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https://api.github.com/repos/huggingface/transformers/issues/3113 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3113/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3113/comments | https://api.github.com/repos/huggingface/transformers/issues/3113/events | https://github.com/huggingface/transformers/pull/3113 | 574,999,270 | MDExOlB1bGxSZXF1ZXN0MzgzMjI1MzY1 | 3,113 | model cards for both aubmindlab/bert-base-arabert models | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3113?src=pr&el=h1) Report\n> Merging [#3113](https://codecov.io/gh/huggingface/transformers/pull/3113?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e9e6efdc452b74947d40a5a2e8af2fc444c63b5b?src=pr&el=desc) will **d... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3112 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3112/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3112/comments | https://api.github.com/repos/huggingface/transformers/issues/3112/events | https://github.com/huggingface/transformers/pull/3112 | 574,967,370 | MDExOlB1bGxSZXF1ZXN0MzgzMTk3Njcz | 3,112 | Adds failing tests for the fast tokenizers | {
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"I can't assign reviewers, but you asked me in #3058 to ping @LysandreJik and @mfuntowicz when I do this.",
"I added a test for the issue from #3088.",
"Added another test for #3091.",
"@dirkgr thanks for taking the time to include your tests into ours. \r\n\r\nIt will definitively help making sure everything... | 1,583 | 1,595 | 1,595 | CONTRIBUTOR | null | This ports some of the tests over that started failing on the AllenNLP side when the new fast tokenizers came out.
Note: These tests are failing right now. They will need updates to the fast transformers before this can be merged. Maybe it would be better to merge this branch into the branch where the tokenizers are... | {
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https://api.github.com/repos/huggingface/transformers/issues/3111 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3111/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3111/comments | https://api.github.com/repos/huggingface/transformers/issues/3111/events | https://github.com/huggingface/transformers/pull/3111 | 574,964,663 | MDExOlB1bGxSZXF1ZXN0MzgzMTk1MzI5 | 3,111 | Create README.md | {
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"You have to add the thumbnail",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3111?src=pr&el=h1) Report\n> Merging [#3111](https://codecov.io/gh/huggingface/transformers/pull/3111?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e9e6efdc452b74947d40a5a2e8af2f... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | - Thumbnail is not set! | {
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https://api.github.com/repos/huggingface/transformers/issues/3110 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3110/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3110/comments | https://api.github.com/repos/huggingface/transformers/issues/3110/events | https://github.com/huggingface/transformers/pull/3110 | 574,919,248 | MDExOlB1bGxSZXF1ZXN0MzgzMTU2NTM5 | 3,110 | BartForSequenceClassification: fix num_labels, add test | {
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https://api.github.com/repos/huggingface/transformers/issues/3109 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3109/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3109/comments | https://api.github.com/repos/huggingface/transformers/issues/3109/events | https://github.com/huggingface/transformers/pull/3109 | 574,905,015 | MDExOlB1bGxSZXF1ZXN0MzgzMTQ0OTc2 | 3,109 | [WIP] Add tests that ensure that copied functions remain in sync | {
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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"
] | 1,583 | 1,589 | 1,589 | CONTRIBUTOR | null | Adding some tests that use ast to check that functions that were originally copied stay in sync.
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https://api.github.com/repos/huggingface/transformers/issues/3108 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3108/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3108/comments | https://api.github.com/repos/huggingface/transformers/issues/3108/events | https://github.com/huggingface/transformers/issues/3108 | 574,810,265 | MDU6SXNzdWU1NzQ4MTAyNjU= | 3,108 | BART: <mask> token ID is outside vocab bounds | {
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"if you install from master it seems to work on 'bart-large'. \r\nSeems like it's only an issue on 'bart-large-cnn'\r\n```\r\ntokenizer = BartTokenizer.from_pretrained('bart-large')\r\nmodel = BartForMaskedLM.from_pretrained('bart-large',output_past=True)\r\n\r\nARTICLE_TO_SUMMARIZE = \"My friends are <mask> but th... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): BART
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)
The tasks I am working on is:
* ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3107 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3107/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3107/comments | https://api.github.com/repos/huggingface/transformers/issues/3107/events | https://github.com/huggingface/transformers/issues/3107 | 574,780,410 | MDU6SXNzdWU1NzQ3ODA0MTA= | 3,107 | BertTokenizer.save_pretrained() ignores do_lower_case | {
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"When I copy/paste your code, I get this in `dumped/model/`:\r\n`vocab.txt`, `tokenizer_config.json` and `special_tokens_map.json`\r\n\r\nThe tokenizer config does save the lowercase, and the output of the code is two `False`.\r\n\r\nBelow the code snippet I had to actually run it:\r\n\r\n```py\r\nimport transforme... | 1,583 | 1,589 | 1,589 | CONTRIBUTOR | null | # 🐛 Bug
## Information
When saving a tokenizer with the purpose of sharing, `init` arguments are not saved to a config.
## To reproduce
Steps to reproduce the behavior:
Initialize a tokenizer with `do_lower_case=False`, save pretrained, initialize from pretrained. The default `do_lower_case=True` will n... | {
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https://api.github.com/repos/huggingface/transformers/issues/3106 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3106/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3106/comments | https://api.github.com/repos/huggingface/transformers/issues/3106/events | https://github.com/huggingface/transformers/pull/3106 | 574,767,209 | MDExOlB1bGxSZXF1ZXN0MzgzMDMyNjE5 | 3,106 | fix beam_search behavior when sampling | {
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"> is do_sample=True tested anywhere?\r\n\r\nYes for the randomly initialized models, with dummy input and also for some Integration tests",
"Good to merge for me"
] | 1,583 | 1,583 | 1,583 | MEMBER | null | This PR aims to fix the beam search behavior when sampling for language generation.
For once, when doing beam_search decoding for language generation, one would usually do greedy decoding (do_sample=False), so this case should not be used very often, but it should be logical nevertheless.
It's kind of hard to see ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3105 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3105/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3105/comments | https://api.github.com/repos/huggingface/transformers/issues/3105/events | https://github.com/huggingface/transformers/pull/3105 | 574,750,323 | MDExOlB1bGxSZXF1ZXN0MzgzMDE4NTkz | 3,105 | Change back pipeline signatures | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3105?src=pr&el=h1) Report\n> Merging [#3105](https://codecov.io/gh/huggingface/transformers/pull/3105?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b31f7150190cdf13950607f8ee1efe11b352c909?src=pr&el=desc) will **i... | 1,583 | 1,583 | 1,583 | MEMBER | null | As discussed with @julien-c in the merged #3055. | {
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https://api.github.com/repos/huggingface/transformers/issues/3104 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3104/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3104/comments | https://api.github.com/repos/huggingface/transformers/issues/3104/events | https://github.com/huggingface/transformers/issues/3104 | 574,720,365 | MDU6SXNzdWU1NzQ3MjAzNjU= | 3,104 | BART -- RuntimeError: expected device cuda:0 but got device cpu | {
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"Can you reproduce with the code on master?\r\n\r\n```\r\ngit clone https://github.com/huggingface/transformers\r\ncd transformers\r\npip install .\r\n```\r\n(duplicate of https://github.com/huggingface/transformers/issues/3079)\r\n",
"Closing for now, reopen if this is broken on the latest code. Otherwise, it wi... | 1,583 | 1,583 | 1,583 | NONE | null | # 🐛 Bug
@sshleifer
I'm using BART model (bart-large), when I try to use the BartForMaskedLM i'm getting the above error. The reason is that in the _combine_masks (line 146 in modeling_bart) is creating a tensor without the device. so by default it is on CPU. To reproduce - simply use BartForMaskedLM model with GP... | {
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https://api.github.com/repos/huggingface/transformers/issues/3103 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3103/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3103/comments | https://api.github.com/repos/huggingface/transformers/issues/3103/events | https://github.com/huggingface/transformers/pull/3103 | 574,697,751 | MDExOlB1bGxSZXF1ZXN0MzgyOTc1Mzg4 | 3,103 | Support keras JSON/HDF5 serialization of main layers | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3103?src=pr&el=h1) Report\n> Merging [#3103](https://codecov.io/gh/huggingface/transformers/pull/3103?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a088d75e510d5641808ccd72f5dca4df36d95b8e?src=pr&el=desc) will **i... | 1,583 | 1,585 | 1,583 | CONTRIBUTOR | null | Fixes #3101 | {
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https://api.github.com/repos/huggingface/transformers/issues/3102 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3102/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3102/comments | https://api.github.com/repos/huggingface/transformers/issues/3102/events | https://github.com/huggingface/transformers/pull/3102 | 574,670,141 | MDExOlB1bGxSZXF1ZXN0MzgyOTUyNTQx | 3,102 | bert-base-arabic model card | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3102?src=pr&el=h1) Report\n> Merging [#3102](https://codecov.io/gh/huggingface/transformers/pull/3102?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/eec5ec807135ae61fa2266f3c7ad947cc207abda?src=pr&el=desc) will **d... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | Please add model card file to the newly added asafaya/bert-base-arabic model | {
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https://api.github.com/repos/huggingface/transformers/issues/3101 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3101/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3101/comments | https://api.github.com/repos/huggingface/transformers/issues/3101/events | https://github.com/huggingface/transformers/issues/3101 | 574,669,114 | MDU6SXNzdWU1NzQ2NjkxMTQ= | 3,101 | Keras layers should override get_config to be JSON-serializable | {
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"Thanks a lot for investigating this and submitting a fix, it's awesome.\r\n\r\nI respond in the PR it-self"
] | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | # 🚀 Feature request
Support JSON serialization of Keras layers by overriding `get_config`, so that they can be sent to Tensorboard to display a conceptual graph of the model.
## Motivation
### 1. Without this, can't write model graph to Tensorboard
From https://github.com/tensorflow/tensorflow/blob/d1786ea... | {
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https://api.github.com/repos/huggingface/transformers/issues/3100 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3100/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3100/comments | https://api.github.com/repos/huggingface/transformers/issues/3100/events | https://github.com/huggingface/transformers/pull/3100 | 574,654,339 | MDExOlB1bGxSZXF1ZXN0MzgyOTM5Njg3 | 3,100 | Fix QA models binding for Flaubert, XLNet and XLM. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3100?src=pr&el=h1) Report\n> Merging [#3100](https://codecov.io/gh/huggingface/transformers/pull/3100?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/eec5ec807135ae61fa2266f3c7ad947cc207abda?src=pr&el=desc) will **d... | 1,583 | 1,583 | 1,583 | MEMBER | null | Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
Fix #2893 | {
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https://api.github.com/repos/huggingface/transformers/issues/3099 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3099/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3099/comments | https://api.github.com/repos/huggingface/transformers/issues/3099/events | https://github.com/huggingface/transformers/pull/3099 | 574,625,849 | MDExOlB1bGxSZXF1ZXN0MzgyOTE2MTgz | 3,099 | Don't crash if fine-tuned model doesn't end with a number | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3099?src=pr&el=h1) Report\n> Merging [#3099](https://codecov.io/gh/huggingface/transformers/pull/3099?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/eec5ec807135ae61fa2266f3c7ad947cc207abda?src=pr&el=desc) will **d... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | That's the same fix applied in https://github.com/huggingface/transformers/issues/2258 , but for the GLUE example | {
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https://api.github.com/repos/huggingface/transformers/issues/3098 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3098/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3098/comments | https://api.github.com/repos/huggingface/transformers/issues/3098/events | https://github.com/huggingface/transformers/issues/3098 | 574,524,709 | MDU6SXNzdWU1NzQ1MjQ3MDk= | 3,098 | why BertModel' object has no attribute 'bias' | {
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"Could you try with `BertForPreTraining` instead of `BertModel` ?",
"> Could you try with `BertForPreTraining` instead of `BertModel` ?\r\n\r\n@LysandreJik the error not occurs,thank you very much, but in the previous,when I use \r\n\r\n```py\r\nmodel = BertForNextSentencePrediction.from_pretrained('bert-base-ca... | 1,583 | 1,587 | 1,583 | NONE | null | ```py
from torch.nn.functional import softmax
from transformers import BertForNextSentencePrediction, BertTokenizer,BertConfig,BertModel
seq_A = 'I like cookies !'
seq_B = 'Do you like them ?'
'''
model = BertForNextSentencePrediction.from_pretrained('bert-base-cased')
'''
tokenizer = BertTokenizer.from_pretr... | {
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https://api.github.com/repos/huggingface/transformers/issues/3097 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3097/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3097/comments | https://api.github.com/repos/huggingface/transformers/issues/3097/events | https://github.com/huggingface/transformers/issues/3097 | 574,464,756 | MDU6SXNzdWU1NzQ0NjQ3NTY= | 3,097 | Some question about training BERT after change the Vocab.txt size | {
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"You can't really change the vocabulary without re-training the whole model. Is there some overlap between the BERT vocabulary and your custom vocabulary? If so, you can add the 20k+ tokens using `add_tokens` (which will probably slow down things, as that's a lot of added tokens).",
"This issue has been automatic... | 1,583 | 1,664 | 1,589 | NONE | null | The original bert-base-chinese-vocab.txt size is 21128.
I use my own vocab.txt size is 44900
When I try to train Fine-tune BERT Model using BertForMaskedLM it have some promble about size mismatch.
I have try to change the BertEmbeddings its self.word_embeddings config.vocab_size to 44900 like this
`self.word_embed... | {
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https://api.github.com/repos/huggingface/transformers/issues/3096 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3096/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3096/comments | https://api.github.com/repos/huggingface/transformers/issues/3096/events | https://github.com/huggingface/transformers/issues/3096 | 574,417,588 | MDU6SXNzdWU1NzQ0MTc1ODg= | 3,096 | BART BartForSequenceClassification example | {
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"Thanks for reporting this!"
] | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | # 🐛 Bug
I'm trying the run the code on the documentation.
https://huggingface.co/transformers/model_doc/bart.html#bartforsequenceclassification
```
from transformers import BartTokenizer, BartForSequenceClassification
import torch
tokenizer = BartTokenizer.from_pretrained('bart-large')
model = BartForSequen... | {
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"Hi @Damiox, \r\n\r\nFirst, you have to be careful if you do LM inference with batches of different lengths. Besides using an attention mask, you also have to change the position_ids when using GPT2 (see #3021 ). Considering batch language generation with GPT2 I highly recommend making sure that the input_ids batch... | 1,583 | 1,585 | 1,583 | NONE | null | # ❓ Questions & Help
Hi. I'm having some issues when using the past + attention mask functionality. Results are not the ones I was expecting to get... I'm using latest master as the latest release 2.5.1 is failing (`RuntimeError: The size of tensor a (6) must match the size of tensor b (3) at non-singleton dimension... | {
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https://api.github.com/repos/huggingface/transformers/issues/3094 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3094/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3094/comments | https://api.github.com/repos/huggingface/transformers/issues/3094/events | https://github.com/huggingface/transformers/pull/3094 | 574,395,518 | MDExOlB1bGxSZXF1ZXN0MzgyNzI3MjA3 | 3,094 | [Bart] dont call .forward | {
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"@julien-c any idea why this would cause \r\n```\r\nFAILED tests/test_pipelines.py::MonoColumnInputTestCase::test_tf_feature_extraction \r\n```",
"I think this one might sometimes fail randomly"
] | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3093 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3093/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3093/comments | https://api.github.com/repos/huggingface/transformers/issues/3093/events | https://github.com/huggingface/transformers/issues/3093 | 574,350,885 | MDU6SXNzdWU1NzQzNTA4ODU= | 3,093 | wrong 'label2id' and 'id2label' in config when loading from pretrained | {
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## Information
Model I am using (Bert, XLNet ...): Bert
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)
The tasks I am working on is:
* ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3092 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3092/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3092/comments | https://api.github.com/repos/huggingface/transformers/issues/3092/events | https://github.com/huggingface/transformers/issues/3092 | 574,320,255 | MDU6SXNzdWU1NzQzMjAyNTU= | 3,092 | ċ in gpt2 | {
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"The proper way to decode a value is using the `decode` method:\r\n\r\n```py\r\nfrom transformers import GPT2Tokenizer\r\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n\r\ntokenizer.decode([198]) # '\\n'\r\n```\r\n\r\nSome byte indices are shifted in the GPT-2 vocabulary, especially the control characters... | 1,583 | 1,583 | 1,583 | NONE | null | There's a 'ċ' in gpt2.
```
vocab = list(tokenizer_gpt2.encoder.keys())
vocab[198]
```
output: ċ
Based on some examples, I guess it means "with_break". But I can't find this parameter in gpt2 tokenizer document. Can anyone tell me the meaning? thank you. | {
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https://api.github.com/repos/huggingface/transformers/issues/3091 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3091/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3091/comments | https://api.github.com/repos/huggingface/transformers/issues/3091/events | https://github.com/huggingface/transformers/issues/3091 | 574,288,594 | MDU6SXNzdWU1NzQyODg1OTQ= | 3,091 | Fast tokenizers fail when the input is just spaces | {
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"This is now fixed on `master`:\r\n```\r\n>>> t = transformers.AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=True)\r\n>>> t.encode_plus(\" \", add_special_tokens=False)\r\n{'input_ids': [], 'token_type_ids': [], 'attention_mask': []}\r\n```\r\nAlso, `add_special_tokens` now works exactly the same for ... | 1,583 | 1,587 | 1,587 | CONTRIBUTOR | null | Slow tokenizers:
```
>>> import transformers
>>> t = transformers.AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False)
>>> t.encode_plus(" ", add_special_tokens=False)
{'input_ids': [], 'token_type_ids': []}
```
Fast tokenizers:
```
>>> import transformers
>>> t = transformers.AutoTokenizer.from... | {
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https://api.github.com/repos/huggingface/transformers/issues/3090 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3090/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3090/comments | https://api.github.com/repos/huggingface/transformers/issues/3090/events | https://github.com/huggingface/transformers/issues/3090 | 574,286,713 | MDU6SXNzdWU1NzQyODY3MTM= | 3,090 | Cuda error during evaluation - CUBLAS_STATUS_NOT_INITIALIZED | {
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"Tried debugging with CPU (an aside - this has an issue in itself apparently when --no_cuda flag is used --> run_language_modeling.py needs to set args.n_gpu to 0)\r\n\r\nFound the fix -> Needed to call model.resize_token_embeddings(len(tokenizer)) after adding tokens in the eval mode as well. "
] | 1,583 | 1,618 | 1,583 | NONE | null | # 🐛 Bug
## Information
Overview:
I am using the Bert pre-trained model and trying to finetune it using a customized dataset which requires me to add new tokens so that the tokenizer doesn't wordpiece them (these tokens are of the form <1234> and </1234> where 1234 can be any int converted to string).
I was abl... | {
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https://api.github.com/repos/huggingface/transformers/issues/3089 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3089/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3089/comments | https://api.github.com/repos/huggingface/transformers/issues/3089/events | https://github.com/huggingface/transformers/pull/3089 | 574,270,057 | MDExOlB1bGxSZXF1ZXN0MzgyNjI1MzI2 | 3,089 | add models cards for camembert-base-fquad camembert-base-squad | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3089?src=pr&el=h1) Report\n> Merging [#3089](https://codecov.io/gh/huggingface/transformers/pull/3089?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f169957d0cf17b110f27cacc1b1fb43efaa01218?src=pr&el=desc) will **i... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | Following #2893
Model links:
- [`fmikaelian/camembert-base-fquad`](https://huggingface.co/fmikaelian/camembert-base-fquad)
- [`fmikaelian/camembert-base-squad`](https://huggingface.co/fmikaelian/camembert-base-squad) | {
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https://api.github.com/repos/huggingface/transformers/issues/3088 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3088/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3088/comments | https://api.github.com/repos/huggingface/transformers/issues/3088/events | https://github.com/huggingface/transformers/issues/3088 | 574,261,994 | MDU6SXNzdWU1NzQyNjE5OTQ= | 3,088 | Fast tokenizers can't `encode_plus` a list of ids; slow tokenizers can | {
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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"
] | 1,583 | 1,588 | 1,588 | CONTRIBUTOR | null | With the slow tokenizers:
```
>>> import transformers
>>> t = transformers.AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False)
>>> t.encode_plus([1000])
{'input_ids': [101, 1000, 102],
'token_type_ids': [0, 0, 0],
'attention_mask': [1, 1, 1]}
```
With the fast tokenizers:
```
>>> import tran... | {
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https://api.github.com/repos/huggingface/transformers/issues/3087 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3087/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3087/comments | https://api.github.com/repos/huggingface/transformers/issues/3087/events | https://github.com/huggingface/transformers/issues/3087 | 574,198,533 | MDU6SXNzdWU1NzQxOTg1MzM= | 3,087 | Error when runnig run_tf_ner.py | {
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"You should not have any issues with this as `GradientAccumulator` is correctly imported in the __init__.py file:\r\n\r\nhttps://github.com/huggingface/transformers/blob/298bed16a841fae3608d334441ccae4d9043611f/src/transformers/__init__.py#L426-L427\r\n\r\nIs `transformers` correctly installed in your pip environme... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | # 🐛 Bug
## Information
```python
!python3 /content/transformers/examples/ner/run_tf_ner.py --data_dir /content/ner_dataset \
--model_type bert \
--labels /content/labels.txt \
--model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
--output_dir model_output \
--max_seq_length 256 \
--num_train_epochs 5... | {
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https://api.github.com/repos/huggingface/transformers/issues/3086 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3086/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3086/comments | https://api.github.com/repos/huggingface/transformers/issues/3086/events | https://github.com/huggingface/transformers/issues/3086 | 574,189,410 | MDU6SXNzdWU1NzQxODk0MTA= | 3,086 | Disabling Eager Mode Prevents Loading Pre-Trained BERT | {
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"TFBertModel can auto use gpu? i find model(np.array(input_ids)) is slow,cost 100+ms\r\nIn [14]: %timeit model_outputs = model(np.array(input_ids))\r\n133 ms ± 3.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\r\n\r\nIn [15]: %time model_outputs = model(np.array(input_ids))\r\nCPU times: user 330 ms, sys... | 1,583 | 1,646 | 1,591 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...):
**BERT**
Language I am using the model on (English, Chinese ...):
**English**
## To reproduce
Steps to reproduce the behavior:
1. Disable Tensorflow eager mode `tf.compat.v1.disable_v2_behavior()`
2. Create a pretrained BERT instance `model ... | {
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- Update metrics | {
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"I agree that an on-the-fly tokenisation would be neat as an alternative to pre-processing the whole input file and saving the tensors in memory. ",
"Hi @BramVanroy , this is mentioned in the blog post bout training models from scratch, as something that could be done (https://huggingface.co/blog/how-to-train). I... | 1,583 | 1,590 | 1,590 | NONE | null |
line 107 in run_language_model.py
with open(file_path, encoding="utf-8") as f:
text = f.read()
any idea how to use generators to load large files? | {
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https://api.github.com/repos/huggingface/transformers/issues/3082 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3082/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3082/comments | https://api.github.com/repos/huggingface/transformers/issues/3082/events | https://github.com/huggingface/transformers/pull/3082 | 574,082,363 | MDExOlB1bGxSZXF1ZXN0MzgyNDczMTQ2 | 3,082 | Summarization Examples: add Bart CNN Evaluation | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3082?src=pr&el=h1) Report\n> Merging [#3082](https://codecov.io/gh/huggingface/transformers/pull/3082?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b74c9b106b97b0722ff8f98e77e2e2210c688b23?src=pr&el=desc) will **i... | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,583 | 1,589 | 1,589 | NONE | null | Hello, huggingface! Thank you for the great work with doing neat interfaces for transformers family!
I'm analyzing performance of transformers on a task of texts classification (like SST-2 in GLUE). I found that many architectures has `<ModelName>ForSequenceClassification` class, But `transformerXL` has restricted s... | {
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https://api.github.com/repos/huggingface/transformers/issues/3080 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3080/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3080/comments | https://api.github.com/repos/huggingface/transformers/issues/3080/events | https://github.com/huggingface/transformers/issues/3080 | 574,072,632 | MDU6SXNzdWU1NzQwNzI2MzI= | 3,080 | Docker Hub automatic tests failing | {
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"Should be fixed, this tests were running because I asked to build on every master's commit. \r\n\r\nNow it should only build on new tags."
] | 1,583 | 1,583 | 1,583 | CONTRIBUTOR | null | The automated docker tests are failing on HEAD and when you click details you get 404, so tough to debug | {
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https://api.github.com/repos/huggingface/transformers/issues/3079 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3079/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3079/comments | https://api.github.com/repos/huggingface/transformers/issues/3079/events | https://github.com/huggingface/transformers/issues/3079 | 574,041,379 | MDU6SXNzdWU1NzQwNDEzNzk= | 3,079 | Bart CUDA not working | {
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"I just merged a PR and your example works for me. Would you mind seeing if it is still broken in your system? Thanks for posting! \r\n(note that the fix won't be in v 2.5.1 if you pip installed). \r\n",
"Reopen if still broken!"
] | 1,583 | 1,583 | 1,583 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): BART - bart-large
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)
The tasks I am worki... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3078?src=pr&el=h1) Report\n> Merging [#3078](https://codecov.io/gh/huggingface/transformers/pull/3078?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/c0135194ebc5de4b1bbef98b31f9c457a0bf746a?src=pr&el=desc) will **d... | 1,583 | 1,583 | 1,583 | MEMBER | null | This PR changes the behavior of greedy beam search generation as discussed and wished in #2415 .
Also two assertion statements are added:
1. It is not allowed to generate multiple sequences from the same input_ids when greedy generation (`num_return_sequences > 1`, `do_sample=False`, `num_beams` == 1 => `Assertio... | {
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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"
] | 1,583 | 1,588 | 1,588 | NONE | null | XLNet was designed to handle multiple segment modeling (i.e. > 2 sentences) by using relative segment encodings. For sentence-level classification tasks with arbitrary sentence counts, what is the structure of the segment (token type) ids? I’ve found from the documentation that 2-sequence classification is supported by... | {
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"Maybe this is a little late but you could take a look in both `examples/run_tf_glue.py` and [this function](https://github.com/huggingface/transformers/blob/master/src/transformers/data/processors/glue.py#L31-L168) from`src/transformers/data/processors/glue.py` and write a custom training script based from those."... | 1,583 | 1,591 | 1,591 | NONE | null | I am working on a TextClassification problem, for which I am trying to traing my model on TFBertForSequenceClassification given in huggingface-transformers library.
I followed the example given on their github page, I am able to run the sample code with given sample data using tensorflow_datasets.load('glue/mrpc'). ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3074 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3074/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3074/comments | https://api.github.com/repos/huggingface/transformers/issues/3074/events | https://github.com/huggingface/transformers/issues/3074 | 573,558,797 | MDU6SXNzdWU1NzM1NTg3OTc= | 3,074 | Only enable some labels in BERT fine-tuned NER | {
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"I'm not sure I understand your question. The O tag is here in order to identify a token which is not an entity in the IOB1 tagging scheme. If you don't have such a tag, every token will have to be classified as an entity, which does not make sense?\r\n\r\nIf you would like to define a custom tagging scheme and tra... | 1,583 | 1,590 | 1,590 | CONTRIBUTOR | null | # ❓ Questions & Help
Only enable some labels in BERT fine-tuned NER
## Details
I would be interested to enable only some labels in BERT fine-tuned NER prediction.
For example, I know what are the entities and I would like to train a model that will classify entities - but I am not interested to train/predict O t... | {
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https://api.github.com/repos/huggingface/transformers/issues/3073 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3073/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3073/comments | https://api.github.com/repos/huggingface/transformers/issues/3073/events | https://github.com/huggingface/transformers/issues/3073 | 573,558,193 | MDU6SXNzdWU1NzM1NTgxOTM= | 3,073 | Finetuned BERT model does not seem to predict right labels/work properly? | {
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"Please post your code using [code blocks](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks). Don't post screenshots.",
"FYI: \r\nphoto of **format input data**; https://i.stack.imgur.com/t472b.png\r\nphoto of **tag2name** ; https://i.stack.imgur.com/RO7dp.png\r\n\r\n\r\n... | 1,583 | 1,588 | 1,588 | NONE | null | # ❓ Questions & Help
I am trying out a finetuned BERT model for token classification (--> https://huggingface.co/bert-base-cased-finetuned-conll03-english), but when I observe the model output (i.e. the logits after applying the softmax) and compare it with the true label_ids, they are totally uncorrelated (see pictur... | {
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https://api.github.com/repos/huggingface/transformers/issues/3072 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3072/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3072/comments | https://api.github.com/repos/huggingface/transformers/issues/3072/events | https://github.com/huggingface/transformers/issues/3072 | 573,532,646 | MDU6SXNzdWU1NzM1MzI2NDY= | 3,072 | Chinese BERT model can be used represented by words instead of character | {
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"I am not sure which Chinese BERT you are referring to, but the original multilingual BERT has trained Chinese on the character-level. From their [README](https://github.com/google-research/bert/blob/master/multilingual.md):\r\n\r\n> Because Chinese (and Japanese Kanji and Korean Hanja) does not have whitespace cha... | 1,583 | 1,583 | 1,583 | NONE | null | # ❓ Questions & Help
I want to ask about the Chinese BERT model can be used represented by words instead of character?
Because when I do BERT visual for Chinese can only see its attention from character to character.
I want to see attention from words to words. Can I change this?
Thanks a lot for your help | {
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https://api.github.com/repos/huggingface/transformers/issues/3071 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3071/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3071/comments | https://api.github.com/repos/huggingface/transformers/issues/3071/events | https://github.com/huggingface/transformers/issues/3071 | 573,520,345 | MDU6SXNzdWU1NzM1MjAzNDU= | 3,071 | Predict the next word in sentence context from the list of possible words in Russian | {
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"Example\r\nMy sentence: Мой кот ... (My cat ...)\r\n3 word is ест (eat)\r\nList of possible words: ест (eat), поглощает (absorb), глотает (swallow), кушают (eats) etc.\r\nI need to determine the probabilities of each word from the given list in the context of the phrase and make the most correct sentence.\r\nOutpu... | 1,583 | 1,583 | 1,583 | NONE | null | Hello from Russia. I have a task of predicting the next word from the list of possible words in Russian. How can I do this?
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https://api.github.com/repos/huggingface/transformers/issues/3070 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3070/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3070/comments | https://api.github.com/repos/huggingface/transformers/issues/3070/events | https://github.com/huggingface/transformers/issues/3070 | 573,488,746 | MDU6SXNzdWU1NzM0ODg3NDY= | 3,070 | load_tf_weights_in_bert : 'BertModel' object has no attribute 'bias' | {
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"change \r\n`BertModel.form_pretrained`\r\nto\r\n`BertForPreTraining.from_pretrained`\r\nit seems to work",
"Glad you could get it to work! Indeed, `BertForPreTraining` should be used to convert from official BERT models."
] | 1,583 | 1,583 | 1,583 | NONE | null | ```
AttributeError Traceback (most recent call last)
<ipython-input-14-0d66155b396d> in <module>
12
13 K.clear_session()
---> 14 model = create_model()
15 optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5)
16 model.compile(loss... | {
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https://api.github.com/repos/huggingface/transformers/issues/3069 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3069/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3069/comments | https://api.github.com/repos/huggingface/transformers/issues/3069/events | https://github.com/huggingface/transformers/issues/3069 | 573,477,072 | MDU6SXNzdWU1NzM0NzcwNzI= | 3,069 | No Causal Attention Masking in GPT-2 LM Finetuning Script | {
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"Hi @alvinchangw, \r\n\r\nGPT2 always uses causal masking no matter what kind of attention_mask you give it. \r\nIt's easy to see when you print out the computed attentions for each layer (by setting `output_attentions=True`) => see for this also #2975. \r\n\r\nIn the code this is done in this line:\r\nhttps://gith... | 1,583 | 1,583 | 1,583 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): GPT-2
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [X] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on is:
*... | {
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https://api.github.com/repos/huggingface/transformers/issues/3068 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3068/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3068/comments | https://api.github.com/repos/huggingface/transformers/issues/3068/events | https://github.com/huggingface/transformers/issues/3068 | 573,046,241 | MDU6SXNzdWU1NzMwNDYyNDE= | 3,068 | Problem with using pretrained BertTokenizer for Korean | {
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"Intuitively, I would say that this might not be a bug but a limitation of the size of the vocabulary. In the cased version, all data that the tokenizer is 'trained' on is cased, meaning that there are tokens in the vocabulary that only differs by case (e.g. `Be` and `be`). As a consequence, this may cause the voca... | 1,582 | 1,588 | 1,588 | NONE | null | I have a corpus that contains Korean sentences. Here is the output of a berttokenizer for a token:
`tok = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')`
`tok.convert_tokens_to_ids(tok.tokenize('잘해놨습니다'))`
`[44628, 14840, 97071, 97089, 97104, 13212, 79427]`
`tok = BertTokenizer.from_pretrained('b... | {
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https://api.github.com/repos/huggingface/transformers/issues/3067 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3067/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3067/comments | https://api.github.com/repos/huggingface/transformers/issues/3067/events | https://github.com/huggingface/transformers/issues/3067 | 573,020,947 | MDU6SXNzdWU1NzMwMjA5NDc= | 3,067 | No speed diference when doing prediction between BERT and ALBERT | {
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"Hi, there's no reason ALBERT would be faster than BERT. They have the same number of layers, ALBERT just uses repeating layers (its `n` layers are a just a single one) instead of different layers (BERT's `n` layers are `n` different layers).",
"Hi @LysandreJik !\r\nIf ALBERT is a lot smaller than BERT (in terms ... | 1,582 | 1,590 | 1,590 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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https://api.github.com/repos/huggingface/transformers/issues/3066 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3066/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3066/comments | https://api.github.com/repos/huggingface/transformers/issues/3066/events | https://github.com/huggingface/transformers/issues/3066 | 573,001,235 | MDU6SXNzdWU1NzMwMDEyMzU= | 3,066 | Documentation and code mismatch in BertForMaskedLM forward method | {
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## Information
Model I am using (Bert, XLNet ...):
BertForMaskedLM
Language I am using the model on (English, Chinese ...):
Not important.
The problem arises when using:
* [x] my own modified scripts: (give details below)
The tasks I am working on is:
I am trying to obtain LM probabili... | {
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https://api.github.com/repos/huggingface/transformers/issues/3065 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3065/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3065/comments | https://api.github.com/repos/huggingface/transformers/issues/3065/events | https://github.com/huggingface/transformers/issues/3065 | 572,936,346 | MDU6SXNzdWU1NzI5MzYzNDY= | 3,065 | XLM-RoBERTa can't add new tokens. | {
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"Can you post a minimal verifiable example that we can just copy-and-paste to try? I guess you don't really try to add empty strings as tokens?",
"Hi @BramVanroy, no ofc, probably i've pasted the wrong snippet of code. \r\nIf you try to expand the vocabulary of the tokenizer with the following code: \r\n\r\n```\r... | 1,582 | 1,589 | 1,589 | NONE | null | # 🐛 Bug
Model I am using (Bert, XLNet ...): XLM-RoBERTa
## To reproduce
Steps to reproduce the behavior:
1. tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large', do_lower_case=True)
2. tokenizer.add_tokens(['<a1>', '</a1>', '<a2>', '</a2>'])
3. tokenizer.convert_tokens_to_ids('<a1>')
It ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3064 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3064/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3064/comments | https://api.github.com/repos/huggingface/transformers/issues/3064/events | https://github.com/huggingface/transformers/issues/3064 | 572,911,248 | MDU6SXNzdWU1NzI5MTEyNDg= | 3,064 | Add LM capabilities to TFTransfoXLLMHead | {
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"follo... | [] | 1,582 | 1,584 | 1,584 | MEMBER | null | # 🚀 Feature request
It is currently not possible to generate language with the TFTransfoXLLMHeadModel because
the `lm_head` is not implemented (see https://github.com/huggingface/transformers/blob/908fa43b543cf52a3238129624f502240725a6a6/src/transformers/modeling_tf_transfo_xl.py#L745)
Doing:
```
from tra... | {
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https://api.github.com/repos/huggingface/transformers/issues/3063 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3063/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3063/comments | https://api.github.com/repos/huggingface/transformers/issues/3063/events | https://github.com/huggingface/transformers/pull/3063 | 572,795,543 | MDExOlB1bGxSZXF1ZXN0MzgxNDIzNDAy | 3,063 | Add generate() functionality to TF 2.0 | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3063?src=pr&el=h1) Report\n> Merging [#3063](https://codecov.io/gh/huggingface/transformers/pull/3063?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/eec5ec807135ae61fa2266f3c7ad947cc207abda?src=pr&el=desc) will **i... | 1,582 | 1,583 | 1,583 | MEMBER | null | I added the `_generate_no_beam_search` functionality for TF 2.0. It works for the following models:
'gpt2', 'openai', 'xlnet', 'xlm', 'ctrl'. Only for the model 'transfo-xl' it doesn't work because the
lm_head is not implemented yet in TF 2.0 (added an issue here: #3064 ).
Also I checked whether the pytorch 'dis... | {
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https://api.github.com/repos/huggingface/transformers/issues/3062 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3062/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3062/comments | https://api.github.com/repos/huggingface/transformers/issues/3062/events | https://github.com/huggingface/transformers/issues/3062 | 572,619,732 | MDU6SXNzdWU1NzI2MTk3MzI= | 3,062 | Should weight distribution change more when fine-tuning transformers-based classifier? | {
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"I am curious: what kind of behaviour do you see when you freeze the whole base model, and only train the classifier? Also, you may want avoid the use of the `cls` variable name because it is a reserved keyword for the class. In general, the classifier is trained quite quickly (often in two or epochs or less), so y... | 1,582 | 1,590 | 1,590 | CONTRIBUTOR | null | ## ❓Should weight distribution change more when fine-tuning transformers-based classifier?
This question was posted on DataScience stack exchange:
[https://datascience.stackexchange.com/questions/68641/should-weight-distribution-change-more-when-fine-tuning-transformers-based-class](https://datascience.stackexchange.... | {
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"I like this idea. Perhaps it can be added as an example, or even as an argument to the generation script."
] | 1,582 | 1,585 | 1,585 | NONE | null | # 🚀 Feature request
Add a word of lists that you do not want the model to generate for whatever reason.
## Motivation
When creating a text generation model, especially if you will serve that model publicly, it is desirable to have assurances that a model is physically incapable of outputting certain tokens.
Su... | {
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https://api.github.com/repos/huggingface/transformers/issues/3060 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3060/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3060/comments | https://api.github.com/repos/huggingface/transformers/issues/3060/events | https://github.com/huggingface/transformers/issues/3060 | 572,490,874 | MDU6SXNzdWU1NzI0OTA4NzQ= | 3,060 | How to init a subclass of BertForTokenClassification | {
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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"
] | 1,582 | 1,588 | 1,588 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
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https://api.github.com/repos/huggingface/transformers/issues/3059 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3059/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3059/comments | https://api.github.com/repos/huggingface/transformers/issues/3059/events | https://github.com/huggingface/transformers/pull/3059 | 572,480,785 | MDExOlB1bGxSZXF1ZXN0MzgxMTcwNDg2 | 3,059 | Bart-CNN | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3059?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@271344f`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage is `86.95%`... | 1,582 | 1,583 | 1,583 | CONTRIBUTOR | null | ## Sources
- copy pastes code from generate but does not share very much in an effort to simplify. there is no big abstraction yet.
- also copy pastes some code from fairseq
- encoder_outputs and previous decoder attentions are cached.
## Differences with PretrainedModel.generate
these are all bc thats the w... | {
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https://api.github.com/repos/huggingface/transformers/issues/3058 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3058/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3058/comments | https://api.github.com/repos/huggingface/transformers/issues/3058/events | https://github.com/huggingface/transformers/issues/3058 | 572,460,345 | MDU6SXNzdWU1NzI0NjAzNDU= | 3,058 | Fast tokenizers calculate wrong offsets when special characters are present | {
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"@mfuntowicz, would it make sense for me to integrate our tokenizer tests into your code, so you can see these things immediately? I'd be happy to do so.",
"Looks like this is a duplicate of #2917.",
"On second reading, this is not the same issue as #2917, though they may be related.",
"Roberta has another re... | 1,582 | 1,587 | 1,587 | CONTRIBUTOR | null | Example:
```
>>> import transformers
>>> t_fast = transformers.AutoTokenizer.from_pretrained("bert-base-cased", use_fast=True, add_special_tokens=False)
>>> sentence = "A, naïve [MASK] AllenNLP sentence."
>>> tokenized = t_fast.encode_plus(sentence, add_special_tokens=False, return_offsets_mapping=True)
>>> for... | {
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https://api.github.com/repos/huggingface/transformers/issues/3057 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3057/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3057/comments | https://api.github.com/repos/huggingface/transformers/issues/3057/events | https://github.com/huggingface/transformers/issues/3057 | 572,456,125 | MDU6SXNzdWU1NzI0NTYxMjU= | 3,057 | Fast tokenizers don't properly tokenize special tokens | {
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"I think it's a duplicate of #2919. Btw, I should have told you when I saw you open the PR, [I opened a bunch of issues related to AllenNLP usage](https://github.com/huggingface/transformers/issues?utf8=%E2%9C%93&q=is%3Aissue+author%3Abryant1410). I think one that was closed it's not completely solved, but not sure... | 1,582 | 1,587 | 1,587 | CONTRIBUTOR | null | Slow tokenizers:
```
>>> import transformers
>>> t_slow = transformers.AutoTokenizer.from_pretrained("roberta-base", use_fast=False)
>>> t_slow.encode_plus("A <mask> sentence.")
{'input_ids': [0, 83, 50264, 3645, 4, 2],
'token_type_ids': [0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1]}
>>> t_slow... | {
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https://api.github.com/repos/huggingface/transformers/issues/3055 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3055/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3055/comments | https://api.github.com/repos/huggingface/transformers/issues/3055/events | https://github.com/huggingface/transformers/pull/3055 | 572,345,835 | MDExOlB1bGxSZXF1ZXN0MzgxMDYyMTg3 | 3,055 | Pipeline doc | {
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"Hmm, why do we want to be able to spawn a `NerPipeline` or a `QuestionAnsweringPipeline` without a model or a tokenizer?\r\n\r\nIsn't this what `pipeline(\"ner\")` or `pipeline(\"question-answering\")` is for?",
"I think the question is rather why can't we spawn a `NerPipeline` or a `QuestionAnsweringPipeline` e... | 1,582 | 1,583 | 1,583 | MEMBER | null | This PR adds documentation to the pipelines and slightly modifies their behavior:
- `modelcard` is no longer available when using the `pipeline` factory method. As discussed with @thomwolf and @mfuntowicz, it doesn't serve any purpose for the user.
- All task-specific pipelines can now be instantiated without any m... | {
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https://api.github.com/repos/huggingface/transformers/issues/3054 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3054/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3054/comments | https://api.github.com/repos/huggingface/transformers/issues/3054/events | https://github.com/huggingface/transformers/pull/3054 | 572,300,701 | MDExOlB1bGxSZXF1ZXN0MzgxMDI2OTk5 | 3,054 | Bart: Use bool attention_mask for encoder | {
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"I believe the `bool` operator was introduced in PyTorch 1.2.0, won't this break compatibility with PyTorch 1.0.0?",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3054?src=pr&el=h1) Report\n> Merging [#3054](https://codecov.io/gh/huggingface/transformers/pull/3054?src=pr&el=desc) into [master](... | 1,582 | 1,583 | 1,583 | CONTRIBUTOR | null | Wasn't breaking because bool(-1e4) is True, but clearer this way. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3053?src=pr&el=h1) Report\n> Merging [#3053](https://codecov.io/gh/huggingface/transformers/pull/3053?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8bcb37bfb80d77e06001f989ad982c9961a69c31?src=pr&el=desc) will **d... | 1,582 | 1,582 | 1,582 | CONTRIBUTOR | null | * Simplify the NER example to support new features added for us by pytorch-lightning.
* Pull out all rank and backend special casing in the code base.
* Setup data so that TPU examples work using the new code base.
Testing:
* Verify that standard examples of training work.
* Confirm that new TPU code works... | {
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https://api.github.com/repos/huggingface/transformers/issues/3052 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3052/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3052/comments | https://api.github.com/repos/huggingface/transformers/issues/3052/events | https://github.com/huggingface/transformers/issues/3052 | 572,210,623 | MDU6SXNzdWU1NzIyMTA2MjM= | 3,052 | Is ALBERT the right implement from paper ? | {
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"I read the code carefully, and it do share."
] | 1,582 | 1,582 | 1,582 | NONE | null | I read ALBERT paper and code form Google: github.com/google-research/ALBERT. One of the main contribute of ALBert is cross-layer parameter sharing and i can see it on Google's code. But i can't see sharing on this code. Every Layers(or Blocks) are new object and their parameter will be different after fine-tuning.
I... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=h1) Report\n> Merging [#3051](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/53ce3854a16ad2a715bc6ac8af3e30c18b5a1d11?src=pr&el=desc) will **n... | 1,582 | 1,583 | 1,583 | MEMBER | null | Docker images are as follow:
- transformers-cpu (PyTorch + TF)
- transformers-gpu (PyTorch + TF)
- transformers-pytorch-cpu
- transformers-pytorch-gpu
- transformers-tensorflow-cpu
- transformers-tensorflow-gpu
Images are tagged according to the version of the library they bring and always use the latest ver... | {
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"Any progress on this? Has anyone found a way to disable the logging this?\r\n\r\n\r\nThe issue appears to be tqdm. A work-around is to disable it before importing transformers:\r\n```\r\nimport tqdm\r\n\r\ndef nop(it, *a, **k):\r\n return it\r\ntqdm.tqdm = nop\r\nimport transformers\r\n```",
"I agree that it'... | 1,582 | 1,604 | 1,604 | NONE | null | # 🚀 Feature request
When doing a simple pipeline, I want to supress:
```
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 230/230 [00:00<00:00, 136kB/s]
convert squad examples to features: 100%|█████████████████████████████████████... | {
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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"
] | 1,582 | 1,588 | 1,588 | NONE | null | I receive the attention of all the 6 layers with all the 12 heads while exporting the tfdistilbert model. I just want to take the attention of dimension equal to the sequence length. Which layer and head attention is the most effective attention value that I should take in order to get the best value of attention score... | {
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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"
] | 1,582 | 1,588 | 1,588 | NONE | null | # 🚀 Feature request
Add a config term `head_hidden_size` to the model configurations that will be used for the head of models such as `RobertaForSequenceClassification`
## Motivation
HuggingFace transformers library provides a very accessible API and PyTorch models that can be used "plug n play" for various t... | {
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https://api.github.com/repos/huggingface/transformers/issues/3047 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3047/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3047/comments | https://api.github.com/repos/huggingface/transformers/issues/3047/events | https://github.com/huggingface/transformers/pull/3047 | 572,070,438 | MDExOlB1bGxSZXF1ZXN0MzgwODM5MDI3 | 3,047 | [WIP] deleted special tokens as attributes from model config | {
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"Not feasible for bart at the moment, sadly.",
"As mentioned in #3011, I think #3011 is the way to go."
] | 1,582 | 1,583 | 1,583 | MEMBER | null | Given our conversation in #3011 , I thought about deleting all model special config attributes to make sure that no data is duplicated between the tokenizer of a model and the model.
There are two instances where config special attributes were used (e.g. self.config.pad_token_id)
1. the generate() function. But ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3046?src=pr&el=h1) Report\n> Merging [#3046](https://codecov.io/gh/huggingface/transformers/pull/3046?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/298bed16a841fae3608d334441ccae4d9043611f?src=pr&el=desc) will **i... | 1,582 | 1,583 | 1,583 | MEMBER | null | - [ ] add prepare_input_for_generation() to all bert masked lm models
- [ ] add slow integration tests to check results (also include camembert for this one) | {
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"Do you mean just for inference? Or fine-tuning too, like in the one you linked?",
"Yes, fine-tuning as well.",
"The notebook described in #2676 is a good example of something that could work; however the current implementation is not very user friendly, which was the design goal of the `gpt-2-simple` notebook.... | 1,582 | 1,598 | 1,598 | NONE | null | Please provide a barebones "pick up and go" GPT-2 colab notebook for text generation, [just like gpt-2-simple does](https://colab.research.google.com/drive/1VLG8e7YSEwypxU-noRNhsv5dW4NfTGce) | {
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"That answer links to an old version of the codebase. Here is an example using the current-day implementation. The documentation can be improved for this particular task, though, since as currently written the given example doesn't seem to do any NSP.\r\n\r\n```python\r\nfrom torch.nn.functional import softmax\r\nf... | 1,582 | 1,582 | 1,582 | NONE | null | I wan to call the next sentence prediction function on new data.
and this webpage tell it
https://stackoverflow.com/questions/55111360/using-bert-for-next-sentence-prediction
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids ... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,582 | 1,588 | 1,588 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert ...):
Language I am using the model on (English):
The problem arises when using:
* [x ] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on is:
* [x ] an official GLUE/SQUaD tas... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3042?src=pr&el=h1) Report\n> Merging [#3042](https://codecov.io/gh/huggingface/transformers/pull/3042?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b370cc7e99c5b8c7436154d4694c33b461ea0f08?src=pr&el=desc) will **d... | 1,582 | 1,583 | 1,582 | CONTRIBUTOR | null | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3041?src=pr&el=h1) Report\n> Merging [#3041](https://codecov.io/gh/huggingface/transformers/pull/3041?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b370cc7e99c5b8c7436154d4694c33b461ea0f08?src=pr&el=desc) will **i... | 1,582 | 1,582 | 1,582 | CONTRIBUTOR | null | Fix #3037 | {
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"The attention outputs are indeed softmax probabilities and should all be > 0. Could you post a code snippet that reproduces negative attention outputs? Or a code snippet of what exactly you are doing with the attention outputs? ",
"This issue has been automatically marked as stale because it has not had recent a... | 1,582 | 1,588 | 1,588 | NONE | null | # ❓ Questions & Help
I am trying to implement above paper for the GPT2 model. The attention outputs are softmax probabilities as seen from modeling_gpt2.py line (152-162). If KLD loss is computed on these values from teacher and student I am getting negative value of KLD indicating that the inputs are not probabilit... | {
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https://api.github.com/repos/huggingface/transformers/issues/3039 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3039/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3039/comments | https://api.github.com/repos/huggingface/transformers/issues/3039/events | https://github.com/huggingface/transformers/issues/3039 | 571,747,868 | MDU6SXNzdWU1NzE3NDc4Njg= | 3,039 | Paragraph re-ranking using MS MARCO 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"
] | 1,582 | 1,588 | 1,588 | NONE | null | How can I use transformers package to train on MS MARCO dataset by adding my own domain data. Or can I use pre trained model to add my own domain data?
SO link: https://stackoverflow.com/questions/60424723/paragraph-re-ranking-using-ms-marco-dataset | {
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"Looks like `PreTrainedEncoderDecoder.prepare_model_kwargs()` was removed in #2745\r\nIs there a reason for that? either `prepare_model_kwargs` should be added again or the line: \r\n`kwargs_encoder, kwargs_decoder = self.prepare_model_kwargs(**kwargs)`\r\nshould be removed from the forward call. I'd be happy to s... | 1,582 | 1,588 | 1,588 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...):
Model2Model
Language I am using the model on (English, Chinese ...):
English
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)
based on the quick start g... | {
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https://api.github.com/repos/huggingface/transformers/issues/3037 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3037/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3037/comments | https://api.github.com/repos/huggingface/transformers/issues/3037/events | https://github.com/huggingface/transformers/issues/3037 | 571,734,995 | MDU6SXNzdWU1NzE3MzQ5OTU= | 3,037 | Wrong logic in `batch_encode_plus()` | {
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I'm trying to use `batch_encode_plus()` with already tokenized ID as input.
So I have a `list of list of int` as input. For example :
```python
x = [[0, 83, 313, 11, 10551, 2278, 16, 2183, 1958, 804, 7, 916, 11, 13933, 982, 4, 286, 68, 5046, 6, 37, 40, 3627, 231, 2697, 9, 1958, 11, 41, 36027, 27894, ... | {
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"`transformers`'s implementation of the GradientAccumulator has been inspired by the OpenNMT implementation. Can you try their (updated) version and report the results? You can find it here:\r\n\r\nhttps://github.com/OpenNMT/OpenNMT-tf/blob/0311e473d9788b0363ca663cafbdfaf7777e53f9/opennmt/optimizers/utils.py#L64\r\... | 1,582 | 1,589 | 1,589 | CONTRIBUTOR | null | # 🐛 Bug
I'm using gradient accumulation to get larger batch sizes when pretraining BERT, and GradientAccumulator works wonderfully. Thanks for including that!
However, tf.AutoGraph fails when I wrap in the gradient accumulation step in tf.function. I have to write code like this:
```code
@tf.function
def ba... | {
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As a remedy I think it is alright if a new pad_token is added or wheth... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3034?src=pr&el=h1) Report\n> Merging [#3034](https://codecov.io/gh/huggingface/transformers/pull/3034?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9df74b8bc42eedc496f7148b9370728054ca3b6a?src=pr&el=desc) will **i... | 1,582 | 1,582 | 1,582 | CONTRIBUTOR | null | Hi, feel free to reject if you think these changes aren't necessary. Just saw a couple typos while reading the documentation and wanted to help 😄. The first change looks a bit hard to tell what changed, but it was 'superseeds' to 'supersedes'. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3033?src=pr&el=h1) Report\n> Merging [#3033](https://codecov.io/gh/huggingface/transformers/pull/3033?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/bb7c46852051f7d031dd4be0240c9c9db82f6ed9?src=pr&el=desc) will **d... | 1,582 | 1,582 | 1,582 | MEMBER | null | - fixed issue #3031
- updated doc-string for GPT2
- added two test for GPT2 that I think are important:
- check that when using past as an input to speed up decoding, the results are equivalent to not using past.
- check that when using past and attn_mask as an input to speed up decoding, the resul... | {
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https://api.github.com/repos/huggingface/transformers/issues/3032 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3032/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3032/comments | https://api.github.com/repos/huggingface/transformers/issues/3032/events | https://github.com/huggingface/transformers/issues/3032 | 571,461,730 | MDU6SXNzdWU1NzE0NjE3MzA= | 3,032 | Loading custom weights for BERT in pytorch | {
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"Yes. Have a look at the documentation for [`from_pretrained`](https://huggingface.co/transformers/main_classes/model.html#transformers.PreTrainedModel.from_pretrained), particularly the `pretrained_model_name_or_path ` argument.",
"Yes sorry I looked after I asked and found everything I needed it "
] | 1,582 | 1,582 | 1,582 | NONE | null | Is it possible to load custom pre-trained weights for BERT, other than the one you provide ? | {
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https://api.github.com/repos/huggingface/transformers/issues/3031 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3031/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3031/comments | https://api.github.com/repos/huggingface/transformers/issues/3031/events | https://github.com/huggingface/transformers/issues/3031 | 571,430,501 | MDU6SXNzdWU1NzE0MzA1MDE= | 3,031 | Forward pass with GPT2 using both past and attention_mask as an input leads to dimension error | {
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"FYI - I created a stackoverflow question about this here: https://stackoverflow.com/questions/60459292/using-past-and-attention-mask-at-the-same-time-for-gpt2",
"@Damiox - answered you stackoverflow question :-)"
] | 1,582 | 1,583 | 1,582 | MEMBER | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): GPT2
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ x ] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on is:
... | {
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https://api.github.com/repos/huggingface/transformers/issues/3030 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3030/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3030/comments | https://api.github.com/repos/huggingface/transformers/issues/3030/events | https://github.com/huggingface/transformers/issues/3030 | 571,428,060 | MDU6SXNzdWU1NzE0MjgwNjA= | 3,030 | run_tf_glue with AdamW optimizer and distributed training | {
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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"
] | 1,582 | 1,588 | 1,588 | NONE | null | # ❓ Questions & Help
Can you please update the run_tf_glue example (Tensorflow version) that includes the Adam optimizer with Warm-up and weight decay. If you can also show how to run the code on multiple GPUs and on TPU.
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https://api.github.com/repos/huggingface/transformers/issues/3029 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3029/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3029/comments | https://api.github.com/repos/huggingface/transformers/issues/3029/events | https://github.com/huggingface/transformers/issues/3029 | 571,290,984 | MDU6SXNzdWU1NzEyOTA5ODQ= | 3,029 | How to add data to pretrained model. | {
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"Have a look at the language modeling example:\r\n\r\nhttps://huggingface.co/transformers/examples.html#language-model-training\r\n\r\nhttps://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py",
"Thank you for your answering.\r\n\r\nI did that^^",
"This issue has been automatical... | 1,582 | 1,589 | 1,589 | NONE | null | I want to add wiki data to pretrained model.
i.e., kor wiki data is added to XLMRoberta Model weight.
I think that files in /transformers/templates/adding_a_new_model do
Is it right?
please let me know way to add data.
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https://api.github.com/repos/huggingface/transformers/issues/3028 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3028/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3028/comments | https://api.github.com/repos/huggingface/transformers/issues/3028/events | https://github.com/huggingface/transformers/issues/3028 | 571,239,136 | MDU6SXNzdWU1NzEyMzkxMzY= | 3,028 | AttributeError: 'Tensor' object has no attribute 'size' | {
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"You are using TF. Did you mean to use `TFDistilBertModel` instead of `DistilBertModel`?",
"@BramVanroy got it. Thanks.",
"I wonder if it would be possible to know in advance whether a model supports TF or pyTorch, or both... and throw an exception accordingly? "
] | 1,582 | 1,641 | 1,582 | NONE | null | This is the model that I have defined:
```
input_layer = keras.layers.Input(shape = (attention_mask.shape[1],), dtype='int64')
bert = DistilBertModel.from_pretrained("distilbert-base-cased")(input_layer)
bert = bert[0][:,0,:]
# bert = keras.layers.Dense(units=10, activation='relu')(bert)
classifie... | {
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https://api.github.com/repos/huggingface/transformers/issues/3027 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3027/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3027/comments | https://api.github.com/repos/huggingface/transformers/issues/3027/events | https://github.com/huggingface/transformers/issues/3027 | 571,104,664 | MDU6SXNzdWU1NzExMDQ2NjQ= | 3,027 | This class and module cannot be found | {
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"You can do `pip install tokenizers` or [install from source](https://github.com/huggingface/tokenizers).",
"> You can do `pip install tokenizers` or [install from source](https://github.com/huggingface/tokenizers).\r\n\r\nOK， I've got it ,thank you",
"> pip install tokenizers\r\n\r\n```bash\r\nRequirement alre... | 1,582 | 1,618 | 1,582 | NONE | null | I want to use BertForNextSentencePrediction in ' modeling_bert.py' line 1020 ,but when I run Examples in line 1065, error happens
File "E:\work\pycharm\transformers-master\src\transformers\tokenization_bert.py", line 24, in <module>
from tokenizers import BertWordPieceTokenizer
ImportError: No module named... | {
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https://api.github.com/repos/huggingface/transformers/issues/3026 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3026/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3026/comments | https://api.github.com/repos/huggingface/transformers/issues/3026/events | https://github.com/huggingface/transformers/issues/3026 | 571,094,325 | MDU6SXNzdWU1NzEwOTQzMjU= | 3,026 | language_modeling.py doesn't continue from last global step | {
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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"
] | 1,582 | 1,588 | 1,588 | CONTRIBUTOR | null | # 🐛 Bug
Hey, The checkpoint's suffix is the last optimization step rather than the last global step (I'm working with accumulation steps)
## Information
The problem arises when using:
* [ ] the official example scripts: language_modeling.py
The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: l... | {
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"Please don't post a screenshot. Copy-and-paste your code or output instead.",
"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",
"@Vvegetables , does this problem still exist?",
"it'... | 1,582 | 1,593 | 1,593 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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https://api.github.com/repos/huggingface/transformers/issues/3024 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3024/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3024/comments | https://api.github.com/repos/huggingface/transformers/issues/3024/events | https://github.com/huggingface/transformers/pull/3024 | 570,991,180 | MDExOlB1bGxSZXF1ZXN0Mzc5OTQyMDMw | 3,024 | Fix (non-slow) tests on GPU (torch) | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3024?src=pr&el=h1) Report\n> Merging [#3024](https://codecov.io/gh/huggingface/transformers/pull/3024?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/bb7c46852051f7d031dd4be0240c9c9db82f6ed9?src=pr&el=desc) will **n... | 1,582 | 1,582 | 1,582 | MEMBER | null | Fixes ~29 failing tests
Also cf previous commit from december: https://github.com/huggingface/transformers/pull/2055/commits/61978c1dd3f340a545e74537c3dae41a4514e867
(not sure why T5 and the common methods were not failing back then) | {
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https://api.github.com/repos/huggingface/transformers/issues/3023 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3023/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3023/comments | https://api.github.com/repos/huggingface/transformers/issues/3023/events | https://github.com/huggingface/transformers/issues/3023 | 570,987,485 | MDU6SXNzdWU1NzA5ODc0ODU= | 3,023 | BART : host `bart-large-cnn` | {
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"IIRC @sshleifer is working on getting summarization working with that model."
] | 1,582 | 1,583 | 1,583 | CONTRIBUTOR | null | # 🌟 New model addition
BART pretrained model `bart-large` is currently provided, as well as fine-tuned BART model `bart-large-mnli`.
**How about [`bart-large-cnn`](https://github.com/pytorch/fairseq/tree/master/examples/bart#pre-trained-models) ?** | {
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https://api.github.com/repos/huggingface/transformers/issues/3022 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3022/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3022/comments | https://api.github.com/repos/huggingface/transformers/issues/3022/events | https://github.com/huggingface/transformers/pull/3022 | 570,896,940 | MDExOlB1bGxSZXF1ZXN0Mzc5ODU2Njc4 | 3,022 | Make format consistent with that of PreTrainedTokenizer | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3022?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@c913eb9`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage is `100%`.\... | 1,582 | 1,588 | 1,588 | NONE | null | {
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"It seems possible to by-pass this issue by setting appropriate `attention_mask` so that no tokens will attend the positions that are supposed to be paddings, this way you can use whatever token as padding. I'm working on this issue too, will try to follow up if it works out.",
"I tried a rough version, basically... | 1,582 | 1,678 | 1,582 | NONE | null | Given GPT2 tokenizer do not have an internal pad_token_id, how do I pad sentences and do batch inference using GPT2LMHeadModel?
Specifically my code as:
```
prompt_text = [
'in this paper we',
'we are trying to',
'The purpose of this workshop is to check whether we can', ]
tokens = [tokenizer.conve... | {
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https://api.github.com/repos/huggingface/transformers/issues/3020 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3020/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3020/comments | https://api.github.com/repos/huggingface/transformers/issues/3020/events | https://github.com/huggingface/transformers/pull/3020 | 570,835,585 | MDExOlB1bGxSZXF1ZXN0Mzc5ODAzNzk1 | 3,020 | [ci] Run all tests on (self-hosted) GPU | {
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https://api.github.com/repos/huggingface/transformers/issues/3019 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3019/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3019/comments | https://api.github.com/repos/huggingface/transformers/issues/3019/events | https://github.com/huggingface/transformers/pull/3019 | 570,823,600 | MDExOlB1bGxSZXF1ZXN0Mzc5NzkzNTk1 | 3,019 | Delete Model2Model | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3019?src=pr&el=h1) Report\n> Merging [#3019](https://codecov.io/gh/huggingface/transformers/pull/3019?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e8ce63ff2163259276fc0a4a2f35b836fe9f4aa0?src=pr&el=desc) will **i... | 1,582 | 1,583 | 1,582 | CONTRIBUTOR | null | - the quickstart code doesn't work
- the tests don't test a forward pass
if you need it `git checkout e8ce63ff` | {
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"I can't seem to find who is pushing the commits, but can whoever is doing that please add commit messages? This is really messy and impossible to go over as currently written.",
"Sorry, @BramVanroy . I didn't realize people were reviewing this branch. This is a WIP We are still trying to work this out with new p... | 1,582 | 1,582 | 1,582 | CONTRIBUTOR | null | Some changes to PLT to support our workflow. Prelim support for TPUs.
Still testing. | {
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"Hi, where did you obtain this model? Is it using the official google-research implementation or using huggingface/transformers?",
"@LysandreJik Thank you for your reply! I have pretrained BERT base using my own pytorch code and generated checkpoint for BERT base. Then I want to do squad finetuning using my own p... | 1,582 | 1,588 | 1,588 | NONE | null | How can I use my own pretrained BERT model in SQUAD finetuing? How to do this in the code? Can anyone provide any instructions? | {
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https://api.github.com/repos/huggingface/transformers/issues/3015 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3015/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3015/comments | https://api.github.com/repos/huggingface/transformers/issues/3015/events | https://github.com/huggingface/transformers/issues/3015 | 570,752,675 | MDU6SXNzdWU1NzA3NTI2NzU= | 3,015 | Latest version of transformers available via conda? | {
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"It would be nice if it had it's own channel like PyTorch has, as put a distribution of the packages (transformers, tokenizers, etc.) there.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contribution... | 1,582 | 1,591 | 1,591 | NONE | null | # 🚀 Feature request
I notice that the version of transformers available via conda-forge is quite outdated (v2.1.1). Could you make later versions available there too? Thanks!
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3014?src=pr&el=h1) Report\n> Merging [#3014](https://codecov.io/gh/huggingface/transformers/pull/3014?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e693cd1e877aa191d3317faed33e87d1558c9406?src=pr&el=desc) will **d... | 1,582 | 1,582 | 1,582 | MEMBER | null | It's quite easy to get real numbers for the XLM-R moder from [fairseq](https://github.com/pytorch/fairseq/tree/master/examples/xlmr), so I added integration tests for `xlm_roberta_modeling.py` and `xlm_roberta_tokenization.py`
Since `XLMRobertaModel` is the same model as `RobertaModel`, I think intergration tests f... | {
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