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https://api.github.com/repos/huggingface/transformers/issues/5520 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5520/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5520/comments | https://api.github.com/repos/huggingface/transformers/issues/5520/events | https://github.com/huggingface/transformers/issues/5520 | 650,950,830 | MDU6SXNzdWU2NTA5NTA4MzA= | 5,520 | AdamW step device error | {
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```
class M(nn.Module):
def __init__(self, ...):
self.A = nn.Parameter(...)
self.B = nn.Parameter(...)
self.C = torch.einsum(..., self.A, self.B)
def forward(self, D):
return func(self.C.to(D.device), D)
```
However, I'm getting the following error when training this mo... | {
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"I just noticed the same.\r\n\r\nIt seems that you have to include the `labels` parameter as well to get the predictions for your `decoder_input_ids`. You didn't have to do this in versions prior to 3.0, so it appears as if something has changed so that it's required now.\r\n\r\nMaybe @sshleifer knows since I thin... | 1,593 | 1,594 | 1,594 | NONE | null | # โ 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/5518 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5518/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5518/comments | https://api.github.com/repos/huggingface/transformers/issues/5518/events | https://github.com/huggingface/transformers/pull/5518 | 650,911,929 | MDExOlB1bGxSZXF1ZXN0NDQ0Mjk4NzI3 | 5,518 | Make T5 compatible with ONNX | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5518?src=pr&el=h1) Report\n> Merging [#5518](https://codecov.io/gh/huggingface/transformers/pull/5518?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/58cca47c16149e43d1b516623d59e3c5d97f695e&el=desc) will **decrease... | 1,593 | 1,603 | 1,594 | CONTRIBUTOR | null | This is a small PR to make T5 exportable to ONNX with any op>9. It addresses an issue outlined in #5075 where T5 would not export to ONNX. In order to make it exportable, 2 changes are made:
- A torch.einsum is replaced with a tensor multiplication in 96d0ec7 since onnx does not currently support this notation
- Deco... | {
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https://api.github.com/repos/huggingface/transformers/issues/5517 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5517/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5517/comments | https://api.github.com/repos/huggingface/transformers/issues/5517/events | https://github.com/huggingface/transformers/issues/5517 | 650,880,601 | MDU6SXNzdWU2NTA4ODA2MDE= | 5,517 | getting different model result from tokenizer vs tokenizer.encode function | {
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I am using gpt2 model to encode sentences. I am confused between tokenizer.encode vs tokenizer :
If I am using tokenizer.encode:
```
sentence = 'checking single sentences'
from transformers import GPT2Tokenizer, GPT2Model
import torch
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-m... | {
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- Creation of a DialoguePipeline to process Conversations using the history context
- Integration tests for DialoguePipeline using `microsoft/DialoGPT-medium` | {
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https://api.github.com/repos/huggingface/transformers/issues/5514 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5514/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5514/comments | https://api.github.com/repos/huggingface/transformers/issues/5514/events | https://github.com/huggingface/transformers/pull/5514 | 650,859,890 | MDExOlB1bGxSZXF1ZXN0NDQ0MjYyNDQ2 | 5,514 | added model card for ukr-roberta-base | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5514?src=pr&el=h1) Report\n> Merging [#5514](https://codecov.io/gh/huggingface/transformers/pull/5514?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/58cca47c16149e43d1b516623d59e3c5d97f695e&el=desc) will **decrease... | 1,593 | 1,594 | 1,594 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/5513 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5513/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5513/comments | https://api.github.com/repos/huggingface/transformers/issues/5513/events | https://github.com/huggingface/transformers/issues/5513 | 650,852,405 | MDU6SXNzdWU2NTA4NTI0MDU= | 5,513 | Fail in some tests (with detailed description) | {
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"It seems most of the tests that fail are due to `CUDA error: out of memory`?",
"> It seems most of the tests that fail are due to `CUDA error: out of memory`?\r\n\r\nYes. As the message shows, they are due to `CUDA error: out of memory`. \r\nBut I am sure that **no** other processes are using CUDA **before an... | 1,593 | 1,599 | 1,599 | NONE | null | # โ Questions & Help
I have followed the README to install transformers.
However, there are some failures in the test. (mainly about multigpu_data_paralle_forward)
```
======================================================================================================================== short test summary info ===... | {
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"@sshleifer Hope you are doing well. sorry for the delay, I have created the PR for some of the related issues which were mentioned in #5057.\r\n> 1. They never use Cuda or fp16, even if they are available.\r\n\r\nI have some doubts which had encountered when making this PR\r\n\r\n---\r\n**1.** As you had said ther... | 1,593 | 1,598 | 1,598 | CONTRIBUTOR | null | The tests in examples didn't use the Cuda or fp16 even if they were available.
- The text classification example (`run_glue.py`) didn't use the fp16 even if it was available but
the device was take based on the availability(Cuda/CPU).
- The language-modeling example (`run_language_modeling.py`) was having `--no_... | {
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```
In [27]: encoding = tokenizer(text_batch, return_tensors='pt', padding=True, truncation=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-27... | {
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https://api.github.com/repos/huggingface/transformers/issues/5510 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5510/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5510/comments | https://api.github.com/repos/huggingface/transformers/issues/5510/events | https://github.com/huggingface/transformers/pull/5510 | 650,817,931 | MDExOlB1bGxSZXF1ZXN0NDQ0MjMyNDg5 | 5,510 | Fix typo in training | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5510?src=pr&el=h1) Report\n> Merging [#5510](https://codecov.io/gh/huggingface/transformers/pull/5510?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/58cca47c16149e43d1b516623d59e3c5d97f695e&el=desc) will **decrease... | 1,593 | 1,594 | 1,594 | CONTRIBUTOR | null | Fixes a small typo in the code example.
Ref code: https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py#L1211 | {
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https://api.github.com/repos/huggingface/transformers/issues/5509 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5509/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5509/comments | https://api.github.com/repos/huggingface/transformers/issues/5509/events | https://github.com/huggingface/transformers/issues/5509 | 650,792,768 | MDU6SXNzdWU2NTA3OTI3Njg= | 5,509 | TPU Trainer memory leak and memory requirements | {
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"Hi @misrasaurabh1 I have also faced this issue. One immediate solution is to use lazy loading dataset, as xla by default loads the dataset in all processes. Something like `nlp` can really help here. as posted by @thomwolf here https://gist.github.com/thomwolf/13ca2b2b172b2d17ac66685aa2eeba62\r\n\r\n`nlp` can lo... | 1,593 | 1,602 | 1,602 | CONTRIBUTOR | null | # ๐ Bug
The TPU Trainer for Pytorch is extremely memory inefficient as it eats up lot of CPU memory while training. Moreover the training pipeline has a memory leak somewhere that leads to increased memory utilization as the training proceeds.
## Information
I am using a huge machine (n1-highmem-32) with 208 GB o... | {
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https://api.github.com/repos/huggingface/transformers/issues/5508 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5508/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5508/comments | https://api.github.com/repos/huggingface/transformers/issues/5508/events | https://github.com/huggingface/transformers/issues/5508 | 650,781,481 | MDU6SXNzdWU2NTA3ODE0ODE= | 5,508 | T5 Masking: | {
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"`min_length` corresponds to the minimum number of tokens, not words.\r\n\r\nFeel free to reopen if this does not answer your question."
] | 1,593 | 1,594 | 1,594 | NONE | null | While this works, the outputs are not according to what is specified in "model.generate."
```
# tokenizer = T5Tokenizer.from_pretrained(model_name)
# model = T5ForConditionalGeneration.from_pretrained(model_name)
# # Input text
# text = 'This <extra_id_0> sentence. </s>'
# encoded = tokenizer.encode_plus(te... | {
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https://api.github.com/repos/huggingface/transformers/issues/5507 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5507/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5507/comments | https://api.github.com/repos/huggingface/transformers/issues/5507/events | https://github.com/huggingface/transformers/issues/5507 | 650,776,509 | MDU6SXNzdWU2NTA3NzY1MDk= | 5,507 | What's the correct way to use add_prefix_space for the fast RoBERTa tokenizer in 3.0.0/3.0.1? | {
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"Thanks for flagging, the docstrings were indeed very confusing!\r\nI hope the new doc introduced in #5559 is clearer and solves your problem. Please reopen if needed.",
"Thanks for the clarification in the new doc! Does it mean RoBERTa was pre-trained **without** this add_prefix_space behavior? In other words, i... | 1,593 | 1,604 | 1,594 | CONTRIBUTOR | null | The documentation says we should pass this flag to **the encoding methods**.
https://github.com/huggingface/transformers/blob/58cca47c16149e43d1b516623d59e3c5d97f695e/src/transformers/tokenization_roberta.py#L259-L260
However, the encoding methods don't take such an argument, and passing it in actually causes an ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5506 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5506/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5506/comments | https://api.github.com/repos/huggingface/transformers/issues/5506/events | https://github.com/huggingface/transformers/issues/5506 | 650,756,275 | MDU6SXNzdWU2NTA3NTYyNzU= | 5,506 | Why is `encoder_extended_attention_mask = None` when `config.is_decoder == False` | {
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"The `encoder_attention_mask` is only relevant if BERT is uses as a Encoder-Decoder model using the `EncoderDecoderModel` wrapper class. In this case the decoder should be able to accept an `encoder_attention_mask` for its cross-attention layers. \r\n\r\nIn all other cases this mask is not relevant and should be se... | 1,593 | 1,594 | 1,594 | NONE | null | Potential Bug(?)
Reading the codebase I see that attention masks are ignored for many of the pretrained model configs such as `'bert-base-uncased'`. We can see [here](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py#L743) that the attention mask is simply cleared out. Is this... | {
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https://api.github.com/repos/huggingface/transformers/issues/5505 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5505/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5505/comments | https://api.github.com/repos/huggingface/transformers/issues/5505/events | https://github.com/huggingface/transformers/issues/5505 | 650,747,379 | MDU6SXNzdWU2NTA3NDczNzk= | 5,505 | 3.0.1 BertTokenizer batch_encode_plus() shows warnings "Truncation was not explicitely activated but `max_length` is provided a specific value" | {
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"same problem here",
"same problem here",
"I bumped the same problem. After a plenty of warning messages my colab stuck and web-page didn't answer. As a temporary solution I installed previous version of transformers library ```!pip install transformers==3.0.0``` and evething with ```BertTokenizer.batch_encode_... | 1,593 | 1,597 | 1,594 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): I'm using Transformers 3.0.1 and the BERT model to do two-sentence NLI-style classification.
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ ] the official example scripts: (give details below)... | {
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https://api.github.com/repos/huggingface/transformers/issues/5504 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5504/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5504/comments | https://api.github.com/repos/huggingface/transformers/issues/5504/events | https://github.com/huggingface/transformers/issues/5504 | 650,744,942 | MDU6SXNzdWU2NTA3NDQ5NDI= | 5,504 | Write With Transformers | {
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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"
] | 1,593 | 1,599 | 1,599 | NONE | null | # ๐ Bug
## Information
Model: XLNet
Outputs are extremely unusual, and I suspect there is an issue.
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"Hi @santhoshkolloju what is the xla and pytorch version ?",
"Pytorch - 1.7.0a0+542ac74\r\ntorch_xla - 1.6+30b65e9\r\nThe setup file is taken from \r\nVERSION = \"nightly\" #@param [\"1.5\" , \"20200325\", \"nightly\"]\r\n!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o... | 1,593 | 1,599 | 1,599 | NONE | null | I am trying to train T5 on TPU .Training started without errors but its very slow . I think its not using TPU backend.
https://colab.research.google.com/drive/10TN0zgPWCIAzbA0PKYIo3fF8wOvfa1P7?usp=sharing | {
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https://api.github.com/repos/huggingface/transformers/issues/5502 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5502/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5502/comments | https://api.github.com/repos/huggingface/transformers/issues/5502/events | https://github.com/huggingface/transformers/issues/5502 | 650,626,185 | MDU6SXNzdWU2NTA2MjYxODU= | 5,502 | licens | {
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## Information
Model I am using (Bert, XLNet ...):
Language I am using the model on (English, Chinese ...):
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on is:
* [ ] an offici... | {
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https://api.github.com/repos/huggingface/transformers/issues/5501 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5501/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5501/comments | https://api.github.com/repos/huggingface/transformers/issues/5501/events | https://github.com/huggingface/transformers/pull/5501 | 650,602,171 | MDExOlB1bGxSZXF1ZXN0NDQ0MDYyMjc0 | 5,501 | Merge pull request #1 from huggingface/master | {
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https://api.github.com/repos/huggingface/transformers/issues/5500 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5500/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5500/comments | https://api.github.com/repos/huggingface/transformers/issues/5500/events | https://github.com/huggingface/transformers/issues/5500 | 650,584,256 | MDU6SXNzdWU2NTA1ODQyNTY= | 5,500 | batch_encode_plus model output is different from tokenizer.encode model's output | {
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"Indeed, if you are using padding you need to provide the attention masks to your model otherwise it doesn't know which tokens it should not attend to.\r\n\r\nHere is the correct version of `batch_encoding` which will give the same output as the non batched version:\r\n```python\r\ndef batch_encoding(sentences):\r\... | 1,593 | 1,597 | 1,594 | NONE | null | I am trying to encode multiple sentences with BertTokenizer. I tried batch_encode_plus but I am getting different output when I am feeding BertTokenizer's output vs batch_encode_plus's output to model.
```
single_sentence = 'checking single sentences'
sentences = ['checking single sentences', 'many sentences encod... | {
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https://api.github.com/repos/huggingface/transformers/issues/5499 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5499/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5499/comments | https://api.github.com/repos/huggingface/transformers/issues/5499/events | https://github.com/huggingface/transformers/issues/5499 | 650,583,810 | MDU6SXNzdWU2NTA1ODM4MTA= | 5,499 | [ERROR] add_special_tokens = True not working in version 3.0.0 | {
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"Hi, can you provide more information (basically fill all the filed in the issue templates) in particular provide a clear example so we can try to reproduce the behavior?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. T... | 1,593 | 1,599 | 1,599 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): BERT
Language I am using the model on (English, Chinese ...): Multi-Lingual
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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"We're back up, and should now be setup to auto-restart when it fails. Thanks for the heads up!",
"Awsome! Thanks.",
"Hi! It seems the page is down again.",
"Man, streamlit's killing me. Thanks, rebooting now.",
"Ouch, this means we users are indirectly killing you! Thanks a lot. ๐ ",
"This page is givin... | 1,593 | 1,598 | 1,593 | CONTRIBUTOR | null | Hi, this page was very interesting.
https://huggingface.co/zero-shot/
It is down since yesterday.
What is up with it? Do you plan to bring it up again? | {
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"Turning off the eager mode of tensorflow makes inference much faster."
] | 1,593 | 1,593 | 1,593 | NONE | null | **Environment:**
- OS: Ubuntu 18.04
- Python: 3.7.6
- Transformers: 3.0.0
- PyTorch: 1.4.0
- Tensorflow: 2.2.0
- CUDA: 10.1
- CUDNN: 7.6
- GPU: V100
**My code:**
```import time
import torch
import tensorflow as tf
from transformers import AutoTokenizer, AutoModelWithLMHead, TFAutoModelWithLMHead
TIMES... | {
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"This is related to https://github.com/huggingface/transformers/issues/2917. In the slow tokenizers, when `do_lower_case=False` we don't strip accents, while we do it when `do_lower_case=True`. In the fast tokenizers, this is controlled by the `strip_accents` option, which is `True` here.\r\n\r\n@thomwolf How do yo... | 1,593 | 1,594 | 1,594 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): BERT
Language I am using the model on (English, Chinese ...): 'bert-base-multilingual-cased'
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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https://api.github.com/repos/huggingface/transformers/issues/5489 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5489/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5489/comments | https://api.github.com/repos/huggingface/transformers/issues/5489/events | https://github.com/huggingface/transformers/issues/5489 | 650,381,330 | MDU6SXNzdWU2NTAzODEzMzA= | 5,489 | encoder_outputs are always the same when generating with different inputs | {
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"Hmm, this will be hard to debug here. I'm currently working on getting a working example of a Bert2Bert model, so I will keep an eye on `encoder_output` bugs!\r\nSee conversation here: https://github.com/huggingface/transformers/issues/4443#issuecomment-656691026",
"Thank you for your reply.\r\nI am looking forw... | 1,593 | 1,595 | 1,595 | NONE | null | # โ 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/5488 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5488/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5488/comments | https://api.github.com/repos/huggingface/transformers/issues/5488/events | https://github.com/huggingface/transformers/issues/5488 | 650,368,481 | MDU6SXNzdWU2NTAzNjg0ODE= | 5,488 | Cannot train RoBERTa from scratch with multiple nodes and multiple GPUs | {
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"I figured out the issue. I should not set `--local_rank=$SLURM_LOCALID` in the argument. `torch.distributed.launch` will automatically pass the right --local_rank value to run_language_modeling.py. "
] | 1,593 | 1,594 | 1,594 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...):
Language I am using the model on (English, Chinese ...):
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:
* [ ] an offici... | {
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https://api.github.com/repos/huggingface/transformers/issues/5487 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5487/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5487/comments | https://api.github.com/repos/huggingface/transformers/issues/5487/events | https://github.com/huggingface/transformers/issues/5487 | 650,360,907 | MDU6SXNzdWU2NTAzNjA5MDc= | 5,487 | Better TPU Support in examples | {
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"I agree with this request. TPU training pipeline is very fragile, lacking and needs more attention. Encouraging more use of TPU by providing easy examples would increase its usage resulting in a higher quality TPU Training system over time as more people contribute.",
"This issue has been automatically marked as... | 1,593 | 1,599 | 1,599 | NONE | null | # ๐ Feature request
<!-- A clear and concise description of the feature proposal.
Please provide a link to the paper and code in case they exist. -->
I tried to train BERT on TPU recently, and found your [examples](https://github.com/huggingface/transformers/blob/master/examples) has done work on this topi... | {
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"This might help you: https://stackoverflow.com/questions/62691279/how-to-disable-tokenizers-parallelism-true-false-warning",
"I suspect this may be caused by loading data. In my case, it happens when my dataloader starts working.",
"This is happening whenever you use `multiprocessing` (Often used by data loade... | 1,593 | 1,705 | 1,594 | NONE | null | I know this warning is because the transformer library is updated to 3.x.
I know the warning saying to set TOKENIZERS_PARALLELISM = true / false
My question is where should i set TOKENIZERS_PARALLELISM = true / false
is this when defining tokenizers like
```
tok = Tokenizer.from_pretrained('xyz', TOKENIZERS_PA... | {
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https://api.github.com/repos/huggingface/transformers/issues/5485 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5485/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5485/comments | https://api.github.com/repos/huggingface/transformers/issues/5485/events | https://github.com/huggingface/transformers/issues/5485 | 650,333,781 | MDU6SXNzdWU2NTAzMzM3ODE= | 5,485 | Bert-extractive-summaizer importing issue | {
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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"
] | 1,593 | 1,599 | 1,599 | NONE | null | Hi ,
I am facing issues while import summarizer.
NameError Traceback (most recent call last)
<ipython-input-6-3b4384c20fe2> in <module>()
----> 1 from summarizer import Summarizer
2
3 body = 'Text body that you want to summarize with BERT'
4 body2 = 'Somethi... | {
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https://api.github.com/repos/huggingface/transformers/issues/5484 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5484/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5484/comments | https://api.github.com/repos/huggingface/transformers/issues/5484/events | https://github.com/huggingface/transformers/issues/5484 | 650,331,874 | MDU6SXNzdWU2NTAzMzE4NzQ= | 5,484 | Error using t5-base-cnn | {
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"Use the standard `T5Tokenizer.from_pretrained('t5-base')`",
"and I would love to hear your results!",
"The outputs of t5-base-cnn are good! Will let you know when I run over a bigger dataset.\r\nMy doubt is how many epochs is ideal for fine-tuning t5 models over cnn/dm?\r\nMy t5-small fine-tuned over cnn/dm co... | 1,593 | 1,601 | 1,595 | NONE | null | I'm trying to fine-tune all the t5 models over CNN/DailyMail to see how they perform compared to the BART ones. I came across your t5-base-cnn model day. I tried using it in the way mentioned but got interrupted by an error that says :
OSError: Model name 'sshleifer/t5-base-cnn' was not found in tokenizers model na... | {
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https://api.github.com/repos/huggingface/transformers/issues/5483 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5483/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5483/comments | https://api.github.com/repos/huggingface/transformers/issues/5483/events | https://github.com/huggingface/transformers/issues/5483 | 650,282,132 | MDU6SXNzdWU2NTAyODIxMzI= | 5,483 | can't get models directory after running python run_squad.py | {
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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"
] | 1,593 | 1,599 | 1,599 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...):
Bert
Language I am using the model on (English, Chinese ...):
English
The problem arises when using:
after running "python run_squad.py ", I didn't get models directory.
running time of my code in colab is only 20 minutes, I think the trainin... | {
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"Hi, ok I can reproduce, this is a bug in the `AddedToken`class of `huggingface/tokenizers`. Moving this up."
] | 1,593 | 1,594 | 1,594 | CONTRIBUTOR | null | Tokenizers are losing all their special tokens when un-pickled. Not sure if there are other attributes that aren't being rehydrated as well ...
```
hf_tokenizer
# <transformers.tokenization_roberta.RobertaTokenizer at 0x7fc4ce625f10>
hf_tokenizer.special_tokens_map
# {'bos_token': '<s>',
# 'eos_token': '</s>... | {
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https://api.github.com/repos/huggingface/transformers/issues/5480 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5480/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5480/comments | https://api.github.com/repos/huggingface/transformers/issues/5480/events | https://github.com/huggingface/transformers/issues/5480 | 650,251,847 | MDU6SXNzdWU2NTAyNTE4NDc= | 5,480 | 'Size' Error while loading t5-large model | {
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"Hi! This is because the return of `tokenizer('sentence embeddings from t5 model', return_tensors=\"pt\")` is a dict containing values that can be used by the model. It's not a tensor, so it's not `input_ids`.\r\n\r\nChange the following line:\r\n```py\r\ntokenizer('sentence embeddings from t5 model', return_tensor... | 1,593 | 1,593 | 1,593 | NONE | null | # ๐ Bug
## Information
Model I am using t5-large:
Language I am using the model on English
The problem arises when using:
```
from transformers import T5Tokenizer, T5Model
tokenizer = T5Tokenizer.from_pretrained('t5-large')
model = T5Model.from_pretrained('t5-large')
input_ids = tokenize... | {
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https://api.github.com/repos/huggingface/transformers/issues/5479 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5479/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5479/comments | https://api.github.com/repos/huggingface/transformers/issues/5479/events | https://github.com/huggingface/transformers/pull/5479 | 650,224,604 | MDExOlB1bGxSZXF1ZXN0NDQzNzU5MTEy | 5,479 | Exposing prepare_for_model for both slow & fast tokenizers | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5479?src=pr&el=h1) Report\n> Merging [#5479](https://codecov.io/gh/huggingface/transformers/pull/5479?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ef0e9d806c51059b07b98cb0279a20d3ba3cbc1d&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | MEMBER | null | With version v3.0.0, two breaking changes that could have been avoided have been introduced. After discussion with @n1to and @thomwolf, this PR aims to revert these changes, by implementing two changes:
- The `prepare_for_model` method for both slow and tokenizers is now publicly exposed (it was only the case for th... | {
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https://api.github.com/repos/huggingface/transformers/issues/5478 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5478/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5478/comments | https://api.github.com/repos/huggingface/transformers/issues/5478/events | https://github.com/huggingface/transformers/issues/5478 | 650,223,803 | MDU6SXNzdWU2NTAyMjM4MDM= | 5,478 | Possible breaking undetected change to "data/processors/squad.py" | {
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"Hi, overflowing tokens are handled differently between slow and fast tokenizers with fast tokenizers having better support.\r\nWhich kind of tokenizer are you using?",
"I am using the fast tokenizers ",
"Ok, currently we don't handle overflowing in fast tokenizers with this processing script.\r\nThis is on the... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | # โ Questions & Help
It looks like the squad data utils hasn't been updated to the new version.
https://github.com/huggingface/transformers/blob/fcf0652460753f8a81f7576e8abdaa6b3742f00e/src/transformers/data/processors/squad.py#L136
```
encoded_dict = tokenizer.encode_plus( # TODO(thom) update this l... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5477?src=pr&el=h1) Report\n> Merging [#5477](https://codecov.io/gh/huggingface/transformers/pull/5477?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/58cca47c16149e43d1b516623d59e3c5d97f695e&el=desc) will **increase... | 1,593 | 1,594 | 1,594 | CONTRIBUTOR | null | Add DeeBERT (entropy-based early exiting for *BERT).
Paper: https://www.aclweb.org/anthology/2020.acl-main.204/
Based on its original repository: https://github.com/castorini/DeeBERT | {
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https://api.github.com/repos/huggingface/transformers/issues/5476 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5476/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5476/comments | https://api.github.com/repos/huggingface/transformers/issues/5476/events | https://github.com/huggingface/transformers/issues/5476 | 650,181,077 | MDU6SXNzdWU2NTAxODEwNzc= | 5,476 | Seq2Seq: Option to not store whole dataset in memory | {
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"Do you still need help? I can help out and contribute here. ",
"Yes that would be super helpful! The goal is to avoid using so much CPU memory here:\r\nhttps://github.com/huggingface/transformers/blob/353b8f1e7a7361c0afd9e391381bc226b4a5ca8f/examples/seq2seq/utils.py#L101\r\n\r\nby only reading a few batches fro... | 1,593 | 1,595 | 1,595 | CONTRIBUTOR | null | https://github.com/huggingface/transformers/blob/ef0e9d806c51059b07b98cb0279a20d3ba3cbc1d/examples/seq2seq/utils.py#L93 | {
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"142 AutoTokenizer Failures (the original 35 +107 more).\r\nWhat would help with the `sshleifer` ones (at least) is if I could somehow say \"this is the same as the `BartTokenizer` without uploading the same files all over again. Sadly, S3 does not support symlinks.\r\n\r\n```\r\n{'DeBERTa/base': ('Unrecognized mod... | 1,593 | 1,629 | 1,593 | CONTRIBUTOR | null | Here is what I ran:
```python
from transformers.hf_api import HfApi
from tqdm import tqdm
import pandas as pd
model_list = HfApi().model_list()
model_ids = [x.modelId for x in model_list]
from transformers import AutoConfig
def check_hub(cls, model_ids):
results = {}
failure_data = {}
f... | {
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https://api.github.com/repos/huggingface/transformers/issues/5474 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5474/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5474/comments | https://api.github.com/repos/huggingface/transformers/issues/5474/events | https://github.com/huggingface/transformers/issues/5474 | 650,172,589 | MDU6SXNzdWU2NTAxNzI1ODk= | 5,474 | Can't use AutoModelForCausalLM with bert | {
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"Can reproduce :-) Opened a PR to fix it - thanks for the issue @sshearing !"
] | 1,593 | 1,594 | 1,594 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): bert-base-uncased
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ ] my own modified scripts: (give details below)
Here is a simple 3 lines of code you can try to replicate the bug:
from transfo... | {
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"Hi! Thanks for opening this issue. This should have been fixed by https://github.com/huggingface/transformers/pull/5414."
] | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | # ๐ Bug
## Information
`TFAutoModelForSequenceClassification` does not work on v3.0.0 / can't load a model that was working on v2.11.0
## To reproduce
Steps to reproduce the behavior:
- This work:
```python
!pip install transformers==2.11.0
from transformers import TFAutoModelForSequenceClassificat... | {
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It outputs the following warnings:
```
ERROR:transformers.tokenization_utils:We need to remove 186 to truncate the inputbut the first sequence has a length 25. Please select another truncation strategy than TruncationStrategy.ONLY_FI... | {
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"Hi! The SQuAD example doesn't have trainer support yet. We're in the process of adding it. You can see the supported tasks [here](https://github.com/huggingface/transformers/tree/master/examples#the-big-table-of-tasks), only the tasks with Trainer, TFTrainer or pytorch-lightning support can run on TPU."
] | 1,593 | 1,593 | 1,593 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...):
Electra
Language I am using the model on (English, Chinese ...):
ENG
The problem arises when using:
* [ ] the official example scripts: RUN_squad.py + xla_spawn.py
The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5469 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5469/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5469/comments | https://api.github.com/repos/huggingface/transformers/issues/5469/events | https://github.com/huggingface/transformers/pull/5469 | 650,059,684 | MDExOlB1bGxSZXF1ZXN0NDQzNjIzMTc1 | 5,469 | [Discussion] fix zero divison error (Reformer batch size bug) | {
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During the training of the reformer model, I noticed that when you increase the batch size, a zero divison error often occurs
With these changes the error no longer occurs | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5468?src=pr&el=h1) Report\n> Merging [#5468](https://codecov.io/gh/huggingface/transformers/pull/5468?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5a0dac53bfd6e69ae64fb3119d607445e1a308d8&el=desc) will **increase... | 1,593 | 1,599 | 1,596 | CONTRIBUTOR | null | Fix a bug when a saved model is created the parameters `output_hidden_states` and `output_attentions` was ignored.
Reproducibility with TF 2.2:
```python
import tensorflow as tf
from transformers import TFBertModel, BertTokenizer, BertConfig
config = BertConfig.from_pretrained("bert-base-multilingual-uncased",... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5467?src=pr&el=h1) Report\n> Merging [#5467](https://codecov.io/gh/huggingface/transformers/pull/5467?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/35befd9ce31c23a774fd34f57bc44033ce70141d&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | COLLABORATOR | null | This PR introduces a mid/high-level summary of the different tokenizer types used in the library (a bit like the model summary).
Preview is [here](https://56179-155220641-gh.circle-artifacts.com/0/docs/_build/html/tokenizer_summary.html). | {
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https://api.github.com/repos/huggingface/transformers/issues/5465 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5465/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5465/comments | https://api.github.com/repos/huggingface/transformers/issues/5465/events | https://github.com/huggingface/transformers/pull/5465 | 649,814,338 | MDExOlB1bGxSZXF1ZXN0NDQzNDIwMzYw | 5,465 | Fixing missing arguments for TransfoXL tokenizer when using TextGenerationPipeline | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5465?src=pr&el=h1) Report\n> Merging [#5465](https://codecov.io/gh/huggingface/transformers/pull/5465?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6726416e4a9780e7a92b5681e1446f15f7ef83d3&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | As discussed with @LysandreJik and @mfuntowicz , `TextGenerationPipeline` gives imperfect results when using TransfoXL as the tokenizer lacks the `add_space_before_punct_symbol` argument. In order to fix this, this PR overrides `_parse_and_tokenize` for this pipeline in order to pass tokenizer arguments. | {
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https://api.github.com/repos/huggingface/transformers/issues/5463 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5463/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5463/comments | https://api.github.com/repos/huggingface/transformers/issues/5463/events | https://github.com/huggingface/transformers/issues/5463 | 649,788,247 | MDU6SXNzdWU2NDk3ODgyNDc= | 5,463 | Pre-Trained Model (ipuneetrathore/bert-base-cased-finetuned-finBERT) loads in PyTorch but not Tensorflow | {
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"Hello! That's because the user that uploaded that model didn't upload a TensorFlow version, only a PyTorch version. You can see it when you click on \"show all files\", you'll see that there is a `pytorch_model.bin`, but no `tf_model.h5`.\r\n\r\nHere you can solve this by telling the TF model that you want to load... | 1,593 | 1,594 | 1,593 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): TFBertModel
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 ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5462?src=pr&el=h1) Report\n> Merging [#5462](https://codecov.io/gh/huggingface/transformers/pull/5462?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/35befd9ce31c23a774fd34f57bc44033ce70141d&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | #4826 fixed TransfoXL's `prepare_inputs_for_generation` function. This PR changes the expected outputs in the TransfoXL generation test to match the new correct outputs. | {
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"I'm not sure I completely understand your process. You're loading a Reformer model - which one, with which checkpoint?\r\n\r\nYou want to use another tokenizer. Which one, loaded from which checkpoint?",
"I am using this notebook: https://github.com/patrickvonplaten/notebooks/blob/master/Reformer_For_Masked_LM.i... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): reformer
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) it's based on the reformer mlm n... | {
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https://api.github.com/repos/huggingface/transformers/issues/5460 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5460/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5460/comments | https://api.github.com/repos/huggingface/transformers/issues/5460/events | https://github.com/huggingface/transformers/issues/5460 | 649,657,582 | MDU6SXNzdWU2NDk2NTc1ODI= | 5,460 | BERT Huggingface trainer api: ValueError: expected sequence of length 128 at dim 1 (got 314) | {
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"I can reproduce! Thank you for opening an issue, I'm looking into it now.",
"Same error (StackOverflow--> https://stackoverflow.com/questions/67004233/typeerror-zeros-like-argument-input-when-fine-tuning-on-mlm) what was the fix in the end @LysandreJik ?",
"@LysandreJik Is there any way to fix this (for what I... | 1,593 | 1,623 | 1,593 | NONE | null | # โ 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/5459 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5459/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5459/comments | https://api.github.com/repos/huggingface/transformers/issues/5459/events | https://github.com/huggingface/transformers/issues/5459 | 649,642,911 | MDU6SXNzdWU2NDk2NDI5MTE= | 5,459 | Error while saving model: TypeError: ('Not JSON Serializable:', DistilBertConfig | {
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"Hi! The way to save the transformers model is using the `save_pretrained` method, which saves both the configuration and the model as an h5 file. Can you try using it instead?",
"> Hi! The way to save the transformers model is using the `save_pretrained` method, which saves both the configuration and the model a... | 1,593 | 1,594 | 1,594 | NONE | null | # ๐ Bug
## Information
In this problem, I am using the pre-trained **distillbert** model embedding to build a custom model (See the code snippet below). Everything works perfectly fine except saving the model (See error below). I am using the latest version of the transformer, which is 3.0.0. I could not even sa... | {
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"Ha, I didn't know the Tokenizer between Bart and mBart is different. I just noticed there is a class `MBartTokenizer`.\r\n\r\nIt seems like this class is not documented on [HuggingFace documentation](https://huggingface.co/transformers/model_doc/bart.html). Maybe we should consider adding it ?\r\n\r\n_Also the mod... | 1,593 | 1,594 | 1,594 | CONTRIBUTOR | null | # ๐ Bug
From [`sshleifer/mbart-large-cc25`](https://huggingface.co/sshleifer/mbart-large-cc25) :
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("sshleifer/mbart-large-cc25")
```
---
Running this code yield an error :
>OSError: Model name 'sshleifer/mbart-... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5457?src=pr&el=h1) Report\n> Merging [#5457](https://codecov.io/gh/huggingface/transformers/pull/5457?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/306f1a269504b781f886d75105acabf8ae95bd11&el=desc) will **decrease... | 1,593 | 1,594 | 1,594 | CONTRIBUTOR | null | This sets `test_torchscript=True` for BART and removes unneeded asserts in `test_tie_weights`. | {
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"Hi @RayLei Have a look at this https://huggingface.co/transformers/preprocessing.html"
] | 1,593 | 1,595 | 1,595 | NONE | null | # โ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
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"+1, I got the same error.",
"Hi, do you mind pasting your environment information? Especially related to your transformers and tokenizers versions.",
"Hi @LysandreJik, thanks for checking in. I am using the version 2.11.0 of the transformers library, and tokenizers==0.7.0. \r\n\r\nFollowing is the associated ... | 1,593 | 1,609 | 1,600 | NONE | null | Thanks for the transformers library!
## Information
I am trying to finetune a pre-trained model of type `LongformerForQuestionAnswer` on a custom QA dataset using a custom script morphed from `run_squad.py`. The pre-trained model is `allenai/longformer-large-4096-finetuned-triviaqa`
While saving the pretraine... | {
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https://api.github.com/repos/huggingface/transformers/issues/5453 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5453/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5453/comments | https://api.github.com/repos/huggingface/transformers/issues/5453/events | https://github.com/huggingface/transformers/issues/5453 | 649,432,908 | MDU6SXNzdWU2NDk0MzI5MDg= | 5,453 | The output to be used for getting sentence embeddings from BERT | {
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"Hi @AkshitaJha , what is your downstream task ? \r\nAlso you may wanna try this out for sentence embeddings \r\nhttps://huggingface.co/deepset/sentence_bert\r\nhttps://github.com/UKPLab/sentence-transformers\r\n",
"This issue has been automatically marked as stale because it has not had recent activity. It will ... | 1,593 | 1,599 | 1,599 | NONE | null | What is the output that we should be using to get embeddings for a sentence using BERT? When I load the pre-trained BERT model ([BertModel](https://huggingface.co/transformers/model_doc/bert.html#transformers.BertModel)) from huggingface for inference, should I be using the `pooler_output', the output of the last hidde... | {
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"You could manually cast it to a namespace with \r\n```python\r\nargparse.Namespace(**ckpt[\"hparams\"])\r\n```\r\n\r\nBut @williamFalcon may have a cleaner solution\r\n",
"I added it with a very *very* dirty fix, in GLUETransformer init added this to avoid cast it to Namespace if it was a dict\r\n\r\n`if type(hp... | 1,593 | 1,601 | 1,601 | COLLABORATOR | null | Hi,
after manually resolving the `n_gpu` attribute issue in `lightning_base.py` (see #5385), I found another strange behaviour in the Text Classification example.
I used PL in version *0.8.1* with the `run_pl.sh` script. Training works, but after reloading the model for evaluation, the following error message is ... | {
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"Think it's required for some weird keras inner workings, or @LysandreJik ? I remember I had to change them to `inputs` in TF T5 at some point as well.",
"### Background for Keras inner workings:\r\n\r\n(taken from the docs)\r\n\r\nTF 2.0 models accepts two formats as inputs:\r\n\r\n- having all inputs as keyword... | 1,593 | 1,598 | 1,598 | CONTRIBUTOR | null | Why should TF encoder_decoder models take inputs instead of input_ids ?
https://github.com/huggingface/transformers/blob/master/tests/test_modeling_tf_common.py#L328
@patrickvonplaten | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5449?src=pr&el=h1) Report\n> Merging [#5449](https://codecov.io/gh/huggingface/transformers/pull/5449?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d16e36c7e525aab4c08a6e60a7478e209498dc14&el=desc) will **increase... | 1,593 | 1,598 | 1,594 | CONTRIBUTOR | null | This post / guide is inspired by this recent [Twitter discussion](https://twitter.com/myleott/status/1245840363262283776) and [this gist](https://gist.github.com/myleott/cdf685b8b3ce20b0221e1842782bce74) on the different ways that perplexity can be evaluated and the optimal strategy of a strided "sliding window".
In... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5448?src=pr&el=h1) Report\n> Merging [#5448](https://codecov.io/gh/huggingface/transformers/pull/5448?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d16e36c7e525aab4c08a6e60a7478e209498dc14&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | - fixed grammar and spelling
- added an intro
- updated Training data references | {
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"Hi! Why were you using `tokenize` + `prepare_for_model` instead of `encode`/`encode_plus` ? Let's see how to fit your use-case with the best approach!",
"Sure.\r\n\r\nI'm the developer of [this library](https://ohmeow.github.io/blurr/) which integrates huggingface with fastai. Probably the best thing is to look... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | I'm working with already numericalized data (e.g., where the text has been converted to ids via `tokenizer.tokenize()`) and was using `prepare_for_model` to build the appropriate input dictionary ... ***but*** that method is gone in 3.0.
So ... what should I use/do now?
Thanks | {
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"Can you maybe just use the script provided here: https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb? In Reformer you have to be careful with the length you use for training. The docs can be helpful here as well: https://huggingface.co/transformers/model_doc/reformer.html",
"Thank yo... | 1,593 | 1,593 | 1,593 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): Reformer
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)
- using run_language_modeling... | {
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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",
"This issue still occurs. How do I reopen?"
] | 1,593 | 1,615 | 1,599 | NONE | null | # ๐ 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: Write With Transformer
* [ ] my own modified scripts: (give details below)
The tasks I am working on is:
... | {
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"Yes, similarly to the `good_tokenizer` where you enabled truncation, you should enable it for the `bad_tokenizer`:\r\n\r\n```py\r\nfrom transformers import RobertaTokenizerFast\r\n\r\nactually_very_good_tokenizer = RobertaTokenizerFast.from_pretrained(\"./BERT-Esperanto/\", max_len=512)\r\n\r\ntxt = \"Mi estas Jul... | 1,593 | 1,593 | 1,593 | NONE | null | Hi, it seems that at least the RobertaTokenizerFast is not actually truncating encodings to the max_len when encoding(the same issue occurs with the other encoding functions). The BPE tokenizer from tokenizers does.
Below the problem is shown based on the 'how to train from scratch' example.
```
from tokenizers.... | {
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"Hey @gyin-ai, \r\n\r\nThanks a lot for the issue! Currently `generate` does not seem to be compatible with `tf.function`. I will open an issue about this and hopefully fix generate so that it will become possible to generate using `tf.function`. You're use case should definitely be possible in the future!\r\n\r\nI... | 1,593 | 1,650 | 1,600 | NONE | null | # โ Questions & Help
How can we wrap the model.generate and export it as a part of savedModel pb file? In this way, we can use beam search or topK during the tf serving or converting it to coremltools model.
## Details
I am trying to find a way to wrap the model in a Keras Model. But apparently model.generate ... | {
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"That looks like some solid batch parallelization :D Yeah these results don't look very accurate.\r\n\r\nTo be honest TPU Benchmarking is not very well tested yet and probably not very reliable, also partly because PyTorch/XLA is not very robust yet either. I will try to see if I can find the reason for this.",
"... | 1,593 | 1,600 | 1,600 | NONE | null | # ๐ Bug
## Information
I'm trying to benchmark performance of TPUs and the results don't make sense: they are the same for all batch sizes.
It was mentioned [in the pull request that added the feature](https://github.com/huggingface/transformers/pull/4850#issuecomment-640751636) but the PR was merged anyway.
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https://api.github.com/repos/huggingface/transformers/issues/5439 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5439/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5439/comments | https://api.github.com/repos/huggingface/transformers/issues/5439/events | https://github.com/huggingface/transformers/pull/5439 | 649,052,138 | MDExOlB1bGxSZXF1ZXN0NDQyNzU4MDAy | 5,439 | Don't discard entity_group when token is the last in the sequence. | {
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"LGTM!\r\n\r\nThanks @mfuntowicz !\r\n\r\nBefore:\r\n\r\n```bash\r\nIn [6]: nlp(\"My name is Wolfgang and I live in Berlin\") \r\nOut[6]: [{'entity_group': 'I-PER', 'score': 0.99... | 1,593 | 1,593 | 1,593 | MEMBER | null | Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com> | {
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"The old occurrences of `isinstance(item, tuple)` can be replaced by `isinstance(item, tuple) or is_dataclass(item)` (to catch the return_tuple behavior) (`is_dataclass` comes from the dataclasses module).",
"General question (haven't dived deeply into this PR): \r\n\r\ndo we really want to maintain backward comp... | 1,593 | 1,594 | 1,594 | COLLABORATOR | null | This PR addresses #5226 with no breaking changes. Instead of returning tuples, all PyTorch models now return a subclass of `ModelOutput` that is appropriate. Here is an example on a base model:
```
from transformers import BertTokenizer, BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pr... | {
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"@LysandreJik is rebooting the Raspberry Pi right now",
"Joking, I mean the Hugging Face data center",
"It's back up!",
"Thanks! It does work now, but it seems slower to respond and sometimes it times out. This is to the point where it's close to unusable. Do you happen to know whether this is on my end or yo... | 1,593 | 1,593 | 1,593 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): (Distil-)GPT2 on WriteWithTransformer
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [X] the official example scripts: Write With Transformer
* [ ] my own modified scripts: (give details below)
... | {
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https://api.github.com/repos/huggingface/transformers/issues/5436 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5436/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5436/comments | https://api.github.com/repos/huggingface/transformers/issues/5436/events | https://github.com/huggingface/transformers/issues/5436 | 648,932,662 | MDU6SXNzdWU2NDg5MzI2NjI= | 5,436 | Squad2 processor error | {
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"You have to add a \"[CLS]\" token to Reformer tokenizer here to make the script work. The one tokenizer that is online for reformer: `tok = ReformerTokenizer.from_pretrained(\"google/reformer-crime-and-punishment\")` does not have a CLS token. If you add a new token via:\r\n\r\n`tok.add_special_tokens` \r\n\r\nthe... | 1,593 | 1,667 | 1,593 | CONTRIBUTOR | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...):
Language I am using the model on (English, Chinese ...):
The problem arises when using:
* [x] the official example scripts: (give details below)
question answering
* [ ] my own modified scripts: (give details below)
The tasks I am working on ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5435 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5435/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5435/comments | https://api.github.com/repos/huggingface/transformers/issues/5435/events | https://github.com/huggingface/transformers/issues/5435 | 648,921,074 | MDU6SXNzdWU2NDg5MjEwNzQ= | 5,435 | I want to load pre-trained model from file instead of file name | {
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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"
] | 1,593 | 1,599 | 1,599 | NONE | null | Thanks for your excellent code. I recently encountered such a probelm, I want to load pretrained model from anthor machine, and this server could not map the path to my code. But I could load this model in a buffer. So I want to use this buffer becoming the args. What should I do ๏ผ tks. | {
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https://api.github.com/repos/huggingface/transformers/issues/5434 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5434/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5434/comments | https://api.github.com/repos/huggingface/transformers/issues/5434/events | https://github.com/huggingface/transformers/issues/5434 | 648,875,680 | MDU6SXNzdWU2NDg4NzU2ODA= | 5,434 | MiniLM transformers inconsistent log posteriors in multiple runs | {
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"What was the issue?",
"MiniLM is not distilled with Masked LM task, only [Self-Attention distillation](https://github.com/huggingface/transformers/tree/master/model_cards/microsoft/MiniLM-L12-H384-uncased). It doesn't have LM head in the weights file. They are initialised randomly at each run ๐ค \r\n\r\n```\r\n{... | 1,593 | 1,593 | 1,593 | NONE | null | # ๐ Bug
## Information
**Describe the bug**
Using MiniLM for computing log likelihood of test sentences. Cross posted [here](https://github.com/microsoft/unilm/issues/196)
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (attached below)... | {
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https://api.github.com/repos/huggingface/transformers/issues/5433 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5433/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5433/comments | https://api.github.com/repos/huggingface/transformers/issues/5433/events | https://github.com/huggingface/transformers/pull/5433 | 648,873,762 | MDExOlB1bGxSZXF1ZXN0NDQyNjA4NzA5 | 5,433 | [Reformer] Add QA head to reformer model | {
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Checked all tests including RUN_SLOW on GPU => all pass. | {
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"I have tested reproducing **[Fine Tuning Transformer for MultiClass Text Classification](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb)** successfully. But I tried to load the model and vocab files from a spirited file predict_... | 1,593 | 1,594 | 1,594 | NONE | null | How to load and predict a fine tuning DistilBert Multi Classification Model? | {
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"Maybe we should fix this upstream. I wanted to keep identical behavior for `squad_convert_examples_to_features` while moving the code to the new tokenizer API but maybe I missed something.",
"@thomwolf I removed the commit on the padding part to make sure things continue to work at very short term. \r\n\r\nAlso,... | 1,593 | 1,593 | 1,593 | MEMBER | null | **1. Some newly introduced models such as [bart-large-finetuned-squadv1](https://huggingface.co/valhalla/bart-large-finetuned-squadv1) have more than 2 outputs by default on the QA pipeline which is not supported.**
- This PR makes it possible to support such outputs and assumes the 2 first elements are the actual ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5428 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5428/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5428/comments | https://api.github.com/repos/huggingface/transformers/issues/5428/events | https://github.com/huggingface/transformers/issues/5428 | 648,824,383 | MDU6SXNzdWU2NDg4MjQzODM= | 5,428 | How to use (and preferably finetune) BART for text infilling? | {
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"@julien-c , @sshleifer ?",
"Sorry for the slow response.\r\nUnfortunately, text infilling is not yet supported. It would be a welcome contribution! I think the equivalent fairseq task is called `DenoisingTask` \r\nhttps://github.com/pytorch/fairseq/blob/aa79bb9c37b27e3f84e7a4e182175d3b50a79041/fairseq/tasks/deno... | 1,593 | 1,600 | 1,600 | NONE | null | [Here](https://huggingface.co/transformers/model_doc/bart.html#bartforconditionalgeneration) is shown how to use BART for simple mask filling (one <mask> token = one generated token), but how to use it for text infilling? The BART paper states that the model was pretrained on such task so it should be possible.
Is t... | {
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https://api.github.com/repos/huggingface/transformers/issues/5427 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5427/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5427/comments | https://api.github.com/repos/huggingface/transformers/issues/5427/events | https://github.com/huggingface/transformers/issues/5427 | 648,809,243 | MDU6SXNzdWU2NDg4MDkyNDM= | 5,427 | WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss. WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when min... | {
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"I am also encountering a similar issue from yesterday. It never happened before.\r\n```\r\nWARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model_1/bert/pooler/dense/kernel:0', 'tf_bert_model_1/bert/pooler/dense/bias:0'] when minimizing the loss.\r\nWARNING:tensorflow:Gradients do not exist for va... | 1,593 | 1,663 | 1,594 | NONE | null | # ๐ Bug
## 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)
* [ ] my own modified scripts: (give details below)
ip1 = Input(shape = (max_length+2... | {
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https://api.github.com/repos/huggingface/transformers/issues/5426 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5426/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5426/comments | https://api.github.com/repos/huggingface/transformers/issues/5426/events | https://github.com/huggingface/transformers/pull/5426 | 648,782,339 | MDExOlB1bGxSZXF1ZXN0NDQyNTMyNjAw | 5,426 | [Reformer] Add Masked LM Reformer | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5426?src=pr&el=h1) Report\n> Merging [#5426](https://codecov.io/gh/huggingface/transformers/pull/5426?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/35befd9ce31c23a774fd34f57bc44033ce70141d&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | MEMBER | null | Similar to BERT, Reformer LM model is split into two:
- The standard Causal Language Modeling Reformer `ReformerModelWithLMHead`: Here we have a tiny breaking change as `ReformerModelWithLMHead` can no longer be used with bi-directional self-attention. This option should not really have been used anyways as there are ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5425 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5425/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5425/comments | https://api.github.com/repos/huggingface/transformers/issues/5425/events | https://github.com/huggingface/transformers/issues/5425 | 648,767,250 | MDU6SXNzdWU2NDg3NjcyNTA= | 5,425 | [Quick poll] Give your opinion on the future of ๐ค transformers | {
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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"
] | 1,593 | 1,599 | 1,599 | MEMBER | null | The ๐ค transformers library is at a crossroad ๐ and could evolve in many directions, from teaching to research & applications.
We made a quick poll to get your opinion.
If you have 2-3 minutes and want to participate in shaping the future of the library ๐ https://docs.google.com/forms/d/e/1FAIpQLSeKWNE1SyaSvqLY... | {
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https://api.github.com/repos/huggingface/transformers/issues/5424 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5424/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5424/comments | https://api.github.com/repos/huggingface/transformers/issues/5424/events | https://github.com/huggingface/transformers/issues/5424 | 648,758,667 | MDU6SXNzdWU2NDg3NTg2Njc= | 5,424 | Bart EncoderLayer masked_fill not working properly with pytorch 1.4 | {
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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",
"Hello, I still hava this problem when my pytorch were upgraded to 1.5 . I don't know if it's related to the python version . \r\nCan... | 1,593 | 1,617 | 1,599 | NONE | null | # ๐ Bug
## Information
Model I am using (Bert, XLNet ...): Bart
I'm trying to use the EncoderLayer of Bart but I realized that `attn_weights = attn_weights.masked_fill(reshaped, float("-inf"))` at line [659](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bart.py#L659) does ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5423 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5423/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5423/comments | https://api.github.com/repos/huggingface/transformers/issues/5423/events | https://github.com/huggingface/transformers/issues/5423 | 648,639,186 | MDU6SXNzdWU2NDg2MzkxODY= | 5,423 | Error Instantiating T5-11B from conributed models | {
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"same result. I can't download",
"Please see https://github.com/huggingface/transformers/issues/5986#issuecomment-663090043",
"Works when I use:\r\n\r\n```python\r\nimport transformers\r\n\r\nt5 = transformers.AutoModel.from_pretrained('t5-11b', use_cdn = False)\r\n```\r\n\r\nThank You!"
] | 1,593 | 1,595 | 1,595 | CONTRIBUTOR | null | # ๐ Bug
## Information
Model I am using : T5-11B
Language I am using the model on: English
The problem arises when using:
when I try downloading the T5-11B model
The tasks I am working on is:
Evaluating ROGUE score on CNN dataset
## To reproduce
Steps to reproduce the behavior:
Just try instant... | {
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https://api.github.com/repos/huggingface/transformers/issues/5422 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5422/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5422/comments | https://api.github.com/repos/huggingface/transformers/issues/5422/events | https://github.com/huggingface/transformers/pull/5422 | 648,607,773 | MDExOlB1bGxSZXF1ZXN0NDQyMzg5NDQ4 | 5,422 | Create README.md | {
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"Cool",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5422?src=pr&el=h1) Report\n> Merging [#5422](https://codecov.io/gh/huggingface/transformers/pull/5422?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/fcf0652460753f8a81f7576e8abdaa6b3742f00e&el=desc) will ... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Card for my model | {
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https://api.github.com/repos/huggingface/transformers/issues/5421 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5421/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5421/comments | https://api.github.com/repos/huggingface/transformers/issues/5421/events | https://github.com/huggingface/transformers/issues/5421 | 648,604,879 | MDU6SXNzdWU2NDg2MDQ4Nzk= | 5,421 | What to do about this warning message: "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification" | {
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"Not sure what's happening with the multiple duplicate opened issues, @ohmeow?\r\n\r\nIs GitHub flaky again? :)",
"I am also encountering the same warning. \r\n\r\nWhen loading the model\r\n```\r\nSome weights of the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['nsp___cls', ... | 1,593 | 1,704 | 1,593 | CONTRIBUTOR | null | ```
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
```
returns this warning message:
```
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cl... | {
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