 412,565,139 | MDU6SXNzdWU0MTI1NjUxMzk= | 304 | Can I do a code reference in implementing my code? | {
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"Hi @graykode,\r\nWhat do you mean by \"code reference\"?",
"@thomwolf \r\nHello thomwolf!\r\nIt mean that I apply your code about `GPT-2 model and transferring tensorflow checkpoint to pytorch` in my project!\r\n I show the origin of the information in my project code comment when I refer to your code.\r\nThanks... | 1,550 | 1,550 | 1,550 | NONE | null | @thomwolf
I am trying simply implementing gpt-2 on Pytorch
I have trouble in trasfering tensorflow checkpoint to pytorch :(
https://github.com/graykode/gpt-2-Pytorch
Could I do this code reference in implementing my code? I'll write reference in my code!!
Thanks for awesome sharing! | {
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"This could be related to #266 - are you using the latest version of `pytorch-pretrained-BERT`?",
"No, I was on 0.4, I upgraded to 0.6.1 and it worked."
] | 1,550 | 1,550 | 1,550 | NONE | null | There is an assertion error in the example code in the README.
The text that is input to the model
`"[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"`
is expected to be tokenized and masked like so
`['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', '[MASK]', 'was', 'a', 'pupp... | {
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https://api.github.com/repos/huggingface/transformers/issues/301 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/301/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/301/comments | https://api.github.com/repos/huggingface/transformers/issues/301/events | https://github.com/huggingface/transformers/issues/301 | 412,222,150 | MDU6SXNzdWU0MTIyMjIxNTA= | 301 | `train_dataset` and `eval_dataset` in run_openai_gpt.py | {
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"Hi Ben,\r\nPlease read the [relevant example section in the readme](https://github.com/huggingface/pytorch-pretrained-BERT#fine-tuning-openai-gpt-on-the-rocstories-dataset).",
"@thomwolf I see that the data is downloaded and cached in case of not providing the `train_dataset` and `eval_dataset` parameters: https... | 1,550 | 1,560 | 1,550 | CONTRIBUTOR | null | Running `examples/run_openai_gpt.py` w/ the default arguments throws an error:
```
$ python run_openai_gpt.py --output_dir tmp --do_eval
Traceback (most recent call last):
File "run_openai_gpt.py", line 259, in <module>
main()
File "run_openai_gpt.py", line 153, in main
train_dataset = load_rocstorie... | {
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"Hi Jasdeep,\r\nNo, they are initialized from Google's pretrained model (they are trained for next sentence prediction task during pretraining)."
] | 1,550 | 1,550 | 1,550 | CONTRIBUTOR | null | Hey! Sorry if this is redundant.
I saw 3 other issues asking similar questions but couldn't find these exact layers mentioned.
Are bert.pooler.dense.weight & bert.pooler.dense.bias randomly initialized?
Thank you so much!
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"Update:\r\n\r\nI manually uninstalled PyTorch (`torch | 1.0.1.post2 | 1.0.1.post2` in the list above) from my venv and installed PyTorch 0.4.0 (I couldn't find the whl file for 0.4.1 for Mac OS, which is my platform) by running\r\n\r\n`pip install https://download.pytorch.org/whl/torch-0.4.0-cp36-cp36m-macosx_10_7... | 1,550 | 1,551 | 1,551 | NONE | null | Steps to reproduce:
1. Clone the repo.
2. Set up a plain virtual environment `venv` for the repo with Python 3.6.
3. Run `pip install .` (using the `[--editable]` didn't work and there was some error, so I just removed it)
4. Run `pip install spacy ftfy==4.4.3` and `python -m spacy download en` -- SUCCESSFUL.
5.... | {
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"Hm, I guess I was using the wrong `checkpoint` file? When I used `/mnt/transformer-xl/tf/sota/pretrained_xl/tf_lm1b/model/model.ckpt-1191000` weights are loaded, but another error occurs:\r\n\r\n```bash\r\nLoading TF weight transformer/r_r_bias/Adam_1 with shape [24, 16, 80]\r\nLoading TF weight transformer/r_w_bi... | 1,550 | 1,575 | 1,557 | COLLABORATOR | null | Hi,
I wanted to convert the TensorFlow checkpoint for the ` lm1b` model to PyTorch with the `convert_transfo_xl_checkpoint_to_pytorch.py` script.
I downloaded the checkpoint with the [download.sh](https://github.com/kimiyoung/transformer-xl/blob/master/tf/sota/download.sh) script.
Then I called the convert sc... | {
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https://api.github.com/repos/huggingface/transformers/issues/297 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/297/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/297/comments | https://api.github.com/repos/huggingface/transformers/issues/297/events | https://github.com/huggingface/transformers/issues/297 | 412,147,861 | MDU6SXNzdWU0MTIxNDc4NjE= | 297 | Sudden catastrophic classification output during NER training | {
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"I manage to solve this problem. There is an issue in the calculation of the total optimization steps in `run_squad.py` example that results in a negative learning rate because of the `warmup_linear` schedule. This happens because `t_total` is calculated based on `len(train_examples)` instead of `len(train_features... | 1,550 | 1,569 | 1,550 | CONTRIBUTOR | null | Hi,
I am fine-tuning BERT model (based on `BertForTokenClassification`) to a NER task with 9 labels ("O" + BILU tags for 2 classes) and sometimes during training I run into this odd behavior: a network with 99% accuracy that is showing a converging trend suddenly shifts all of its predictions to a single class. This h... | {
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https://api.github.com/repos/huggingface/transformers/issues/296 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/296/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/296/comments | https://api.github.com/repos/huggingface/transformers/issues/296/events | https://github.com/huggingface/transformers/issues/296 | 412,063,102 | MDU6SXNzdWU0MTIwNjMxMDI= | 296 | How to change config parameters when loading the model with `from_pretrained` | {
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"I have the same question. I need to change the `hidden_dropout_prob`. How is that possible?",
"Same question. Any best practice? ",
"Oh, I find this code works:\r\n```python\r\nhidden_droput_prob = 0.3\r\nconfig = BertConfig.from_pretrained(\"bert-base-uncased\", num_labels=num_labels, hidden_dropout_prob=hidd... | 1,550 | 1,657 | 1,550 | CONTRIBUTOR | null | I have created a model by extending `PreTrainedBertModel`:
```python
class BertForMultiLabelClassification(PreTrainedBertModel):
def __init__(self, config, num_labels=2):
super(BertForMultiLabelClassification, self).__init__(config)
self.num_labels = num_labels
self.bert = BertMo... | {
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https://api.github.com/repos/huggingface/transformers/issues/295 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/295/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/295/comments | https://api.github.com/repos/huggingface/transformers/issues/295/events | https://github.com/huggingface/transformers/pull/295 | 411,902,273 | MDExOlB1bGxSZXF1ZXN0MjU0MjM2MDA0 | 295 | fix broken link in readme | {
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"Thanks!"
] | 1,550 | 1,550 | 1,550 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/294 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/294/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/294/comments | https://api.github.com/repos/huggingface/transformers/issues/294/events | https://github.com/huggingface/transformers/issues/294 | 411,855,459 | MDU6SXNzdWU0MTE4NTU0NTk= | 294 | Extract Features for GPT2 and Transformer-XL | {
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"Hi Dan,\r\nYou can extract all the hidden-states of Transformer-XL using the snippet indicated in the readme [here](https://github.com/huggingface/pytorch-pretrained-BERT#12-transfoxlmodel).\r\nFor the GPT-2 it's not possible right now.\r\nI can add it in the next release (or you can submit a PR).",
"@thomwolf ... | 1,550 | 1,584 | 1,550 | NONE | null | Hi everyone,
I'm interested in extracting token-level embeddings from the pre-trained GPT2 and Transformer-XL models and noticed that extract_features.py seems to be specific to BERT.
Can you let us know if you have any plans to provide a similar implementation for models other than BERT?
Alternatively, could ... | {
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"Thanks!"
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https://api.github.com/repos/huggingface/transformers/issues/292 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/292/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/292/comments | https://api.github.com/repos/huggingface/transformers/issues/292/events | https://github.com/huggingface/transformers/pull/292 | 411,581,192 | MDExOlB1bGxSZXF1ZXN0MjUzOTk1MTQw | 292 | Fix typo in `GPT2Model` code sample | {
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"Oh that's right, this one should be a logging.info event like the other ones.",
"Fixed"
] | 1,550 | 1,551 | 1,551 | NONE | null | As a library, it is preferred to have no unnecessary `print`s in the repo. Using the `pytorch-pretrained-BERT` makes it impossible to use `stdout` as main output mechanism for my code.
For example: it prints directly to `stdout` "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.... | {
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https://api.github.com/repos/huggingface/transformers/issues/290 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/290/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/290/comments | https://api.github.com/repos/huggingface/transformers/issues/290/events | https://github.com/huggingface/transformers/pull/290 | 411,431,440 | MDExOlB1bGxSZXF1ZXN0MjUzODgxMTA3 | 290 | Typo/formatting fixes in README | {
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"Changes are... even more minor then. I'll close this and open another (hope that's OK)."
] | 1,550 | 1,550 | 1,550 | CONTRIBUTOR | null | {
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"Thanks, I'll fix that in a future release."
] | 1,550 | 1,551 | 1,551 | CONTRIBUTOR | null | Thought to flag, also given the terrific work on this repo (and others), that the company name in the code here seems to be systematically spelt wrong (?)
https://github.com/huggingface/pytorch-pretrained-BERT/search?q=hugginface&unscoped_q=hugginface | {
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https://api.github.com/repos/huggingface/transformers/issues/288 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/288/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/288/comments | https://api.github.com/repos/huggingface/transformers/issues/288/events | https://github.com/huggingface/transformers/pull/288 | 411,417,662 | MDExOlB1bGxSZXF1ZXN0MjUzODcwNTEz | 288 | forgot to add regex to requirements.txt :( | {
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A test on OpenAI GPT-2 tokenizer module would have caught that.
But (byte-level) BPE tokenization tests are such a pain to make properly.
Let's add one in the next release, after the ACL deadline. | {
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"Thanks!"
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https://api.github.com/repos/huggingface/transformers/issues/285 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/285/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/285/comments | https://api.github.com/repos/huggingface/transformers/issues/285/events | https://github.com/huggingface/transformers/issues/285 | 411,074,179 | MDU6SXNzdWU0MTEwNzQxNzk= | 285 | Anyone tried this model to write a next sentence? | {
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"Closing for now.",
"Why ? @thomwolf ",
"I'm trying to clean up the issues to get a better view of what needs to be fixed.\r\nBut you are right opening/closing issue is too binary. Let's add labels instead.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be... | 1,550 | 1,557 | 1,557 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/284 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/284/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/284/comments | https://api.github.com/repos/huggingface/transformers/issues/284/events | https://github.com/huggingface/transformers/issues/284 | 410,782,598 | MDU6SXNzdWU0MTA3ODI1OTg= | 284 | Error in Apex's FusedLayerNorm | {
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"This was an error in `apex`, due to mismatched compiled libraries. Fix can be found [here](https://github.com/NVIDIA/apex/issues/156#issuecomment-465301976).\r\n\r\n> Try a full `pip uninstall apex`, then `cd apex_repo_dir; rm-rf build; python setup.py install --cuda_ext --cpp_ext` and see if the segfault persists... | 1,550 | 1,550 | 1,550 | NONE | null | After installing `apex` with the cuda extensions and running BERT, I get the following error in `FusedLayerNormAffineFunction`, [apex/normalization/fused_layer_norm.py](https://github.com/NVIDIA/apex/blob/master/apex/normalization/fused_layer_norm.py#L16) (line 21).
```
RuntimeError: a Tensor with 2482176 elements ... | {
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"Yes, the examples are not adapted for Python 2, only the library.\r\nI don't plan to adapt or maintain them but feel free to submit a PR!",
"env: python2.7\r\nline 662: writer.write(json.dumps(all_predictions, indent=4) + \"\\n\")\r\nchange as :writer.write(json.dumps(all_predictions, indent=4).decode('utf-8') +... | 1,550 | 1,560 | 1,550 | NONE | null | The general run_squad.py doesn't appear to work properly for python 2.7 because of the json dumping string vs unicode issues during the eval.
python2.7 run_squad.py \
--bert_model bert-base-uncased \
--do_train \
--do_predict \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predi... | {
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https://api.github.com/repos/huggingface/transformers/issues/282 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/282/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/282/comments | https://api.github.com/repos/huggingface/transformers/issues/282/events | https://github.com/huggingface/transformers/pull/282 | 410,648,826 | MDExOlB1bGxSZXF1ZXN0MjUzMzM5NDI4 | 282 | Fix some bug about SQuAD code | {
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"Ok, thanks @wlhgtc!"
] | 1,550 | 1,550 | 1,550 | CONTRIBUTOR | null | Fix issue in #207
This error occurs when 'nbest' only contain 1 item, but none 'text'. So the code to add empty will not work.
I add another condition to solve it.
https://github.com/huggingface/pytor... | {
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https://api.github.com/repos/huggingface/transformers/issues/281 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/281/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/281/comments | https://api.github.com/repos/huggingface/transformers/issues/281/events | https://github.com/huggingface/transformers/issues/281 | 410,646,108 | MDU6SXNzdWU0MTA2NDYxMDg= | 281 | Conversion of gpt-2 small model | {
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"I'd like to help out on this. I will have a look and try to understand the earlier bridges in this repo. Let me know if you see anywhere a newcomer can be helpful with.",
"Sure, would be happy to welcome a PR.\r\nYou can start from `modeling_openai.py` and `tokenization_openai.py`'s codes.\r\nIt's pretty much th... | 1,550 | 1,552 | 1,550 | NONE | null | Hey! This seems like something a lot of folks will want. I'd like to be able to load GPT-2 117M and fine-tune it. What's necessary to convert it? I looked at the tensorflow code a little and it looks vaguely related to transformer xl, but I haven't looked at the paper yet or etc. | {
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https://api.github.com/repos/huggingface/transformers/issues/279 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/279/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/279/comments | https://api.github.com/repos/huggingface/transformers/issues/279/events | https://github.com/huggingface/transformers/issues/279 | 410,143,066 | MDU6SXNzdWU0MTAxNDMwNjY= | 279 | DataParallel imbalanced memory usage | {
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"Managed to get volatile GPU to work properly but memory allocation is sitll imbalanced\r\n\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 410.78 Driver Version: 410.78 CUDA Version: 10.0 |\r\n|-------------------------------+----------------------... | 1,550 | 1,551 | 1,551 | NONE | null | Simialr to this issue: https://discuss.pytorch.org/t/dataparallel-imbalanced-memory-usage/22551/12, when I run run_lm_finetuning.py using 4 GPUs on Microsoft Azure, the first GPU will have 4000MB Memory usage while the other 3 are at 700MB. The Volatile Util for the first GPU also is at 100% while the rest are at 0%. ... | {
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https://api.github.com/repos/huggingface/transformers/issues/278 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/278/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/278/comments | https://api.github.com/repos/huggingface/transformers/issues/278/events | https://github.com/huggingface/transformers/issues/278 | 410,074,977 | MDU6SXNzdWU0MTAwNzQ5Nzc= | 278 | PAD symbols change the output | {
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"Hi Judit:\r\n- Regarding the padding: you should send an `attention_mask` with the input if the input is smaller than the tensor you are sending in (see the description on `BertModel` in the README).\r\n- Regarding the seed: don't forget to put your model in eval mode (`model.eval()`) to disable the dropout layer... | 1,550 | 1,594 | 1,550 | NONE | null | Adding `[PAD]` symbols to an input sentence changes the output of the model. I put together a small example here:
https://gist.github.com/juditacs/8be068d5f9063ad68e3098a473b497bd
I also noticed that the seed state affects the output as well. Resetting it in every run ensures that the output is always the same. I... | {
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https://api.github.com/repos/huggingface/transformers/issues/277 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/277/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/277/comments | https://api.github.com/repos/huggingface/transformers/issues/277/events | https://github.com/huggingface/transformers/issues/277 | 409,870,543 | MDU6SXNzdWU0MDk4NzA1NDM= | 277 | 80min training time to fine-tune BERT-base on the SQuAD dataset instead of 24min? | {
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"You should use 16bit training (`--fp16` argument). You can use the dynamic loss scaling or tune the loss scale yourself if the results are not the best.",
"@thomwolf Thanks! I enabled 16bit training and it took about 20min/epoch. Is that what you experienced?",
"Sounds good.",
"@thomwolf \r\nMay I know what ... | 1,550 | 1,561 | 1,551 | NONE | null | I just fine-tuned BERT-base on the SQuAD dataset with an AWS EC2 `p3.2xlarge` Deep Learning AMI with a single Tesla V100 16GB:
I used the config in your README:
```
export SQUAD_DIR=/path/to/SQUAD
python run_squad.py \
--bert_model bert-base-uncased \
--do_train \
--do_predict \
--do_lower_case \
... | {
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https://api.github.com/repos/huggingface/transformers/issues/276 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/276/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/276/comments | https://api.github.com/repos/huggingface/transformers/issues/276/events | https://github.com/huggingface/transformers/issues/276 | 409,861,122 | MDU6SXNzdWU0MDk4NjExMjI= | 276 | Argument do_lower_case is repeated in run_lm_finetuning.py | {
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"Hi @dileep1996, this has just been fixed in master (#275)!"
] | 1,550 | 1,550 | 1,550 | NONE | null | Hi, I am trying to finetune LM and am facing the following issue.
**argparse.ArgumentError: argument --do_lower_case: conflicting option string: --do_lower_case** | {
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https://api.github.com/repos/huggingface/transformers/issues/275 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/275/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/275/comments | https://api.github.com/repos/huggingface/transformers/issues/275/events | https://github.com/huggingface/transformers/pull/275 | 409,832,484 | MDExOlB1bGxSZXF1ZXN0MjUyNzE1NDQ3 | 275 | --do_lower_case is duplicated in parser args | {
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"Thanks @davidefiocco!"
] | 1,550 | 1,550 | 1,550 | CONTRIBUTOR | null | I'm therefore deleting one repetition (please review!) | {
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https://api.github.com/repos/huggingface/transformers/issues/274 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/274/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/274/comments | https://api.github.com/repos/huggingface/transformers/issues/274/events | https://github.com/huggingface/transformers/issues/274 | 409,715,950 | MDU6SXNzdWU0MDk3MTU5NTA= | 274 | Help: how to get index/symbol from last_hidden, on text8? | {
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"Hi,\r\nThere is no pretrained character-level model for text8 right now.\r\nOnly a word-level model trained on wikitext 103."
] | 1,550 | 1,551 | 1,551 | NONE | null | I am trying on text8 dataset. I want to print next token. The model in source code forward() output is loss, but I want to get logits and softmax result, and finally get next token in vocab.
how to get index/symbol from last_hidden, on text8?
Thanks | {
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https://api.github.com/repos/huggingface/transformers/issues/273 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/273/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/273/comments | https://api.github.com/repos/huggingface/transformers/issues/273/events | https://github.com/huggingface/transformers/pull/273 | 409,701,518 | MDExOlB1bGxSZXF1ZXN0MjUyNjE0NzI4 | 273 | Update to fifth release | {
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- this fixes a bug in the loading of the pretrained `TransfoXLModel` from the s3 dump (which is a converted `TransfoXLLMHeadModel`) and the weights were not loaded.
- I also added a fallback of `OpenAIGPTTokenizer` on BERT's `BasicTokenizer` when SpaCy... | {
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https://api.github.com/repos/huggingface/transformers/issues/272 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/272/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/272/comments | https://api.github.com/repos/huggingface/transformers/issues/272/events | https://github.com/huggingface/transformers/issues/272 | 409,598,865 | MDU6SXNzdWU0MDk1OTg4NjU= | 272 | Facing issue in Run Fine tune LM | {
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"Yes, you need documents with multiple lines because only sentences from the same doc are used as positive examples for the nextSentence prediction. ",
"Seems like the expected behavior. Feel free to open a PR to extend the example if you want @tuhinjubcse."
] | 1,550 | 1,551 | 1,551 | NONE | null | So my LM sample.txt is such that each doc has only one line
So in BERTDataSet len is giving negative
I tried changing it to self.num_docs - 1
def __len__(self):
print(self.corpus_lines ,self.num_docs)
return self.corpus_lines - self.num_docs - 1
I am also getting errors at multiple steps, Is the... | {
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https://api.github.com/repos/huggingface/transformers/issues/271 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/271/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/271/comments | https://api.github.com/repos/huggingface/transformers/issues/271/events | https://github.com/huggingface/transformers/issues/271 | 409,585,974 | MDU6SXNzdWU0MDk1ODU5NzQ= | 271 | Transformer-XL: wrong encoding in the vocab | {
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"Yeah, the re-encoding seems to fix the bug:\r\n```\r\nIn [6]: \"'EnquΓΒͺtes\".encode('latin1').decode('utf8')\r\nOut[6]: \"'EnquΓͺtes\"\r\n```",
"which version of python are you using?",
"It's python3.6. Does the snippet above gives different result on other version?\r\n\r\nJFYI: I'm using the script below to cr... | 1,550 | 1,557 | 1,557 | NONE | null | Seems that something odd happened during vocab serialization as many symbols with non-latin symbols are broken.
E.g.:
```
In [1]: import pytorch_pretrained_bert
In [2]: tokenizer = pytorch_pretrained_bert.TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
In [4]: print(tokenizer.idx2sym[224178])
'EnquΓΒͺte... | {
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https://api.github.com/repos/huggingface/transformers/issues/270 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/270/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/270/comments | https://api.github.com/repos/huggingface/transformers/issues/270/events | https://github.com/huggingface/transformers/issues/270 | 409,516,530 | MDU6SXNzdWU0MDk1MTY1MzA= | 270 | Transformer-XL: hidden states are nan | {
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"This is only Transformer-XL related. For GPT the output is:\r\n\r\n```python\r\ntensor([[[ 0.1963, 0.0367, -0.2051, ..., 0.7062, -0.2786, 0.1352],\r\n [-0.4705, 0.1581, 0.0452, ..., 0.7809, -0.2519, 0.4257],\r\n [-0.2602, -0.7126, -0.7966, ..., 0.6364, -0.1560, -0.6084],\r\n ...,... | 1,550 | 1,550 | 1,550 | COLLABORATOR | null | Hi,
I followed the code in the Transformer-XL section:
```python
import torch
from pytorch_pretrained_bert import TransfoXLTokenizer, TransfoXLModel, TransfoXLLMHeadModel
# Load pre-trained model tokenizer (vocabulary from wikitext 103)
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
#... | {
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https://api.github.com/repos/huggingface/transformers/issues/269 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/269/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/269/comments | https://api.github.com/repos/huggingface/transformers/issues/269/events | https://github.com/huggingface/transformers/issues/269 | 409,385,626 | MDU6SXNzdWU0MDkzODU2MjY= | 269 | Get hidden states from all layers of Transformer-XL? | {
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"Hi @hugochan, actually that what's in the `mems` of the Transformer-XL are (maybe you can read again the paper).\r\n\r\nOne thing to be careful about is that the `mems` have transposed first dimensions and are longer (see the readme). Here is how to extract the hidden states from the model output:\r\n```python\r\n... | 1,549 | 1,550 | 1,550 | NONE | null | Hi,
Thank you for supporting the pretrained Transformer-XL model! I was wondering if it makes sense to get hidden states from all layers of Transformer-XL as the output, just as what can be done for BERT. It seems this is not supported currently. Practically I found this strategy worked well for BERT and gave better... | {
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https://api.github.com/repos/huggingface/transformers/issues/268 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/268/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/268/comments | https://api.github.com/repos/huggingface/transformers/issues/268/events | https://github.com/huggingface/transformers/pull/268 | 409,265,270 | MDExOlB1bGxSZXF1ZXN0MjUyMjg5MzEy | 268 | fixed a minor bug in README.md | {
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"Thanks @wangxiaodiu "
] | 1,549 | 1,550 | 1,550 | CONTRIBUTOR | null | Assertion failed if one followed the instructions in README.md->Usage->BERT.
https://github.com/huggingface/pytorch-pretrained-BERT/issues/266#issuecomment-462730151 | {
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https://api.github.com/repos/huggingface/transformers/issues/267 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/267/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/267/comments | https://api.github.com/repos/huggingface/transformers/issues/267/events | https://github.com/huggingface/transformers/issues/267 | 409,194,189 | MDU6SXNzdWU0MDkxOTQxODk= | 267 | Missing files for Transformer-XL examples | {
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"Oh yes, that was a typo, there is only one example for Transformer-XL and it's the `run_transfo_xl.py` file detailed [here](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/README.md#openai-gpt-and-transformer-xl-running-the-examples). Read the API details in the readme for more information on th... | 1,549 | 1,549 | 1,549 | COLLABORATOR | null | Hi,
thanks so much for the new *0.5.0* release. I wanted to train a `TransfoXLModel` model, as described in the `README` [here](https://github.com/huggingface/pytorch-pretrained-BERT/blame/master/README.md#L132).
Unfortunately, the files `transfo_xl_train.py` and `transfo_xl_eval.py` are not located in the `examp... | {
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"Though we are not facing the same issueβ¦β¦\r\n\r\nI followed the instruction from the readme, the `tokenized_text` is expected by assertion to be:\r\n `['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', '[MASK]', 'was', 'a', 'puppet', '##eer', '[SEP]']`. \r\n\r\nHowever, the actual `tokenized_text` is:\r... | 1,549 | 1,575 | 1,550 | NONE | null | The tokenizer is not working correctly for me for e.g. [CLS] is gettig broken '[' , 'cl ', '##s', ']'
In [1]: import torch
...: from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMas
...: kedLM
...:
...: # Load pre-trained model tokenizer (vocabulary)
...: tokenizer = BertTok... | {
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https://api.github.com/repos/huggingface/transformers/issues/265 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/265/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/265/comments | https://api.github.com/repos/huggingface/transformers/issues/265/events | https://github.com/huggingface/transformers/issues/265 | 408,771,087 | MDU6SXNzdWU0MDg3NzEwODc= | 265 | Variance Sources | {
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"Hi Carolin,\r\n\r\nDepending on the model you are using, not all the weights are initialized from the pre-trained models. Check the details in the [overview](https://github.com/huggingface/pytorch-pretrained-BERT#overview) section of the readme to see if it's the case for you.\r\n\r\nApart from weights initializat... | 1,549 | 1,549 | 1,549 | NONE | null | Hi,
when I change the `--seed` argument, I get a high variance between different runs on my dataset. So I was wondering where the sources of variance might come from. I see that the seed is set (e.g. in `run_squad.py`) via:
`random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)`
But h... | {
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https://api.github.com/repos/huggingface/transformers/issues/264 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/264/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/264/comments | https://api.github.com/repos/huggingface/transformers/issues/264/events | https://github.com/huggingface/transformers/issues/264 | 408,729,916 | MDU6SXNzdWU0MDg3Mjk5MTY= | 264 | RuntimeError: cuda runtime error while running run_classifier.py with 'bert-large-uncased' bert model | {
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"reduce batch size?",
"It is 32 as of now . What do you think I should reduce it to ?",
"Start very low and increase while looking at `nvidia-smi` or a similar GPU memory visualization tool.",
"Closing this for now, feel free to re-open if you have other issues."
] | 1,549 | 1,551 | 1,551 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/263 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/263/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/263/comments | https://api.github.com/repos/huggingface/transformers/issues/263/events | https://github.com/huggingface/transformers/issues/263 | 408,343,730 | MDU6SXNzdWU0MDgzNDM3MzA= | 263 | potential bug in extract_features.py | {
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"Hi Jie,\r\n`extract_feature.py` is an example script. If you want to adapt it for sentences-pair, we would be happy to welcome a PR :)"
] | 1,549 | 1,549 | 1,549 | NONE | null | Hi,
`token_type_ids` is not set for this line:
`all_encoder_layers, _ = model(input_ids, token_type_ids=None, attention_mask=input_mask)`
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/extract_features.py#L267, this does not affect single sequence feature extraction, but for a pair o... | {
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https://api.github.com/repos/huggingface/transformers/issues/262 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/262/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/262/comments | https://api.github.com/repos/huggingface/transformers/issues/262/events | https://github.com/huggingface/transformers/issues/262 | 407,849,628 | MDU6SXNzdWU0MDc4NDk2Mjg= | 262 | speed becomes slow | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"I know this is closed, but I'm running into a similar issue. For the first ~100 batches, the script runs with an OK speed (1s/it, batch... | 1,549 | 1,622 | 1,557 | NONE | null | Hi,
I'm trying to fine-tune Bert-base-uncased model for Squad v1.1 on microsoft azure.
And I'm experiencing slow speed as the training continues.
[logs from first epoch]
Iteration: 3%|β | 452/14774 [01:46<57:06, 4.18it/s][A
Iteration: 3%|β | 453/14774 [01:46<57:07, 4.18it/s][A
Iterat... | {
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https://api.github.com/repos/huggingface/transformers/issues/261 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/261/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/261/comments | https://api.github.com/repos/huggingface/transformers/issues/261/events | https://github.com/huggingface/transformers/pull/261 | 407,599,866 | MDExOlB1bGxSZXF1ZXN0MjUxMDM3Mjc3 | 261 | removing unused argument eval_batch_size from LM finetuning #256 | {
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"Nice!"
] | 1,549 | 1,549 | 1,549 | CONTRIBUTOR | null | Removing unused eval_batch_size argument for simplification. As requested in #256. | {
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https://api.github.com/repos/huggingface/transformers/issues/260 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/260/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/260/comments | https://api.github.com/repos/huggingface/transformers/issues/260/events | https://github.com/huggingface/transformers/issues/260 | 407,499,189 | MDU6SXNzdWU0MDc0OTkxODk= | 260 | pretrained model(s) in onnx format | {
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"Hi @WilliamTambellini, have you tried to follow the standard ONNX procedure for converting a PyTorch model?\r\nThe model in this repo are just regular PyTorch models.",
"Hello Thomas, I ve not yet tried, just seen : \r\nhttps://github.com/onnx/models/issues/130\r\nhttps://stackoverflow.com/questions/54220042/how... | 1,549 | 1,570 | 1,564 | CONTRIBUTOR | null | hi, would you assist/help in order to export/convert at least 1 model into the onnx format ?
https://onnx.ai
kind | {
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https://api.github.com/repos/huggingface/transformers/issues/259 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/259/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/259/comments | https://api.github.com/repos/huggingface/transformers/issues/259/events | https://github.com/huggingface/transformers/issues/259 | 407,475,195 | MDU6SXNzdWU0MDc0NzUxOTU= | 259 | please add option to load fine-tuned file to CPU if trained on GPU | {
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"Indeed, this will be in the next release, thanks!",
"is there is a change according the CPU issue ?",
"Should be fixed now. Do you still have an error?",
"i am loading the model and i dont know how to load on CPU , gives me \"model.to\" to is not defined. Can you tell me how to send the model to run on CPU i... | 1,549 | 1,652 | 1,549 | NONE | null | I fine-tuned the pytorch_model.bin on a GPU machine (google cloud) but need to use it on my home computer (no GPU). When I tried to open it using `model = BertForMaskedLM.from_pretrained(bert_version)` I got the following error:
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_a... | {
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https://api.github.com/repos/huggingface/transformers/issues/258 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/258/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/258/comments | https://api.github.com/repos/huggingface/transformers/issues/258/events | https://github.com/huggingface/transformers/pull/258 | 407,373,796 | MDExOlB1bGxSZXF1ZXN0MjUwODYzMTI1 | 258 | Fix the undefined variable in squad example | {
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"Thanks @BoeingX !"
] | 1,549 | 1,549 | 1,549 | CONTRIBUTOR | null | `train_dataset` is undefined | {
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https://api.github.com/repos/huggingface/transformers/issues/257 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/257/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/257/comments | https://api.github.com/repos/huggingface/transformers/issues/257/events | https://github.com/huggingface/transformers/issues/257 | 407,218,110 | MDU6SXNzdWU0MDcyMTgxMTA= | 257 | Minor redundancy in model defintion? | {
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"Yes, feel free to submit a PR. Otherwise, I'll fix it in the next release."
] | 1,549 | 1,551 | 1,551 | NONE | null | this is a _major_ nitpick but it was a bit confusing at first:
https://github.com/huggingface/pytorch-pretrained-BERT/blob/822915142b2f201c0b01acd7cffe1b05994d2d82/pytorch_pretrained_bert/modeling.py#L206-L212
L212 can simply be replaced by `self.all_head_size = config.hidden_size` as you already error out if the... | {
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https://api.github.com/repos/huggingface/transformers/issues/256 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/256/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/256/comments | https://api.github.com/repos/huggingface/transformers/issues/256/events | https://github.com/huggingface/transformers/issues/256 | 407,051,972 | MDU6SXNzdWU0MDcwNTE5NzI= | 256 | does run_lm_finetuning.py actually use --eval_batch_size? | {
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"No, there's no evaluation step in the example script yet. What I can recommend is using your downstream task for evaluation of the pretrained BERT. Alternatively, you could of course also add some evaluation of the LM / nextSentence loss on a validation set.",
"Perhaps for clarity then, that parameter should be ... | 1,549 | 1,551 | 1,551 | NONE | null | I'm looking through this code (thanks so much for writing it, btw) and I'm not seeing whether it actually uses eval_batch_size at all. If it doesn't, is it still performing an evaluation step to assess goodness of fit? | {
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https://api.github.com/repos/huggingface/transformers/issues/255 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/255/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/255/comments | https://api.github.com/repos/huggingface/transformers/issues/255/events | https://github.com/huggingface/transformers/issues/255 | 407,024,928 | MDU6SXNzdWU0MDcwMjQ5Mjg= | 255 | Error while using Apex | {
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"Hi @chenyangh,\r\nYou need to install `apex` with the C++ and CUDA extensions:\r\n```bash\r\ngit clone https://github.com/NVIDIA/apex.git\r\ncd apex\r\npython setup.py install --cuda_ext --cpp_ext\r\n```",
"@thomwolf \r\nThanks!",
"@thomwolf After doing what you wrote, I got this error.\r\n\r\ntorch.__version_... | 1,549 | 1,576 | 1,549 | NONE | null | Hi, I am trying to do mixed precision training, but I have countered a problem that seems to be related to the LayerNorm implementation of Apex. I have the following error msg while running the example (same error for my other code).
`
Traceback (most recent call last):
File "run_lm_finetuning.py", line 648, i... | {
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https://api.github.com/repos/huggingface/transformers/issues/253 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/253/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/253/comments | https://api.github.com/repos/huggingface/transformers/issues/253/events | https://github.com/huggingface/transformers/pull/253 | 406,979,960 | MDExOlB1bGxSZXF1ZXN0MjUwNTU2Njk0 | 253 | Merge pull request #1 from huggingface/master | {
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https://api.github.com/repos/huggingface/transformers/issues/252 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/252/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/252/comments | https://api.github.com/repos/huggingface/transformers/issues/252/events | https://github.com/huggingface/transformers/issues/252 | 406,919,939 | MDU6SXNzdWU0MDY5MTk5Mzk= | 252 | BERT tuning all parameters? | {
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"In the examples scripts, it tunes the whole model.\r\nBut BERT models classes are just regular PyTorch `nn.Modules` so you can also freeze layer like you would do in any PyTorch module."
] | 1,549 | 1,549 | 1,549 | CONTRIBUTOR | null | Just a clarification question:
when tuning bert parameters (say, for SQUAD), does it tune the parameters of the final parameter or the whole BERT model?
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https://api.github.com/repos/huggingface/transformers/issues/251 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/251/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/251/comments | https://api.github.com/repos/huggingface/transformers/issues/251/events | https://github.com/huggingface/transformers/pull/251 | 406,419,974 | MDExOlB1bGxSZXF1ZXN0MjUwMTIyMDA2 | 251 | Only keep the active part mof the loss for token classification | {
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"Thanks Thibault!"
] | 1,549 | 1,549 | 1,549 | CONTRIBUTOR | null | If attention mask is not none, then we want to restrict our loss to the items which are not padding (hereby assumed those that have attention_mask = 1). This is important if doing e.g NER. | {
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https://api.github.com/repos/huggingface/transformers/issues/250 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/250/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/250/comments | https://api.github.com/repos/huggingface/transformers/issues/250/events | https://github.com/huggingface/transformers/pull/250 | 406,086,511 | MDExOlB1bGxSZXF1ZXN0MjQ5ODc2Mjk2 | 250 | Fix squad answer start and end position | {
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"Thanks @cooelf, there was a PR merging `run_squad` and `run_squad2` that also fixed this issue."
] | 1,549 | 1,549 | 1,549 | CONTRIBUTOR | null | Previous version might miss some overlong answer start and end indices (which should be 0), and sometimes the start/end positions would be outside the model inputs.
Doc_start and doc_end are based on tokenized subword sequences while example.start_position and example.end_position are indices in original word-level,... | {
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"The weights of the BERT base model are getting updated while finetuning the network.",
"Indeed"
] | 1,549 | 1,549 | 1,549 | NONE | null | I just wanted to ask if the weights of the Bert base model are getting updated while fine tuning Bert for Question answering. I see that the Bert for QA is a model with A linear layer on top of Bert pre-trained model. I am trying to reproduce the same model in keras. Could any one tell me if i should freeze the layers... | {
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https://api.github.com/repos/huggingface/transformers/issues/248 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/248/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/248/comments | https://api.github.com/repos/huggingface/transformers/issues/248/events | https://github.com/huggingface/transformers/pull/248 | 405,819,449 | MDExOlB1bGxSZXF1ZXN0MjQ5Njk0NjIx | 248 | fix prediction on run-squad.py example | {
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"Thanks @JoeDumoulin "
] | 1,549 | 1,549 | 1,549 | CONTRIBUTOR | null | run_squad.py exits with an error when running do_predict without training. The error is due to the model_state_dict not existing when --do_predict is selected.
Traceback (most recent call last):
File "run_squad.py", line 980, in <module>
main()
File "run_squad.py", line 923, in main
model_state_dict... | {
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https://api.github.com/repos/huggingface/transformers/issues/247 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/247/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/247/comments | https://api.github.com/repos/huggingface/transformers/issues/247/events | https://github.com/huggingface/transformers/issues/247 | 405,757,654 | MDU6SXNzdWU0MDU3NTc2NTQ= | 247 | Multilabel classification and diverging loss | {
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"Hey, I am working on something similar. I feel like the original code might be incorrect. They seem to directly take the output of the model as 'loss' without applying any criteria. But I might be totally wrong. ",
"Hey! :)\r\n\r\nReally? What do you mean by criteria?\r\n\r\nI tried to artificially change my dat... | 1,549 | 1,559 | 1,549 | NONE | null | Hi,
I'm not sure I'm posting this at the right spot but I am trying to use your excellent implementation to do some multi label classification on some text. I basically adapted the run_classifier.py code to a Jupyter Notebook and change a little bit the BERT Sequence Classifier model so it can handle multilabel clas... | {
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https://api.github.com/repos/huggingface/transformers/issues/246 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/246/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/246/comments | https://api.github.com/repos/huggingface/transformers/issues/246/events | https://github.com/huggingface/transformers/pull/246 | 405,581,136 | MDExOlB1bGxSZXF1ZXN0MjQ5NTA5Njg5 | 246 | Accurate SQuAD answer start and end position | {
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Doc_start and doc_end are based on tokenized subword sequences while example.start_position and example.end_position are indices in original word-level, w... | {
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https://api.github.com/repos/huggingface/transformers/issues/245 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/245/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/245/comments | https://api.github.com/repos/huggingface/transformers/issues/245/events | https://github.com/huggingface/transformers/issues/245 | 405,396,619 | MDU6SXNzdWU0MDUzOTY2MTk= | 245 | can you do a new release + pypi | {
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"Hi Joel, yes the new release (0.5.0) is pretty much ready (remaining work on branches `fifth-release` and `transfo-xl` to finish testing the newly added pre-trained OpenAI GPT and Transformer-XL).\r\n\r\nLikely next week.",
"Awesome, thanks!\n\nOn Thu, Jan 31, 2019, 11:06 AM Thomas Wolf <notifications@github.com... | 1,548 | 1,549 | 1,549 | CONTRIBUTOR | null | we've been getting some requests to incorporate newer features into allennlp that are only on master (e.g. `never_split`).
thanks! | {
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https://api.github.com/repos/huggingface/transformers/issues/244 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/244/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/244/comments | https://api.github.com/repos/huggingface/transformers/issues/244/events | https://github.com/huggingface/transformers/pull/244 | 405,184,953 | MDExOlB1bGxSZXF1ZXN0MjQ5MTk4NTU1 | 244 | Avoid confusion of inplace LM masking | {
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"Great, thanks @tholor!"
] | 1,548 | 1,549 | 1,549 | NONE | null | Fix confusion of LM masking that happens inplace. Discussed in https://github.com/huggingface/pytorch-pretrained-BERT/issues/243 and https://github.com/huggingface/pytorch-pretrained-BERT/issues/226 | {
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https://api.github.com/repos/huggingface/transformers/issues/243 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/243/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/243/comments | https://api.github.com/repos/huggingface/transformers/issues/243/events | https://github.com/huggingface/transformers/issues/243 | 405,156,658 | MDU6SXNzdWU0MDUxNTY2NTg= | 243 | seems there is a bug in fine tuning language model | {
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"I believe this is not a bug you are referring to, but indeed some confusing part of the code that we should probably change to avoid future confusion. `tokens_a` get masked **inplace** by the method `random_word`. `tokens_a` and `t1_random` refer indeed to the same objects. You can see that the input got masked al... | 1,548 | 1,548 | 1,548 | NONE | null | For masked language model, the input should be tokens masked. But in examples/run_lm_finetuning.py, input is not masked.
In method convert_example_to_features, is it supposed to use masked output as token input?
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for to... | {
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https://api.github.com/repos/huggingface/transformers/issues/242 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/242/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/242/comments | https://api.github.com/repos/huggingface/transformers/issues/242/events | https://github.com/huggingface/transformers/pull/242 | 404,944,006 | MDExOlB1bGxSZXF1ZXN0MjQ5MDEyODU5 | 242 | Fix argparse type error | {
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"Thanks Surya!"
] | 1,548 | 1,549 | 1,549 | CONTRIBUTOR | null | Resolved the following error on executing `run_squad2.py --help`
```TypeError: %o format: an integer is required, not dict``` | {
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https://api.github.com/repos/huggingface/transformers/issues/241 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/241/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/241/comments | https://api.github.com/repos/huggingface/transformers/issues/241/events | https://github.com/huggingface/transformers/issues/241 | 404,905,842 | MDU6SXNzdWU0MDQ5MDU4NDI= | 241 | Tokenization doesn't seem to match BERT paper | {
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"WordPiece tokenization depends on the particular BERT model: In general, one model, say, bert-based-cased will produce a different tokenization than another, say, bert-large-uncased.\r\n\r\nIf you try all models, one or more might produce the tokenization shown in the example in the paper.\r\nIt might also happen ... | 1,548 | 1,548 | 1,548 | CONTRIBUTOR | null | In the [BERT paper](https://arxiv.org/abs/1810.04805) section 4.3 ("Named Entity Recognition") there is an example of some tokenized text:
```python
['Jim', 'Hen', '##son', 'was', 'a', 'puppet', '##eer']
```
However, when I take that sentence and try to tokenize it myself with `BertTokenizer` from this repo
... | {
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https://api.github.com/repos/huggingface/transformers/issues/240 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/240/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/240/comments | https://api.github.com/repos/huggingface/transformers/issues/240/events | https://github.com/huggingface/transformers/pull/240 | 404,901,084 | MDExOlB1bGxSZXF1ZXN0MjQ4OTc5MjIw | 240 | Minor update in README | {
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"Thanks Girishkumar!"
] | 1,548 | 1,549 | 1,549 | CONTRIBUTOR | null | Updated links to classes in `modeling.py` | {
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https://api.github.com/repos/huggingface/transformers/issues/239 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/239/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/239/comments | https://api.github.com/repos/huggingface/transformers/issues/239/events | https://github.com/huggingface/transformers/issues/239 | 404,850,329 | MDU6SXNzdWU0MDQ4NTAzMjk= | 239 | cannot load BERTAdam when restoring from BioBert | {
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"I see. This is because they didn't use the same names for the adam optimizer variables than the Google team. I'll see if I can find a simple way around this for future cases.\r\n\r\nIn the mean time, you can install `pytorch-pretrained-bert` from the master (`git clone ...` and `pip install -e .`) and add the name... | 1,548 | 1,551 | 1,551 | NONE | null | I am trying to convert the recently released BioBert checkpoint: https://github.com/naver/biobert-pretrained
The conversion script loads the checkpoint, but appears to balk at BERTAdam when building the Pytorch model.
```
...
Building PyTorch model from configuration: {
"attention_probs_dropout_prob": 0.1,
... | {
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https://api.github.com/repos/huggingface/transformers/issues/238 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/238/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/238/comments | https://api.github.com/repos/huggingface/transformers/issues/238/events | https://github.com/huggingface/transformers/issues/238 | 404,624,962 | MDU6SXNzdWU0MDQ2MjQ5NjI= | 238 | padded positions are ignored when embedding position ids | {
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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"
] | 1,548 | 1,557 | 1,557 | NONE | null | When embedding position ids, all positions are considered.
```
seq_length = input_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
```
This is different from most transformer implementations.
Should it be
```
position_ids = np.array([
[pos_i+1 if w_i != ... | {
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https://api.github.com/repos/huggingface/transformers/issues/237 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/237/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/237/comments | https://api.github.com/repos/huggingface/transformers/issues/237/events | https://github.com/huggingface/transformers/issues/237 | 404,604,613 | MDU6SXNzdWU0MDQ2MDQ2MTM= | 237 | How can I change vocab size for pretrained model? | {
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"Hi,\r\n\r\nIf you want to modify the vocabulary, you should refer to this part of the original repo `README` https://github.com/google-research/bert#learning-a-new-wordpiece-vocabulary",
"If you don't want a complete new vocabulary (which would require training from scratch), but extend the pretrained one with a... | 1,548 | 1,689 | 1,549 | NONE | null | Is there way to change (expand) vocab size for pretrained model?
When I input the new token id to model, it returns:
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1108 with torch.no_grad():
11... | {
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"Ok. I see you included this. (max_sent_length, max_query_length)\r\nI will debug my error.You probably can close this issue. "
] | 1,548 | 1,548 | 1,548 | NONE | null | Hi, I tried to train squad model on a different dataset where I have lengthier questions/contexts. It gave memory error
CUDA out of memory. Tried to allocate 4.50 MiB (GPU 5; 11.78 GiB total capacity;
This error seems to happen in pytorch when there are lengthier data points ( pytorch tells how much it tried to ... | {
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"You can download the Tensorflow weights from Google's BERT repo and convert them as detailed in the readme of the present repo.",
"Hi tomwolf,\r\n\r\nI am new to XLNet and have the same issue as above. \r\n\r\nCould you direct me to the readme of the issue? I am not able to find it.\r\n\r\nI modified my code to ... | 1,548 | 1,620 | 1,549 | NONE | null | When I try to run BERT training, I get the following error during the vocabulary download:
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-large-uncased-vocab.txt (Caused by NewConnectionError('<urllib3.conne... | {
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"1. For evaluation I would advise the maximum batch size that your GPU allows. You will be able to use more efficiently this way.\r\n\r\n2. I think you will be better off by using a single thread.",
"Thanks! How can i figure out optimal batch size? I want to try tesla k80",
"You increase it gradually and when t... | 1,548 | 1,551 | 1,551 | NONE | null | Hi!
1) Help me please figure out, what would be optimal batch size for evaluating nextSentencePrediction model? For performance. Is it same as used during pre-training (128)?
2) If i building high performance evaluating backend on CUDA, would it be a good idea to use several threads with bert model in each, or its ... | {
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"You can use it to get the current learning rate of the `BertAdam` optimizer (which vary according to the schedules discussed in #195)."
] | 1,548 | 1,549 | 1,549 | NONE | null | I use a Model based on BertModel, and when I use the BertAdam the learning rate isn't changed. And when I use `get_lr()`, the return result is `[0]`. And I see the length of state isn't 0, but why I get that? | {
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https://api.github.com/repos/huggingface/transformers/issues/231 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/231/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/231/comments | https://api.github.com/repos/huggingface/transformers/issues/231/events | https://github.com/huggingface/transformers/issues/231 | 403,574,123 | MDU6SXNzdWU0MDM1NzQxMjM= | 231 | Why is the output bias computed separately? | {
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"The code section you linked follows the original TensorFlow code: https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_pretraining.py#L257",
"Exactly."
] | 1,548 | 1,548 | 1,548 | NONE | null | Hi !
Sorry if this is a dumb question, but I don't understand why is the bias [added separately](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L379) to the decoder weights instead of using `self.decoder = nn.Linear(num_features, num_tokens, bias=True)`? Isn't ... | {
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https://api.github.com/repos/huggingface/transformers/issues/230 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/230/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/230/comments | https://api.github.com/repos/huggingface/transformers/issues/230/events | https://github.com/huggingface/transformers/issues/230 | 403,494,487 | MDU6SXNzdWU0MDM0OTQ0ODc= | 230 | Cleaning `~/.pytorch_pretrained_bert` | {
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"This folder contains the pretrained model weights as they have been trained by google and the vocabulary files for the tokenizer.\r\n\r\nI would not remove it unless you are really tight on disk space, in this case I guess you could only keep the `.json` files with the vocabulary and load your finetuned model.",
... | 1,548 | 1,548 | 1,548 | CONTRIBUTOR | null | What is inside `~/.pytorch_pretrained_bert`? Is it just the downloaded pre-trained model weights? Is it safe to remove this directory? | {
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"It is, check the nice recent work of Guillaume Lample and Alexis Conneau: https://arxiv.org/abs/1901.07291"
] | 1,548 | 1,548 | 1,548 | NONE | null | If true, is there an example? | {
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https://api.github.com/repos/huggingface/transformers/issues/228 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/228/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/228/comments | https://api.github.com/repos/huggingface/transformers/issues/228/events | https://github.com/huggingface/transformers/issues/228 | 403,423,004 | MDU6SXNzdWU0MDM0MjMwMDQ= | 228 | Freezing base transformer weights | {
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"Hi!\r\n\r\nYou can modify the trainable attributes as described in #95.",
"Thanks!"
] | 1,548 | 1,548 | 1,548 | CONTRIBUTOR | null | As I understand, say if I'm doing a classification task, then the transformer weights, along with the top classification layer weights, are both trainable (i.e. `requires_grad=True`), correct? If so, is there a way to freeze the transformer weights, but only train the top layer? Is that a good idea in general when I ha... | {
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https://api.github.com/repos/huggingface/transformers/issues/227 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/227/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/227/comments | https://api.github.com/repos/huggingface/transformers/issues/227/events | https://github.com/huggingface/transformers/issues/227 | 403,186,108 | MDU6SXNzdWU0MDMxODYxMDg= | 227 | RuntimeError: Expected object of backend CUDA but got backend CPU for argument #3 'index' | {
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"You failed to move the tensors to GPU. \r\nReplace your code with this:\r\n```\r\ninput_ids_tensor = input_ids_tensor.to(self.device)\r\nsegment_ids_tensor = segment_ids_tensor.to(self.device)\r\ninput_mask_tensor = input_mask_tensor.to(self.device)\r\n```",
"Ah I didn't realize they don't work in-place (unlike ... | 1,548 | 1,594 | 1,548 | CONTRIBUTOR | null | Here is the complete error message:
```
Traceback (most recent call last):
File "app/set_expantion_eval.py", line 118, in <module>
map_n=flags.map_n)
File "app/set_expantion_eval.py", line 62, in Eval
expansionWithScores = BE.set_expansion_tensorized(seeds, ["1"])
File "/mnt/castor/seas_home/d/d... | {
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https://api.github.com/repos/huggingface/transformers/issues/226 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/226/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/226/comments | https://api.github.com/repos/huggingface/transformers/issues/226/events | https://github.com/huggingface/transformers/issues/226 | 403,125,784 | MDU6SXNzdWU0MDMxMjU3ODQ= | 226 | Logical error in the run_lm_finetuning? | {
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"Hi, @snakers4,\r\n\r\nI think this part is correct. \r\nThe input comes from `tokens` and `input_ids` in line 371, some of which are _already_ masked/altered, and the LM targets are `lm_label_ids`, which contain the original tokens. \r\nNote that `random_word`, called in line 331 and 332, masks the words in `token... | 1,548 | 1,548 | 1,548 | NONE | null | Hi,
@thomwolf @nhatchan
@tholor @deepset-ai
Many thanks for amazing work with this repository =)
I maybe grossly wrong or just missed some line of the code somewhere, but it seems to me that there is a glaring issue in the overall logic of `examples/run_lm_finetuning.py` - I guess you never pre-trained the m... | {
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"Hi, 512 tokens if you use the pre-trained models. Any length you want if you train your models from scratch.",
"could we set it smaller ? cause if i set it as 512, then result is out of memory",
"You can just send a smaller input in the model, no need to go to the max",
"thank you @thomwolf "
] | 1,548 | 1,550 | 1,548 | NONE | null | is there an max sentence length for this bert code? | {
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"Hi @hahmyg, please refer to the relevant section in the original implementation repository: https://github.com/google-research/bert#learning-a-new-wordpiece-vocabulary."
] | 1,548 | 1,548 | 1,548 | NONE | null | for specific task, it is required to add new vocabulary for tokenizer.
It is ok that re-training for those vocabulary for me :)
Is it possible to add new vocabulary for tokenizer?
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https://api.github.com/repos/huggingface/transformers/issues/222 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/222/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/222/comments | https://api.github.com/repos/huggingface/transformers/issues/222/events | https://github.com/huggingface/transformers/issues/222 | 402,509,287 | MDU6SXNzdWU0MDI1MDkyODc= | 222 | ConnectionError returned if Internet network is not stable | {
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"I'm guessing you're using some of the classes defined in modeling.py, such as one of the Bert \"pretrained models\" (e.g. any of the models that inherit from `PreTrainedBertModel`)? On construction, each of these classes takes a `config` argument, where `config` is a BertConfig object (also defined in modeling.py)... | 1,548 | 1,548 | 1,548 | NONE | null | Hi,
although I have download BERT pretrained model, "ConnectionError" returned if my Internet network is not very stable.
Function ```file_utils.cached_path``` needs stable internet.
Is there any way to avoid checking for amazonaws before loading bert-embedding? | {
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https://api.github.com/repos/huggingface/transformers/issues/221 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/221/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/221/comments | https://api.github.com/repos/huggingface/transformers/issues/221/events | https://github.com/huggingface/transformers/issues/221 | 402,169,653 | MDU6SXNzdWU0MDIxNjk2NTM= | 221 | Using BERT with custom QA dataset | {
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"I think that you should start by pretraining a BERT model on SQuAD to give it a sense on how to perform question answering and then try finetuning it to your task. This may already give you good results, if it doesn't you might have to dig a bit deeper in the model.\r\n\r\nI don't really know how adding your domai... | 1,548 | 1,562 | 1,548 | NONE | null | Hi,
I want to use BERT to train a QA model on a custom SQuAD-like dataset. Ideally, I would like to leverage the learning from the SQuAD dataset, and add fine-tuning on my custom dataset, which has specific vocabulary.
What is the best way to do this? | {
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https://api.github.com/repos/huggingface/transformers/issues/220 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/220/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/220/comments | https://api.github.com/repos/huggingface/transformers/issues/220/events | https://github.com/huggingface/transformers/issues/220 | 402,120,223 | MDU6SXNzdWU0MDIxMjAyMjM= | 220 | Questions Answering Example | {
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"Hi!\r\n\r\nYou can check this file that implements question answering on the SQuAD dataset: https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_squad.py",
"Thank you very much!",
"How can we use pre trained BertForQuestionAnswering model? I have looked into BertForNextSentencePredic... | 1,548 | 1,618 | 1,548 | NONE | null | Hello folks! Can you provide simple example how to use pytorch bert with pretrained model for questions answering? | {
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https://api.github.com/repos/huggingface/transformers/issues/219 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/219/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/219/comments | https://api.github.com/repos/huggingface/transformers/issues/219/events | https://github.com/huggingface/transformers/issues/219 | 402,103,567 | MDU6SXNzdWU0MDIxMDM1Njc= | 219 | How can I get the confidence score for the classification task | {
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"You can use `torch.nn.functional.softmax` on the `logits` that the model outputs here:\r\n\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/blob/0a9d7c7edb20a3e82cfbb4b72515575543784823/examples/run_classifier.py#L589-L591\r\n\r\nIt will give you the confidence score for each class."
] | 1,548 | 1,548 | 1,548 | NONE | null | In evaluation step, it seems it only shows the predicted label for the data instance.
How can I get the confidence score for each class? | {
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https://api.github.com/repos/huggingface/transformers/issues/218 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/218/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/218/comments | https://api.github.com/repos/huggingface/transformers/issues/218/events | https://github.com/huggingface/transformers/pull/218 | 401,987,478 | MDExOlB1bGxSZXF1ZXN0MjQ2Nzc4NTEz | 218 | Fix learning rate problems in run_classifier.py | {
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"Thanks @matej-svejda.\r\nSo this problem was actually introduced by adding NVIDIA's fp16 optimizer (`FusedAdam`) to the examples. This optimizer is a simple Adam which doesn't incorporate a learning rate schedule so we had to add a manual learning rate schedule in the examples.\r\nSo a better solution is to keep t... | 1,548 | 1,549 | 1,549 | CONTRIBUTOR | null | - Don't do warmup twice (in BertAdam and manually)
- Compute num_train_steps correctly for the case where gradient_accumulation_steps > 1. The current version might lead the the LR never leaving the warmup phase, depending on the value of gradient_accumulation_steps.
With these changes I get > 84% accuracy on MRPC,... | {
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https://api.github.com/repos/huggingface/transformers/issues/217 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/217/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/217/comments | https://api.github.com/repos/huggingface/transformers/issues/217/events | https://github.com/huggingface/transformers/issues/217 | 401,971,392 | MDU6SXNzdWU0MDE5NzEzOTI= | 217 | Loading fine_tuned BertModel fails due to prefix error | {
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"I think that you have find the problem but I'm not sure if your fix is the most appropriate way to deal with it. As this problem will only happen when we are loading a `BertModel` pretrained instance, maybe \r\n\r\n```\r\nload(model, prefix='' if hasattr(model, 'bert') or cls == BertModel else 'bert.')\r\n```\r\nw... | 1,548 | 1,592 | 1,548 | NONE | null | I am loading a pretrained BERT model with `BertModel.from_pretrained` as I feed the `pooled_output` representation directly to a loss without a head. After fine-tuning the model, I save it as in [`run_classifier.py`](https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py#L553).
... | {
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https://api.github.com/repos/huggingface/transformers/issues/216 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/216/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/216/comments | https://api.github.com/repos/huggingface/transformers/issues/216/events | https://github.com/huggingface/transformers/issues/216 | 401,890,579 | MDU6SXNzdWU0MDE4OTA1Nzk= | 216 | Training classifier does not work for more than two classes | {
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"What version of `pytorch-pretrained-BERT` are you using?\r\n\r\nIt seems to me that the change you are describing is already implemented.\r\n\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/blob/0a9d7c7edb20a3e82cfbb4b72515575543784823/examples/run_classifier.py#L560",
"Okay. It is my bad that I did no... | 1,548 | 1,548 | 1,548 | NONE | null | I am trying to run a classifier on the AGN data which has four classes. I am using the following command to train and evaluate the classifier.
python examples/run_classifier.py \
--task_name agn \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/AGN/ \
--bert_model bert-base-uncased ... | {
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https://api.github.com/repos/huggingface/transformers/issues/215 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/215/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/215/comments | https://api.github.com/repos/huggingface/transformers/issues/215/events | https://github.com/huggingface/transformers/issues/215 | 401,444,984 | MDU6SXNzdWU0MDE0NDQ5ODQ= | 215 | Loading fine tuned BertForMaskedLM | {
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"Maybe update to a recent version of `pytorch-pretrained-bert`?",
"I'm already using the last release.\r\n\r\nI don't have any issues running it on gpu. The problem append when using map_location\r\n",
"yeah yes. if you trained model in GPU, can't be loaded. we will change map_location=\"CPU\" in modeling.py li... | 1,548 | 1,600 | 1,548 | NONE | null | Hi,
I tried to fine tune BertForMaskedLM and it works. But i'm facing issues when I try to load the fine tuned model.
Here is the code I used to load the model :
```
model_state_dict = torch.load("./finetunedmodel/pytorch_model.bin", map_location='cpu')
model_fine = BertForMaskedLM.from_pretrained(pretrain... | {
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https://api.github.com/repos/huggingface/transformers/issues/214 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/214/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/214/comments | https://api.github.com/repos/huggingface/transformers/issues/214/events | https://github.com/huggingface/transformers/issues/214 | 401,264,959 | MDU6SXNzdWU0MDEyNjQ5NTk= | 214 | SQuAD output layer and the computation loss | {
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"Yes you can also do that."
] | 1,548 | 1,548 | 1,548 | NONE | null | Hi, I noticed that in the final linear layer of `BertForQuestionAnswering`, the loss is computed based on `start_logits` and `end_logits `. That means the positions of questions are also considered to compute loss. Maybe we should only care about the positions of context? e.g., by setting the question part of `start_lo... | {
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https://api.github.com/repos/huggingface/transformers/issues/213 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/213/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/213/comments | https://api.github.com/repos/huggingface/transformers/issues/213/events | https://github.com/huggingface/transformers/issues/213 | 401,219,022 | MDU6SXNzdWU0MDEyMTkwMjI= | 213 | will examples update the parameters of bert model? | {
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"Could you please show the part of the paper where you have seen mentioned, I haven't found it.\r\n\r\nAre you talking about this paragraph?\r\n\r\n>In this section we evaluate how well BERT performs in the feature-based approach by generating ELMo-like pre-trained contextual representations on the CoNLL-2003 NER t... | 1,548 | 1,550 | 1,549 | NONE | null | on the examples, it loads bert-base model and do some tasks, the paper says that it will fix the parameters of bert and only update the parameters of our tasks, but i find that it seems not fix parameters of bert? just load the model, and adds some layers to train | {
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https://api.github.com/repos/huggingface/transformers/issues/212 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/212/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/212/comments | https://api.github.com/repos/huggingface/transformers/issues/212/events | https://github.com/huggingface/transformers/issues/212 | 401,080,530 | MDU6SXNzdWU0MDEwODA1MzA= | 212 | Pytorch-Bert: Why this command: pip install pytorch-pretrained-bert doesn't work for me | {
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"Hi, most likely you'll have to switch to python 3.5 or newer!\r\nhttps://pypi.org/project/pytorch-pretrained-bert/ (check for requirements in the page: `Requires: Python >=3.5.0`) ",
"Indeed",
"Need some help. I'm experiencing the same problem with Python 3.7.3\r\n\r\nError code:\r\nERROR: Could not find a ve... | 1,547 | 1,566 | 1,549 | NONE | null | I try to install pytorch-bert using the command: pip install pytorch-pretrained-bert
However this doesn't work for me.
And the feedback is below:
Could not find a version that satisfies the requirement pytorch-pretrained-be
rt (from versions: )
No matching distribution found for pytorch-pretrained-bert
I al... | {
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https://api.github.com/repos/huggingface/transformers/issues/211 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/211/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/211/comments | https://api.github.com/repos/huggingface/transformers/issues/211/events | https://github.com/huggingface/transformers/issues/211 | 401,008,858 | MDU6SXNzdWU0MDEwMDg4NTg= | 211 | How convert pytorch to tf checkpoint? | {
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"I don't think such a conversion is currently implemented in this repository, but I have my own implementation here (if you're interested in adapting it for your use-case): https://github.com/nikitakit/self-attentive-parser/blob/8238e79e2089300db059eddff78229a09e254f70/export/export_bert.py#L94-L141",
"Thanks @ni... | 1,547 | 1,561 | 1,558 | NONE | null | How convert pytorch to tf checkpoint? | {
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https://api.github.com/repos/huggingface/transformers/issues/210 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/210/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/210/comments | https://api.github.com/repos/huggingface/transformers/issues/210/events | https://github.com/huggingface/transformers/issues/210 | 401,006,360 | MDU6SXNzdWU0MDEwMDYzNjA= | 210 | error: the following arguments are required: --bert_model, --output_dir | {
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"Check this site : https://github.com/huggingface/pytorch-pretrained-BERT and find your 2 Parameters \"--bert_model\",\" --output_dir\" . You'll find the Below example π \r\n\r\nExample π π― \r\n --bert_model : We can use BERT Models like : bert-base-uncased, bert-base-cased, bert-large-uncased, bert-large-cased... | 1,547 | 1,564 | 1,547 | NONE | null | the above error arose when i ran the run_squad.py in pycharm(i just copied and ran locally). so can anbody tell how to input these two parameters "--bert_model"," --output_dir" in the IDE? | {
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https://api.github.com/repos/huggingface/transformers/issues/209 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/209/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/209/comments | https://api.github.com/repos/huggingface/transformers/issues/209/events | https://github.com/huggingface/transformers/issues/209 | 400,968,613 | MDU6SXNzdWU0MDA5Njg2MTM= | 209 | Missing softmax in BertForQuestionAnswering after linear layer? | {
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"It depends on what you use as loss, as mentioned in the [documentation](https://pytorch.org/docs/stable/nn.html#crossentropyloss):\r\n\r\n>This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class. "
] | 1,547 | 1,547 | 1,547 | NONE | null | https://github.com/huggingface/pytorch-pretrained-BERT/blob/0a9d7c7edb20a3e82cfbb4b72515575543784823/pytorch_pretrained_bert/modeling.py#L1089-L1113
It seems there should be a softmax after the linear layer, or did I miss something? | {
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