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https://api.github.com/repos/huggingface/transformers/issues/2412 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2412/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2412/comments | https://api.github.com/repos/huggingface/transformers/issues/2412/events | https://github.com/huggingface/transformers/pull/2412 | 545,651,674 | MDExOlB1bGxSZXF1ZXN0MzU5NDgxNTAy | 2,412 | Update Mish activation function to use torchscript JIT | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2412?src=pr&el=h1) Report\n> Merging [#2412](https://codecov.io/gh/huggingface/transformers/pull/2412?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0ffc8eaf53542092271a208a52e881668e753e72?src=pr&el=desc) will **d... | 1,578 | 1,584 | 1,584 | NONE | null | This PR modifies the implementation of Mish to match that of the [fastai library](https://github.com/fastai/fastai_dev/blob/0f613ba3205990c83de9dba0c8798a9eec5452ce/dev/local/layers.py#L441). A discussion of the benefits of JIT for the Mish function can be found on the [fastai forums](https://forums.fast.ai/t/meet-mish... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,578 | 1,584 | 1,584 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I notice that for T5 model, there are more choices(T5Model, T5WithLMHeadModel, T5PreTrainedModel) than BERT or GPT. What is the difference between these three? I think all three are pre-trained model. We do not use T5PreTrainedModel in ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2410 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2410/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2410/comments | https://api.github.com/repos/huggingface/transformers/issues/2410/events | https://github.com/huggingface/transformers/issues/2410 | 545,502,460 | MDU6SXNzdWU1NDU1MDI0NjA= | 2,410 | Typo in XLM moses pipeline. | {
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"Indeed, thanks @alvations !",
"BTW, https://github.com/huggingface/transformers/blob/master/src/transformers/tokenization_xlm.py#L621 could also be simplified to the normalizer object from https://github.com/alvations/sacremoses/blob/master/sacremoses/normalize.py#L129\r\n\r\n\r\n```python\r\n def moses_punct... | 1,578 | 1,578 | 1,578 | NONE | null | The replacement on for the unicode punct replacement has a mistake at https://github.com/huggingface/transformers/blob/master/src/transformers/tokenization_xlm.py#L477 | {
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https://api.github.com/repos/huggingface/transformers/issues/2409 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2409/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2409/comments | https://api.github.com/repos/huggingface/transformers/issues/2409/events | https://github.com/huggingface/transformers/issues/2409 | 545,473,336 | MDU6SXNzdWU1NDU0NzMzMzY= | 2,409 | Error in pipeline() when model left as None | {
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"Upgraded to Python 3.6.7, and two of _tasks_ (sentiment-analysis and question-answering) works as expected (i.e., no error without specifying `model` args).\r\n\r\nThe remaining two _tasks_ (ner and feature-extraction) fail on a new (similar) error:\r\n\r\n#### feature-extraction\r\n```py\r\n>>> from transformers ... | 1,578 | 1,584 | 1,584 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): Default models as per `SUPPORTED_TASKS` config in [pipeline.py](https://github.com/huggingface/transformers/blob/master/src/transformers/pipelines.py)
Language I am using the model on (English, Chinese....): English
The problem ari... | {
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https://api.github.com/repos/huggingface/transformers/issues/2408 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2408/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2408/comments | https://api.github.com/repos/huggingface/transformers/issues/2408/events | https://github.com/huggingface/transformers/issues/2408 | 545,438,391 | MDU6SXNzdWU1NDU0MzgzOTE= | 2,408 | Can't download models or model config | {
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"Hi, I'm not sure I see what exactly is your problem ? Was there something following this message, like an error or a warning ?",
"OSError: Couldn't reach server at 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin' to download pretrained weights. \r\nit is disconnect servi... | 1,578 | 1,578 | 1,578 | NONE | null | ## ❓ Questions & Help
when I run fine-tune examples as run_squad.py,it turns out error like this:
E:\tensorflow_natural_question\transformers\examples>python run_squad.py --model_type bert --model_name_or_path bert-base-cased --do_train --do_eval --do_lower_case --train_file $SQUAD_DIR/train-v2.0.json --predict_fil... | {
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https://api.github.com/repos/huggingface/transformers/issues/2407 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2407/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2407/comments | https://api.github.com/repos/huggingface/transformers/issues/2407/events | https://github.com/huggingface/transformers/pull/2407 | 545,424,991 | MDExOlB1bGxSZXF1ZXN0MzU5MzA2NzY2 | 2,407 | [cli] Add support for T5 model conversion | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2407?src=pr&el=h1) Report\n> Merging [#2407](https://codecov.io/gh/huggingface/transformers/pull/2407?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/80faf22b4ac194061a08fde09ad8b202118c151e?src=pr&el=desc) will **d... | 1,578 | 1,584 | 1,584 | NONE | null | I have added support for converting t5 model from CLI.
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https://api.github.com/repos/huggingface/transformers/issues/2406 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2406/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2406/comments | https://api.github.com/repos/huggingface/transformers/issues/2406/events | https://github.com/huggingface/transformers/issues/2406 | 545,421,060 | MDU6SXNzdWU1NDU0MjEwNjA= | 2,406 | BERT's Embedding/Vocab Size in Code is Different from Provided Pretrained Config | {
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"The default configuration in `configuration_bert.py` is for `bert-base-uncased` model. I am not sure what you are trying to do here will work or not but here is what I would suggest try doing:\r\n\r\nFirst Load configuration manually from `bert-base-case` json. Then change the parameters you want to change and the... | 1,578 | 1,648 | 1,578 | NONE | null | ## 🐛 A Subtle Bug
Hi, I really appreciate your work but I found a subtle problem here. Could you take a look of it?
- The model I am using is **BERT**.
- The language I am using the model on is **English**.
- The problem arises when using:
- The task I am working on is to simply initialize a BERT object... | {
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https://api.github.com/repos/huggingface/transformers/issues/2405 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2405/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2405/comments | https://api.github.com/repos/huggingface/transformers/issues/2405/events | https://github.com/huggingface/transformers/issues/2405 | 545,411,968 | MDU6SXNzdWU1NDU0MTE5Njg= | 2,405 | weird resize during the initialization in the PreTrainedModel | {
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"This layer is resized in `self.init_weights` because it is sharing weights with the embedding layer. They need to be the same size.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,578 | 1,584 | 1,584 | NONE | null | Hi
I am using BertForMaskedLM in the run_lm_finetuning.py code. This module call the module of BertLMPredictionHead, in which there is a decoder layer which is of the size of hidden_size*vocab_size. I would like to change the dimension of this layer. when I change it, I realize that during the call to elf.init_weight... | {
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https://api.github.com/repos/huggingface/transformers/issues/2404 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2404/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2404/comments | https://api.github.com/repos/huggingface/transformers/issues/2404/events | https://github.com/huggingface/transformers/issues/2404 | 545,410,457 | MDU6SXNzdWU1NDU0MTA0NTc= | 2,404 | Pretrained Model not available | {
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"Could you describe your issue in more details? e.g. share some code on what you are trying to do and what is not working?",
"I have the same issue.\r\n\r\n\r\n\r\n",
"I download a pretrained model and un... | 1,578 | 1,627 | 1,584 | NONE | null | ## ❓ Questions & Help
01/05/2020 12:18:00 - INFO - root - finetuned model not available - loading standard pretrained model
01/05/2020 12:18:00 - INFO - transformers.tokenization_utils - Model name '/opt/ml/code/pretrained_models/bert-base-uncased' not found in model shortcut name list (bert-base-uncased, bert-... | {
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https://api.github.com/repos/huggingface/transformers/issues/2403 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2403/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2403/comments | https://api.github.com/repos/huggingface/transformers/issues/2403/events | https://github.com/huggingface/transformers/pull/2403 | 545,398,064 | MDExOlB1bGxSZXF1ZXN0MzU5Mjg4MDYw | 2,403 | Add support for Albert and XLMRoberta for the Glue example | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2403?src=pr&el=h1) Report\n> Merging [#2403](https://codecov.io/gh/huggingface/transformers/pull/2403?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/80faf22b4ac194061a08fde09ad8b202118c151e?src=pr&el=desc) will **n... | 1,578 | 1,578 | 1,578 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/2402 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2402/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2402/comments | https://api.github.com/repos/huggingface/transformers/issues/2402/events | https://github.com/huggingface/transformers/issues/2402 | 545,361,440 | MDU6SXNzdWU1NDUzNjE0NDA= | 2,402 | BertForTokenClassification can not from_pretrained the fine-tuned model? | {
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"Hi there, I had this same issue.\r\nIn my case, it was a tokenizer issue. For \r\n`--tokenizer_name` use \"bert-base-multilingual-cased\" or \"bert-base-multilingual-uncased\" solved the problem.",
"> Hi there, I had this same issue.\r\n> In my case, it was a tokenizer issue. For\r\n> `--tokenizer_name` use \"be... | 1,578 | 1,578 | 1,578 | NONE | null | ## ❓ Questions & Help
Thanks the great work!
While, when I wrap the `run_ner.py `scripts in sklearn api style for non-specialists, I met some problem.
It 's ok for training and evaluating, but when predicting the F1-score is much lower than that of evaluating. As shown in following:
Evaluating result: the F1-sco... | {
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https://api.github.com/repos/huggingface/transformers/issues/2401 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2401/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2401/comments | https://api.github.com/repos/huggingface/transformers/issues/2401/events | https://github.com/huggingface/transformers/issues/2401 | 545,286,778 | MDU6SXNzdWU1NDUyODY3Nzg= | 2,401 | Batch size affecting output. | {
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"It is possible to get slightly different results. Could you share more details on which evaluation script are you running and for which model/configuration etc?",
"I'm getting having the same issue. But with XLM-R:\r\n\r\nI decided to write a simple script to demonstrate the difference between encoding individua... | 1,578 | 1,707 | 1,584 | NONE | null | ## ❓ Questions & Help
When running evaluation, why am i getting slightly different output when running a batch size of 1 compared to batch size greater than 1?
| {
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https://api.github.com/repos/huggingface/transformers/issues/2400 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2400/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2400/comments | https://api.github.com/repos/huggingface/transformers/issues/2400/events | https://github.com/huggingface/transformers/pull/2400 | 545,284,405 | MDExOlB1bGxSZXF1ZXN0MzU5MjEzNDU1 | 2,400 | fix #2399 an ImportError in official example | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2400?src=pr&el=h1) Report\n> Merging [#2400](https://codecov.io/gh/huggingface/transformers/pull/2400?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/78528742f169fb9481865aa25726ceca5499e036?src=pr&el=desc) will **n... | 1,578 | 1,578 | 1,578 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/2399 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2399/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2399/comments | https://api.github.com/repos/huggingface/transformers/issues/2399/events | https://github.com/huggingface/transformers/issues/2399 | 545,280,617 | MDU6SXNzdWU1NDUyODA2MTc= | 2,399 | import Error from official example caused by fastprogress | {
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"Thanks for reporting. (and hello @sgugger :)\r\n\r\nI'll merge this to fix the immediate issue, but maybe @jplu can chime in: maybe we don't need the fastprogress dependency here?",
"Closed by #2400 ",
"Oh, I forgot to update the `__init__` with the new version. Will add back the functions there to make compat... | 1,578 | 1,581 | 1,578 | CONTRIBUTOR | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): BERT
Language I am using the model on (English, Chinese....): ALL
The problem arise when using:
* [x] the official example scripts: (give details)
* [ ] my own modified scripts: (give details)
The tasks I am working on is:
* ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2398 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2398/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2398/comments | https://api.github.com/repos/huggingface/transformers/issues/2398/events | https://github.com/huggingface/transformers/issues/2398 | 545,279,816 | MDU6SXNzdWU1NDUyNzk4MTY= | 2,398 | Distilbert predicting mask | {
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"In full `bert` case you are using `BertForMaskedLM` but for distill bert you are using `DistilBertModel` which is not for masked language modelling. Try using `DistilBertForMaskedLM`. Check it, it works:\r\n\r\nhttps://colab.research.google.com/drive/1GYt9H9QRUa5clFfAke6KPYl0mi4H1F3H",
"Well, in hindsight that w... | 1,578 | 1,578 | 1,578 | NONE | null | Hi,
This is probably me doing something wrong, but I can't get distilbert to give me a sensible prediciton when I mask part of a sentence.
This setup for BERT (based on the examples):
```
import logging
logging.basicConfig(level=logging.INFO)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')... | {
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https://api.github.com/repos/huggingface/transformers/issues/2397 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2397/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2397/comments | https://api.github.com/repos/huggingface/transformers/issues/2397/events | https://github.com/huggingface/transformers/issues/2397 | 545,206,566 | MDU6SXNzdWU1NDUyMDY1NjY= | 2,397 | unable to use distilbert multilingual model | {
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"Hi\r\nI have verified that it is working. Could you please share your environment details etc.\r\n\r\nhttps://colab.research.google.com/drive/1Bo0luU5q7bztalw5-trWsvl7G0J6zE10",
"It seems you're not actually running on transformers 2.3.0. Could you please tell me the output of this code in your environment?\r\n\... | 1,578 | 1,584 | 1,584 | NONE | null | ## ❓ Questions & Help
I'm trying to use the distilbert-base-multilingual-cased model but have been unable to do so.
I have checked and I am using transformers version 2.3.0. I have already tried these things:
1) tokenizer = AutoTokenizer.from_pretrained("https://s3.amazonaws.com/models.huggingface.co/bert/dist... | {
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https://api.github.com/repos/huggingface/transformers/issues/2396 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2396/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2396/comments | https://api.github.com/repos/huggingface/transformers/issues/2396/events | https://github.com/huggingface/transformers/issues/2396 | 545,200,978 | MDU6SXNzdWU1NDUyMDA5Nzg= | 2,396 | Model2Model quickstart attention_mask dimensionality problem | {
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"same issue:\r\n\r\nLinux (ubuntu 18.04.3 LTS)\r\n\r\nPython 3.6.9\r\nTorch Version: 1.3.1\r\n\r\nno GPU - regular DELL box, \r\n\r\ntransformers installed following this part on installation guide (under python3 venv):\r\n...\r\ngit clone https://github.com/huggingface/transformers\r\ncd transformers\r\npip instal... | 1,578 | 1,583 | 1,583 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....):
BERT-base-uncased
Language I am using the model on (English, Chinese....):
The problem arise when using:
* [X] the official example scripts: (give details)
[model2model tutorial code](https://huggingface.co/transformers/quicks... | {
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"If you just change your config.json's `hidden_act` value locally you should still be able to load the pretrained weights and be able to convert the model to TFLite, right? ",
"Yes. Another option (my current workaround) is to explicitly specify hidden_act when creating model instance (via .from_pretrained(...)) ... | 1,578 | 1,582 | 1,582 | CONTRIBUTOR | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): ALBERT
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [x] my own modified scripts: i'm fine-tuning albert for multilabel-classifcation problem and then converting model into tf-lit... | {
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https://api.github.com/repos/huggingface/transformers/issues/2394 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2394/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2394/comments | https://api.github.com/repos/huggingface/transformers/issues/2394/events | https://github.com/huggingface/transformers/issues/2394 | 545,059,580 | MDU6SXNzdWU1NDUwNTk1ODA= | 2,394 | Pretrained model installation issue | {
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"The file on S3 seems accessible right now. Did you try to reach it directly from your machine and from console to check you have no network issue?",
"No, my network is having no issues I verified it, and from console it is not accessible. The required pretrained model is to be installed from command only as per ... | 1,578 | 1,583 | 1,583 | NONE | null | I run the script for this repo "https://github.com/alexa/wqa_tanda" in which i need to run the run_glue.py file from Transformer Model, while running that script it gives an error-
Couldn't reach server at "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json to download pretrained model c... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,578 | 1,584 | 1,584 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): DistilBERT and BERT
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [ ] the official example scripts: run_squad_w_distillation.py
The tasks I am working on is:
* [ ] an official... | {
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"I confirm what you see... in current master code, `bert-large-cased-finetuned-conll03-english` has no mapping in tokenizers or models so it can't find it in the same way as `bert-base-uncased` for example.\r\n\r\nbut it works if you target it directly:\r\n\r\n```python\r\nAutoTokenizer.from_pretrained(\"https://s3... | 1,578 | 1,579 | 1,579 | NONE | null | ## 🐛 Bug
Model I am using (Bert, XLNet....): `bert-base-cased-finetuned-conll03-english`
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [x] the official example scripts: running a small snippet from docs (see below)
* [ ] my own modified scripts: (give detai... | {
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https://api.github.com/repos/huggingface/transformers/issues/2391 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2391/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2391/comments | https://api.github.com/repos/huggingface/transformers/issues/2391/events | https://github.com/huggingface/transformers/issues/2391 | 544,813,178 | MDU6SXNzdWU1NDQ4MTMxNzg= | 2,391 | What dataset was used for the NER results reported in the docs for bert/roberta-large-cased and distilbert-base-uncased models? | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,578 | 1,583 | 1,583 | CONTRIBUTOR | null | ## ❓ Questions & Help
Regarding [this section in the docs](https://huggingface.co/transformers/examples.html#comparing-bert-large-cased-roberta-large-cased-and-distilbert-base-uncased) and the NER results using bert-large-cased, roberta-large-cased, and distillbert-base-uncased ...
**What dataset was used?**
W... | {
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https://api.github.com/repos/huggingface/transformers/issues/2390 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2390/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2390/comments | https://api.github.com/repos/huggingface/transformers/issues/2390/events | https://github.com/huggingface/transformers/issues/2390 | 544,598,208 | MDU6SXNzdWU1NDQ1OTgyMDg= | 2,390 | Pipelines support | {
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"Hi @AlexanderKUA, thanks for reporting this issue.\r\n\r\nCan you give a try to the following commit 088daf78d45bed144fe2af84b538f573573bd01d and let us know if it solves your issue ?\r\n\r\n```python\r\nfrom transformers import pipeline\r\nnlp = pipeline('feature-extraction', model='distilbert-base-uncased', devi... | 1,577 | 1,578 | 1,578 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....):
I'm using roberta-base model for feature extraction through pipeline functionality.
Language I am using the model on English texts.
The problem arise when using:
* [x ] my own modified scripts: (give details)
```
from trans... | {
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https://api.github.com/repos/huggingface/transformers/issues/2389 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2389/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2389/comments | https://api.github.com/repos/huggingface/transformers/issues/2389/events | https://github.com/huggingface/transformers/pull/2389 | 544,582,032 | MDExOlB1bGxSZXF1ZXN0MzU4NjYxNTI3 | 2,389 | update the config.is_decoder=True before initialize the decoder | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2389?src=pr&el=h1) Report\n> Merging [#2389](https://codecov.io/gh/huggingface/transformers/pull/2389?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9261c7f771fccfa2a2cb78ae544adef2f6eb402b?src=pr&el=desc) will **n... | 1,577 | 1,578 | 1,578 | NONE | null | Currently the `PreTrainedEncoderDecoder` class fails to initialize the "cross-attention layer" since it updates `decoder.config.is_decoder = True` after decoder initialization. | {
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https://api.github.com/repos/huggingface/transformers/issues/2388 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2388/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2388/comments | https://api.github.com/repos/huggingface/transformers/issues/2388/events | https://github.com/huggingface/transformers/issues/2388 | 544,568,124 | MDU6SXNzdWU1NDQ1NjgxMjQ= | 2,388 | Can't load finetuned model properly. | {
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"Hi, I met the same problem as you mentioned! Do you fix it? \r\nmy question is here, https://github.com/huggingface/transformers/issues/2402",
"No, not yet :/ @trueto \r\nI think it saves somehow wrong model but i am not sure. ",
"This issue has been automatically marked as stale because it has not had recent ... | 1,577 | 1,584 | 1,584 | NONE | null | I am making a model for joint bert. After trained my model, i try to eval before saving and it gives with %95 accuracy. But the problem is when i save this trained model and load it, i get the awful result. I hope you can help me about finding why i cant load properly.
Here is some part of my code
```
class JointB... | {
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https://api.github.com/repos/huggingface/transformers/issues/2387 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2387/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2387/comments | https://api.github.com/repos/huggingface/transformers/issues/2387/events | https://github.com/huggingface/transformers/issues/2387 | 544,556,827 | MDU6SXNzdWU1NDQ1NTY4Mjc= | 2,387 | Pre-trained model returns different outputs(random outputs) | {
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"I found too. \"last_hidden_states\" was not fixed when I reload pretrain model. I think we miss something. My question is here <https://github.com/huggingface/transformers/issues/2386>, maybe help you.",
"Hi @houdaM97, this is due to the fact that the pretrained archive `xlnet-base-cased` does not contain keys f... | 1,577 | 1,584 | 1,584 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hello,
I had recently play around Huggingface library, i wrote a simple script for question answering task. and for that, i used TFXLNetForQuestionAnsweringSimple model (pre-trained model), but i had different outputs for the same inp... | {
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https://api.github.com/repos/huggingface/transformers/issues/2386 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2386/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2386/comments | https://api.github.com/repos/huggingface/transformers/issues/2386/events | https://github.com/huggingface/transformers/issues/2386 | 544,506,682 | MDU6SXNzdWU1NDQ1MDY2ODI= | 2,386 | Different usage between BertModel and AlbertModel | {
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"Did you do model.eval() to disable dropout and norm before torch.no_grad()? ",
"Yes. Because they didn‘t’ throw any exception, I'm a little confused about their usage.\r\n```\r\nimport torch\r\nfrom transformers import BertTokenizer, BertModel\r\nfrom transformers import AlbertTokenizer, AlbertModel\r\nfrom tra... | 1,577 | 1,578 | 1,578 | NONE | null | ## ❓ Questions & Help
Hi~
```
bert_path = 'D:/pretrain/pytorch/albert_base/'
tokenizer = BertTokenizer.from_pretrained(bert_path)
BERT = BertModel.from_pretrained(bert_path)
...
with torch.no_grad():
last_hidden_states = BERT(input_ids)[0]
```
I found ```last_hidden_states``` was not fixed when I reload... | {
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https://api.github.com/repos/huggingface/transformers/issues/2385 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2385/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2385/comments | https://api.github.com/repos/huggingface/transformers/issues/2385/events | https://github.com/huggingface/transformers/issues/2385 | 544,428,158 | MDU6SXNzdWU1NDQ0MjgxNTg= | 2,385 | The method os.rename() in file_utils.py make a permissionError | {
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"I have the same problem when downloading the pre-trained tokenizer. I also need help!",
"> I have the same problem when downloading the pre-trained tokenizer. I also need help!\r\n\r\nOnline download often occur different problems,so i download it first and use it locally.",
"Ok this should be solved on master... | 1,577 | 1,579 | 1,578 | NONE | null | ## ❓ Questions & Help
Is someone happen to this question?
<!-- A clear and concise description of the question. -->
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2384?src=pr&el=h1) Report\n> Merging [#2384](https://codecov.io/gh/huggingface/transformers/pull/2384?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/80faf22b4ac194061a08fde09ad8b202118c151e?src=pr&el=desc) will **n... | 1,577 | 1,579 | 1,579 | CONTRIBUTOR | null | `With` scope creates a file lock, which leads to the following error:
INFO:filelock:Lock 1408081097608 released on C:\Users\dimag\.cache\torch\transformers\26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084.lock
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https://api.github.com/repos/huggingface/transformers/issues/2383 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2383/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2383/comments | https://api.github.com/repos/huggingface/transformers/issues/2383/events | https://github.com/huggingface/transformers/issues/2383 | 544,403,685 | MDU6SXNzdWU1NDQ0MDM2ODU= | 2,383 | clarification on output | {
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On using bert_uncased on the following sentence:
`Hello this is my dog`
and to get attentions I use:
`last_hidden_states, pooler_outputs, hidden_states, attentions = outputs`
`attentions` gives:
a tuple of 12 tensors where each tensor is of size [1,12,5,5]
I was wondering if the 12 tensors in the... | {
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https://api.github.com/repos/huggingface/transformers/issues/2382 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2382/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2382/comments | https://api.github.com/repos/huggingface/transformers/issues/2382/events | https://github.com/huggingface/transformers/pull/2382 | 544,391,545 | MDExOlB1bGxSZXF1ZXN0MzU4NTEyNjIx | 2,382 | Proposition to include community models in readme | {
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https://api.github.com/repos/huggingface/transformers/issues/2381 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2381/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2381/comments | https://api.github.com/repos/huggingface/transformers/issues/2381/events | https://github.com/huggingface/transformers/issues/2381 | 544,371,457 | MDU6SXNzdWU1NDQzNzE0NTc= | 2,381 | how to use distilledgpt2 | {
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"Hi, you can use it as such:\r\n\r\n```py\r\nfrom transformers import GPT2Model, GPT2Tokenizer\r\n\r\nmodel = GPT2Model.from_pretrained(\"distilgpt2\")\r\ntokenizer = GPT2Tokenizer.from_pretrained(\"distilgpt2\")\r\n```\r\n\r\nYou can see the list of available models in the [pretrained section of our documentation]... | 1,577 | 1,583 | 1,583 | NONE | null | Hi
I want to use distilledgpt2, I cannot see the config file and modeling files, could you please assist me how to use it
thanks | {
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https://api.github.com/repos/huggingface/transformers/issues/2380 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2380/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2380/comments | https://api.github.com/repos/huggingface/transformers/issues/2380/events | https://github.com/huggingface/transformers/issues/2380 | 544,369,808 | MDU6SXNzdWU1NDQzNjk4MDg= | 2,380 | errors encountered with run_lm_finetuning.py | {
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"Hello I also got the same error while running BERT.\r\n\r\nTraceback (most recent call last):\r\n File \"code/transformers-2.3.0/examples/run_lm_finetuning.py\", line 713, in <module>\r\n main()\r\n File \"code/transformers-2.3.0/examples/run_lm_finetuning.py\", line 663, in main\r\n global_step, tr_loss =... | 1,577 | 1,589 | 1,589 | NONE | null | Hi
I am using run_lm_finetuning.py, I encountered the following issues:
- block_size value is by default = -1, which creates the following error, can be solved by setting the default value to 512:
```
File "run_lm_finetuning.py", line 712, in <module>
main()
File "run_lm_finetuning.py", line 662, in main
... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | Hi
I greatly appreciate showing me how to pretrain a transformer model like BERT, I mean not finetuning but pretraining, Is there any code in your repo doing this? thanks a lot for your help. | {
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https://api.github.com/repos/huggingface/transformers/issues/2378 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2378/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2378/comments | https://api.github.com/repos/huggingface/transformers/issues/2378/events | https://github.com/huggingface/transformers/pull/2378 | 544,239,450 | MDExOlB1bGxSZXF1ZXN0MzU4Mzk4MDI3 | 2,378 | added pad_to_max_length option to batch_encode_plus | {
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"Thanks @ameasure, do you think you could run the quality tool as defined in the contributing guidelines for that test `check_code_quality` to pass?",
"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 cont... | 1,577 | 1,584 | 1,584 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/2377 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2377/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2377/comments | https://api.github.com/repos/huggingface/transformers/issues/2377/events | https://github.com/huggingface/transformers/pull/2377 | 544,177,875 | MDExOlB1bGxSZXF1ZXN0MzU4MzQ4NDI1 | 2,377 | Text generation on GPU: Moved the encoded_prompt to correct device | {
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"Thank you @alberduris !"
] | 1,577 | 1,578 | 1,578 | CONTRIBUTOR | null | Moved the `encoded_prompt` to the correct device to solve the problem when using GPU.
Solves the problem mentioned in #227 #1414 #2360 | {
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https://api.github.com/repos/huggingface/transformers/issues/2376 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2376/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2376/comments | https://api.github.com/repos/huggingface/transformers/issues/2376/events | https://github.com/huggingface/transformers/issues/2376 | 544,177,102 | MDU6SXNzdWU1NDQxNzcxMDI= | 2,376 | Classification of sentence pair with two different languages | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | I have been working on multi-lingual sentence similarity (English-Hindi)
### for example:
> Sentence 1 (English)
> Sentence 2 (Translation in Hindi of Sentence 1)
> Sentence Similarity Score.
Any idea on how do I train for sentence similarity using `xlm-mlm-xnli15-1024`?
| {
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https://api.github.com/repos/huggingface/transformers/issues/2375 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2375/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2375/comments | https://api.github.com/repos/huggingface/transformers/issues/2375/events | https://github.com/huggingface/transformers/issues/2375 | 544,120,731 | MDU6SXNzdWU1NDQxMjA3MzE= | 2,375 | Is the position of the scheduler.step() correct? | {
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"Why this issue were left without an answer? I don't know what is the answer, but it seems that this scheduler should be executed at batch-level since there are other examples using batch-level instead of epoch-level: https://github.com/huggingface/transformers/blob/8e8384663d716d4b5a4f510070ff954fc0ba4a52/examples... | 1,577 | 1,658 | 1,577 | CONTRIBUTOR | null | ## ❓ Questions & Help
In the [lm_finetuning_file](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py#L285-L319), the scheduler used is "get_linear_schedule_with_warmup" which in turn uses the underlying "LambdaLR" [ref](https://github.com/huggingface/transformers/blob/594ca6deadb6b... | {
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https://api.github.com/repos/huggingface/transformers/issues/2374 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2374/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2374/comments | https://api.github.com/repos/huggingface/transformers/issues/2374/events | https://github.com/huggingface/transformers/issues/2374 | 544,074,027 | MDU6SXNzdWU1NDQwNzQwMjc= | 2,374 | Fine-tuning BertAbs on new dataset? | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"duplicate of #2597, no update yet sadly."
] | 1,577 | 1,583 | 1,583 | NONE | null | ## 🚀 Feature
Hi,
I wonder if there can be a script for fine-tuning BertAbs on new dataset?
Or if you have some hint to provide about this task? Not sure how to access loss from ```modeling_bertabs.py```.
Thanks
| {
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https://api.github.com/repos/huggingface/transformers/issues/2373 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2373/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2373/comments | https://api.github.com/repos/huggingface/transformers/issues/2373/events | https://github.com/huggingface/transformers/issues/2373 | 544,060,016 | MDU6SXNzdWU1NDQwNjAwMTY= | 2,373 | RuntimeError: The size of tensor a (30524) must match the size of tensor b (30522) at non-singleton dimension 2 --- run_lm_finetuning.py | {
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"Can you share the full command you are using to run the script?",
"> Can you share the full command you are using to run the script?\r\n\r\n> Can you share the full command you are using to run the script?\r\n\r\nHi sure, this is the command (basically the same as the document):\r\n```\r\npython run_lm_finetunin... | 1,577 | 1,584 | 1,584 | NONE | null | ## 🐛 Bug
I am using ```run_lm_finetuning.py``` to fine-tune bert-base-uncased on my dataset and I am getting the following error:
I also truncated my dataset to have num examples dividable by the batch_size.
Note that fine-tuning gpt2 on the same dataset works fine.
```
12/30/2019 17:23:28 - INFO - __main__... | {
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https://api.github.com/repos/huggingface/transformers/issues/2372 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2372/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2372/comments | https://api.github.com/repos/huggingface/transformers/issues/2372/events | https://github.com/huggingface/transformers/issues/2372 | 544,030,819 | MDU6SXNzdWU1NDQwMzA4MTk= | 2,372 | What is the "could not find answer" warning in squad.py | {
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"This means that the script that converts the examples to features can't find the answers it should be finding. Rather than trying to predict those, it ignores them.\r\n\r\nThis means that these examples won't be used for training, reducing the total number of examples that will be used. If it is a small portion of... | 1,577 | 1,598 | 1,584 | NONE | null | Hello,
I am trying to run run_squad.py for BERT (italian-cased) with an italian version of squad.
During the creation of features from dataset, I got some answer skipped like in the following:
<img width="478" alt="Screenshot 2019-12-30 at 23 30 19" src="https://user-images.githubusercontent.com/26765504/71603... | {
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"BTW, I manually downloaded the pretrained model, and save it in dir \"./bert-base-chinese\"",
"@Yuejiang-li \r\nTo me, the error msg indicates that the seq generated after tokenization of \"weibo_content\" has more than 512 tokens. The 512 is the max num of tokens in one seq allowed for the BERT model (embeddin... | 1,577 | 1,601 | 1,577 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hi!
I'm currently using BERT to obtain sentence embeddings for Chinese text inputs.
Things are fine for most cases. However when I am dealing with this text:
```python
weibo_content = "貌似还是没有完全修复。 http://As.international.anger.grow... | {
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https://api.github.com/repos/huggingface/transformers/issues/2370 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2370/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2370/comments | https://api.github.com/repos/huggingface/transformers/issues/2370/events | https://github.com/huggingface/transformers/issues/2370 | 543,841,417 | MDU6SXNzdWU1NDM4NDE0MTc= | 2,370 | Pipelines: add PoS support | {
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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",
"We now have a more general `TokenClassificationPipeline`, @arnaudmiribel (this is just an alias to the `NerPipeline`)"
] | 1,577 | 1,583 | 1,583 | CONTRIBUTOR | null | ## 🚀 Feature
As `Pipelines` were recently added for many tasks including NER, Sentiment Analysis, it'd be great to also enable Part-of-Speech tagging.
## Motivation
PoS tagging is a very useful task, and often used as an evaluating downstream task for new models.
## Additional context
Current available ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2369 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2369/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2369/comments | https://api.github.com/repos/huggingface/transformers/issues/2369/events | https://github.com/huggingface/transformers/pull/2369 | 543,832,095 | MDExOlB1bGxSZXF1ZXN0MzU4MDcyNDk3 | 2,369 | few changes due to the torch version inconsistency in summarization example | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | This small change intends to fix the issue #2297.
it's generally a version inconsistent issue.
in ver 1.1.0, torch.gt outputs:
_torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 0, 1],
[ 0, 0]], dtype=torch.uint8)_
while in ver 1.2.0, it outputs:
_torch.ge(torch.tensor([[1,... | {
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"the `past` variable in GTP-2 stores all previously computed key and value vectors. Because GPT-2 uses masked self-attention only the query vectors of previous tokens are updated at every step, but not the key and value vectors. Therefore the `past` variable can be used to speed up decoding. \r\n\r\nTo better under... | 1,577 | 1,584 | 1,584 | CONTRIBUTOR | null | ## ❓ Questions & Help
I'm hoping to get some clarification regarding how past/layer_past are meant to work in GPT-2. My prior impression was that the query/key/value at every layer (other than the first) should be influenced by all tokens the model is able to see. As such, it shouldn't make much sense to use pre-com... | {
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https://api.github.com/repos/huggingface/transformers/issues/2367 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2367/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2367/comments | https://api.github.com/repos/huggingface/transformers/issues/2367/events | https://github.com/huggingface/transformers/issues/2367 | 543,759,668 | MDU6SXNzdWU1NDM3NTk2Njg= | 2,367 | Load the google bert model(ckpt) from TFBertForPreTraining error | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | I want use the google chinese bert ckpt model in transforms, and env use tf2.
the transforms can load the ckpt into pytorch model
But I want load the ckpt into tf.keras model, How can I do this ?
mo... | {
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https://api.github.com/repos/huggingface/transformers/issues/2366 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2366/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2366/comments | https://api.github.com/repos/huggingface/transformers/issues/2366/events | https://github.com/huggingface/transformers/issues/2366 | 543,727,652 | MDU6SXNzdWU1NDM3Mjc2NTI= | 2,366 | How Can I load the google bert model(ckpt)? | {
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] | 1,577 | 1,583 | 1,583 | NONE | null | TF2.0
## ❓ Questions & Help
import os
pretrained_path = 'chinese_L-12_H-768_A-12'
config_path = os.path.join(pretrained_path, 'bert_config.json')
checkpoint_path = os.path.join(pretrained_path, 'bert_model.ckpt')
vocab_path = os.path.join(pretrained_path, 'vocab.txt')
config = BertConfig.from_json_file(config_... | {
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https://api.github.com/repos/huggingface/transformers/issues/2365 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2365/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2365/comments | https://api.github.com/repos/huggingface/transformers/issues/2365/events | https://github.com/huggingface/transformers/issues/2365 | 543,575,714 | MDU6SXNzdWU1NDM1NzU3MTQ= | 2,365 | upgrading new transformer doesn't work | {
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"Previously you were relying on `transformers` being implicitly added to `PYTHONPATH` when you were working from the source of the repository. This breaks if you move to another directory, like `examples`.\r\n\r\n`pip install .` makes `transformers` available in your virtualenv regardless of where you're working.\r... | 1,577 | 1,577 | 1,577 | NONE | null | ## ❓ Questions & Help
Hi,
I have pulled the repo again since a lot of stuff changed/added.
When I try to use ```pip install --upgrade .``` command, nothing changes and I am stuck in the following step forever:
```
(py36) pytorch-transformers$ pip install --upgrade .
Processing /home/pytorch-transformers
``... | {
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https://api.github.com/repos/huggingface/transformers/issues/2364 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2364/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2364/comments | https://api.github.com/repos/huggingface/transformers/issues/2364/events | https://github.com/huggingface/transformers/issues/2364 | 543,507,442 | MDU6SXNzdWU1NDM1MDc0NDI= | 2,364 | How to fine-tune PreTrainedEncoderDecoder on new dataset? | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Seconded. Is there a way to fine-tune any seq2seq model in huggingface?",
"@Josh-Payne You can have a look at https://github.com/hugg... | 1,577 | 1,590 | 1,590 | NONE | null | ## ❓ Questions & Help
Hi,
Many thanks for your recent work implementing [this paper](https://arxiv.org/pdf/1907.12461.pdf).
I wonder if you have or can provide script and documentation for fine-tuning PreTrainedEncoderDecoder on new dataset?
Many thanks | {
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https://api.github.com/repos/huggingface/transformers/issues/2363 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2363/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2363/comments | https://api.github.com/repos/huggingface/transformers/issues/2363/events | https://github.com/huggingface/transformers/issues/2363 | 543,473,827 | MDU6SXNzdWU1NDM0NzM4Mjc= | 2,363 | Finetuning on several tasks | {
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"The `BertForSequenceClassification` model has a classifier head transforming Bert output into `num_labels` output. So if you change the classification output, it can't be loaded as you could see.\r\nThe only hack you could do is to load the fine-tuned model with previous `num_labels`, then remove classifier head a... | 1,577 | 1,583 | 1,583 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Hello,
Is it possible to finetune a Transformer on some dataset, and then finetune the model again on another dataset with a different number of output labels ?
I tried this and got the following error:
```
RuntimeError: Er... | {
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https://api.github.com/repos/huggingface/transformers/issues/2362 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2362/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2362/comments | https://api.github.com/repos/huggingface/transformers/issues/2362/events | https://github.com/huggingface/transformers/issues/2362 | 543,436,736 | MDU6SXNzdWU1NDM0MzY3MzY= | 2,362 | Why albert has a print statement during forward? | {
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"This was an issue with a previous version of transformers. Please upgrade it to a more recent version for the warning to go away. Thank you.",
"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 contributio... | 1,577 | 1,584 | 1,584 | NONE | null | ## ❓ Questions & Help
both AlbertTransformer and AlbertLayerGroup have print statements in the forward method, which messes up logging / printing during training
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https://api.github.com/repos/huggingface/transformers/issues/2361 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2361/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2361/comments | https://api.github.com/repos/huggingface/transformers/issues/2361/events | https://github.com/huggingface/transformers/pull/2361 | 543,378,999 | MDExOlB1bGxSZXF1ZXN0MzU3NjYwNTEw | 2,361 | Improve logging message in feature conversion functions | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2361?src=pr&el=h1) Report\n> Merging [#2361](https://codecov.io/gh/huggingface/transformers/pull/2361?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f75bf05ce6a05ef316363de129c29f2e00cacd7b?src=pr&el=desc) will **n... | 1,577 | 1,578 | 1,578 | NONE | null | This PR adds the total number of examples to process to the log message produced during the feature conversion step. | {
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https://api.github.com/repos/huggingface/transformers/issues/2360 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2360/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2360/comments | https://api.github.com/repos/huggingface/transformers/issues/2360/events | https://github.com/huggingface/transformers/issues/2360 | 543,340,652 | MDU6SXNzdWU1NDMzNDA2NTI= | 2,360 | CTRL - RuntimeError: Expected object of backend CUDA but got backend CPU for argument #3 'index' | {
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"Upgrading torch to 1.3.1 solves the issue",
"I have the same problem even with torch==1.3.1\r\n\r\nI think this should be re-opened",
"I have the same issue for generating with gpt2 \r\n\r\nHere is the error log:\r\n```\r\n File \"run_generation.py\", line 236, in <module>\r\n main()\r\n File \"run_genera... | 1,577 | 1,582 | 1,577 | NONE | null | ## 🐛 Bug
Hi,
The error
`RuntimeError: Expected object of backend CUDA but got backend CPU for argument #3 'index'`
arise while running CTRL using examples/run_generation.py
Model I am using (Bert, XLNet....): **CTRL**
Language I am using the model on (English, Chinese....): **English**
The problem a... | {
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https://api.github.com/repos/huggingface/transformers/issues/2359 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2359/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2359/comments | https://api.github.com/repos/huggingface/transformers/issues/2359/events | https://github.com/huggingface/transformers/issues/2359 | 543,336,794 | MDU6SXNzdWU1NDMzMzY3OTQ= | 2,359 | Confusion about the target_mapping parameter of the xlnet model | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | Why code at https://github.com/huggingface/transformers/blob/f75bf05ce6a05ef316363de129c29f2e00cacd7b/src/transformers/modeling_xlnet.py#L1029 is ` target_mapping[0, 0, -1] = 1.0`, i think it should be ` target_mapping[:, :, -1] = 1.0`
And I‘m confused about `taget_mapping` parameter, What is the difference between... | {
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https://api.github.com/repos/huggingface/transformers/issues/2358 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2358/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2358/comments | https://api.github.com/repos/huggingface/transformers/issues/2358/events | https://github.com/huggingface/transformers/issues/2358 | 543,331,776 | MDU6SXNzdWU1NDMzMzE3NzY= | 2,358 | Quickstart BERT Example: Assertion Error | {
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"Not quite sure what happened. Restarted my kernel after installing packages from ```examples/requirements.txt``` and is fixed. Closing the issue."
] | 1,577 | 1,577 | 1,577 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): Bert
Language I am using the model on (English, Chinese....): English
The problem arises when:
* I run the official BERT Example in my local Jupyter Lab environment: Copy pasted the code and ran it in one cell.
## To Reproduce... | {
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https://api.github.com/repos/huggingface/transformers/issues/2357 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2357/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2357/comments | https://api.github.com/repos/huggingface/transformers/issues/2357/events | https://github.com/huggingface/transformers/issues/2357 | 543,311,525 | MDU6SXNzdWU1NDMzMTE1MjU= | 2,357 | GLUE benchmark score for XLNet_base_cased? | {
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"Have you looked at the [run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py) script ?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,584 | 1,584 | NONE | null | ## ❓ Questions & Help
Can someone provide the GLUE benchmark scores for different GLUE tasks. Or a script for preforming predictions on the test files will be really helpful.
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https://api.github.com/repos/huggingface/transformers/issues/2356 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2356/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2356/comments | https://api.github.com/repos/huggingface/transformers/issues/2356/events | https://github.com/huggingface/transformers/pull/2356 | 543,234,113 | MDExOlB1bGxSZXF1ZXN0MzU3NTQzMzc5 | 2,356 | GPT2 should not store/compute cached activations during finetuning | {
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"Not sure which size of GPT-2 you're testing with, but the 355M version utilizes gradient checkpointing for finetuning in gpt-2-simple, which is not the case with the 124M version w/ Transformers.\r\n\r\nThat might be a useful test case.",
"I just tried this with gpt-2-medium on my poetry dataset and have the sam... | 1,577 | 1,651 | 1,583 | MEMBER | null | This PR tries to fix the issue with large memory usage from GPT2 during fine-tuning.
## Quick estimations
@LysandreJik compared memory usage with @minimaxir GPT2-simple (https://github.com/minimaxir/gpt-2-simple):
*Small model*, batch size 4, sequence length 512 (roughly similar):
- us => 9.9GB,
- GPT2-simpl... | {
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https://api.github.com/repos/huggingface/transformers/issues/2355 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2355/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2355/comments | https://api.github.com/repos/huggingface/transformers/issues/2355/events | https://github.com/huggingface/transformers/issues/2355 | 543,087,068 | MDU6SXNzdWU1NDMwODcwNjg= | 2,355 | transformers command not found after installing transformers using pip | {
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"I have this problem also. \r\nInstalled with pip3 (maybe this is the necessary information)",
"Hi @ManasRMohanty, @DaniilRoman,\r\n\r\nIn 2.3.0 we introduced some new commands from the cli, which are now provided through **transformers-cli**.\r\n\r\nCan you please try the following: \r\n\r\n```bash\r\ntransforme... | 1,577 | 1,578 | 1,577 | NONE | null | I wanted to convert TF checkpoints to pytorch saved files and thus I followed instructions as mentioned in the link https://huggingface.co/transformers/converting_tensorflow_models.html
Thus, I installed PyTorch, Tensorflow and then transformers. But after doing so, when I ran the command, my system prompted me tra... | {
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https://api.github.com/repos/huggingface/transformers/issues/2354 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2354/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2354/comments | https://api.github.com/repos/huggingface/transformers/issues/2354/events | https://github.com/huggingface/transformers/pull/2354 | 543,066,120 | MDExOlB1bGxSZXF1ZXN0MzU3MzkxODk0 | 2,354 | [debug] Debug Heisenbug, the old school way. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2354?src=pr&el=h1) Report\n> Merging [#2354](https://codecov.io/gh/huggingface/transformers/pull/2354?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/bfe870be654a1fc54c5479f9ad0875492d9cd959?src=pr&el=desc) will **n... | 1,577 | 1,577 | 1,577 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/2352 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2352/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2352/comments | https://api.github.com/repos/huggingface/transformers/issues/2352/events | https://github.com/huggingface/transformers/pull/2352 | 543,061,144 | MDExOlB1bGxSZXF1ZXN0MzU3Mzg3NjAz | 2,352 | Cli tweaks | {
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"Awesome!"
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https://api.github.com/repos/huggingface/transformers/issues/2351 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2351/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2351/comments | https://api.github.com/repos/huggingface/transformers/issues/2351/events | https://github.com/huggingface/transformers/issues/2351 | 543,026,761 | MDU6SXNzdWU1NDMwMjY3NjE= | 2,351 | GLUE Benchmark Hyperparameters | {
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"Hi, each result available on the [example page](https://huggingface.co/transformers/examples.html) shows the command that was used, displaying the hyper-parameters that are different from the defaults.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed ... | 1,577 | 1,584 | 1,584 | NONE | null | ## ❓ Questions & Help
In the `run_glue.py` script, are the hyperparameters for running BERT, RoBERTa, ALBERT, etc. the exact same? The documentation does not seem to outline separate hyperparameters, but the papers of each respective model show different hyperparameter ranges. I'm wondering if this was taken account... | {
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https://api.github.com/repos/huggingface/transformers/issues/2350 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2350/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2350/comments | https://api.github.com/repos/huggingface/transformers/issues/2350/events | https://github.com/huggingface/transformers/issues/2350 | 543,017,081 | MDU6SXNzdWU1NDMwMTcwODE= | 2,350 | Trouble fine tuning BERT language model | {
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"I have the same question but no answer. In my case, I ran it in google colab and used easydict to deal with arg parser. \r\n\r\n/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)\r\n 1836 .format... | 1,577 | 1,580 | 1,580 | NONE | null | ## 🐛 Bug
Hello, I'm having trouble running **run_lm_finetuning.py** script. I'm using pytorch 1.2, python 3.5, CUDA 9.2, Ubuntu 18.04.
When I run
```
$python run_lm_finetuning.py
--output_dir= my_output_dir/
--model_type=bert
--model_name_or_path=bert-base-uncased
--do_train
--train... | {
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https://api.github.com/repos/huggingface/transformers/issues/2349 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2349/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2349/comments | https://api.github.com/repos/huggingface/transformers/issues/2349/events | https://github.com/huggingface/transformers/pull/2349 | 543,000,485 | MDExOlB1bGxSZXF1ZXN0MzU3MzM3MDQ3 | 2,349 | Enforce target version for black. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2349?src=pr&el=h1) Report\n> Merging [#2349](https://codecov.io/gh/huggingface/transformers/pull/2349?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/bfe870be654a1fc54c5479f9ad0875492d9cd959?src=pr&el=desc) will **n... | 1,577 | 1,578 | 1,578 | CONTRIBUTOR | null | This should stabilize formatting.
As suggested by @julien-c. | {
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https://api.github.com/repos/huggingface/transformers/issues/2348 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2348/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2348/comments | https://api.github.com/repos/huggingface/transformers/issues/2348/events | https://github.com/huggingface/transformers/issues/2348 | 542,995,393 | MDU6SXNzdWU1NDI5OTUzOTM= | 2,348 | CamembertForQuestionAnswering | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | Hi,
is it possibile tu add _CamembertForQuestionAnswering_ class that extend _RobertaForQuestionAnswering_ into **src/transformers/modeling_camembert.py**, **src/transformers/__init__.py** and **examples/run_squad.py** ?
I had to manually force it in order to execute _run_squad.py_ with a CamemBERT-like network.
... | {
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https://api.github.com/repos/huggingface/transformers/issues/2347 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2347/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2347/comments | https://api.github.com/repos/huggingface/transformers/issues/2347/events | https://github.com/huggingface/transformers/pull/2347 | 542,969,141 | MDExOlB1bGxSZXF1ZXN0MzU3MzEwODky | 2,347 | revise T5 code to support one step decoding during generation | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | @thomwolf Hi, I'm new to contribute to this project. I revised the T5 code to support one step decoding during generation based on your implementation. Besides adding `decode_step` function, I also revised some others to pass in `cache` variable. Meanwhile, I added `bos_token` in tokenizer_t5 so that `bos_token` can be... | {
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https://api.github.com/repos/huggingface/transformers/issues/2346 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2346/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2346/comments | https://api.github.com/repos/huggingface/transformers/issues/2346/events | https://github.com/huggingface/transformers/issues/2346 | 542,916,889 | MDU6SXNzdWU1NDI5MTY4ODk= | 2,346 | Why does the BertForQuestionAnswering sample code duplicate the [CLS] token? | {
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"Indeed, this is a mistake, thank you for raising an issue. It should have been fixed with 74755c89b92e0c0c027221c13fd034afed4d2136.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,584 | 1,584 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
The BertForQuestionAnswering sample code creates duplicate [CLS] tokens. Wondering why:
```
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-w... | {
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https://api.github.com/repos/huggingface/transformers/issues/2345 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2345/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2345/comments | https://api.github.com/repos/huggingface/transformers/issues/2345/events | https://github.com/huggingface/transformers/issues/2345 | 542,913,668 | MDU6SXNzdWU1NDI5MTM2Njg= | 2,345 | Feature Request: Pipeline for Query/Document relevance | {
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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",
"unstale because this is very interesting",
"This issue has been automatically marked as stale because it has not had recent activity.... | 1,577 | 1,589 | 1,589 | NONE | null | # Pipelines for IR tasks
## Justification
In the last few years, a bunch of deep architectures were proposed for Ad-hoc retrieval, most with limited success (if any). However, BERT(et al)-based models are finally pushing the state of the art for Ad-hoc retrieval. In fact in the last TREC had a [Deep Learning trac... | {
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https://api.github.com/repos/huggingface/transformers/issues/2344 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2344/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2344/comments | https://api.github.com/repos/huggingface/transformers/issues/2344/events | https://github.com/huggingface/transformers/issues/2344 | 542,894,470 | MDU6SXNzdWU1NDI4OTQ0NzA= | 2,344 | How to run bert without checkpoints | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | I would like to run BERT from scratch with no checkpoints for my language (PT-BR) and make a comparison with the multilingual model!
I am currently running BERT-Native provided by google to get checkpoints from scratch and then converting to pytorch. But it is a time consuming process!
can anybody help me? | {
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https://api.github.com/repos/huggingface/transformers/issues/2343 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2343/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2343/comments | https://api.github.com/repos/huggingface/transformers/issues/2343/events | https://github.com/huggingface/transformers/issues/2343 | 542,861,840 | MDU6SXNzdWU1NDI4NjE4NDA= | 2,343 | How to finetune PreTrainedEncoderDecoder | {
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"Hi,\r\n\r\nThanks for the nice work. I have the same question.\r\nWould appreciate your reply. ",
"Yes , example would be nice",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
PreTrainedEncoderDecoder is great.
Now I have the following questions :
(1) How to use my data to finetune the PreTrainedEncoderDecoder?
(2) If I want to use the pretrained RoBERTa as encoder and deocder, what should I do ? | {
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https://api.github.com/repos/huggingface/transformers/issues/2342 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2342/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2342/comments | https://api.github.com/repos/huggingface/transformers/issues/2342/events | https://github.com/huggingface/transformers/pull/2342 | 542,856,814 | MDExOlB1bGxSZXF1ZXN0MzU3MjE3NjM0 | 2,342 | Tokenizers as optional dependency | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,651 | 1,583 | MEMBER | null | - `tokenizers` as an optional dependency (`pip install -e .[fast]`)
- code formating with `make style` `make quality` | {
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https://api.github.com/repos/huggingface/transformers/issues/2341 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2341/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2341/comments | https://api.github.com/repos/huggingface/transformers/issues/2341/events | https://github.com/huggingface/transformers/issues/2341 | 542,764,020 | MDU6SXNzdWU1NDI3NjQwMjA= | 2,341 | "Reformer: The Efficient Transformer" looks awesome. I'd love to see it in the library. | {
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"I have started to refactor the original source code in Pytorch if you'd like to help I'd greatly appreciate it! [https://github.com/zbloss/reformer](https://github.com/zbloss/reformer)",
"I have a working implementation at https://github.com/lucidrains/reformer-pytorch !",
"Any update on adding this to the lib... | 1,577 | 1,590 | 1,590 | NONE | null | # 🌟New model addition
## Model description
Efficient Transformer with locality-sensitive hashing and reversible layers
https://openreview.net/forum?id=rkgNKkHtvB
<!-- Important information -->
## Open Source status
* [. ] the model implementation is available: (give details)
There is an implementation fro... | {
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https://api.github.com/repos/huggingface/transformers/issues/2340 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2340/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2340/comments | https://api.github.com/repos/huggingface/transformers/issues/2340/events | https://github.com/huggingface/transformers/issues/2340 | 542,752,109 | MDU6SXNzdWU1NDI3NTIxMDk= | 2,340 | Bert cross attention | {
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"I would imagine the idea is to incorporate a strong presence of the encoder hidden states - else the conditioning on the encoder might be weak. \r\n\r\nWe do an attention without the encoder hidden states anyways before the cross attention."
] | 1,577 | 1,581 | 1,579 | NONE | null | ## ❓ Questions & Help
In the standard Transformer/Bert architecture, what is the intuition behind cross attention doing a weighted average over the encoder_hidden_states? What happens if we set the value layer to decoder hidden_state instead?
See the cross attention value layer being set to the encoder_hidden_sta... | {
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https://api.github.com/repos/huggingface/transformers/issues/2339 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2339/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2339/comments | https://api.github.com/repos/huggingface/transformers/issues/2339/events | https://github.com/huggingface/transformers/pull/2339 | 542,743,310 | MDExOlB1bGxSZXF1ZXN0MzU3MTI0NzY3 | 2,339 | read each lines, require less memory | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2339?src=pr&el=h1) Report\n> Merging [#2339](https://codecov.io/gh/huggingface/transformers/pull/2339?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/537a1de53d824b5851bce32cb5eafaef3f9ce5ef?src=pr&el=desc) will **i... | 1,577 | 1,580 | 1,580 | NONE | null | The original code reads whole data at once, so it requires so much memory to handle huge corpus.
This change is:
* read corpus by each lines
* flatten 2-dimension array by itertools.chain, it requies less memory and fast | {
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https://api.github.com/repos/huggingface/transformers/issues/2338 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2338/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2338/comments | https://api.github.com/repos/huggingface/transformers/issues/2338/events | https://github.com/huggingface/transformers/issues/2338 | 542,695,809 | MDU6SXNzdWU1NDI2OTU4MDk= | 2,338 | Summarization ROGUE scores don't equal that of the paper ... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | CONTRIBUTOR | null | ## ❓ Questions & Help
Just ran the `run_summarization.py` script, with the parameters specified [here](https://github.com/huggingface/transformers/tree/master/examples/summarization) and the ROGUE scores are far off from what is reported in the related paper.
The ROGUE scores reported in [PreSumm paper](https://g... | {
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https://api.github.com/repos/huggingface/transformers/issues/2337 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2337/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2337/comments | https://api.github.com/repos/huggingface/transformers/issues/2337/events | https://github.com/huggingface/transformers/issues/2337 | 542,670,080 | MDU6SXNzdWU1NDI2NzAwODA= | 2,337 | Dropout rates to be updated in all ALBERT v2 configs | {
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"I found it already been updated: https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-config.json\r\n\r\nDid I miss something?",
"Base, large and xlarge v2 configs have to be updated too, as confirmed by this [issue](https://github.com/google-research/ALBERT/issues/23) in the official Google Res... | 1,577 | 1,582 | 1,582 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): ALBERT v2
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [x] the official example scripts: run_squad.py
* [ ] my own modified scripts:
The tasks I am working on is:
* [x] an ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2336 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2336/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2336/comments | https://api.github.com/repos/huggingface/transformers/issues/2336/events | https://github.com/huggingface/transformers/issues/2336 | 542,644,580 | MDU6SXNzdWU1NDI2NDQ1ODA= | 2,336 | TypeError: Expected Operation, Variable, or Tensor, got None while saving tensorflow model | {
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"The training of the model is successful, but getting errors only while saving the model",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): TFAlbert
Language I am using the model on (English, Chinese....): English
The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name): GLUE
* [ ] my own task or dataset: (give details)
## Environment
* ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2335 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2335/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2335/comments | https://api.github.com/repos/huggingface/transformers/issues/2335/events | https://github.com/huggingface/transformers/issues/2335 | 542,633,932 | MDU6SXNzdWU1NDI2MzM5MzI= | 2,335 | XLNet and RoBERTa embeddings | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | Referring to Jay Alammar's awesome blog post wherein he showed how to create sentence embeddings from BERT (DistilBert as well), can we use the workings he showed here for XLNet and RoBERTa models as well?
http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/
I was thinking majorly to use eve... | {
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https://api.github.com/repos/huggingface/transformers/issues/2334 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2334/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2334/comments | https://api.github.com/repos/huggingface/transformers/issues/2334/events | https://github.com/huggingface/transformers/issues/2334 | 542,618,685 | MDU6SXNzdWU1NDI2MTg2ODU= | 2,334 | relativeattentionbias.weight in block 0 EncDecAttention of T5 Model not in original tf model. Where do we get it from? | {
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"It should also be in the TF version, this is the shared relative attention bias (shared among layers).\r\n\r\nDo you want to give more details on how you compared both lists of weights and what make you think it's missing?",
"Sure. By the way, when we say the TF version, I mean the weights released by Google. So... | 1,577 | 1,622 | 1,585 | NONE | null | ## ❓ Questions & Help
Hi, I was comparing the weights in original tf model and the pytorch t5 model and it looks like there is an extra embedding in the EncDecAttention layer (layer_1) in block_0 (relative_attention_bias.weight). I could find and compare the other embedding weights in the model but not this particul... | {
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https://api.github.com/repos/huggingface/transformers/issues/2333 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2333/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2333/comments | https://api.github.com/repos/huggingface/transformers/issues/2333/events | https://github.com/huggingface/transformers/pull/2333 | 542,612,586 | MDExOlB1bGxSZXF1ZXN0MzU3MDE3NTc0 | 2,333 | Add 'keep_accents' flag to basic tokenizer | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2333?src=pr&el=h1) Report\n> Merging [#2333](https://codecov.io/gh/huggingface/transformers/pull/2333?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/77b0a385ffac5964030d08b1c3611b61370b1918?src=pr&el=desc) will **i... | 1,577 | 1,584 | 1,584 | NONE | null | Hello!
Recently we released our Spanish Bert Model (https://github.com/dccuchile/beto) and we found problems with the tokenization for Spanish.
The problem relates to that the basic tokenizer convert the text to NFD.
For example:
```
text = "[CLS] compañera [SEP]"
tokenized_text = tokenizer.tokenize(text)
... | {
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https://api.github.com/repos/huggingface/transformers/issues/2332 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2332/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2332/comments | https://api.github.com/repos/huggingface/transformers/issues/2332/events | https://github.com/huggingface/transformers/issues/2332 | 542,604,542 | MDU6SXNzdWU1NDI2MDQ1NDI= | 2,332 | What does 'output of the embeddings' mean? | {
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"The output of the embeddings is the sum of the token embeddings + the segment embeddings + the position embeddings. This value is the value that will be fed to the first layer of the transformer.",
"@LysandreJik \r\n\r\nHello,\r\n\r\nThank you very much for your reply.\r\n\r\nSo according to the Hugging Face Tra... | 1,577 | 1,612 | 1,578 | NONE | null | Hello,
According to Hugging Face Transformers documentation, (https://huggingface.co/transformers/model_doc/gpt2.html#gpt2doubleheadsmodel)
the transformer's output ```hidden_state``` is defined as the following:
```
hidden_states: (optional, returned when config.output_hidden_states=True)
list of torch.FloatT... | {
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".2f is not enough to represent learning rate",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | Hello,
Outside of the training function, I set:
```python
# define the hyperparameters for running the train function.
optimizer_ch2 = AdamW(model_ch2.parameters(), lr = lr, correct_bias = True)
scheduler_ch2 = get_linear_schedule_with_warmup(optimizer = optimizer_ch2,
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https://api.github.com/repos/huggingface/transformers/issues/2330 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2330/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2330/comments | https://api.github.com/repos/huggingface/transformers/issues/2330/events | https://github.com/huggingface/transformers/issues/2330 | 542,601,912 | MDU6SXNzdWU1NDI2MDE5MTI= | 2,330 | BERT adapted to time series | {
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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",
"Can this issue be opened again? I recon there is a need to discuss this possibility",
"@jbechara / @MJimitater : Hello! \r\n\r\nI hap... | 1,577 | 1,612 | 1,583 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Is there a better way of modifying BERT to take time series as input (i.e. numerical data instead of text) than editing my local library to skip the word embedding? If not, what is the easiest way to do the latter?
Thanks! | {
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https://api.github.com/repos/huggingface/transformers/issues/2329 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2329/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2329/comments | https://api.github.com/repos/huggingface/transformers/issues/2329/events | https://github.com/huggingface/transformers/pull/2329 | 542,570,038 | MDExOlB1bGxSZXF1ZXN0MzU2OTgzMTYw | 2,329 | refactoring the code | {
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"We already have a `make style` command which automates formatting (with a setup that we chose).\r\n\r\nThanks for your contribution, closing this issue now."
] | 1,577 | 1,577 | 1,577 | NONE | null | code formatting, following PEP8 convention | {
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https://api.github.com/repos/huggingface/transformers/issues/2328 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2328/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2328/comments | https://api.github.com/repos/huggingface/transformers/issues/2328/events | https://github.com/huggingface/transformers/pull/2328 | 542,561,633 | MDExOlB1bGxSZXF1ZXN0MzU2OTc2Mjg3 | 2,328 | Refactoring the code | {
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https://api.github.com/repos/huggingface/transformers/issues/2327 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2327/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2327/comments | https://api.github.com/repos/huggingface/transformers/issues/2327/events | https://github.com/huggingface/transformers/issues/2327 | 542,558,785 | MDU6SXNzdWU1NDI1NTg3ODU= | 2,327 | load_and_cache_examples crashes on windows | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using ALBERT:
Language I am using the model on (English):
The problem arise when using:
[examples/run_squad.py] the official example scripts: (run evaluation for offline model)
It crashes in "load_and_cache_examples" for paths with windows format. It's... | {
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https://api.github.com/repos/huggingface/transformers/issues/2326 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2326/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2326/comments | https://api.github.com/repos/huggingface/transformers/issues/2326/events | https://github.com/huggingface/transformers/issues/2326 | 542,550,271 | MDU6SXNzdWU1NDI1NTAyNzE= | 2,326 | run_generation.py gives TypeError when using xlnet due to empty dict being passed as token | {
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"I have the same problem, did you find any solutions?\r\n@nanne-aben ",
"No, not really. I removed the empty dictionary, which makes the code run,\nbut the generated text is just kinda bad. GPT2 (in which case {} is not\nadded) creates much better text. So I guess that the {} was added for a\nreason, but I can't ... | 1,577 | 1,586 | 1,586 | NONE | null | ## 🐛 Bug
When I run
```
python run_generation.py --model_type=xlnet --model_name_or_path=xlnet-large-cased
```
I get the following error
```
Traceback (most recent call last):
File "run_generation.py", line 236, in <module>
main()
File "run_generation.py", line 214, in main
encoded_prompt = ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2325 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2325/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2325/comments | https://api.github.com/repos/huggingface/transformers/issues/2325/events | https://github.com/huggingface/transformers/issues/2325 | 542,542,936 | MDU6SXNzdWU1NDI1NDI5MzY= | 2,325 | How to make FP16 quantization on gpt/xl? | {
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"Closing this in favor of https://github.com/huggingface/tflite-android-transformers/issues/4"
] | 1,577 | 1,577 | 1,577 | NONE | null | How could I fix this error?
`ValueError: Message tensorflow.GraphDef exceeds maximum protobuf size of 2GB: 6234365906` | {
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https://api.github.com/repos/huggingface/transformers/issues/2324 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2324/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2324/comments | https://api.github.com/repos/huggingface/transformers/issues/2324/events | https://github.com/huggingface/transformers/pull/2324 | 542,527,329 | MDExOlB1bGxSZXF1ZXN0MzU2OTQ3NjU0 | 2,324 | Typo in serving.py | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2324?src=pr&el=h1) Report\n> Merging [#2324](https://codecov.io/gh/huggingface/transformers/pull/2324?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/aeef4823ab6099249679756182700e6800024c36?src=pr&el=desc) will **n... | 1,577 | 1,577 | 1,577 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/2323 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2323/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2323/comments | https://api.github.com/repos/huggingface/transformers/issues/2323/events | https://github.com/huggingface/transformers/issues/2323 | 542,497,542 | MDU6SXNzdWU1NDI0OTc1NDI= | 2,323 | Where does the pre-trained bert model gets cached in my system by default? | {
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"AFAIK, the cache folder is hidden. You can download the files manually and the save them to your desired location two files to download is config.json and <model--name>.bin and you can call it through pretrained suppose you wanted to instantiate BERT then do `BertForMaskedLM.from_pretrained(Users/<Your location>/<... | 1,577 | 1,705 | 1,577 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I used model_class.from_pretrained('bert-base-uncased') to download and use the model. The next time when I use this command, it picks up the model from cache. But when I go into the cache, I see several files over 400M with large rando... | {
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https://api.github.com/repos/huggingface/transformers/issues/2322 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2322/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2322/comments | https://api.github.com/repos/huggingface/transformers/issues/2322/events | https://github.com/huggingface/transformers/issues/2322 | 542,469,582 | MDU6SXNzdWU1NDI0Njk1ODI= | 2,322 | I am getting repetitive output when running "python run_generation.py" | {
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"I guess you can tune the model for better results like selecting medium large gpt model changin temp and top - p to get different predictions. \r\nIf your new try using [write with transformer](https://transformer.huggingface.co/doc/gpt2-large) to get an idea about it.",
"You could add a `repetition_penalty`. R... | 1,577 | 1,582 | 1,582 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
Here is the command I used to run the code:
python run_generation.py --model_type=gpt2 --model_name_or_path=gpt2 --length 100
Here is the input and output I got:
Model prompt >>> nice to meet you
nice to meet you.
"I'm sorry, bu... | {
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https://api.github.com/repos/huggingface/transformers/issues/2321 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2321/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2321/comments | https://api.github.com/repos/huggingface/transformers/issues/2321/events | https://github.com/huggingface/transformers/issues/2321 | 542,455,784 | MDU6SXNzdWU1NDI0NTU3ODQ= | 2,321 | Bert Decoder using is_decoder and encoder_hidden_states | {
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"Hi, you're initializing a decoder but you're using it as an encoder. For the task you're showing here, you only need the encoder part, no need to initialize a decoder:\r\n\r\n```py\r\nmodel = BertForMaskedLM.from_pretrained('bert-base-uncased')\r\nmodel.eval()\r\n#\r\n# # Predict all tokens\r\nwith torch.no_grad()... | 1,577 | 1,590 | 1,584 | NONE | null | ```
import torch
from transformers import BertTokenizer, BertModel, BertForMaskedLM
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
text = "[CLS] For an unfamiliar eye, the Porsche Cayenne and the Cayenne Coupe would look similar"
tokenized_text =... | {
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https://api.github.com/repos/huggingface/transformers/issues/2320 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2320/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2320/comments | https://api.github.com/repos/huggingface/transformers/issues/2320/events | https://github.com/huggingface/transformers/issues/2320 | 542,420,530 | MDU6SXNzdWU1NDI0MjA1MzA= | 2,320 | how to do a simple multi-classifier by bert 2.0,training set ,and label set all lines | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,577 | 1,583 | 1,583 | NONE | null | how to do a simple multi-classifier by bert 2.0,training set ,and label set all lines | {
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https://api.github.com/repos/huggingface/transformers/issues/2319 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2319/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2319/comments | https://api.github.com/repos/huggingface/transformers/issues/2319/events | https://github.com/huggingface/transformers/issues/2319 | 542,418,432 | MDU6SXNzdWU1NDI0MTg0MzI= | 2,319 | help: couldn't find such vocabulary files at this path or url | {
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"Did you manage to solve your issue? (if you did, how?)"
] | 1,577 | 1,577 | 1,577 | NONE | null | I want to load the Chinese Roberta model of pre-trained. When I use RobertaModel.from_pretrained() to load pre-trained model, it can't work.
<img width="1108" alt="屏幕快照 2019-12-25 下午11 56 11" src="https://user-images.githubusercontent.com/25845940/71454755-21f98100-27cd-11ea-8d0d-37beed6cc235.png">
<img width="105... | {
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https://api.github.com/repos/huggingface/transformers/issues/2318 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2318/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2318/comments | https://api.github.com/repos/huggingface/transformers/issues/2318/events | https://github.com/huggingface/transformers/issues/2318 | 542,406,032 | MDU6SXNzdWU1NDI0MDYwMzI= | 2,318 | How can I read my bert model by using transformers? | {
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"Not clearly sure what your question is but i guess u need to change\r\n`from pytorch_pretrained_bert import BertModel, BertTokenizer` to `from transformers import BertModel, BertTokenizer` \r\nDownload latest version if not-found-module error occurs...",
"thank u for your replay, I tryed from transformers i... | 1,577 | 1,577 | 1,577 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
when I use pytorch_pretrained_bert, i can read my model like this:
from pytorch_pretrained_bert import BertModel, BertTokenizer
tokenizer = BertTokenizer.from_pretrained(bert_vocab_path)
bert = BertModel.from_pretrained(bert_model_... | {
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https://api.github.com/repos/huggingface/transformers/issues/2317 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2317/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2317/comments | https://api.github.com/repos/huggingface/transformers/issues/2317/events | https://github.com/huggingface/transformers/pull/2317 | 542,394,083 | MDExOlB1bGxSZXF1ZXN0MzU2ODQzMjgw | 2,317 | Fix beam search when sampling in language generation | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2317?src=pr&el=h1) Report\n> Merging [#2317](https://codecov.io/gh/huggingface/transformers/pull/2317?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/aeef4823ab6099249679756182700e6800024c36?src=pr&el=desc) will **n... | 1,577 | 1,583 | 1,583 | MEMBER | null | I think there is a problem with beam search when setting `do_sample=True`
As it was implemented before, the variable `next_words` in previous line 829 would always contains
word ids < `vocab_size` which forces all `beam_idx` to always be == 0.
This way all words would actually always be appended to the `input_ids`... | {
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https://api.github.com/repos/huggingface/transformers/issues/2316 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2316/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2316/comments | https://api.github.com/repos/huggingface/transformers/issues/2316/events | https://github.com/huggingface/transformers/pull/2316 | 542,392,268 | MDExOlB1bGxSZXF1ZXN0MzU2ODQyMDk1 | 2,316 | Delete [dev] behind pip install -e . | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2316?src=pr&el=h1) Report\n> Merging [#2316](https://codecov.io/gh/huggingface/transformers/pull/2316?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/aeef4823ab6099249679756182700e6800024c36?src=pr&el=desc) will **n... | 1,577 | 1,577 | 1,577 | MEMBER | null | I might be wrong here, but I think it should simply be
```bash
$ pip install -e .
```
without the [dev]
When executing
```bash
$ pip install -e .[dev]
```
in my terminal I get the error:
`no matches found: .[dev]` | {
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https://api.github.com/repos/huggingface/transformers/issues/2315 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2315/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2315/comments | https://api.github.com/repos/huggingface/transformers/issues/2315/events | https://github.com/huggingface/transformers/pull/2315 | 542,392,016 | MDExOlB1bGxSZXF1ZXN0MzU2ODQxOTEy | 2,315 | Add hint to install pytest-xdist | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2315?src=pr&el=h1) Report\n> Merging [#2315](https://codecov.io/gh/huggingface/transformers/pull/2315?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/aeef4823ab6099249679756182700e6800024c36?src=pr&el=desc) will **n... | 1,577 | 1,577 | 1,577 | MEMBER | null | Just a small hint that pytest-xdist should be installed before running the make test step | {
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https://api.github.com/repos/huggingface/transformers/issues/2314 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2314/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2314/comments | https://api.github.com/repos/huggingface/transformers/issues/2314/events | https://github.com/huggingface/transformers/issues/2314 | 542,387,644 | MDU6SXNzdWU1NDIzODc2NDQ= | 2,314 | Is there a uncased gpt2? | {
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"Hi, all the available models are listed in the [pretrained models section of the documentation](https://huggingface.co/transformers/pretrained_models.html). For GPT-2, there are four different models (`gpt2`, `gpt2-medium`, `gpt2-large`, `gpt2-xl`), which are all cased."
] | 1,577 | 1,578 | 1,578 | NONE | null | ## ❓ Questions & Help
Hi, thanks for everything. Quick question: Is there a pre-trained uncased gpt2, like bert-uncased? | {
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https://api.github.com/repos/huggingface/transformers/issues/2313 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2313/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2313/comments | https://api.github.com/repos/huggingface/transformers/issues/2313/events | https://github.com/huggingface/transformers/pull/2313 | 542,369,693 | MDExOlB1bGxSZXF1ZXN0MzU2ODI1OTM4 | 2,313 | Add dropout to WordpieceTokenizer and BPE | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2313?src=pr&el=h1) Report\n> Merging [#2313](https://codecov.io/gh/huggingface/transformers/pull/2313?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/81db12c3ba0c2067f43c4a63edf5e45f54161042?src=pr&el=desc) will **d... | 1,577 | 1,597 | 1,584 | CONTRIBUTOR | null | We can add dropout not only to model weights but and to a tokenizer. The paper, proposed by Ivan Provilkov (2019, https://arxiv.org/pdf/1910.13267.pdf), describes all benefits from this approach and shows that it's almost always better to use dropout during tokenization. (use only for training, for inference dropout sh... | {
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