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https://api.github.com/repos/huggingface/transformers/issues/3612 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3612/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3612/comments | https://api.github.com/repos/huggingface/transformers/issues/3612/events | https://github.com/huggingface/transformers/issues/3612 | 593,251,872 | MDU6SXNzdWU1OTMyNTE4NzI= | 3,612 | training GPT2 from scratch : implement causal attention mask? | {
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"You'd want to look at the `run_language_modeling.py` script which implements causal language modeling. (do not pass the `--mlm` flag)",
"I'm thinking some edit to run_language_modeling.py script maybe would make it work. I don't think just to not pass the --mlm flag you solve the problem @julien-c. Have you foun... | 1,585 | 1,591 | 1,591 | NONE | null | # ❓ Questions & Help
I am trying to train a ```GPT2``` model from scratch but I noticed, by looking into the code here https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_gpt2.py that there doesn’t seem to be an implementation for a causal mask. Maybe it is in another repo and I missed ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3610 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3610/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3610/comments | https://api.github.com/repos/huggingface/transformers/issues/3610/events | https://github.com/huggingface/transformers/issues/3610 | 593,203,724 | MDU6SXNzdWU1OTMyMDM3MjQ= | 3,610 | How can u make sure that my transformer model should only one GPU, though the serve has multiple GPU cards. | {
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"You should have used the template, because now we don't have enough information to help you: how are you running the script (torch launch utility? Which command?), which script are you using (your own (give details) or one of the example scripts)?\r\n\r\nBy default, PyTorch will only use one GPU unless you specify... | 1,585 | 1,586 | 1,586 | NONE | null | I have transformer BERT model and I am trying to train on lambda server which has 8 GPU cards, How can u make sure that this model should use only once GPU out of 8, by default , it is using all GPUs. even after setting
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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https://api.github.com/repos/huggingface/transformers/issues/3609 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3609/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3609/comments | https://api.github.com/repos/huggingface/transformers/issues/3609/events | https://github.com/huggingface/transformers/issues/3609 | 593,198,219 | MDU6SXNzdWU1OTMxOTgyMTk= | 3,609 | Filling more than 1 masked token at a time | {
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"Indeed, this is not supported right now. We'd welcome a PR though :)",
"Before somebody starts on a PR, we need to consider what exactly this should do.\r\n\r\nFor `top_k = 1`, most users probably expect a single forward pass and picking the top prediction for each token. For greater `top_k`, however, picking th... | 1,585 | 1,625 | 1,625 | NONE | null | I am able to use hugging face's mask filling pipeline to predict 1 masked token in a sentence using the below:
```
!pip install -q transformers
from __future__ import print_function
import ipywidgets as widgets
from transformers import pipeline
nlp_fill = pipeline('fill-mask')
nlp_fill("I am going to guess <... | {
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"A PR is welcome!",
"created PR #3621 ",
"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",
"cc @mfuntowicz @n1t0 \r\n\r\nThe problem is that the tokenizer does not allow empty string... | 1,585 | 1,598 | 1,598 | CONTRIBUTOR | null | https://github.com/huggingface/transformers/blob/81484b447b7d8504ff5e1cfff38ec35918383963/src/transformers/tokenization_roberta.py#L239
this will introduce issue if `text == ""`, which will occur if anyone follows `run_glue.py` with QQP task as the `train.tsv` has two lines contains the empty column.
can be corrected... | {
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https://api.github.com/repos/huggingface/transformers/issues/3607 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3607/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3607/comments | https://api.github.com/repos/huggingface/transformers/issues/3607/events | https://github.com/huggingface/transformers/pull/3607 | 593,181,329 | MDExOlB1bGxSZXF1ZXN0Mzk4MDEwMTgw | 3,607 | Allow the creation of "entity groups" for NerPipeline #3548 | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3607?src=pr&el=h1) Report\n> Merging [#3607](https://codecov.io/gh/huggingface/transformers/pull/3607?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/81484b447b7d8504ff5e1cfff38ec35918383963&el=desc) will **decrease... | 1,585 | 1,587 | 1,587 | CONTRIBUTOR | null | This pull request adds an `index` key to the dictionary returned by `NerPipeline`. The index will be necessary in order to identify **entity groups**, where an entity group is a contiguous series of tokens, having the same **entity type**.
Details of what I want to be able to do can be found in issue #3548.
If th... | {
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https://api.github.com/repos/huggingface/transformers/issues/3605 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3605/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3605/comments | https://api.github.com/repos/huggingface/transformers/issues/3605/events | https://github.com/huggingface/transformers/issues/3605 | 593,154,374 | MDU6SXNzdWU1OTMxNTQzNzQ= | 3,605 | 🐛 Summarization pipeline : T5-base much slower than BART-large | {
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"Hi @Colanim, thanks a lot for your speed comparison :-). \r\n\r\nIt might be possible that the pipelines used different default parameters for `T5` and `Bart` under the hood which strongly influence their running times.\r\nBesides `min_length` and `max_length` could you also insert those parameters into both `T5` ... | 1,585 | 1,586 | 1,586 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model : `bart-large-cnn` and `t5-base`
Language : English
The problem arises when using : [this colab notebook](https://colab.research.google.com/drive/1iAIFX1QQiFm1F01vMmnAgFh4oH1H-K8W), using both BART and T5 with pipeline for Summarization.
Dataset : CNN/DM
## To reproduce
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https://api.github.com/repos/huggingface/transformers/issues/3602 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3602/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3602/comments | https://api.github.com/repos/huggingface/transformers/issues/3602/events | https://github.com/huggingface/transformers/pull/3602 | 592,991,279 | MDExOlB1bGxSZXF1ZXN0Mzk3ODY3ODU4 | 3,602 | Multilingual BART - | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3602?src=pr&el=h1) Report\n> Merging [#3602](https://codecov.io/gh/huggingface/transformers/pull/3602?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1a16d9d94a81e95463b166adfce4a8e02cdc47eb&el=desc) will **not chan... | 1,585 | 1,591 | 1,586 | CONTRIBUTOR | null | This adds `mbart-en-ro` model, a BART variant finetuned on english-romanian translation.
### TODO
- [x] (docs) pretrained_model.rst
- [ ] (docs) README.md
- [ ] (docs) bart.rst
### Differences with Bart
`config.normalize_before`: all the `LayerNorm` calls happen before attention calls
`config.add_final_layer... | {
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https://api.github.com/repos/huggingface/transformers/issues/3601 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3601/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3601/comments | https://api.github.com/repos/huggingface/transformers/issues/3601/events | https://github.com/huggingface/transformers/pull/3601 | 592,908,693 | MDExOlB1bGxSZXF1ZXN0Mzk3ODAxMjQ1 | 3,601 | [Generate, Test] Split generate test function into beam search, no beam search | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3601?src=pr&el=h1) Report\n> Merging [#3601](https://codecov.io/gh/huggingface/transformers/pull/3601?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f68d22850ced09bb194b30068ff94ca3409f0879&el=desc) will **decrease... | 1,585 | 1,586 | 1,586 | MEMBER | null | - Clean the generate testing functions
- Also should fix flaky behaviour of bad_word_tokens test (see #3367 and https://circleci.com/gh/huggingface/transformers/27997?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link)
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https://api.github.com/repos/huggingface/transformers/issues/3600 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3600/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3600/comments | https://api.github.com/repos/huggingface/transformers/issues/3600/events | https://github.com/huggingface/transformers/issues/3600 | 592,905,236 | MDU6SXNzdWU1OTI5MDUyMzY= | 3,600 | Why isn't there a SequenceClassificationModel for GPT-2 (and some other models)? | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,585 | 1,591 | 1,591 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
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https://api.github.com/repos/huggingface/transformers/issues/3599 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3599/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3599/comments | https://api.github.com/repos/huggingface/transformers/issues/3599/events | https://github.com/huggingface/transformers/issues/3599 | 592,905,220 | MDU6SXNzdWU1OTI5MDUyMjA= | 3,599 | Why is there not a SequenceClassification model for GPT-2? | {
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"My apologies, my computer glitched and posted twice. Please close this issue and refer to: https://github.com/huggingface/transformers/issues/3600"
] | 1,585 | 1,585 | 1,585 | NONE | null | # ❓ Questions & Help
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https://api.github.com/repos/huggingface/transformers/issues/3598 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3598/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3598/comments | https://api.github.com/repos/huggingface/transformers/issues/3598/events | https://github.com/huggingface/transformers/issues/3598 | 592,904,004 | MDU6SXNzdWU1OTI5MDQwMDQ= | 3,598 | After enable fp16, torch.save model has error | {
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"Please use the template in the future. It is there for a reason. As mentioned in the template, don't post a screenshot. Use code blocks, post your code or the example script that you used, and the error trace. Also provide your version of PyTorch and Python.\r\n\r\nhttps://help.github.com/en/github/writing-on-gith... | 1,585 | 1,591 | 1,591 | NONE | null | # 🐛 Bug
After complete training, the model cannot be saved.
## Information
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https://api.github.com/repos/huggingface/transformers/issues/3597 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3597/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3597/comments | https://api.github.com/repos/huggingface/transformers/issues/3597/events | https://github.com/huggingface/transformers/issues/3597 | 592,898,580 | MDU6SXNzdWU1OTI4OTg1ODA= | 3,597 | CTRL generates French text when I want English texts | {
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"CTRL uses control codes, as is mentioned in our documentation, with examples on the [original repository](https://github.com/salesforce/ctrl#generations). Have you tried using these control codes?",
"How do I specify which control code I want to use? Do I have to do that in the command line and if yes, how? Thi... | 1,585 | 1,593 | 1,593 | NONE | null | # ❓ Questions & Help
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"Hi,\r\n\r\nIs anyone working on this or is it open for someone to take?\r\nI was able to reproduce the issue.\r\nIf not being worked on by anyone, I would like to take it up.\r\n\r\nThanks",
"I'm facing similar issues with batch_encode_plus:\r\n```\r\ntokenizer = transformers.BertTokenizer.from_pretrained('bert-... | 1,585 | 1,592 | 1,592 | NONE | null | # 🐛 Bug
## Information
Model I am using 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:
* [ ] an official GL... | {
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"Can you show how you initialize `tokenizer`? Which vocab are you using?",
"> Can you show how you initialize `tokenizer`? Which vocab are you using?\r\n\r\nsorry I forgot that... I updated the code in the issue already.\r\n\r\nI was using `distilbert-base-multilingual-cased`\r\n\r\n`tokenizer = DistilBertTokeniz... | 1,585 | 1,591 | 1,591 | NONE | null | # 🐛 Bug
## Information
Model I am using (DistillBert):
The problem arises when using:
* my own modified scripts: (give details below)
The tasks I am working on is:
* my own task or dataset: (give details below)
## To reproduce
with transformers 2.3.0:
```python
import torch
from transformers im... | {
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https://api.github.com/repos/huggingface/transformers/issues/3593 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3593/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3593/comments | https://api.github.com/repos/huggingface/transformers/issues/3593/events | https://github.com/huggingface/transformers/issues/3593 | 592,759,313 | MDU6SXNzdWU1OTI3NTkzMTM= | 3,593 | Transformers and BERT: dealing with possessives and apostrophes when encode | {
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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",
"Any idea about how to solve this apostrophe seperating word token with different word id in bert rokenizer ?"
] | 1,585 | 1,681 | 1,591 | NONE | null | Let's consider two sentences:
"why isn't Alex's text tokenizing? The house on the left is the Smiths' house"
Now let's tokenize and decode:
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
tokenizer.decode(tokenizer.conv... | {
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https://api.github.com/repos/huggingface/transformers/issues/3592 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3592/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3592/comments | https://api.github.com/repos/huggingface/transformers/issues/3592/events | https://github.com/huggingface/transformers/issues/3592 | 592,716,038 | MDU6SXNzdWU1OTI3MTYwMzg= | 3,592 | Issues with using SciBERT for Summarizer | {
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"I believe only BART and T5 can do summarization for now. See the [documentation regarding the checkpoints that may summarize](https://huggingface.co/transformers/main_classes/pipelines.html#summarizationpipeline).",
"Here is [a notebook](https://github.com/Nikoschenk/bert-extractive-summarizer/blob/master/colab/... | 1,585 | 1,587 | 1,585 | NONE | null | Hi All, I am not sure if I am doing this right? But i want my summarizer to use SciBERT SciVocab instead of the traditional BERT vocab. Appreciate any help and advice! This is what I am currently using:
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''... | {
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https://api.github.com/repos/huggingface/transformers/issues/3591 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3591/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3591/comments | https://api.github.com/repos/huggingface/transformers/issues/3591/events | https://github.com/huggingface/transformers/issues/3591 | 592,693,446 | MDU6SXNzdWU1OTI2OTM0NDY= | 3,591 | Cannot load model in tranformers | {
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"It seems you have a very old version of this repository (when it was still named `pytorch_transformers`). Please update to the latest version or you won't have all the features (like access to the model hub).",
"I fixed the version and the error was fixed. But now I get \r\n\r\n AttributeError: 'BertTokenizer... | 1,585 | 1,591 | 1,591 | NONE | null | Hello, I tried to upload your model and I get this error:
tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
model = AutoModelWithLMHead.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
ERROR:pytorch_transformers.modeling_utils:Model name 'nlpaueb/bert-base-greek-... | {
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https://api.github.com/repos/huggingface/transformers/issues/3590 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3590/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3590/comments | https://api.github.com/repos/huggingface/transformers/issues/3590/events | https://github.com/huggingface/transformers/issues/3590 | 592,691,560 | MDU6SXNzdWU1OTI2OTE1NjA= | 3,590 | min_length parameter in default pipeline summarization produces output smaller than min_length | {
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"Hi @Weilin37,\r\n\r\nHow did you count the length of your output? The `min_length` corresponds to the minimum number of tokens in the output which is `<=` number of words.\r\nTo count the number of tokens you have in your output text, you could use `tokenizer.tokenize(OUTPUT_TEXT)`",
"> Hi @Weilin37,\r\n> \r\n> ... | 1,585 | 1,619 | 1,585 | NONE | null | Hi, I am using pipeline summarization. My code is below:
```
from transformers import pipeline, AutoTokenizer, AutoModel
summarizer = pipeline("summarization")
abstract_dictionary = {'Introduction':'','Methods':'','Results':'','Discussion':''}
for section in article_dictionary:
if section == 'Introducti... | {
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https://api.github.com/repos/huggingface/transformers/issues/3589 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3589/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3589/comments | https://api.github.com/repos/huggingface/transformers/issues/3589/events | https://github.com/huggingface/transformers/issues/3589 | 592,676,070 | MDU6SXNzdWU1OTI2NzYwNzA= | 3,589 | Evaluation - Output False Positive and False Negative Sentences | {
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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"
] | 1,585 | 1,591 | 1,591 | NONE | null | # ❓ Questions & Help
Hi, regarding the sequential classification task, after the evaluation on test data, how could I output the actual false positive and false negative sentences? Basically convert BERT embedding back to the actual sentences for error analysis?
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https://api.github.com/repos/huggingface/transformers/issues/3588 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3588/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3588/comments | https://api.github.com/repos/huggingface/transformers/issues/3588/events | https://github.com/huggingface/transformers/pull/3588 | 592,637,578 | MDExOlB1bGxSZXF1ZXN0Mzk3NTgzNTAw | 3,588 | added model_cards for polish squad models | {
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"model pages: https://huggingface.co/models?filter=polish,question-answering\r\n\r\nThank you!"
] | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/3587 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3587/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3587/comments | https://api.github.com/repos/huggingface/transformers/issues/3587/events | https://github.com/huggingface/transformers/issues/3587 | 592,502,353 | MDU6SXNzdWU1OTI1MDIzNTM= | 3,587 | How to fine tune T5 like for translation tasks? | {
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"see https://github.com/huggingface/transformers/issues/3576"
] | 1,585 | 1,586 | 1,586 | NONE | null | # ❓ Questions & Help
## Details
<!-- Description of your issue -->
I want to pass 100000's of training instances to this syntax, but it says its limit is only 512. I am passing list of strings.
input_ids = tokenizer.encode('translate English to German: The house is wonderful. </s>', return_tensors='pt')
lm_lab... | {
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https://api.github.com/repos/huggingface/transformers/issues/3586 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3586/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3586/comments | https://api.github.com/repos/huggingface/transformers/issues/3586/events | https://github.com/huggingface/transformers/issues/3586 | 592,477,200 | MDU6SXNzdWU1OTI0NzcyMDA= | 3,586 | when I run transformers in Docker container, it appeared this error | {
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"We would need more information in order to help you debug, namely transformers version, python version, code example, etc.\r\n\r\nHave you seen the issue template? Respecting it will ensure you get help :slightly_smiling_face: "
] | 1,585 | 1,587 | 1,587 | NONE | null | ```
unknown exception: Model name 'bert-base-uncased' was not found in tokenizers model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, be... | {
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https://api.github.com/repos/huggingface/transformers/issues/3585 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3585/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3585/comments | https://api.github.com/repos/huggingface/transformers/issues/3585/events | https://github.com/huggingface/transformers/issues/3585 | 592,469,508 | MDU6SXNzdWU1OTI0Njk1MDg= | 3,585 | Reason behind the layers taken for distilbert-multilingual | {
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"With distillation, the initialization is really important in order to obtain good results. You can read the [paper](https://arxiv.org/pdf/1910.01108.pdf) to have more information, look for section 3 and \"Student Initialization\"!\r\n\r\nThe important part is the following:\r\n\r\n_Student initialization In additi... | 1,585 | 1,602 | 1,586 | NONE | null | Hi,
I went across the training code for distilbert. I can see that the distillation process is used to train the distilbert model from bert model.
What is the reason only the **layers [0, 2, 4, 7, 9, 11]** have been taken to train distilbert model?Is there any idea about the layers behind this?
And,what is the *... | {
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https://api.github.com/repos/huggingface/transformers/issues/3584 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3584/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3584/comments | https://api.github.com/repos/huggingface/transformers/issues/3584/events | https://github.com/huggingface/transformers/issues/3584 | 592,435,854 | MDU6SXNzdWU1OTI0MzU4NTQ= | 3,584 | cased -> uncased in BERT GLUE example | {
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"additionally, the `xlnet-large-cased` should not combine with `--do_lower_case` as `xlnet` model only has a cased version."
] | 1,585 | 1,586 | 1,586 | CONTRIBUTOR | null | similar to https://github.com/huggingface/transformers/issues/3183, the GLUE readme also have this issue, the MRPC example use `bert-base-cased` while at the same time `--do_lower_case`. | {
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https://api.github.com/repos/huggingface/transformers/issues/3583 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3583/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3583/comments | https://api.github.com/repos/huggingface/transformers/issues/3583/events | https://github.com/huggingface/transformers/issues/3583 | 592,367,116 | MDU6SXNzdWU1OTIzNjcxMTY= | 3,583 | Dict in the first positional arguments | {
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"I would assume that you need to unpack the dictionary as you pass it to the model:\r\n\r\n```python\r\nmodel(**features)\r\n```\r\n\r\nEDIT: I was wrong, since the TF version should be used differently than the PT version.",
"Thank you @BramVanroy, but another error arose:\r\n\r\n```python\r\nmodel(**features)\r... | 1,585 | 1,585 | 1,585 | NONE | null | Could someone help me figure out what is wrong in the TFBertModel below?
```python
features = next(iter(dataset))
features
```
which prints:
```python
{'attention_mask': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)>,
'input_ids': <tf.Tenso... | {
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https://api.github.com/repos/huggingface/transformers/issues/3582 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3582/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3582/comments | https://api.github.com/repos/huggingface/transformers/issues/3582/events | https://github.com/huggingface/transformers/issues/3582 | 592,316,295 | MDU6SXNzdWU1OTIzMTYyOTU= | 3,582 | Does the BART model support Chinese? Having the pre-trained Chinese model? | {
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"Have the same problem and do you have any url to download the bart-large-cnn or and other pretrained model",
"No chinese support, yet. \r\nDownload:\r\n```bash\r\nwget https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-cnn/pytorch_model.bin\r\n```",
"This issue has been automatically marke... | 1,585 | 1,591 | 1,591 | NONE | null | # 🚀 Feature request
<!-- A clear and concise description of the feature proposal.
Please provide a link to the paper and code in case they exist. -->
## Motivation
<!-- Please outline the motivation for the proposal. Is your feature request
related to a problem? e.g., I'm always frustrated when [.... | {
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https://api.github.com/repos/huggingface/transformers/issues/3581 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3581/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3581/comments | https://api.github.com/repos/huggingface/transformers/issues/3581/events | https://github.com/huggingface/transformers/issues/3581 | 592,312,163 | MDU6SXNzdWU1OTIzMTIxNjM= | 3,581 | Different outputs in using convert_roberta_original_pytorch_checkpoint_to_pytorch.py | {
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"I had a similar problem. There is a bug in conversion script, this pull request fixes the issue: https://github.com/huggingface/transformers/pull/3642",
"@sdadas Thanks a lot. I've pulled the latest change and it works now.",
"Great to hear, closing!"
] | 1,585 | 1,586 | 1,586 | CONTRIBUTOR | null | # 🐛 Bug
We have an issue in converting roberta model from fairseq format to huggingface format. The conversion function provided in the transformers library gives you different outputs when you pass sample data through the fairseq and huggingface-transformers versions seperately.
⬇️Problem with our pretrained mode... | {
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"Good catch @dmytyar :-) "
] | 1,585 | 1,586 | 1,586 | NONE | null | # 🐛 Bug
## Information
In file src/transformers/modeling_tf_transfo_xl.py present small typo in method parameters call.
In line 491 present method:
def _update_mems(self, hids, mems, qlen, mlen)
And in line 610 on it's call:
new_mems = self._update_mems(hids, mems, mlen, qlen)
As you can see qlen ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3579 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3579/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3579/comments | https://api.github.com/repos/huggingface/transformers/issues/3579/events | https://github.com/huggingface/transformers/issues/3579 | 592,224,844 | MDU6SXNzdWU1OTIyMjQ4NDQ= | 3,579 | Summarization pipeline max_length parameter seems to just cut the summary rather than generating a complete sentence within the max length | {
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"**Try using the T5 summarizer instead like below:**\r\n```python\r\nfrom transformers import T5Tokenizer, T5ForConditionalGeneration\r\n\r\ntokenizer = T5Tokenizer.from_pretrained('t5-small')\r\nmodel = T5ForConditionalGeneration.from_pretrained('t5-small')\r\ninputs = tokenizer.batch_encode_plus([\"summarize: \" ... | 1,585 | 1,588 | 1,585 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): default model from pipeline("summarization")
Language I am using the model on (English, Chinese ...): English
I am using the pipeline for summarization in most up to date version of Transformers. I am inputing a long piece of tax and setting the su... | {
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https://api.github.com/repos/huggingface/transformers/issues/3578 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3578/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3578/comments | https://api.github.com/repos/huggingface/transformers/issues/3578/events | https://github.com/huggingface/transformers/pull/3578 | 592,214,932 | MDExOlB1bGxSZXF1ZXN0Mzk3MjQxNDc1 | 3,578 | [WIP] Adding model parallelism for T5 (should work for other models as well) | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3578?src=pr&el=h1) Report\n> Merging [#3578](https://codecov.io/gh/huggingface/transformers/pull/3578?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a4ee4da18ad659b196582bbdf40785033ee1d26b?el=desc) will **decrease... | 1,585 | 1,652 | 1,619 | MEMBER | null | This PR adds:
- a `get_block_list()` utility method which returns a list of the blocks in a Transformers model (currently only added on T5). Block can be Modules or list/tuple of Modules (if a single transformer block is spread in several ModuleList like in XLM).
- a `spread_on_devices(devices: Optional[List] = None)... | {
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https://api.github.com/repos/huggingface/transformers/issues/3577 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3577/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3577/comments | https://api.github.com/repos/huggingface/transformers/issues/3577/events | https://github.com/huggingface/transformers/issues/3577 | 592,208,430 | MDU6SXNzdWU1OTIyMDg0MzA= | 3,577 | DistilBert not giving hidden states | {
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"@Ierezell I am facing same issue. How did you fix this issue? "
] | 1,585 | 1,590 | 1,585 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): DistilBert
Language I am using the model on (English, Chinese ...): Multilingual
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... | {
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https://api.github.com/repos/huggingface/transformers/issues/3576 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3576/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3576/comments | https://api.github.com/repos/huggingface/transformers/issues/3576/events | https://github.com/huggingface/transformers/issues/3576 | 592,194,682 | MDU6SXNzdWU1OTIxOTQ2ODI= | 3,576 | T5 fine tune for seq2seq generation | {
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"@patrickvonplaten ",
"Not yet :-) For translation you could try to create a `run_train.py` script using the following resources:\r\n\r\n- How to run `T5` for translation: https://github.com/huggingface/transformers/tree/master/examples/translation/t5\r\n- How to train `Bart` for summarization (should be very sim... | 1,585 | 1,620 | 1,587 | 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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"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"
] | 1,585 | 1,591 | 1,591 | NONE | null | # 🖥 Benchmarking `transformers`
## Benchmark
I am trying the Transformers library out on [QUAERO French Medical Corpus](https://quaerofrenchmed.limsi.fr/) NER-dataset (consisting of Medline titles and EMEA documents). Using Camembert-base 'out of the box' with default hyperparameters, I get an F1 measure of 85% ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3573 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3573/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3573/comments | https://api.github.com/repos/huggingface/transformers/issues/3573/events | https://github.com/huggingface/transformers/issues/3573 | 592,014,338 | MDU6SXNzdWU1OTIwMTQzMzg= | 3,573 | How can I use masked_lm_labels correctly? | {
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"Can any one help me\r\nQAQ",
"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"
] | 1,585 | 1,591 | 1,591 | 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/3572 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3572/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3572/comments | https://api.github.com/repos/huggingface/transformers/issues/3572/events | https://github.com/huggingface/transformers/issues/3572 | 591,982,426 | MDU6SXNzdWU1OTE5ODI0MjY= | 3,572 | BART run run_train.sh RuntimeError: expected device cuda:0 but got device cpu | {
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"I meet the same bug when i use bart as an embedding layer.\r\nHave u solve the problem?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"> I meet the same bug when i use bart as a... | 1,585 | 1,611 | 1,592 | NONE | null | # 🐛 Bug
## Information
Model I am using (BART):
Language I am using the model on CNN dailymail (English, Chinese ...):
The problem arises when using:
* [* ] the official example scripts: (give details below)
text summarization of bart, run the script run_train.sh, following the guideline
* [ ] my own mo... | {
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https://api.github.com/repos/huggingface/transformers/issues/3571 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3571/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3571/comments | https://api.github.com/repos/huggingface/transformers/issues/3571/events | https://github.com/huggingface/transformers/issues/3571 | 591,845,403 | MDU6SXNzdWU1OTE4NDU0MDM= | 3,571 | transformers pipeline in GCP cloud functions | {
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"You could cache the model once in your environment and then load it from there. Just point `from_pretrained` to the directory containing the model and configuration (or tokenizer file if loading the tokenizer) instead of the S3 link.",
"@LysandreJik Completely understood and right but this is different case here... | 1,585 | 1,661 | 1,589 | NONE | null | # ❓ Transformers pipeline in GCP
I am trying to use transformers pipeline in GCP cloud functions. While calling the function the downloading of model is happening everytime. How can we sort this issue.
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"@makaveli10 how to solve it ?"
] | 1,585 | 1,596 | 1,585 | NONE | null | I wanted to load model saved on disk but it keeps on throwing this error
File "train.py", line 1, in <module>
import config
File "/media/saurabh/D/code/bert_imdb_sentiment/src/config.py", line 14, in <module>
do_lower_case=True
File "/home/saurabh/anaconda3/lib/python3.7/site-packages/tran... | {
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https://api.github.com/repos/huggingface/transformers/issues/3569 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3569/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3569/comments | https://api.github.com/repos/huggingface/transformers/issues/3569/events | https://github.com/huggingface/transformers/issues/3569 | 591,795,687 | MDU6SXNzdWU1OTE3OTU2ODc= | 3,569 | Regarding distilbert-multilingual-uncased model | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,585 | 1,591 | 1,591 | NONE | null | I am using pretrained distilbert-multilingual-uncased model to get the embeddings of a sentence. I want to ask which layer would be great for taking the semantic embedding of a sentence | {
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https://api.github.com/repos/huggingface/transformers/issues/3568 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3568/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3568/comments | https://api.github.com/repos/huggingface/transformers/issues/3568/events | https://github.com/huggingface/transformers/pull/3568 | 591,768,866 | MDExOlB1bGxSZXF1ZXN0Mzk2ODczNzgy | 3,568 | Create README.md | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3568?src=pr&el=h1) Report\n> Merging [#3568](https://codecov.io/gh/huggingface/transformers/pull/3568?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b38d552a92a0a201c005afae0e1b861ae6de9ce0&el=desc) will **increase... | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | added documentation for our fine-tuned BERT model | {
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https://api.github.com/repos/huggingface/transformers/issues/3567 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3567/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3567/comments | https://api.github.com/repos/huggingface/transformers/issues/3567/events | https://github.com/huggingface/transformers/pull/3567 | 591,692,291 | MDExOlB1bGxSZXF1ZXN0Mzk2ODEyMTM4 | 3,567 | Add tiny-bert-bahasa-cased model card | {
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https://api.github.com/repos/huggingface/transformers/issues/3566 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3566/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3566/comments | https://api.github.com/repos/huggingface/transformers/issues/3566/events | https://github.com/huggingface/transformers/pull/3566 | 591,674,925 | MDExOlB1bGxSZXF1ZXN0Mzk2Nzk3ODk4 | 3,566 | BertJapaneseTokenizer accept options for mecab | {
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"@singletongue Please give your opinion.",
"Great! It looks good to me.",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3566?src=pr&el=h1) Report\n> Merging [#3566](https://codecov.io/gh/huggingface/transformers/pull/3566?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transfo... | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | Now we can pass `mecab_kwargs` to `BertJapaneseTokenizer.__init__` and set tokenizer more accurately.
Changes:
1. `BertJapaneseTokenizer.__init__` accepts `mecab_kwargs` keyword argument. It is directly passed to `MeCabTokenizer.__init__`
2. Now we can disable `normalize_text` in `MeCabTokenizer` through `mecab... | {
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https://api.github.com/repos/huggingface/transformers/issues/3565 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3565/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3565/comments | https://api.github.com/repos/huggingface/transformers/issues/3565/events | https://github.com/huggingface/transformers/issues/3565 | 591,672,207 | MDU6SXNzdWU1OTE2NzIyMDc= | 3,565 | Language model fine tuning using scibert as the base model | {
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"SciBert is a BERT model.\r\n\r\nPlease be more descriptive. Saying that features are not calculated correctly is not very helpful. Please describe the problem in full.",
"Hi @BramVanroy , following is the error stack\r\n```code\r\n04/01/2020 09:10:02 - INFO - __main__ - Training/evaluation parameters Namespace... | 1,585 | 1,588 | 1,588 | NONE | null | # 🐛 Bug
## Information
I am trying to finetune the Scibert model on Covid dataset. Features for the train data is not being calculated properly.
Model I am using (Bert, XLNet ...): allenai/scibert_scivocab_uncased
Language I am using the model on (English, Chinese ...): English
The problem arises when usin... | {
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"You can't set the ids, they are set automatically from the sentence piece model.\r\nBut (1) why are you using the T5Tokenizer for a Bart checkpoint and (2) why do you want to tweak the id?",
"(1) I used the `T5Tokenizer` in order to make a runnable example that did not require checking out my `mbart` branch.\r\n... | 1,585 | 1,651 | 1,591 | CONTRIBUTOR | null | I am unable to set bos_token_id=0 for a new SentencePiece tokenizer (MBART).
Here is what I'm doing?
```bash
wget https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/sentence.bpe.model
```
```python
from transformers import T5Tokenizer
vocab_file = 'sentence.bpe.model'
t2 = T5Toke... | {
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https://api.github.com/repos/huggingface/transformers/issues/3563 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3563/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3563/comments | https://api.github.com/repos/huggingface/transformers/issues/3563/events | https://github.com/huggingface/transformers/pull/3563 | 591,597,278 | MDExOlB1bGxSZXF1ZXN0Mzk2NzQxNDgz | 3,563 | update run_language_modeling.py for high efficiency in Multi GPUs | {
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https://api.github.com/repos/huggingface/transformers/issues/3562 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3562/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3562/comments | https://api.github.com/repos/huggingface/transformers/issues/3562/events | https://github.com/huggingface/transformers/issues/3562 | 591,595,629 | MDU6SXNzdWU1OTE1OTU2Mjk= | 3,562 | can not init tokenizers from third party model , on albert model | {
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"Hi, could you specify which version of `transformers` you're running?",
"I encountered the same problem when using Albert. @voidful \r\n```\r\nAutoTokenizer.from_pretrained('voidful/albert_chinese_xxlarge')\r\n```\r\nwill raise\r\n```\r\n04/04/2020 14:21:28 - INFO - Model name 'voidful/albert_chinese_xxlarge' no... | 1,585 | 1,627 | 1,586 | NONE | null | # 🐛 Bug
## Information
Model I am using (albert.):
Language I am using the model on (English, Chinese ...):
The problem arises when using:
* [ *] the official example scripts: (give details below)
follow the instructions on :
`https://huggingface.co/models`
such as use "voidful/albert_chinese_tin... | {
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"Hi, you could use model.eval() or load pre-tuned model and run against test set\r\n\t# Load a trained model and vocabulary that you have fine-tuned\r\n\tmodel = model_class.from_pretrained(output_dir)\r\n\ttokenizer = tokenizer_class.from_pretrained(output_dir)\r\nSee this post: https://mccormickml.com/2019/07/22/... | 1,585 | 1,586 | 1,586 | 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/3560 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3560/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3560/comments | https://api.github.com/repos/huggingface/transformers/issues/3560/events | https://github.com/huggingface/transformers/issues/3560 | 591,530,669 | MDU6SXNzdWU1OTE1MzA2Njk= | 3,560 | Mean reduce over last hidden state | {
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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"
] | 1,585 | 1,591 | 1,591 | NONE | null | One of the outputs of [TFBert](https://huggingface.co/transformers/model_doc/bert.html#transformers.TFBertModel.call) is the `last_hidden_state` which is a tensor of shape `(batch_size, sequence_length, hidden_size)`.
How someone could proceed to compute the `mean pooling` of the valid embeddings? I mean, as the `at... | {
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https://api.github.com/repos/huggingface/transformers/issues/3559 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3559/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3559/comments | https://api.github.com/repos/huggingface/transformers/issues/3559/events | https://github.com/huggingface/transformers/issues/3559 | 591,525,697 | MDU6SXNzdWU1OTE1MjU2OTc= | 3,559 | How to trace the BertForQuestionAnswering | {
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"I found a workaround solution, just to pass the complete inputs including `input_ids, token_type_ids and attention_mask` to the trace method and invoke forward along with those 3 inputs. \r\nBut it would be great to know how I can just pass in input_ids and token_type_ids",
"This issue has been automatically mar... | 1,585 | 1,591 | 1,591 | NONE | null | I followed the example [here](https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering) and want to convert BertForQuestionAnswering to TorchScript.
Here is my code
```
from transformers import BertTokenizer, BertForQuestionAnswering
import torch
tokenizer = BertTokenizer.from_pretrained... | {
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https://api.github.com/repos/huggingface/transformers/issues/3558 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3558/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3558/comments | https://api.github.com/repos/huggingface/transformers/issues/3558/events | https://github.com/huggingface/transformers/issues/3558 | 591,492,933 | MDU6SXNzdWU1OTE0OTI5MzM= | 3,558 | Metrics are coupled to the run_glue.py tasks. | {
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"As you say yourself, the core of the library is to provide you with models (and recently also pipelines and in the future even more exciting things) to build your own projects. The examples show you some implementations that are in themselves usable but that are by no means meant to be exhaustive for all problems.... | 1,585 | 1,591 | 1,591 | NONE | null | The metrics used for evaluating a run_glue task is coupled to the task itself (i.e. regression or classification). We wrote a `TrinarySentimentProcessor` which grabs a positive, neutral, or negative sentiment from a text, but we found that the simple accuracy measure was not the right one. We wanted to use log loss (cr... | {
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https://api.github.com/repos/huggingface/transformers/issues/3557 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3557/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3557/comments | https://api.github.com/repos/huggingface/transformers/issues/3557/events | https://github.com/huggingface/transformers/pull/3557 | 591,401,720 | MDExOlB1bGxSZXF1ZXN0Mzk2NTgwNDcy | 3,557 | Create model card | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3557?src=pr&el=h1) Report\n> Merging [#3557](https://codecov.io/gh/huggingface/transformers/pull/3557?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b38d552a92a0a201c005afae0e1b861ae6de9ce0&el=desc) will **increase... | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | Create model card for: distilbert-multi-finetuned-for-xqua-on-tydiqa | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3556?src=pr&el=h1) Report\n> Merging [#3556](https://codecov.io/gh/huggingface/transformers/pull/3556?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ae6834e028ecdf7fdbe886c1f86d0e02d5fef6f0&el=desc) will **not chan... | 1,585 | 1,585 | 1,585 | MEMBER | null | As first done by @sshleifer in #3488 , this PR puts a tiny T5 model on S3 to save testing time. | {
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https://api.github.com/repos/huggingface/transformers/issues/3555 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3555/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3555/comments | https://api.github.com/repos/huggingface/transformers/issues/3555/events | https://github.com/huggingface/transformers/issues/3555 | 591,268,337 | MDU6SXNzdWU1OTEyNjgzMzc= | 3,555 | T5 for summarization: pipeline x T5ForConditionalGeneration different results | {
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"Hi @renatoviolin,\r\n\r\nthe T5 pipeline uses special input arguments for the `generate()` function that have been shown to work well for summarization. If you take a look at `task_specific_params` and under `summarization` in T5's config: https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-config.json. Yo... | 1,585 | 1,585 | 1,585 | NONE | null | I've been doing experiments with text summarization and got some different results between pipeline and T5ForConditionalGeneration.
First I use the model.generate() to generate the resume. It runs very fast (even on CPU) and give poors results.
Second I use the pipeline, passing the same model I build on the firs... | {
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https://api.github.com/repos/huggingface/transformers/issues/3554 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3554/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3554/comments | https://api.github.com/repos/huggingface/transformers/issues/3554/events | https://github.com/huggingface/transformers/issues/3554 | 591,234,777 | MDU6SXNzdWU1OTEyMzQ3Nzc= | 3,554 | resize_token_embeddings error for Transformer-XL | {
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"Hi @vsieplus ,\r\n\r\nThis is a known bug and sadly we don't have a solution for this now. TransfoXLLMHead uses adaptive weight embeddings which makes it not very easy to implement this function. Should be implemented in the long run though - I will note it down. @thomwolf @LysandreJik ",
"@patrickvonplaten Does... | 1,585 | 1,699 | 1,591 | NONE | null | # 🐛 Bug
## Information
Model I am using : Transformer-XL
Language I am using the model on : English
The problem arises when using:
* [ ] my own modified scripts: a fine-tuning script for TransfoXLLMHeadModel
## To reproduce
The following code aims to add two new tokens to the vocabulary, 'wug' and '... | {
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"Hi @dhecloud, \r\n\r\nThanks for you issue :-) \r\nDoes the model still work fine? ",
"> Hi @dhecloud,\r\n> \r\n> Thanks for you issue :-)\r\n> Does the model still work fine?\r\n\r\nHi, thanks for your reply.\r\nUsing the examples provided in the doc, the model works fine. \r\nBefore i used `T5WithLMHeadModel` ... | 1,585 | 1,706 | 1,588 | NONE | null | # 🐛 Bug
## Information
Model I am using: T5
## To reproduce
```
model, info = T5ForConditionalGeneration.from_pretrained('t5-small',output_loading_info=True)
```
info is
`
{'missing_keys': ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight'], 'unexpected_keys': ['encoder.b... | {
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https://api.github.com/repos/huggingface/transformers/issues/3552 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3552/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3552/comments | https://api.github.com/repos/huggingface/transformers/issues/3552/events | https://github.com/huggingface/transformers/pull/3552 | 591,146,676 | MDExOlB1bGxSZXF1ZXN0Mzk2MzY2NjUz | 3,552 | Update README.md | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3552?src=pr&el=h1) Report\n> Merging [#3552](https://codecov.io/gh/huggingface/transformers/pull/3552?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/83d1fbcff608f84a27234e20d6531b4404dc059e&el=desc) will **decrease... | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | - Show that the last uploaded version was trained on more data (custom_license files) | {
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https://api.github.com/repos/huggingface/transformers/issues/3551 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3551/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3551/comments | https://api.github.com/repos/huggingface/transformers/issues/3551/events | https://github.com/huggingface/transformers/issues/3551 | 591,138,598 | MDU6SXNzdWU1OTExMzg1OTg= | 3,551 | Recommended preprocessing steps for english sentences in GPT2 | {
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"Just pre-process them as you would for any other NLP task. This may include: normalisation of punctuation, dealing with HTML/XML, white-space normalisation, language verification. Something that people used to do in the age of word2vec is creating new entities for specific items. For instance, any number could be ... | 1,585 | 1,596 | 1,585 | NONE | null | # ❓ Questions & Help
To run inferences in english sentences... I'm not really sure what preprocessing steps I need to do before sending the tokenized text to gpt-2 in order to get the predictions. Any advice?
How to handle non-english words that can be found in an english sentences? Extra punctuation, space, new ... | {
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But some integration tests for T5 would be very nice, to be sure that changes in the future will not break T5.
This PR adds hard-coded integration tests, where the input for summarization is co... | {
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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"
] | 1,585 | 1,591 | 1,591 | NONE | null | nlp = pipeline('fill-mask',
# model=args.bert_model_path,
# config=args.bert_config_path,
# tokenizer=args.bert_model_dir
model = '../data/bert_models/chinese_finetuned_lm/pytorch_model.bin',
config = '../data/bert_mod... | {
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"What do you think @mfuntowicz?",
"[Related issue ](https://github.com/huggingface/transformers/issues/2488)",
"Actually, I realized that instead of `index`, even better would be the word piece `offsets`, similar to that returned by `tokenizer.encode` from the huggingface [tokenizers package](https://github.com... | 1,585 | 1,589 | 1,589 | CONTRIBUTOR | null | Using `NerPipeline`, I want to be able to input a string (sequence of tokens), and extract *entity groups*, where an entity group is a contiguous series of tokens, having the same *entity type*.
**Example:**
For the ner code below:
```
from transformers import pipeline, AutoModelForTokenClassification, AutoTo... | {
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https://api.github.com/repos/huggingface/transformers/issues/3547 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3547/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3547/comments | https://api.github.com/repos/huggingface/transformers/issues/3547/events | https://github.com/huggingface/transformers/pull/3547 | 590,927,217 | MDExOlB1bGxSZXF1ZXN0Mzk2MTgyOTQz | 3,547 | [T5, TF 2.2] change tf t5 argument naming | {
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As shown in #3539, in TF 2.2 errors occur due to the naming of the first argument in the `keras.layer.__call__` function of TF T5.
Previously `decoder_input_ids` was used as the first argument - which did not produce any errors in TF <= 2.1. In TF 2.2, it produces the error:
```
/usr/local/lib/pyth... | {
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"Update\r\n\r\nOn a regular CPU machine (not using GPUs), here's the benchmarks you'll need to load these models into memory and run them:\r\n\r\nBart-large-cnn (default): 5GB of RAM\r\nT5-small: 14GB\r\nT5-base: 20GB\r\nT5-large: 31GB\r\nT5-3b: 68GB\r\nT5-11b: 120GB\r\n\r\nSo, I was initially wrong: **You can run ... | 1,585 | 1,585 | 1,585 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...):
T5 11B param
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 ... | {
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This PR fixes this. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3544?src=pr&el=h1) Report\n> Merging [#3544](https://codecov.io/gh/huggingface/transformers/pull/3544?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e5c393dcebf42eaec9c1e1d619b5a7788a2d7c65&el=desc) will **increase... | 1,585 | 1,586 | 1,586 | CONTRIBUTOR | null | - add lightning to `examples/requirements.txt`
- first lightning unittest! | {
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"Feels like it could be reimplemented in a few lines of code – do we need to add a new dependency for this?",
"I copy pasted it. Would love to understand more about the adding a dependency vs maintaining code tradeoff!",
"I think that's a great addition! Trying to keep the tests short is very important I think ... | 1,585 | 1,588 | 1,588 | CONTRIBUTOR | null | This is a simple way to make sure code doesn't get slower over time. Since it is a new dependency, I wanted to show a tiny PR before I use it more. | {
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"Have you checked whether the Bioasq format suits the Huggingface interface/format?\r\nBecause the Bioasq does not natively support a reading comprehension task as defined in SquaD",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity... | 1,585 | 1,593 | 1,593 | NONE | null | # 🐛 Bug
I am using Bert on BioASQ question answering dataset using the script run_squad.py from Huggingface Transformers.
##To reproduce
Steps to reproduce the behavior:
1.I am using run_squad.py https://github.com/huggingface/transformers/blob/master/examples/run_squad.py from Huggingface Transformers f... | {
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"This should be added to the initializer, not the forward method. Also, the correct parameter is `output_hidden_states`. You can also be more explicit by changing the config:\r\n\r\n```python\r\nmodel_config = AutoConfig.from_pretrained('bert-base-uncased', output_hidden_states=True)\r\nself.bert = AutoModel.from_p... | 1,585 | 1,585 | 1,585 | NONE | null | I am getting an error such as forward () got an unexpected keyword argument 'output_all_encoded_layers', how can I fix it?
class BertBinaryClassifier(nn.Module):
def __init__(self, dropout=0.1):
super(BertBinaryClassifier, self).__init__()
self.bert = BertModel.from_pretrained('bert-base-unc... | {
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"This was fixed yesterday, can you try installing from master?",
"verified that it's fixed in master. However, the bug remains when installing from pip. ",
"We'll ship a new pip release soon, but in any case we'll try to update the code so that the TF script can run with an immutable `InputExample` (as discusse... | 1,585 | 1,694 | 1,693 | NONE | null | # 🐛 Bug
## Information
Model I am using: bert-base-cased
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:
* [x] an ... | {
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"Hi @cformosa, \r\n\r\nThanks for posting this bug. This seems to be related to the new TF 2.2 release. \r\nCould you instead use TF2.1 for the moment:\r\n\r\n```\r\n!pip install transformers\r\n!pip install tensorflow==2.1\r\nfrom transformers import pipeline\r\n\r\nsummarizer = pipeline(\"summarization\", model=\... | 1,585 | 1,585 | 1,585 | NONE | null | # 🐛 Bug
T5 summarization code in pipelines.py file gives an error.
## Information
Model I am using: T5
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
pipelines.py official documentation example at line 1146
```
# use t5 in tf
summarizer = pipeline("summ... | {
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https://api.github.com/repos/huggingface/transformers/issues/3537 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3537/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3537/comments | https://api.github.com/repos/huggingface/transformers/issues/3537/events | https://github.com/huggingface/transformers/pull/3537 | 590,544,482 | MDExOlB1bGxSZXF1ZXN0Mzk1ODU5MDAx | 3,537 | Add model cards | {
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https://api.github.com/repos/huggingface/transformers/issues/3536 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3536/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3536/comments | https://api.github.com/repos/huggingface/transformers/issues/3536/events | https://github.com/huggingface/transformers/pull/3536 | 590,535,333 | MDExOlB1bGxSZXF1ZXN0Mzk1ODUxMzQy | 3,536 | [Encoder-Decoder] Force models outputs to always have batch_size as their first dim | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3536?src=pr&el=h1) Report\n> Merging [#3536](https://codecov.io/gh/huggingface/transformers/pull/3536?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6f5a12a5833d1e3783e4b8a42cb556b64085745e&el=desc) will **decrease... | 1,585 | 1,585 | 1,585 | MEMBER | null | This PR remove the hard-coded variable `encoder_outputs_batch_dim_idx` from Bart and T5 by transposing BART's `encoder_outputs` dimensions before returning them.
**Reasons:**
- When adding more encoder-decoder models, we would always force the newly added model to have this variable
- When adding the modeling_enco... | {
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https://api.github.com/repos/huggingface/transformers/issues/3535 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3535/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3535/comments | https://api.github.com/repos/huggingface/transformers/issues/3535/events | https://github.com/huggingface/transformers/issues/3535 | 590,533,973 | MDU6SXNzdWU1OTA1MzM5NzM= | 3,535 | Error on fine-tuning XLM like model on SQUaD like dataset | {
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"Hi, any follow-up in this thread? I have received the same error with yours, but a different parameter 'model_name_or_path = xlm-mlm-tlm-xnli15-1024' is used in my experiment.",
"I had to revert all the way to 2.5.1 to get this to work (xlnet-base fine-tuning on SQuAD 1.1), FWIW, so it's been broken for a bit..... | 1,585 | 1,604 | 1,602 | CONTRIBUTOR | null | # 🐛 Bug
## Information
I am trying to fine-tune the model [xlm-mlm-100-1280](https://huggingface.co/xlm-mlm-100-1280) on SQuAD v1 dataset like (tydiQA) with the script provided for this task (transformers/examples/run_squad.py) and I get the following error:
```python
Epoch: 0% 0/5 [00:00<?, ?it/s]
Iterat... | {
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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"
] | 1,585 | 1,591 | 1,591 | NONE | null | Hi,
I am trying to replicate the pretraining process mentioned in this blog post: https://huggingface.co/blog/how-to-train
I have time-restricted access to the GPU I'm currently working on and so I wanted to know how to save the checkpoint and resume the pretraining process from the latest checkpoint.
Thanks! | {
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https://api.github.com/repos/huggingface/transformers/issues/3533 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3533/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3533/comments | https://api.github.com/repos/huggingface/transformers/issues/3533/events | https://github.com/huggingface/transformers/issues/3533 | 590,339,617 | MDU6SXNzdWU1OTAzMzk2MTc= | 3,533 | Error when training with distributed training on 4/8 Nvidia v100. | {
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"Out of curiosity, does the same issue arise without fp16?",
"@BramVanroy Yes.",
"Someone's help, please?",
"Have look here: https://github.com/pytorch/pytorch/issues/22436",
"@BramVanroy I'm sorry, the repo is working fine with distributed training. I found the error comes from adding special tokens:\r\n``... | 1,585 | 1,585 | 1,585 | NONE | null | # 🐛 Bug
## Information
I'm getting the following error while training the official implementation in [examples/run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py) on [WikiText-2 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-la... | {
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https://api.github.com/repos/huggingface/transformers/issues/3532 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3532/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3532/comments | https://api.github.com/repos/huggingface/transformers/issues/3532/events | https://github.com/huggingface/transformers/pull/3532 | 590,327,634 | MDExOlB1bGxSZXF1ZXN0Mzk1NjgxNTk2 | 3,532 | Resizing embedding matrix before sending it to the optimizer. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3532?src=pr&el=h1) Report\n> Merging [#3532](https://codecov.io/gh/huggingface/transformers/pull/3532?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d38bbb225f7b847e8be4e969cb9b40e7e4d798a6&el=desc) will **decrease... | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | Hi there,
This is a minor bug when fine-tuning a pre-trained language model with a larger vocabulary using the run_lm_finetuning.py script.
Nothing major but I spent a couple of hours trying to figure out why my token embeddings were not being updated with their corresponding gradient :)
This bug will rise when ... | {
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"Whats also strange, the PyTorch version list the labels as text, e.g: \"B-ORG, I-LOC, [...] in the Model Config Ouput but the TF-version list them like this bellow. Is this okay?\r\n\r\n```\r\n\"id2label\": {\r\n \"0\": \"LABEL_0\",\r\n \"1\": \"LABEL_1\",\r\n \"2\": \"LABEL_2\",\r\n \"3\": \"LABEL_3\"... | 1,585 | 1,702 | 1,592 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): bert-base-multilingual-cased
Language I am using the model on (English, Chinese ...): German
The problem arises when using:
* [X] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3529?src=pr&el=h1) Report\n> Merging [#3529](https://codecov.io/gh/huggingface/transformers/pull/3529?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/75ec6c9e3a7de6cc3e2920f3bb531e7c840b8ada&el=desc) will **increase... | 1,585 | 1,585 | 1,585 | MEMBER | null | Add better explanation to T5 `lm_labels` dosctring. | {
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https://api.github.com/repos/huggingface/transformers/issues/3528 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3528/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3528/comments | https://api.github.com/repos/huggingface/transformers/issues/3528/events | https://github.com/huggingface/transformers/issues/3528 | 590,206,547 | MDU6SXNzdWU1OTAyMDY1NDc= | 3,528 | Unexpected ZeroDivisionError when calling model.prune_heads | {
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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",
"Found the solution to this. When you try to prune all the attention heads in a layer, you will run into this error. This is why it some... | 1,585 | 1,623 | 1,591 | NONE | null | # 🐛 Bug
Traceback (most recent call last):
File "/Users/user/anaconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/Users/user/anaconda3/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/Users/user/Desktop/org/text_models_branche... | {
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https://api.github.com/repos/huggingface/transformers/issues/3527 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3527/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3527/comments | https://api.github.com/repos/huggingface/transformers/issues/3527/events | https://github.com/huggingface/transformers/issues/3527 | 590,174,338 | MDU6SXNzdWU1OTAxNzQzMzg= | 3,527 | Bart.generate requires config.output_past=True | {
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"Bart is a encoder-decoder model. So it should be rather used as translating one sequence to another one. This means that the generation method expects `input_ids` and creates `decoder_input_ids`. \r\n\r\nMaybe you can take a look at this: https://sshleifer.github.io/blog_v2/jupyter/2020/03/12/bart.html",
"I thin... | 1,585 | 1,586 | 1,586 | NONE | null | Is there a way to generate using pre-trained BART like one in
https://huggingface.co/blog/how-to-generate
I am currently using BART for a generation task but finetuning it
I was wondering if it's possible to see generation result from pre-trained BART | {
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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"
] | 1,585 | 1,591 | 1,591 | NONE | null | # 🐛 Bug
## Information
Model I am using (FlauBert ...):
Language I am using the model on (French ...):
The problem arises when using:
* [x] the official example scripts: (give details below)
the script "run_glue" (I did not modified anything) I took it as it is
The tasks I am working on is:
* [x] a... | {
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https://api.github.com/repos/huggingface/transformers/issues/3525 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3525/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3525/comments | https://api.github.com/repos/huggingface/transformers/issues/3525/events | https://github.com/huggingface/transformers/issues/3525 | 590,063,584 | MDU6SXNzdWU1OTAwNjM1ODQ= | 3,525 | Issue loading custom tokenizer for fine-tuning gpt2 | {
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"Can you post a sample code?",
"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"
] | 1,585 | 1,591 | 1,591 | NONE | null | I'm trying to fine-tune gpt2 with a custom tokenizer. It was working fine just over 10 days ago, with --tokenizer_name=/path to vocab and merges folder/ and now it cannot load, asking to check if it's a correct model identifier or contains a config.json file. As if instead of a tokenizer it is now trying to load a mo... | {
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https://api.github.com/repos/huggingface/transformers/issues/3524 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3524/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3524/comments | https://api.github.com/repos/huggingface/transformers/issues/3524/events | https://github.com/huggingface/transformers/pull/3524 | 589,975,629 | MDExOlB1bGxSZXF1ZXN0Mzk1MzkwODcw | 3,524 | Add shoarora/electra and alectra model cards | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3524?src=pr&el=h1) Report\n> Merging [#3524](https://codecov.io/gh/huggingface/transformers/pull/3524?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/33ef7002e17fe42b276dc6d36c07a3c39b1f09ed&el=desc) will **decrease... | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | Add model cards for recently uploaded models:
- shoarora/electra-small-owt (BERT)
- shoarora/alectra-small-owt (ALBERT) | {
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"I found a mistake that in GPT2LMHeadModel, the label is shifted, however, I shift it again when preparing the batch."
] | 1,585 | 1,585 | 1,585 | 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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we direct you to Stack Overflow (SO) where a whole community of PyTorch and
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- [x] Add test | {
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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"
] | 1,585 | 1,595 | 1,595 | NONE | null | Still a work in progress but the contextual embeddings line up with the pytorch version so this is roughly at parity with jax-bert
TODO (mostly notes to myself):
- [x] Add `save_pretrained`
- [ ] Make `from_pretrained` work with names
- [ ] Add dropout at training time, pass through training flag
- [ ] Make sur... | {
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https://api.github.com/repos/huggingface/transformers/issues/3519 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3519/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3519/comments | https://api.github.com/repos/huggingface/transformers/issues/3519/events | https://github.com/huggingface/transformers/pull/3519 | 589,871,846 | MDExOlB1bGxSZXF1ZXN0Mzk1MzEwNTgz | 3,519 | Resizing embedding matrix before sending it to the optimizer. | {
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"Since I just swapped some lines, I guess this code quality check got activated after this file came up in the repo..! 😬",
"Hi Nicolas,\r\nYou have to install the code style tools and run `make style` and `make quality` on your PR.\r\nCheck the contributing guide for the details: https://github.com/huggingface/t... | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | This bug will rise when you add new token types in the Tokenizer.
Since the resizing was done **after** passing the params to the optimizer, the wrong set of params for the embedding table were optimized. | {
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https://api.github.com/repos/huggingface/transformers/issues/3518 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3518/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3518/comments | https://api.github.com/repos/huggingface/transformers/issues/3518/events | https://github.com/huggingface/transformers/issues/3518 | 589,840,657 | MDU6SXNzdWU1ODk4NDA2NTc= | 3,518 | Argument “never_split” not working on bert tokenizer | {
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"Does this problem arise with the fast tokenizer too? Can you try both:\r\n\r\n```python\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased', never_split=['lol'], use_fast=True)\r\n# and\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased', never_split=['lol'], use_fast=False)\r\n```",
">... | 1,585 | 1,591 | 1,591 | NONE | null | I used the ```never_split``` option and tried to retain some tokens. But the tokenizer still divide them into wordpieces.
```
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', never_split=['lol'])
tokenizer.tokenize("lol That's funny")
['lo', '##l', 'that', "'", 's', 'funny']
```
**A link to origi... | {
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https://api.github.com/repos/huggingface/transformers/issues/3517 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3517/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3517/comments | https://api.github.com/repos/huggingface/transformers/issues/3517/events | https://github.com/huggingface/transformers/pull/3517 | 589,834,441 | MDExOlB1bGxSZXF1ZXN0Mzk1MjgyODg3 | 3,517 | [Tokenization] fix edge case for bert tokenization | {
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"@mfuntowicz @n1t0 @LysandreJik - could you check? :-) ",
"Does this mean that `batch_encode_plus` is supposed to handle \"pre-tokenized\" inputs? I thought this was something introduced by https://github.com/huggingface/transformers/pull/3185 with a specific flag `is_pretokenized` (cc @mfuntowicz)",
"# [Codec... | 1,585 | 1,586 | 1,586 | MEMBER | null | This PR fixes #3502 .
The reason why the tests fail in #3502 is because of an edge case.
If the input to `tokenizer.batch_encode_plus()` consists of a tokenized string that results in a list of exactly two strings (``[[16], [.]]`` in issue #3502) then it is treated as a pair of input sequences (=> [CLS] input_s... | {
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https://api.github.com/repos/huggingface/transformers/issues/3516 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3516/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3516/comments | https://api.github.com/repos/huggingface/transformers/issues/3516/events | https://github.com/huggingface/transformers/pull/3516 | 589,808,368 | MDExOlB1bGxSZXF1ZXN0Mzk1MjYzNzc0 | 3,516 | [Docs] examples/summarization/bart: Simplify CNN/DM preprocessing steps | {
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"Merging to unblock patrick. "
] | 1,585 | 1,585 | 1,585 | CONTRIBUTOR | null | I added the preprocessed data in S3.
Evidence that it is the correct size:
 | {
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https://api.github.com/repos/huggingface/transformers/issues/3515 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3515/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3515/comments | https://api.github.com/repos/huggingface/transformers/issues/3515/events | https://github.com/huggingface/transformers/issues/3515 | 589,795,657 | MDU6SXNzdWU1ODk3OTU2NTc= | 3,515 | Isort installed from github branch does not correspond to circle ci isort | {
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"Looking at the CI config, it should install the correct version, though. Not sure what I am missing here.\r\n\r\nhttps://github.com/huggingface/transformers/blob/e5c393dcebf42eaec9c1e1d619b5a7788a2d7c65/.circleci/config.yml#L89",
"Hi @BramVanroy, \r\n\r\nThanks for your answer :-) \r\nI get the feeling that it's... | 1,585 | 1,585 | 1,585 | MEMBER | null | # 🐛 Bug
## Information
Installing isort via:
`$ pip install -U git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort`
does not correspond to the circle ci version anymore, so that `make style` formatting leads to circle-ci make the code quality test fail.
... | {
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https://api.github.com/repos/huggingface/transformers/issues/3514 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3514/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3514/comments | https://api.github.com/repos/huggingface/transformers/issues/3514/events | https://github.com/huggingface/transformers/pull/3514 | 589,788,520 | MDExOlB1bGxSZXF1ZXN0Mzk1MjQ4OTU1 | 3,514 | [Examples] Clean summarization and translation example testing files for T5 and Bart | {
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https://api.github.com/repos/huggingface/transformers/issues/3513 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/3513/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/3513/comments | https://api.github.com/repos/huggingface/transformers/issues/3513/events | https://github.com/huggingface/transformers/issues/3513 | 589,781,536 | MDU6SXNzdWU1ODk3ODE1MzY= | 3,513 | Adding mbart-large-cc25 | {
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"This is a Work in progress but still a few weeks out :)",
"Hi @sshleifer , additional (perhaps bug, or document bug) related to this issue: \r\n\r\nThis model page suggests that we can load mBart-cc25 : \r\nhttps://huggingface.co/facebook/mbart-large-cc25\r\n\r\nHowever, using the instructed command with the new... | 1,585 | 1,594 | 1,594 | CONTRIBUTOR | null | # 🌟 New model addition
Multilingual BART model implemented in fairseq introduced by FAIR
## Model description
This issue is to request adding mBART model existing as a part of fairseq lib.
[Link to the fairseq description of the model](https://github.com/pytorch/fairseq/tree/master/examples/mbart
)
[Link to... | {
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