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https://api.github.com/repos/huggingface/transformers/issues/369 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/369/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/369/comments | https://api.github.com/repos/huggingface/transformers/issues/369/events | https://github.com/huggingface/transformers/issues/369 | 420,149,402 | MDU6SXNzdWU0MjAxNDk0MDI= | 369 | BertForQuestionAnswering: How to split output between query hidden state and context hidden state | {
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"If you want only the context you can find the index from the segment vector by finding the last first 1 in the vector and splitting the query_context on that index. Then the context will be everything after the 1 index and everything before will be the question.",
"This issue has been automatically marked as sta... |
https://api.github.com/repos/huggingface/transformers/issues/604 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/604/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/604/comments | https://api.github.com/repos/huggingface/transformers/issues/604/events | https://github.com/huggingface/transformers/pull/604 | 443,042,368 | MDExOlB1bGxSZXF1ZXN0Mjc4MDEzMTAx | 604 | Fixing issue "Training beyond specified 't_total' steps with schedule 'warmup_linear'" reported in #556 | {
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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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"Looks good.",
"Ok merging thanks, sorry for the delay!"
] |
https://api.github.com/repos/huggingface/transformers/issues/630 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/630/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/630/comments | https://api.github.com/repos/huggingface/transformers/issues/630/events | https://github.com/huggingface/transformers/pull/630 | 447,029,680 | MDExOlB1bGxSZXF1ZXN0MjgxMTA4NTIx | 630 | Update run_squad.py | {
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https://api.github.com/repos/huggingface/transformers/issues/640 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/640/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/640/comments | https://api.github.com/repos/huggingface/transformers/issues/640/events | https://github.com/huggingface/transformers/pull/640 | 448,757,307 | MDExOlB1bGxSZXF1ZXN0MjgyNDM3NjM1 | 640 | Support latest multi language bert fine tune | {
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"merged_at": "2019... | **Affected function**: fine tune example file
**Update summary**:
- Fix issue of bert-base-multilingual not found by fixing uncased version name in argument dict
- Add support for cased version by adding the right name into argument dict | {
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"Great, thanks!"
] |
https://api.github.com/repos/huggingface/transformers/issues/646 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/646/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/646/comments | https://api.github.com/repos/huggingface/transformers/issues/646/events | https://github.com/huggingface/transformers/pull/646 | 450,131,288 | MDExOlB1bGxSZXF1ZXN0MjgzNTE2Njg5 | 646 | Fix link in README | {
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"Thanks!"
] |
https://api.github.com/repos/huggingface/transformers/issues/672 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/672/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/672/comments | https://api.github.com/repos/huggingface/transformers/issues/672/events | https://github.com/huggingface/transformers/pull/672 | 454,644,253 | MDExOlB1bGxSZXF1ZXN0Mjg3MDUyMjg0 | 672 | Add vocabulary and model config to the finetune output | {
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"Nice indeed, thanks @oliverguhr!"
] |
https://api.github.com/repos/huggingface/transformers/issues/679 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/679/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/679/comments | https://api.github.com/repos/huggingface/transformers/issues/679/events | https://github.com/huggingface/transformers/issues/679 | 455,615,467 | MDU6SXNzdWU0NTU2MTU0Njc= | 679 | Why the output of models are random. | {
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The code is:
`from pytorch_pretrained_bert import BertTokenizer, BertMode... | {
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"They won't be able to help you if you don't provide a code for reproducing your issue, as this is not an expected behaviour.",
"Thanks a lot for reminding. The issue is renewed with the code.",
"That's true! I can reproduce it also on my computer... Really weird!",
"You should use `model.eval()` to desacti... |
https://api.github.com/repos/huggingface/transformers/issues/665 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/665/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/665/comments | https://api.github.com/repos/huggingface/transformers/issues/665/events | https://github.com/huggingface/transformers/issues/665 | 453,744,807 | MDU6SXNzdWU0NTM3NDQ4MDc= | 665 | GPT-2 medium and large release? | {
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https://github.com/huggingface/pytorch-pretrained-BERT/blob/ee0308f79ded65dac82c53dfb03e9ff7f06aeee4/pytorch_pretrained_bert/modeling_gpt2.py#L42
When do you plan on supporting the medium (already released by OpenAI) and large versions (not released by OpenAI) of GPT-2?
... | {
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"Take a look at the `attention` branch @g-karthik:\r\n\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT/blob/attention/pytorch_pretrained_bert/modeling_gpt2.py#L42-L45",
"Thanks @julien-c, I had not looked at the file in the `attention` branch!",
"What is the recommended hardware setup for fine-tuning ... |
https://api.github.com/repos/huggingface/transformers/issues/677 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/677/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/677/comments | https://api.github.com/repos/huggingface/transformers/issues/677/events | https://github.com/huggingface/transformers/issues/677 | 455,296,243 | MDU6SXNzdWU0NTUyOTYyNDM= | 677 | Download the model without executing a Python script | {
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Is there a command to download a model (e.g. BertForMaskedLM) without having to execute a Python script?
For example, in Spacy, we can do `python -m spacy download en`. | {
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"Is this what you want?\r\n\r\n```python\r\nPRETRAINED_MODEL_ARCHIVE_MAP = {\r\n 'bert-base-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz\",\r\n 'bert-large-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz\",\r\n 'bert-base... |
https://api.github.com/repos/huggingface/transformers/issues/691 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/691/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/691/comments | https://api.github.com/repos/huggingface/transformers/issues/691/events | https://github.com/huggingface/transformers/pull/691 | 456,537,080 | MDExOlB1bGxSZXF1ZXN0Mjg4NTU2NDE3 | 691 | import class "GPT2MultipleChoiceHead" | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/691?src=pr&el=h1) Report\n> Merging [#691](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/691?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/b3f9e9451b3f999118f2299229bb13f2f69... | |
https://api.github.com/repos/huggingface/transformers/issues/690 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/690/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/690/comments | https://api.github.com/repos/huggingface/transformers/issues/690/events | https://github.com/huggingface/transformers/pull/690 | 456,489,857 | MDExOlB1bGxSZXF1ZXN0Mjg4NTI0NTE2 | 690 | Transformer XL ProjectedAdaptiveLogSoftmax output fix | {
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"Perfect, thanks @shashwath94!"
] |
https://api.github.com/repos/huggingface/transformers/issues/678 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/678/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/678/comments | https://api.github.com/repos/huggingface/transformers/issues/678/events | https://github.com/huggingface/transformers/issues/678 | 455,422,146 | MDU6SXNzdWU0NTU0MjIxNDY= | 678 | Transformer XL ProjectedAdaptiveLogSoftmax bug (maybe?) | {
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"Yes! We don't see that when we use the pre-trained model because the number of clusters is greater than zero anyway. Will fix.",
"Thank you. I created a PR since it was a small bug. #690 "
] |
https://api.github.com/repos/huggingface/transformers/issues/503 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/503/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/503/comments | https://api.github.com/repos/huggingface/transformers/issues/503/events | https://github.com/huggingface/transformers/pull/503 | 434,376,103 | MDExOlB1bGxSZXF1ZXN0MjcxMzgyMTEz | 503 | Fix possible risks of bpe on special tokens | {
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... | Hi developers !
When I use the openai tokenizer, I find it hard to handle the `special tokens` correctly (my library version is v0.6.1) , even though I have already defined them and told the tokenizer NEVER SPLIT them. It is because all tokens, including the special ones will be processed by BPE. So I add one line f... | {
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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"
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https://api.github.com/repos/huggingface/transformers/issues/680 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/680/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/680/comments | https://api.github.com/repos/huggingface/transformers/issues/680/events | https://github.com/huggingface/transformers/issues/680 | 455,627,186 | MDU6SXNzdWU0NTU2MjcxODY= | 680 | Limit on the input text length? | {
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I often get this error:
```
File "/miniconda3/envs/brightwater/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 268, in forward
position_embeddings = self.position_embeddings(position_ids)
File "/miniconda3/envs/brightwater/lib/python3.6/site-packages/torch/nn/modules/module.py"... | {
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"Yes, 512 tokens for Bert.",
"Thank you :) ",
"Is there a way to bypass this limit? To increase the number of words?"
] |
https://api.github.com/repos/huggingface/transformers/issues/450 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/450/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/450/comments | https://api.github.com/repos/huggingface/transformers/issues/450/events | https://github.com/huggingface/transformers/issues/450 | 429,293,867 | MDU6SXNzdWU0MjkyOTM4Njc= | 450 | Understanding pre-training and fine-tuning | {
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},... | closed | false | [] | null | 2 | 2019-04-04T13:55:47Z | 2019-06-17T14:54:30Z | 2019-06-17T14:54:30Z | null | COLLABORATOR | [] | null | null | null | null | I am confused about what these two steps actually do to the model. I would have assumed that pre-training is unsupervised (i.e. no labels) and, thus, the only thing that can be 'learned' is the embedding representations of all tokens. You can then use this pre-trained model (which is an 'empty' model but with pretraine... | {
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"Maybe a good introduction on the topic are the writings of @sebastianruder:\r\n- http://ruder.io/transfer-learning/\r\n- https://thegradient.pub/nlp-imagenet/\r\n- the ULMFiT paper: http://nlp.fast.ai/classification/2018/05/15/introducting-ulmfit.html\r\n\r\nRegarding your specific question of training a Bert mode... |
https://api.github.com/repos/huggingface/transformers/issues/465 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/465/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/465/comments | https://api.github.com/repos/huggingface/transformers/issues/465/events | https://github.com/huggingface/transformers/issues/465 | 431,026,223 | MDU6SXNzdWU0MzEwMjYyMjM= | 465 | Errors when using Apex | {
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```
ubuntu 16.04
nvidia driver 410.48
4 Titan V gpus
python 3.6.8
cuda 9
pytorch 1.0.1.post2
pytorch-pretrained-bert 0.6.1
```
I also tried python 3.7, cuda 10... | {
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"Can you try to run it with `CUDA_LAUNCH_BLOCKING=1` so we can see which exact CUDA call fails?\r\nAlso, do you have a simple way for me to try to reproduce this error?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Tha... |
https://api.github.com/repos/huggingface/transformers/issues/671 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/671/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/671/comments | https://api.github.com/repos/huggingface/transformers/issues/671/events | https://github.com/huggingface/transformers/issues/671 | 454,510,586 | MDU6SXNzdWU0NTQ1MTA1ODY= | 671 | BERT what's different with step and t_total | {
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:param step: which of t_total steps we're on
def get_lr(self, step, nowarn=False):
"""
:param step: which of t_total steps we're on
:param nowarn: set to True to suppress warning regarding training beyond specified '... | {
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https://api.github.com/repos/huggingface/transformers/issues/352 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/352/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/352/comments | https://api.github.com/repos/huggingface/transformers/issues/352/events | https://github.com/huggingface/transformers/issues/352 | 417,772,856 | MDU6SXNzdWU0MTc3NzI4NTY= | 352 | How to incrementally do fine tune train | {
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Please suggest how can I use newly generated pytorch_model.bin file and then increment it with my own training weights to get my own pytorch_suqad_plus_my_model.bin ? | {
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"I think my answer here can help you: https://github.com/huggingface/pytorch-pretrained-BERT/issues/332",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Hey @shuvadibp, did you fig... |
https://api.github.com/repos/huggingface/transformers/issues/693 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/693/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/693/comments | https://api.github.com/repos/huggingface/transformers/issues/693/events | https://github.com/huggingface/transformers/issues/693 | 456,725,533 | MDU6SXNzdWU0NTY3MjU1MzM= | 693 | Have no GPU to train language modelling | {
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I very appreciate what the authors created this repository, help us to more understand how BERT works and implement on several tasks.
So I have a problem with training because I have not GPU to train language modelling, I have Indonesian dataset (about 2G... | {
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"[You can train a tensorflow model using google colab for free](https://github.com/google-research/bert#using-bert-in-colab). After training it, you can [convert your tf model to pytorch](https://github.com/huggingface/pytorch-pretrained-BERT#command-line-interface). ",
"Or use 300 usd credit for google cloud, th... |
https://api.github.com/repos/huggingface/transformers/issues/452 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/452/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/452/comments | https://api.github.com/repos/huggingface/transformers/issues/452/events | https://github.com/huggingface/transformers/issues/452 | 429,634,551 | MDU6SXNzdWU0Mjk2MzQ1NTE= | 452 | Pregenerating data requires multiple documents | {
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I have a small fix for this... | {
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"Hi, I wrote that script! That's a tricky issue, though - what behaviour do you expect when your data is one long text? \r\n\r\nIn the original BERT repo, they used the document breaks to control sampling for the NextSentence task - 'random' next sentences were selected from a different document. I'm not sure there... |
https://api.github.com/repos/huggingface/transformers/issues/424 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/424/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/424/comments | https://api.github.com/repos/huggingface/transformers/issues/424/events | https://github.com/huggingface/transformers/issues/424 | 426,651,083 | MDU6SXNzdWU0MjY2NTEwODM= | 424 | Difference between base and large tokenizer? | {
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"I haven't looked in the details of the vocabularies for each model.\r\nIf you investigate this question, be sure to share the results here, it may interest others as well!",
"I did a diff on the two vocabulary files and there is no difference. As long as you use the uncased version at least. I haven't investigat... |
https://api.github.com/repos/huggingface/transformers/issues/700 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/700/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/700/comments | https://api.github.com/repos/huggingface/transformers/issues/700/events | https://github.com/huggingface/transformers/pull/700 | 458,060,090 | MDExOlB1bGxSZXF1ZXN0Mjg5NzQzNDY1 | 700 | Add an argument --model_size to convert_gpt2_checkpoint_to_pytorch.py | {
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... | Add an argument --model_size to convert_gpt2_checkpoint_to_pytorch.py that lets the user specify whether they want to convert a checkpoint from the 117M model or from the 345M model so that they don't have to create their own 345M json config file. | {
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https://api.github.com/repos/huggingface/transformers/issues/643 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/643/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/643/comments | https://api.github.com/repos/huggingface/transformers/issues/643/events | https://github.com/huggingface/transformers/issues/643 | 449,401,810 | MDU6SXNzdWU0NDk0MDE4MTA= | 643 | FileNotFoundError: [Errno 2] No such file or directory: 'uncased_L-12_H-768_A-12\\pytorch_model.bin' | {
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"I got the same problem. Did you solve the problem?",
"> I got the same problem. Did you solve the problem?\r\n\r\nYes. Actually the project folder of this implementation does not contain the `pytorch_model.bin` file. For loading the actual pretrained model, you have to use `BertModel.from_pretrained('bert-base-u... |
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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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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"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 ... |
https://api.github.com/repos/huggingface/transformers/issues/706 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/706/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/706/comments | https://api.github.com/repos/huggingface/transformers/issues/706/events | https://github.com/huggingface/transformers/pull/706 | 458,819,769 | MDExOlB1bGxSZXF1ZXN0MjkwMzM1NjU0 | 706 | Update run_squad.py | {
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... | redundant else part, model = BertForQuestionAnswering.from_pretrained(args.bert_model) is already written in a different line | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/706?src=pr&el=h1) Report\n> Merging [#706](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/706?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/c304593d8fa93f25febe1458c63497a8467... |
https://api.github.com/repos/huggingface/transformers/issues/150 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/150/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/150/comments | https://api.github.com/repos/huggingface/transformers/issues/150/events | https://github.com/huggingface/transformers/issues/150 | 394,596,898 | MDU6SXNzdWUzOTQ1OTY4OTg= | 150 | BertLayerNorm not loaded in CPU mode | {
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https://github.com/huggingface/pytorch-pretrained-BERT/blob/8da280ebbeca5ebd7561fd05af78c65df9161f92/pytorch_pretrained_bert/... | {
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"Hi @tholor, apex is a GPU specific extension.\r\nWhat kind of use-case do you have in which you have apex installed but no GPU (also fp16 doesn't work on CPU, it's not supported on PyTorch currently)?",
"The two cases I came across this: \r\n1) testing if some code works for both GPU and CPU (on a GPU machine wi... |
https://api.github.com/repos/huggingface/transformers/issues/715 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/715/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/715/comments | https://api.github.com/repos/huggingface/transformers/issues/715/events | https://github.com/huggingface/transformers/pull/715 | 459,477,532 | MDExOlB1bGxSZXF1ZXN0MjkwODI1Nzc5 | 715 | Include a reference for LM finetuning | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/715?src=pr&el=h1) Report\n> Merging [#715](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/715?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/c304593d8fa93f25febe1458c63497a8467... |
https://api.github.com/repos/huggingface/transformers/issues/714 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/714/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/714/comments | https://api.github.com/repos/huggingface/transformers/issues/714/events | https://github.com/huggingface/transformers/pull/714 | 459,465,533 | MDExOlB1bGxSZXF1ZXN0MjkwODE4MDg2 | 714 | Correct a broken link on README | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/714?src=pr&el=h1) Report\n> Merging [#714](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/714?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/c304593d8fa93f25febe1458c63497a8467... |
https://api.github.com/repos/huggingface/transformers/issues/487 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/487/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/487/comments | https://api.github.com/repos/huggingface/transformers/issues/487/events | https://github.com/huggingface/transformers/issues/487 | 432,963,792 | MDU6SXNzdWU0MzI5NjM3OTI= | 487 | BERT multilingual for zero-shot classification | {
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I'm interested in solving a classification problem in which I train the model on one language and make the predictions for another one (zero-shot classification).
It is said in the README for the multilingual BERT model (https://github.com/google-research/bert/blob/master/multilingual.md) that:
> For tokeniz... | {
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"UPD. I tried it with bert-multilingual-cased, but the results are still bad. A number of very simple (text, translated text) give very different probability distributions (the translated versions almost always fall into one major category).\r\n\r\nSpecifiically, **I fine-tune pre-trained bert-multilingual-cased on... |
https://api.github.com/repos/huggingface/transformers/issues/486 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/486/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/486/comments | https://api.github.com/repos/huggingface/transformers/issues/486/events | https://github.com/huggingface/transformers/issues/486 | 432,884,928 | MDU6SXNzdWU0MzI4ODQ5Mjg= | 486 | Difference between this repo and bert-as-service | {
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I wondered if anybody knows the difference between the `BertModel` of this repo and [bert-as-service](https://github.com/hanxiao/bert-as-service).
1. I cannot get the same result between these two even if I use the same checkpoint. pytorch-pretrained-BERT yield a lower acc and slower convergence.
2. The m... | {
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"Yes there are some optimizations. Please take a look here:\r\n\r\nhttps://hanxiao.github.io/2019/01/02/Serving-Google-BERT-in-Production-using-Tensorflow-and-ZeroMQ/#engineering-building-a-scalable-service",
"Hi, there is no specific relation between the present repo (which provides PyTorch implementations of se... |
https://api.github.com/repos/huggingface/transformers/issues/481 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/481/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/481/comments | https://api.github.com/repos/huggingface/transformers/issues/481/events | https://github.com/huggingface/transformers/issues/481 | 432,700,310 | MDU6SXNzdWU0MzI3MDAzMTA= | 481 | BERT does mask-answering or sequence prediction or both??? | {
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I seen BERT adds a word at the end of your input, like sequence prediction, elongating your input text.
But I read that BERT has been trained at (and is a pro at) filling in the blank word mask, and in fact CANNOT do sequence prediction at the end... | {
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"I'm not sure I understand your question.\r\n\r\nBut one thing BERT can do is mask-answering indeed (guessing a word in the middle of a sentence).\r\n\r\nBERT is quite bad at doing sequence prediction at the end of an input because it's not trained on partial sentences.",
"When BERT fills-in a MASK, does it alway... |
https://api.github.com/repos/huggingface/transformers/issues/502 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/502/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/502/comments | https://api.github.com/repos/huggingface/transformers/issues/502/events | https://github.com/huggingface/transformers/issues/502 | 434,217,681 | MDU6SXNzdWU0MzQyMTc2ODE= | 502 | How to obtain attention values for each layer | {
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Please correct me if I am wrong.
From my understanding, The encoded values for each layer (12 of them for base model) would be returned when we run our results through the pre-trained model.
However, I would like to examine the self-attention values for each layer. Is there a way I can extract that ou... | {
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"Not really.\r\nYou should build a new sub-class of `BertPreTrainedModel` which is identical to `BertModel`but send back self-attention values in addition to the hidden states.\r\n",
"I see. Thank you! ",
"Hi, \r\n\r\nJust to add on. If this is what I would be doing, would it be advisable to fine-tune the weigh... |
https://api.github.com/repos/huggingface/transformers/issues/479 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/479/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/479/comments | https://api.github.com/repos/huggingface/transformers/issues/479/events | https://github.com/huggingface/transformers/issues/479 | 432,688,857 | MDU6SXNzdWU0MzI2ODg4NTc= | 479 | Using GPT2 to implement GLTR | {
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Like getting probabilities for each word in a sequence.? | {
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"the allen institute tool may help you....it has probabilities of next 10 words for when adding a next word....i think the prorbablities are shown....may just be the 10 words but the git code is available i think so may be what you want.",
"The output of `GPT2LMHeadModel` are logits so you can just apply a softma... |
https://api.github.com/repos/huggingface/transformers/issues/709 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/709/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/709/comments | https://api.github.com/repos/huggingface/transformers/issues/709/events | https://github.com/huggingface/transformers/issues/709 | 459,154,612 | MDU6SXNzdWU0NTkxNTQ2MTI= | 709 | layer_norm_eps | {
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Or I am... | {
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"Because some people wanted to configure this: https://github.com/huggingface/pytorch-pretrained-BERT/pull/585",
"So I have to add `config.layer_norm_eps = 1e-12` if I am taking the config from the link above ?",
"You don't need to, it's the default value when instantiating a `BertConfig` class.",
"When I pri... |
https://api.github.com/repos/huggingface/transformers/issues/719 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/719/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/719/comments | https://api.github.com/repos/huggingface/transformers/issues/719/events | https://github.com/huggingface/transformers/issues/719 | 459,734,720 | MDU6SXNzdWU0NTk3MzQ3MjA= | 719 | Embedding and predictions in one forward pass | {
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"Yes, just make your own PyTorch model taking inspiration from BertModel and BertForMaskedLM.\r\nIf you sub-class `BertPreTrainedModel`, you'll be able to load the pretrained weights using the `from_pretrained()` method",
"Okay, thank you :) "
] |
https://api.github.com/repos/huggingface/transformers/issues/710 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/710/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/710/comments | https://api.github.com/repos/huggingface/transformers/issues/710/events | https://github.com/huggingface/transformers/issues/710 | 459,174,943 | MDU6SXNzdWU0NTkxNzQ5NDM= | 710 | A way to increase input length limitation? | {
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Is there a way to increase input length limitation of 512 tokens?
Maybe something to change in the code? | {
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"No way as far as I can tell, this is a fundamental limitation for absolute position pre-trained models (i.e. BERT, GPT, GPT-2)",
"Okay thank you!"
] |
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... |
https://api.github.com/repos/huggingface/transformers/issues/703 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/703/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/703/comments | https://api.github.com/repos/huggingface/transformers/issues/703/events | https://github.com/huggingface/transformers/issues/703 | 458,075,677 | MDU6SXNzdWU0NTgwNzU2Nzc= | 703 | "Received 'killed' signal" during the circleci python3 build after submitting PR | {
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Edit: Attached image below
<img widt... | {
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"Yeah I've removed the memory-heavy tests",
"Thanks!"
] |
https://api.github.com/repos/huggingface/transformers/issues/725 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/725/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/725/comments | https://api.github.com/repos/huggingface/transformers/issues/725/events | https://github.com/huggingface/transformers/issues/725 | 460,533,863 | MDU6SXNzdWU0NjA1MzM4NjM= | 725 | BERT Input size reduced to half in forward function | {
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```
class MyBertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config, num_labels=2, output_attentions=False):
super(MyBertForSequenceClassification, self).__init__(config)... | {
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"Maybe you have 2 GPUs?",
"@thomwolf Thanks a lot. I forgot I was running on two gpus. \r\n"
] |
https://api.github.com/repos/huggingface/transformers/issues/676 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/676/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/676/comments | https://api.github.com/repos/huggingface/transformers/issues/676/events | https://github.com/huggingface/transformers/issues/676 | 455,135,026 | MDU6SXNzdWU0NTUxMzUwMjY= | 676 | Importing TF checkpoint as BertForTokenClassificiation | {
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I've been stuck with trying to load TensorFlow checkpoints to be used by `pytorch-pretrained-bert` as `BertForTokenClassification`.
**pytorch-pretrained-BERT Version:** Installed from latest master branch.
**What works:**
```python
config = BertConfig.from_json_file(CONFIG_FILE)
model = Bert... | {
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"Hello everyone,\r\n\r\nI have temporarily come up with a workaround for this. Not sure if it's the best solution but it works. What I did was I essentially merged what `load_tf_weights_in_bert()` and what part of `BertPreTrainedModel.from_pretrained()` was doing. `BertPreTrainedModel` is the parent class of `BertF... |
https://api.github.com/repos/huggingface/transformers/issues/685 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/685/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/685/comments | https://api.github.com/repos/huggingface/transformers/issues/685/events | https://github.com/huggingface/transformers/pull/685 | 456,117,213 | MDExOlB1bGxSZXF1ZXN0Mjg4MjI1NjQw | 685 | Add method to directly load TF Checkpoints for Bert models | {
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... | ## Summary
In this PR, I changed some documentation, and added `from_tf_ckpt()` method to `BertPreTrainedModel`.
This method allows users to directly load TensorFlow checkpoints (e.g. `model.ckpt-XXXX` files) for a task specific Bert model like `BertForTokenClassification` or `BertForSequenceClassification`.
**F... | {
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"Hi, I'm not convinced we need this additional option, see my [comment](https://github.com/huggingface/pytorch-pretrained-BERT/issues/676#issuecomment-502252962) in the associated issue thread.",
"As mentioned in, https://github.com/huggingface/pytorch-pretrained-BERT/issues/676#issuecomment-506134506, I recognis... |
https://api.github.com/repos/huggingface/transformers/issues/359 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/359/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/359/comments | https://api.github.com/repos/huggingface/transformers/issues/359/events | https://github.com/huggingface/transformers/pull/359 | 418,872,236 | MDExOlB1bGxSZXF1ZXN0MjU5NTQwOTU0 | 359 | Update run_gpt2.py | {
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"Thanks Elon"
] | |
https://api.github.com/repos/huggingface/transformers/issues/731 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/731/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/731/comments | https://api.github.com/repos/huggingface/transformers/issues/731/events | https://github.com/huggingface/transformers/pull/731 | 461,122,810 | MDExOlB1bGxSZXF1ZXN0MjkyMTExNDc3 | 731 | merge | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/731?src=pr&el=h1) Report\n> Merging [#731](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/731?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/98dc30b21e3df6528d0dd17f0910ffea12b... | |
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/718?src=pr&el=h1) Report\n> Merging [#718](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/718?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/98dc30b21e3df6528d0dd17f0910ffea12b... |
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... | XLNetForSequenceClassification doesn't have tie_weights() but initialization will call it, or we can made a function in XLNetPretrainedModel? | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/716?src=pr&el=h1) Report\n> Merging [#716](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/716?src=pr&el=desc) into [xlnet](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/c946bb51a61f67b0c9eaae1c9cf6f164a774... |
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https://api.github.com/repos/huggingface/transformers/issues/723 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/723/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/723/comments | https://api.github.com/repos/huggingface/transformers/issues/723/events | https://github.com/huggingface/transformers/pull/723 | 460,283,950 | MDExOlB1bGxSZXF1ZXN0MjkxNDQzMTYz | 723 | Update Adam optimizer to follow pytorch convention for betas parameter (#510) | {
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Update optimiser to follow pytorch convention ([Adam Optimiser](https://pytorch.org/docs/stable/optim.html#torch.optim.Adam)) instead of tensorflow, to allow for better integration with other pytorch libraries and frameworks. | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/723?src=pr&el=h1) Report\n> Merging [#723](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/723?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/98dc30b21e3df6528d0dd17f0910ffea12b... |
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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... |
https://api.github.com/repos/huggingface/transformers/issues/460 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/460/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/460/comments | https://api.github.com/repos/huggingface/transformers/issues/460/events | https://github.com/huggingface/transformers/issues/460 | 430,584,037 | MDU6SXNzdWU0MzA1ODQwMzc= | 460 | run_classifier on CoLA fails with illegal memory access | {
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`CUDA_LAUNCH_BLOCKING=1 python run_classifier.py --task_name CoLA --do_train --do_eval --do_lower_case --data_dir /workspace/glue/CoLA/ --bert_model bert-base-... | {
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"My guess is that the inputs are either larger than the maximum input size of the model (512) or outside the vocabulary (larger than the vocabulary size).\r\n\r\nDo you think you can try to check this?",
"@ananyahjha93 did the implementation of the additional GLUE tasks. Maybe he has some additional insights on t... |
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... | closed | false | [] | null | 2 | 2019-04-22T17:19:40Z | 2019-06-29T09:11:32Z | 2019-06-29T09:11:32Z | null | NONE | [] | null | null | null | 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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"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"
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https://api.github.com/repos/huggingface/transformers/issues/513 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/513/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/513/comments | https://api.github.com/repos/huggingface/transformers/issues/513/events | https://github.com/huggingface/transformers/issues/513 | 435,620,361 | MDU6SXNzdWU0MzU2MjAzNjE= | 513 | How many epochs are necessary for finetuning BERT? | {
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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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"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 ... |
https://api.github.com/repos/huggingface/transformers/issues/517 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/517/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/517/comments | https://api.github.com/repos/huggingface/transformers/issues/517/events | https://github.com/huggingface/transformers/issues/517 | 435,986,221 | MDU6SXNzdWU0MzU5ODYyMjE= | 517 | More SEPs | {
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`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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"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 ... |
https://api.github.com/repos/huggingface/transformers/issues/742 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/742/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/742/comments | https://api.github.com/repos/huggingface/transformers/issues/742/events | https://github.com/huggingface/transformers/pull/742 | 462,306,120 | MDExOlB1bGxSZXF1ZXN0MjkzMDQwNzA1 | 742 | When not loading a pretrained model, all layers are initialized with copies of the same weights | {
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... | Although this repo is mostly used for loading and training pre-trained BERT models, the code does support model initialization too! However, I found an issue with the initialization code - because it just makes one layer and copies it, the weights will be identical across all layers at initialization. This probably isn... | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/742?src=pr&el=h1) Report\n> Merging [#742](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/742?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/dad3c7a485b7ffc6fd2766f349e6ee845ec... |
https://api.github.com/repos/huggingface/transformers/issues/468 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/468/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/468/comments | https://api.github.com/repos/huggingface/transformers/issues/468/events | https://github.com/huggingface/transformers/issues/468 | 431,358,967 | MDU6SXNzdWU0MzEzNTg5Njc= | 468 | GPT-2 fine tunning | {
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"What kind of task are you fine-tuning on? I If it's something like ROCstories task, you need the extra tokens. I think people are doing BERT for up-stream tasks because birectional context gives better results than left-to-right",
"Hi @yaroslavvb, I am mostly focusing on classification tasks(like ROCstories as y... |
https://api.github.com/repos/huggingface/transformers/issues/525 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/525/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/525/comments | https://api.github.com/repos/huggingface/transformers/issues/525/events | https://github.com/huggingface/transformers/issues/525 | 436,513,242 | MDU6SXNzdWU0MzY1MTMyNDI= | 525 | Should I use weight_decay or weight_decay_rate? | {
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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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"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... |
https://api.github.com/repos/huggingface/transformers/issues/522 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/522/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/522/comments | https://api.github.com/repos/huggingface/transformers/issues/522/events | https://github.com/huggingface/transformers/issues/522 | 436,137,071 | MDU6SXNzdWU0MzYxMzcwNzE= | 522 | extending of Transformer-XL for new tasks | {
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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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"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"
] |
https://api.github.com/repos/huggingface/transformers/issues/653 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/653/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/653/comments | https://api.github.com/repos/huggingface/transformers/issues/653/events | https://github.com/huggingface/transformers/issues/653 | 450,697,161 | MDU6SXNzdWU0NTA2OTcxNjE= | 653 | Different Results from version 0.4.0 to version 0.5.0 | {
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"Hi, no we didn't change the weights. Can you share a sample on which the results are different?",
"Hi @thomwolf , thanks for your quick reply. I found even version 0.4.0 is different to version 0.2.0 and 0.3.0. I trained the model on v0.4.0, and then I tried to load the model using v0.2.0, here is the mismatch o... |
https://api.github.com/repos/huggingface/transformers/issues/528 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/528/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/528/comments | https://api.github.com/repos/huggingface/transformers/issues/528/events | https://github.com/huggingface/transformers/issues/528 | 436,680,415 | MDU6SXNzdWU0MzY2ODA0MTU= | 528 | __init__() got an unexpected keyword argument 'do_basic_tokenize' | {
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```
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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"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... |
https://api.github.com/repos/huggingface/transformers/issues/743 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/743/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/743/comments | https://api.github.com/repos/huggingface/transformers/issues/743/events | https://github.com/huggingface/transformers/issues/743 | 462,410,393 | MDU6SXNzdWU0NjI0MTAzOTM= | 743 | Cannot reproduce results from version 0.4.0 | {
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"`pip install pytorch-pretrained-bert==0.4.0` should work normally",
"Though if you did it with the latest release in March 2019 it was probably more 0.6.1 (see the list and dates here: https://github.com/huggingface/pytorch-pretrained-BERT/releases) so `pip install pytorch-pretrained-bert==0.6.1`",
"Thank you!... |
https://api.github.com/repos/huggingface/transformers/issues/648 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/648/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/648/comments | https://api.github.com/repos/huggingface/transformers/issues/648/events | https://github.com/huggingface/transformers/issues/648 | 450,295,950 | MDU6SXNzdWU0NTAyOTU5NTA= | 648 | [Dropout] why there is no dropout for the dev and eval? | {
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"@thomwolf Thanks!",
"line 604",
"> line 604\r\n\r\nThanks so much. my mistake.\r\n\r\nDo you know why there is no dropout for the dev and eval?\r\n",
"first of all, no one uses dropout at evaluation stage as it's a regularizer. The difference of implementation is due to the fact that a dropout layer in pyto... |
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 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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"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... |
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... |
https://api.github.com/repos/huggingface/transformers/issues/741 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/741/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/741/comments | https://api.github.com/repos/huggingface/transformers/issues/741/events | https://github.com/huggingface/transformers/issues/741 | 462,304,734 | MDU6SXNzdWU0NjIzMDQ3MzQ= | 741 | Using BertForNextSentencePrediction and GPT2LMHeadModel in a GAN setup. | {
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https://api.github.com/repos/huggingface/transformers/issues/372 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/372/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/372/comments | https://api.github.com/repos/huggingface/transformers/issues/372/events | https://github.com/huggingface/transformers/issues/372 | 420,279,829 | MDU6SXNzdWU0MjAyNzk4Mjk= | 372 | a single sentence classification task, should the max length of sentence limited to half of 512, that is to say 256 | {
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"Why should it be limited to half of 512?",
"> Why should it be limited to half of 512?\r\n\r\ncause when do train, we have sentence embedding 0 and 1, but in a single sentence classification task ,we just embedding 0, if this get bad influence",
"You can just set the whole sequence to sentence 0. Create a Dat... |
https://api.github.com/repos/huggingface/transformers/issues/748 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/748/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/748/comments | https://api.github.com/repos/huggingface/transformers/issues/748/events | https://github.com/huggingface/transformers/pull/748 | 463,270,020 | MDExOlB1bGxSZXF1ZXN0MjkzNzgyNjkz | 748 | Release 0.7 - Add Torchscript capabilities | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/748?src=pr&el=h1) Report\n> Merging [#748](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/748?src=pr&el=desc) into [xlnet](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/708877958a308a0f0e8fd199f8f327e4797f... |
https://api.github.com/repos/huggingface/transformers/issues/551 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/551/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/551/comments | https://api.github.com/repos/huggingface/transformers/issues/551/events | https://github.com/huggingface/transformers/issues/551 | 438,029,799 | MDU6SXNzdWU0MzgwMjk3OTk= | 551 | Pad inputs to multiple of 8 | {
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@glample's [XLM](https://github.com/facebookresearch/XLM) does that and it seems still relevant with CUDA 10 (cc @yaroslavvb). | {
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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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https://api.github.com/repos/huggingface/transformers/issues/757 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/757/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/757/comments | https://api.github.com/repos/huggingface/transformers/issues/757/events | https://github.com/huggingface/transformers/pull/757 | 464,312,700 | MDExOlB1bGxSZXF1ZXN0Mjk0NjE5MzM1 | 757 | Release 0.7 - Add a real doc | {
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https://api.github.com/repos/huggingface/transformers/issues/553 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/553/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/553/comments | https://api.github.com/repos/huggingface/transformers/issues/553/events | https://github.com/huggingface/transformers/issues/553 | 438,094,501 | MDU6SXNzdWU0MzgwOTQ1MDE= | 553 | How to get back input and predictions as string | {
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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"
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https://api.github.com/repos/huggingface/transformers/issues/449 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/449/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/449/comments | https://api.github.com/repos/huggingface/transformers/issues/449/events | https://github.com/huggingface/transformers/issues/449 | 429,191,528 | MDU6SXNzdWU0MjkxOTE1Mjg= | 449 | Convert_tf_checkpoint_to_pytorch for bert-joint-baseline | {
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I know that the format of Squad and Google NQ is different, but is there a way to convert the bert joint model for Natural Questions (https://github.com/google-research/language/tree/master/language/question_answering/bert_joint) to pytorch? I get this error
'BertForPreTraining' object has no attribute 'answ... | {
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"I also encountered a similar problem AttributeError: 'BertForPreTraining' object has no attribute 'crf_loss'\r\n\r\n@thomwolf \r\n\r\nLooking forward to your reply",
"Hi, from my reading of the [Natural Questions model](https://github.com/google-research/language/blob/master/language/question_answering/bert_jo... |
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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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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"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... |
https://api.github.com/repos/huggingface/transformers/issues/758 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/758/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/758/comments | https://api.github.com/repos/huggingface/transformers/issues/758/events | https://github.com/huggingface/transformers/pull/758 | 464,313,727 | MDExOlB1bGxSZXF1ZXN0Mjk0NjIwMTcx | 758 | Release 0.7 - Add doc | {
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https://api.github.com/repos/huggingface/transformers/issues/745 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/745/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/745/comments | https://api.github.com/repos/huggingface/transformers/issues/745/events | https://github.com/huggingface/transformers/pull/745 | 462,929,669 | MDExOlB1bGxSZXF1ZXN0MjkzNTEyODA5 | 745 | fix evaluation bug | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/745?src=pr&el=h1) Report\n> Merging [#745](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/745?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/dad3c7a485b7ffc6fd2766f349e6ee845ec... |
https://api.github.com/repos/huggingface/transformers/issues/707 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/707/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/707/comments | https://api.github.com/repos/huggingface/transformers/issues/707/events | https://github.com/huggingface/transformers/pull/707 | 458,827,077 | MDExOlB1bGxSZXF1ZXN0MjkwMzQwNjIw | 707 | Update run_squad.py | {
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... | model = BertForQuestionAnswering.from_pretrained(args.bert_model) is written twice.
I think the else part is redundant there | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/707?src=pr&el=h1) Report\n> Merging [#707](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/707?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/c304593d8fa93f25febe1458c63497a8467... |
https://api.github.com/repos/huggingface/transformers/issues/733 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/733/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/733/comments | https://api.github.com/repos/huggingface/transformers/issues/733/events | https://github.com/huggingface/transformers/pull/733 | 461,222,144 | MDExOlB1bGxSZXF1ZXN0MjkyMTkzMDU5 | 733 | Added option to use multiple workers to create training data | {
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The shelve object isn't pickleable, so it can't be used with the Pool | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/733?src=pr&el=h1) Report\n> Merging [#733](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/733?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/98dc30b21e3df6528d0dd17f0910ffea12b... |
https://api.github.com/repos/huggingface/transformers/issues/555 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/555/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/555/comments | https://api.github.com/repos/huggingface/transformers/issues/555/events | https://github.com/huggingface/transformers/issues/555 | 438,298,098 | MDU6SXNzdWU0MzgyOTgwOTg= | 555 | Transformer XL from Pytorch model | {
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] | closed | false | [] | null | 1 | 2019-04-29T12:47:17Z | 2019-07-05T13:18:13Z | 2019-07-05T13:18:13Z | null | CONTRIBUTOR | [] | null | null | null | 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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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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] | closed | false | [] | null | 2 | 2019-04-30T05:01:05Z | 2019-07-06T09:04:23Z | 2019-07-06T09:04:23Z | null | NONE | [] | null | null | null | 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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"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"
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https://api.github.com/repos/huggingface/transformers/issues/557 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/557/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/557/comments | https://api.github.com/repos/huggingface/transformers/issues/557/events | https://github.com/huggingface/transformers/issues/557 | 438,472,035 | MDU6SXNzdWU0Mzg0NzIwMzU= | 557 | Expanding vocab size for GTP2 pre-trained model. | {
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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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"@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 ... |
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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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/565 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/565/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/565/comments | https://api.github.com/repos/huggingface/transformers/issues/565/events | https://github.com/huggingface/transformers/issues/565 | 439,052,352 | MDU6SXNzdWU0MzkwNTIzNTI= | 565 | Results of Fine-tuned model changes in every run | {
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`
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/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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] | closed | false | [] | null | 2 | 2019-05-01T09:48:27Z | 2019-07-08T12:23:21Z | 2019-07-08T12:23:21Z | null | NONE | [] | null | null | null | 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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"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... |
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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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/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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The amount of pre-training data is not issue and we are not looking for the SOTA res... | {
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https://api.github.com/repos/huggingface/transformers/issues/574 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/574/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/574/comments | https://api.github.com/repos/huggingface/transformers/issues/574/events | https://github.com/huggingface/transformers/issues/574 | 439,706,731 | MDU6SXNzdWU0Mzk3MDY3MzE= | 574 | understanding of the output from TransfoXLModel | {
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] | closed | false | [] | null | 1 | 2019-05-02T17:37:39Z | 2019-07-08T18:23:21Z | 2019-07-08T18:23:21Z | null | NONE | [] | null | null | null | 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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https://api.github.com/repos/huggingface/transformers/issues/335 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/335/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/335/comments | https://api.github.com/repos/huggingface/transformers/issues/335/events | https://github.com/huggingface/transformers/issues/335 | 416,195,621 | MDU6SXNzdWU0MTYxOTU2MjE= | 335 | Feature Request: GPT2 fine tuning | {
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"Yes, feel free to open a PR if you want.\r\nIt's just a regular PyTorch model so all the standard ways of training a PyTorch model work.",
"Is is possible to fine-tune GPT2 on downstream tasks currently?",
"same questions"
] | |
https://api.github.com/repos/huggingface/transformers/issues/576 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/576/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/576/comments | https://api.github.com/repos/huggingface/transformers/issues/576/events | https://github.com/huggingface/transformers/issues/576 | 440,001,383 | MDU6SXNzdWU0NDAwMDEzODM= | 576 | key error when using run_classifier.py in predict mode, expecting label? | {
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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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"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"
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https://api.github.com/repos/huggingface/transformers/issues/767 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/767/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/767/comments | https://api.github.com/repos/huggingface/transformers/issues/767/events | https://github.com/huggingface/transformers/pull/767 | 465,828,120 | MDExOlB1bGxSZXF1ZXN0Mjk1NzkyNzM2 | 767 | Documentation | {
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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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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/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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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/773 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/773/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/773/comments | https://api.github.com/repos/huggingface/transformers/issues/773/events | https://github.com/huggingface/transformers/pull/773 | 466,566,367 | MDExOlB1bGxSZXF1ZXN0Mjk2Mzg0NTM3 | 773 | Sphinx doc, XLM Checkpoints | {
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Additionally, patched the XLM weights conversion script and added 5 new checkpoints for XLM. | {
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https://api.github.com/repos/huggingface/transformers/issues/783 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/783/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/783/comments | https://api.github.com/repos/huggingface/transformers/issues/783/events | https://github.com/huggingface/transformers/issues/783 | 467,181,929 | MDU6SXNzdWU0NjcxODE5Mjk= | 783 | how to get the word vector from bert pretrain model ? | {
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I just want to get bert's word vector,but I only can get the encoder's result. How can I get the word vector before data inputing the encoder model ?
Thank you ! | {
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"This will be possible in the new release out soon.",
"I find a method that can get the words embeddings.Thank you all the same!\r\nself.model = BertModel.from_pretrained(config.bert_path)\r\nself.word_emb = self.model.embeddings"
] |
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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] | closed | false | [] | null | 3 | 2019-05-04T05:49:35Z | 2019-07-12T08:08:26Z | 2019-07-12T08:08:26Z | null | NONE | [] | null | null | null | 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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"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... |
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"
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https://api.github.com/repos/huggingface/transformers/issues/724 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/724/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/724/comments | https://api.github.com/repos/huggingface/transformers/issues/724/events | https://github.com/huggingface/transformers/pull/724 | 460,461,332 | MDExOlB1bGxSZXF1ZXN0MjkxNTg3MTM3 | 724 | fixing bugs in load_rocstories_dataset in run_openai_gpt.py | {
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... | The csv reader requires a delimiter argument to read .tsv file in the given example dataset. I've also added link for the dataset and provided a sample eval results in comments. Also, the eval dataset needs to be different from the training dataset, which I've also fixed in the given command to run this script. | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/724?src=pr&el=h1) Report\n> Merging [#724](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/pull/724?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-pretrained-BERT/commit/98dc30b21e3df6528d0dd17f0910ffea12b... |
https://api.github.com/repos/huggingface/transformers/issues/589 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/589/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/589/comments | https://api.github.com/repos/huggingface/transformers/issues/589/events | https://github.com/huggingface/transformers/issues/589 | 440,702,570 | MDU6SXNzdWU0NDA3MDI1NzA= | 589 | Can't save converted checkpoint | {
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https://api.github.com/repos/huggingface/transformers/issues/137 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/137/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/137/comments | https://api.github.com/repos/huggingface/transformers/issues/137/events | https://github.com/huggingface/transformers/issues/137 | 393,079,924 | MDU6SXNzdWUzOTMwNzk5MjQ= | 137 | run_squad.py without GPU.. Without CUPY | {
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In one of my environment I dont have GPU therefore cupy is not getting installed and I am not able to proceed with training.
Can I train on CPU itself?
following is I am trying to run:
```
python run_squ... | null | {
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"@SandeepBhutani What was the conclusion of this issue? ",
"Is this issue still open.. It can be closed.. It was an environment issue..\n\nOn Sat, 13 Jul, 2019, 5:00 AM Peter, <notifications@github.com> wrote:\n\n> @SandeepBhutani <https://github.com/SandeepBhutani> What was the\n> conclusion of this issue?\n>\n>... |
https://api.github.com/repos/huggingface/transformers/issues/370 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/370/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/370/comments | https://api.github.com/repos/huggingface/transformers/issues/370/events | https://github.com/huggingface/transformers/issues/370 | 420,195,472 | MDU6SXNzdWU0MjAxOTU0NzI= | 370 | What is Synthetic Self-Training? | {
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},... | closed | false | [] | null | 8 | 2019-03-12T20:40:50Z | 2019-07-13T20:58:32Z | 2019-06-05T08:50:40Z | null | NONE | [] | null | null | null | null | The current best performing model on[ SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) is BERT + N-Gram Masking + Synthetic Self-Training (ensemble):
What is Synthetic Self-Training?
| {
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"Check Jacob Devlin slides starting from slide 26 [here](https://nlp.stanford.edu/seminar/details/jdevlin.pdf?fbclid=IwAR2TBFCJOeZ9cGhxB-z5cJJ17vHN4W25oWsjI8NqJoTEmlYIYEKG7oh4tlY)",
"@thomwolf thanks, the slides were helpful. Do you know if there is a recording of the talk publicly available somewhere?",
"I don... |
https://api.github.com/repos/huggingface/transformers/issues/784 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/784/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/784/comments | https://api.github.com/repos/huggingface/transformers/issues/784/events | https://github.com/huggingface/transformers/issues/784 | 467,226,420 | MDU6SXNzdWU0NjcyMjY0MjA= | 784 | [bug] from_pretrained error with from_tf | {
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"Yeah this is solved in the coming release"
] |
https://api.github.com/repos/huggingface/transformers/issues/779 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/779/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/779/comments | https://api.github.com/repos/huggingface/transformers/issues/779/events | https://github.com/huggingface/transformers/issues/779 | 467,084,782 | MDU6SXNzdWU0NjcwODQ3ODI= | 779 | Should close the SummaryWriter after using it | {
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I have tried to run the script: [run_glue.py](https://github.com/huggingface/pytorch-pretrained-BERT/blob/xlnet/examples/run_glue.py). When I test with this script, I found some of the scalars add into the SummaryWriter did not appears in TensorBoard. I thi... | {
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"Oh yes you are right, thanks it's fixed in the coming release."
] |
https://api.github.com/repos/huggingface/transformers/issues/774 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/774/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/774/comments | https://api.github.com/repos/huggingface/transformers/issues/774/events | https://github.com/huggingface/transformers/issues/774 | 466,632,277 | MDU6SXNzdWU0NjY2MzIyNzc= | 774 | XLNet text generation ability | {
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I tried running the [XLNet text generation example](https://github.com/huggingface/pytorch-pretrained-BERT/blob/xlnet/examples/generation_xlnet.py)
But the generated text quality is really low.
Tricks used by https://github.com/rusiaaman/XLnet-gen needs to ... | {
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"Indeed, I've now added the text padding trick of Aman (add some padding text to have longer inputs) and the quality is really a lot higher.\r\n\r\nWill merge the xlnet branch in master and release on Monday."
] |
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