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https://api.github.com/repos/huggingface/transformers/issues/5420 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5420/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5420/comments | https://api.github.com/repos/huggingface/transformers/issues/5420/events | https://github.com/huggingface/transformers/pull/5420 | 648,570,076 | MDExOlB1bGxSZXF1ZXN0NDQyMzU4OTYw | 5,420 | Refactor generation sampling parameters (e.g. top k, temperature) into "Sampling" classes | {
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"@sshleifer thanks for taking a look. The run against the tests you mentioned (bart/t5/marian) passed when I gave them a kick. When you say performance, this approach should have the same amount of compute (each enabled Sampler runs once per generation loop) since it is just moving code around unless I missed somet... | 1,593 | 1,600 | 1,600 | NONE | null | #4164 has a full description of the intention here. Basically, to avoid exploding generate(...) with more arguments, I've added one generic Sampler parameter that allows for arbitrary transformations of the generation probability distribution conditioned on the past. This allows users to specify custom ways of sampling... | {
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"Excuse me.\r\nWill this model be added in the future, how long will it take?\r\nIs currently only T5 and Bart can do machine translation?",
"I would guess that I get around to this by the end of July, but I can't be sure.\r\n\r\nWe also have `MarianMTModel` and 1000+ pretrained weights from `Helsinki-NLP/` that ... | 1,593 | 1,600 | 1,600 | CONTRIBUTOR | null | Download instructions from torchub/fairseq: [here](https://github.com/pytorch/fairseq/blob/f03392d11faf1588cb571d19835d6a61ab0d9ca6/examples/wmt19/README.md#L1)
the BART conversion script should be reusable.
## Open source status
* [ x] the model implementation is available: (give details)
* [ x] the model we... | {
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"(Replaced merge with rebase -- see #5420)"
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"Awesome! This PR will leverage many pretrained weights and make them available for TF! \r\nI don't really think there is a workaround for [supporting multiple input types](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_tf_t5.py#L946) especially to make it compatible with Keras a... | 1,593 | 1,603 | 1,603 | CONTRIBUTOR | null | - adds `TFBartForConditionalGeneration`, which can generate summaries that are equivalent to pytorch.
#### TODO this PR:
- [x] fast tests besides two
- [x] reasonable xsum generations
- [x] tests passing
- [x] fix slow cnn test (tf needs to call `adjust_logits_during_generation`)
- [x] functional dropout
- [... | {
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https://api.github.com/repos/huggingface/transformers/issues/5410 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5410/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5410/comments | https://api.github.com/repos/huggingface/transformers/issues/5410/events | https://github.com/huggingface/transformers/pull/5410 | 648,449,848 | MDExOlB1bGxSZXF1ZXN0NDQyMjU3ODc2 | 5,410 | [cleanup] TF T5 tests only init t5-base once. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5410?src=pr&el=h1) Report\n> Merging [#5410](https://codecov.io/gh/huggingface/transformers/pull/5410?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/991172922f9711d7bef160d6aedb2ed1059a88ff&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/5409 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5409/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5409/comments | https://api.github.com/repos/huggingface/transformers/issues/5409/events | https://github.com/huggingface/transformers/pull/5409 | 648,444,819 | MDExOlB1bGxSZXF1ZXN0NDQyMjUzODMz | 5,409 | [CI] gh runner doesn't use -v, cats new result | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5409?src=pr&el=h1) Report\n> Merging [#5409](https://codecov.io/gh/huggingface/transformers/pull/5409?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/27a7fe7a8d3e58d1df7ecc4c5390ac7be728724f&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | This should reduce amount of scrolling required to find error. | {
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https://api.github.com/repos/huggingface/transformers/issues/5408 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5408/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5408/comments | https://api.github.com/repos/huggingface/transformers/issues/5408/events | https://github.com/huggingface/transformers/pull/5408 | 648,442,388 | MDExOlB1bGxSZXF1ZXN0NDQyMjUxODI1 | 5,408 | Fix examples titles and optimization doc page | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5408?src=pr&el=h1) Report\n> Merging [#5408](https://codecov.io/gh/huggingface/transformers/pull/5408?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/87716a6d072b2b66415ce43086c73b04e63fe0fe&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | COLLABORATOR | null | This PR addresses two things:
- first, some of the titles were a bit messy in the navigation bar in the examples and optimization page, fixed that
- second, it expands the optimization documentation, adding mentions of which classes/functions go with which backend (since there is no TF prefix) and expand existing doc... | {
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https://api.github.com/repos/huggingface/transformers/issues/5407 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5407/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5407/comments | https://api.github.com/repos/huggingface/transformers/issues/5407/events | https://github.com/huggingface/transformers/pull/5407 | 648,427,394 | MDExOlB1bGxSZXF1ZXN0NDQyMjM5NjU4 | 5,407 | examples/seq2seq: never override $WANDB_PROJECT | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5407?src=pr&el=h1) Report\n> Merging [#5407](https://codecov.io/gh/huggingface/transformers/pull/5407?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/c4d4e8bdbd25d9463d41de6398940329c89b7fb6&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | cc @borisdayma | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5406?src=pr&el=h1) Report\n> Merging [#5406](https://codecov.io/gh/huggingface/transformers/pull/5406?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/c4d4e8bdbd25d9463d41de6398940329c89b7fb6&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | - New tokenizer API does not put space between `<s/>` and sentence.
- New result: "my name is John" is better than old result: "My name is" so fine to update `expected_result`.
- This is caused by tokenizers upgrade.
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https://api.github.com/repos/huggingface/transformers/issues/5405 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5405/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5405/comments | https://api.github.com/repos/huggingface/transformers/issues/5405/events | https://github.com/huggingface/transformers/issues/5405 | 648,390,826 | MDU6SXNzdWU2NDgzOTA4MjY= | 5,405 | Colab session crash with XLA & Tranformers | {
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"Hi! Could you share a colab notebook reproducing the error?",
"> Hi! Could you share a colab notebook reproducing the error?\r\n\r\nBelow code was sufficient to reproduce the error:\r\n```\r\n!pip3 install transformers\r\n\r\nVERSION = \"nightly\" #@param [\"1.5\" , \"20200325\", \"nightly\"]\r\n!curl https://r... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | I am trying to use xla with transformers but as soon as i import transformers after installing XLA the session is restarted.
Even i tried old version of transformers but same issue, is it related to colab ?
```
!pip3 install transformers
VERSION = "nightly" #@param ["1.5" , "20200325", "nightly"]
!curl ht... | {
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https://api.github.com/repos/huggingface/transformers/issues/5404 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5404/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5404/comments | https://api.github.com/repos/huggingface/transformers/issues/5404/events | https://github.com/huggingface/transformers/issues/5404 | 648,367,434 | MDU6SXNzdWU2NDgzNjc0MzQ= | 5,404 | How to interpret/act on this warning: "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForMaskedLM"? | {
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model = AutoModelForMaskedLM.from_pretrained("bert-base-cased")
```
Returns this warning ...
```
Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']
- This IS expected if you are initializing Bert... | {
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<!-- 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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"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 | 1,598 | 1,598 | CONTRIBUTOR | null | I am a TF2 Noob trying to get TFBart working. Most tests pass besides the ones relying on save_pretrained and pt conversion. Has anybody experienced the following issues?
```
test_tf_compile_model:
h5py/h5o.pyx:202: in h5py.h5o.link
...
RuntimeError: Unable to create link (name already exists)
```
Or
```
test_... | {
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https://api.github.com/repos/huggingface/transformers/issues/5401 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5401/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5401/comments | https://api.github.com/repos/huggingface/transformers/issues/5401/events | https://github.com/huggingface/transformers/issues/5401 | 648,327,487 | MDU6SXNzdWU2NDgzMjc0ODc= | 5,401 | Runtime for BERT and Roberta | {
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"Which version of bert or Roberta you want to use base, large ? It also depends on maximum sequence length",
"I'd like to use the base version with the maximum sequence length of 128.",
"Hi @AkshitaJha , you can run training for few steps 1 or 2, that should give you rough idea of how much time it'll take to fi... | 1,593 | 1,599 | 1,599 | NONE | null | I'd like to train a BERT model from scratch. Approximately, how long should it take to train 800k sentences (batch size of say 32) on a 10GB GeForce RTX 2080 GPU?
If I just fine-tune BERT for 800k sentences for 4 epochs, how long should that take?
Are there any benchmarks available except [exacct](https://blog.e... | {
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https://api.github.com/repos/huggingface/transformers/issues/5400 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5400/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5400/comments | https://api.github.com/repos/huggingface/transformers/issues/5400/events | https://github.com/huggingface/transformers/pull/5400 | 648,319,753 | MDExOlB1bGxSZXF1ZXN0NDQyMTQ4NzMz | 5,400 | Create model card for schmidek/electra-small-cased | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5400?src=pr&el=h1) Report\n> Merging [#5400](https://codecov.io/gh/huggingface/transformers/pull/5400?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/87716a6d072b2b66415ce43086c73b04e63fe0fe&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/5399 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5399/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5399/comments | https://api.github.com/repos/huggingface/transformers/issues/5399/events | https://github.com/huggingface/transformers/pull/5399 | 648,319,303 | MDExOlB1bGxSZXF1ZXN0NDQyMTQ4MzU0 | 5,399 | Add support for past states | {
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https://api.github.com/repos/huggingface/transformers/issues/5398 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5398/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5398/comments | https://api.github.com/repos/huggingface/transformers/issues/5398/events | https://github.com/huggingface/transformers/issues/5398 | 648,166,642 | MDU6SXNzdWU2NDgxNjY2NDI= | 5,398 | Inference time difference between pipeline and with standalone model and tokenizer | {
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"Anyone looking into this issue ? :)",
"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 | 1,600 | 1,600 | NONE | null | # 🐛 Bug
## Information
Model I am using: distilbert-base-cased-distilled-squad
Language I am using the model on : English
The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on is:
* [x] an ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5397 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5397/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5397/comments | https://api.github.com/repos/huggingface/transformers/issues/5397/events | https://github.com/huggingface/transformers/issues/5397 | 648,119,148 | MDU6SXNzdWU2NDgxMTkxNDg= | 5,397 | tokenizer started throwing this warning, ""Truncation was not explicitely activated but `max_length` is provided a specific value, please use `truncation=True` to explicitely truncate examples to max length. Defaulting to 'only_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer... | {
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"This is because we recently upgraded the library to version v3.0.0, which has an improved tokenizers API. You can either disable warnings or put `truncation=True` to remove that warning (as indicated in the warning).",
"how do you disable the warnings for this? I'm encountering the same issue. But I don't want t... | 1,593 | 1,620 | 1,593 | NONE | null | Recently while experimenting, BertTokenizer start to throw this warning
```bash
Truncation was not explicitely activated but `max_length` is provided a specific value, please use `truncation=True` to explicitely truncate examples to max length. Defaulting to 'only_first' truncation strategy. If you encode pairs of se... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5396?src=pr&el=h1) Report\n> Merging [#5396](https://codecov.io/gh/huggingface/transformers/pull/5396?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/331d8d2936e7a140225cf60301ba6469930fd216&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Create model card for electicidad-small (Spanish Electra) fine-tuned on SQUAD-esv1 | {
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https://api.github.com/repos/huggingface/transformers/issues/5395 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5395/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5395/comments | https://api.github.com/repos/huggingface/transformers/issues/5395/events | https://github.com/huggingface/transformers/pull/5395 | 648,070,821 | MDExOlB1bGxSZXF1ZXN0NDQxOTQyODE5 | 5,395 | [Almost all TF models] TF clean up: add missing CLM / MLM loss; fix T5 naming and keras compile | {
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"@LysandreJik @julien-c @jplu @thomwolf @sgugger - can you take a look at this example if the CLM loss is correctly added? If yes, I will add this loss to all other CLM models and add tests.",
"Looks good to me!!",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5395?src=pr&el=h1) Report\n> Mer... | 1,593 | 1,594 | 1,594 | MEMBER | null | This PR aligns TF code more with PT code and adds full training support to all CLM and MLM models applying @jplu's loss design to the remaining models. In more detail the following things are included in the PR:
- Add `TFMaskedLanguageModelingLoss` and `TFCausalLanguageModelingLoss` to all CLM and MLM TF models. Onl... | {
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"Hello! Do you mind giving me your tokenizers version?\r\n\r\nIt shouldn't fail like this, `save_pretrained` is still supported and the recommended way to save tokenizers.",
"``tokenizers-0.8.0rc4`` is installed when I use ``pip install transformers``.\r\n\r\nActually, I'm running a CI for my project. You can see... | 1,593 | 1,594 | 1,594 | CONTRIBUTOR | null | # 🐛 Bug
## Information
I am using GPT2Tokenizer and try to save the information about the tokenizer.
In v2.7, I always do
```python
>>> from transformers import GPT2Tokenizer
>>> a = GPT2Tokenizer('./tests/dataloader/dummy_gpt2vocab/vocab.json', './tests/dataloader/dummy_gpt2vocab/merges.txt')
>>> a.sav... | {
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https://api.github.com/repos/huggingface/transformers/issues/5392 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5392/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5392/comments | https://api.github.com/repos/huggingface/transformers/issues/5392/events | https://github.com/huggingface/transformers/issues/5392 | 648,006,147 | MDU6SXNzdWU2NDgwMDYxNDc= | 5,392 | Windows: No matching distribution found for lightning_base | {
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"Also I am a bit worried, that my RTx 2070 with 8GB will be too small for training, since 13GB were recommended for a batch size of 1 with fp16. I appreciate any hints, what I could do to make it run. Thanks you",
"Also I followed the basic installation process from here:\r\nhttps://github.com/huggingface/transfo... | 1,593 | 1,595 | 1,595 | CONTRIBUTOR | null | # 🐛 Bug
I followed the seq2seq readme and wanted to try the sshleifer/distilbart-cnn-12-6 model for absractive text summarization.
I got the bug above, it seems like lightning_base was part of this project before it was moved/removed.
## Information
Model I am using: sshleifer/distilbart-cnn-12-6
Language... | {
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"I try to train a GPT-2 from scratch in Greek with an older version of run_language_modeling.py (https://github.com/huggingface/transformers/tree/master/examples/language-modeling) script, but I get a low perplexity score of 7 after 15 epochs.\r\n\r\nMy data for train is about 4.6Gb and is constructed as 5 sentence... | 1,593 | 1,600 | 1,600 | 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/5390 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5390/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5390/comments | https://api.github.com/repos/huggingface/transformers/issues/5390/events | https://github.com/huggingface/transformers/issues/5390 | 647,970,936 | MDU6SXNzdWU2NDc5NzA5MzY= | 5,390 | model.generate source code | {
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"`generate()` is defined in the `PretrainedModel` class here https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_utils.py#L872. Given that `BartModel`, `BartForConditionalGeneration`, etc, inherit from `PretrainedModel` they all have access to this generate function. \r\n\r\nHope this... | 1,593 | 1,697 | 1,598 | NONE | null | Hi there,
I am trying to use BART to do NLG task. During my reading the BART tutorial on the website, I couldn't find the definition of 'model.generate()" function. Could you please add some explaination on that? thanks in advance | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5389?src=pr&el=h1) Report\n> Merging [#5389](https://codecov.io/gh/huggingface/transformers/pull/5389?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/64e3d966b1131c15b5905b1e1e582d4bebac1ef0&el=desc) will **decrease... | 1,593 | 1,608 | 1,593 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/5388 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5388/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5388/comments | https://api.github.com/repos/huggingface/transformers/issues/5388/events | https://github.com/huggingface/transformers/issues/5388 | 647,940,489 | MDU6SXNzdWU2NDc5NDA0ODk= | 5,388 | Why T5 do not generate the whole next sentence as one of the pretrain loss? | {
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"Hi @guotong1988 what do you exactly mean by not generating the whole next sentence ?",
"Sorry, my mistake.\r\nInput the previous sentence and generate the next sentence, instead of input the two sentences.",
"I don't really understand the question here. Are you asking why T5 was not trained with the objective... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | T5 has the next-sentence-predict loss.
And the next-sentence-predict loss is not generating the whole next sentence but generating is_next or not_next token.
Thank you very much. | {
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https://api.github.com/repos/huggingface/transformers/issues/5387 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5387/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5387/comments | https://api.github.com/repos/huggingface/transformers/issues/5387/events | https://github.com/huggingface/transformers/issues/5387 | 647,918,029 | MDU6SXNzdWU2NDc5MTgwMjk= | 5,387 | BART fine tuning on gpu issue | {
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"Have you checked if your setup is working properly and if \r\n`torch.cuda.is_available()`\r\nreturns True?",
"Yeah\r\nIt gives initial output as:\r\nSome weights of the model checkpoint at facebook/bart-large were not used when initializing BartForConditionalGeneration: ['encoder.version', 'decoder.version']\r\n... | 1,593 | 1,599 | 1,599 | NONE | null | # ❓ Questions & Help
I am fine tuning BART by executing transformers/examples/seq2seq/finetune.sh as instructed.
I am getting the following error:
```
Traceback (most recent call last):
File "finetune.py", line 346, in <module>
main(args)
File "finetune.py", line 324, in main
logger=logger,
F... | {
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https://api.github.com/repos/huggingface/transformers/issues/5386 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5386/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5386/comments | https://api.github.com/repos/huggingface/transformers/issues/5386/events | https://github.com/huggingface/transformers/issues/5386 | 647,912,400 | MDU6SXNzdWU2NDc5MTI0MDA= | 5,386 | bart-large-cnn training related information | {
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"bart-large-cnn is trained on CNN/DM summarization dataset. See this for dataset info https://www.tensorflow.org/datasets/catalog/cnn_dailymail",
"Also available in the Hugging Face `nlp` [library](https://huggingface.co/datasets/cnn_dailymail) :) \r\n\r\nYou can explore the dataset [here](https://huggingface.co/... | 1,593 | 1,644 | 1,598 | NONE | null | # ❓ Questions & Help
Hi Team HuggingFace
Can we please know the training data set,,total number of summary pairs used and the configuration used to train BART Summarizer (facebook/bart-large-cnn).
Thanks in advance!! | {
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https://api.github.com/repos/huggingface/transformers/issues/5385 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5385/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5385/comments | https://api.github.com/repos/huggingface/transformers/issues/5385/events | https://github.com/huggingface/transformers/issues/5385 | 647,754,832 | MDU6SXNzdWU2NDc3NTQ4MzI= | 5,385 | Example: PyTorch Lightning returns missing attribute error (Token Classification) | {
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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 | COLLABORATOR | null | Hi,
the following error message is currently thrown (version 3.0.0 of Transformers) when running the `run_ner_pl.sh` example from the token classification example:
```bash
Traceback (most recent call last):
File "run_pl_ner.py", line 198, in <module>
trainer = generic_train(model, args)
File "/mnt/tra... | {
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https://api.github.com/repos/huggingface/transformers/issues/5384 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5384/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5384/comments | https://api.github.com/repos/huggingface/transformers/issues/5384/events | https://github.com/huggingface/transformers/issues/5384 | 647,744,088 | MDU6SXNzdWU2NDc3NDQwODg= | 5,384 | Positional and Segment Embeddings in BERT | {
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"They are not predicted, they are learnt alongside the token embedding themselves. They work exactly the same w.r.t. to training: they are all simply embeddings but with a different vocab size.\r\n\r\nhttps://github.com/huggingface/transformers/blob/9a473f1e43221348334b9e7f95bb45770b7ef268/src/transformers/modeling... | 1,593 | 1,603 | 1,603 | NONE | null | #### Question
I'm trying to train a BERT language model from scratch using the huggingface library. I'm not sure I completely understand how positional and segment embeddings work in BERT. The original BERT paper states that unlike transformers, positional and segment embeddings are learned. What exactly does this ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5383 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5383/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5383/comments | https://api.github.com/repos/huggingface/transformers/issues/5383/events | https://github.com/huggingface/transformers/pull/5383 | 647,696,556 | MDExOlB1bGxSZXF1ZXN0NDQxNjY5MDk0 | 5,383 | Documentation for the Trainer API | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5383?src=pr&el=h1) Report\n> Merging [#5383](https://codecov.io/gh/huggingface/transformers/pull/5383?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0267668c3d648c6e41afda97f5df8671ee880ac3&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | COLLABORATOR | null | This PR introduces documentation for the Trainer/TFTrainer classes and the argument dataclasses.
While documenting, I noticed that the method `Trainer.evaluate` and `TFTrainer.evaluate` had an argument `pred_loss_only` which was not used at all (my guess is that it's the same argument passed at init that is used). I... | {
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"what's your `transformers-cli env` output? You should be on 3.0.0 or install from source for this to work."
] | 1,593 | 1,595 | 1,595 | NONE | null | cannot import name 'AutoModelForSeq2SeqLM' from transformers | {
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"I have no problem loading this in `transformers`. These files have not been updated in the past two months either; can you try doing the same in `transformers` or are you constrained by Python 2?",
"I'm constrained to Python 2 right now (due to a decently large codebase which I do not have enough time to upgrade... | 1,593 | 1,593 | 1,593 | NONE | null | ### Information
Model I am using: XLM
### To reproduce
```
from pytorch_transformers import XLMModel, XLMConfig, XLMTokenizer
model_class, tokenizer_class, pretrained_weights = (XLMModel, XLMTokenizer, 'xlm-mlm-en-2048')
config = XLMConfig.from_pretrained(pretrained_weights)
xlm_model = model_class.from_pr... | {
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https://api.github.com/repos/huggingface/transformers/issues/5380 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5380/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5380/comments | https://api.github.com/repos/huggingface/transformers/issues/5380/events | https://github.com/huggingface/transformers/issues/5380 | 647,601,050 | MDU6SXNzdWU2NDc2MDEwNTA= | 5,380 | Unable to load pre-trained model/tokenizer when using Kubernetes | {
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"Do you have a code sample to reproduce this?",
"@LysandreJik sure, create a file named `train.py` containing only the following 2 lines:\r\n```\r\nfrom transformers import GPT2Tokenizer\r\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n```\r\n\r\nCreate `helm/mpijob_chart/templates/job.yaml` as follows:\... | 1,593 | 1,594 | 1,594 | NONE | null | # 🐛 Bug
## Information
Model I am using: GPT-2
The problem arises when using:
* [Y] my own modified scripts: (give details below)
The tasks I am working on is:
* [Y] my own task or dataset: (give details below)
## To reproduce
Steps to reproduce the behavior:
1. Create a Kubernetes cluster, a Do... | {
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"Hi! Do you mind filling in the template?",
"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 | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): gpt2
Language I am using the model on (English, Chinese ...):
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 o... | {
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https://api.github.com/repos/huggingface/transformers/issues/5378 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5378/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5378/comments | https://api.github.com/repos/huggingface/transformers/issues/5378/events | https://github.com/huggingface/transformers/pull/5378 | 647,575,372 | MDExOlB1bGxSZXF1ZXN0NDQxNTc1OTMz | 5,378 | Mention openAI model card and merge content | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5378?src=pr&el=h1) Report\n> Merging [#5378](https://codecov.io/gh/huggingface/transformers/pull/5378?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0267668c3d648c6e41afda97f5df8671ee880ac3&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | COLLABORATOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/5377 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5377/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5377/comments | https://api.github.com/repos/huggingface/transformers/issues/5377/events | https://github.com/huggingface/transformers/issues/5377 | 647,573,950 | MDU6SXNzdWU2NDc1NzM5NTA= | 5,377 | New tokenizer code in transformer 3.0.0 is creating error with old code | {
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"Hi, this is not an error but a warning. If you want to disable warnings, you can use the following:\r\n\r\n```py\r\nimport logging\r\n\r\nlogging.basicConfig(level=logging.ERROR)\r\n```",
"Oh, I'm sorry for writing \"error\" everywhere, but I want to know, is this default behaviour correct for BeRT, it says by d... | 1,593 | 1,593 | 1,593 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): BeRT and GPT2 for Poly-encoder implementation
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 be... | {
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"Hi! Do you have internet access? Does the folder `allenai/longformer-base-4096` exist on your machine?",
"Ahh...sorry I thought it was included in the library...Thank you for your reply!",
"It is included in the library :) I'm asking because it may be possible that you have a folder that has the same name, so ... | 1,593 | 1,595 | 1,595 | NONE | null | Hi,
I am following the official example to load longformer model by:
```
from transformers import LongformerModel, LongformerTokenizer
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
model = LongformerModel.from_pretrained('allenai/longformer-base-4096')
```
I can load toke... | {
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https://api.github.com/repos/huggingface/transformers/issues/5375 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5375/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5375/comments | https://api.github.com/repos/huggingface/transformers/issues/5375/events | https://github.com/huggingface/transformers/pull/5375 | 647,558,746 | MDExOlB1bGxSZXF1ZXN0NDQxNTYyNDU0 | 5,375 | Update dependency mecab to at least version 1.0 | {
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"No, I pinned this on purpose because v1.0 has breaking changes. Tokenization is not done the same way (once you go through the obvious bugs) because the underlying dictionary seems different. Not sure how we can go to v1.0 until pretrained models using this tokenizer have been updated (if that's ever done).",
"C... | 1,593 | 1,596 | 1,596 | COLLABORATOR | null | Require at least version 1.0 of mecab, which comes with prebuilt wheels for all major platforms (OS X, Linux, Windows). This should do away with some reported incompatibility issues for non-linux systems.
See https://github.com/SamuraiT/mecab-python3/issues/31#issuecomment-651053281
Note: code untested but nothin... | {
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https://api.github.com/repos/huggingface/transformers/issues/5374 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5374/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5374/comments | https://api.github.com/repos/huggingface/transformers/issues/5374/events | https://github.com/huggingface/transformers/pull/5374 | 647,553,101 | MDExOlB1bGxSZXF1ZXN0NDQxNTU3ODM4 | 5,374 | How to share model cards with the CLI | {
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"I would reverse the two options for now (PR to the repo is still the recommended way at the moment)",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5374?src=pr&el=h1) Report\n> Merging [#5374](https://codecov.io/gh/huggingface/transformers/pull/5374?src=pr&el=desc) into [master](https://codeco... | 1,593 | 1,593 | 1,593 | COLLABORATOR | null | As seen offline with @julien-c, adding a new recommended way to upload model cards along with models. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5373?src=pr&el=h1) Report\n> Merging [#5373](https://codecov.io/gh/huggingface/transformers/pull/5373?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b9ee87f5c730d72b326ef65089a574a0b519e827&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | - T5 pic uploaded to a more permanent place | {
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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 | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): **albert-base-v2**
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 ... | {
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"Hello, I'm facing the same issue. Do you find any solutions ? :)",
"Hello, facing the same problem",
"cc @Rocketknight1 @gante :raised_hands: ",
"Directly using `TFRobertaMainLayer` is unusual and not recommended - @jessiewang158 can you try using `TFRobertaModel` instead?",
"This issue has been automatica... | 1,593 | 1,663 | 1,663 | NONE | null | # ❓ Questions & Help
## Details
I wanna build a new NLU model extented by TFRobertaPreTrainedModel.
And I write code with reference to 'TFRobertaForSequenceClassification' class.
```python
from transformers import TFRobertaPreTrainedModel, TFRobertaMainLayer
class TFRobertaForNLU(TFRobertaPreTrainedModel):
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in v3.0.0 | {
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"@sshleifer this looks good now :) \r\n\r\n```\r\nEpoch 1: 0%| | 2/13382 [00:01<3:15:14, 1.14it/s, loss=171200.000, v_num=1t6n9wkw]Gradient overflow. Skipping step, loss scaler 0 reducing loss... | 1,593 | 1,595 | 1,595 | CONTRIBUTOR | null | 1. update to use from_argparse_args
2. deleted args that were manual but trainer has already.
3. fixed optimizer_step.
4. moved the LR logging to the correct hook
5. dropped the rank_zero_only stuff since that's not needed. All loggers and print ops happen only on rank_zero already. This was redundant.
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"Actually will make a separate PR for the examples README.md - to have the docs in v3.0.0.",
"@sgugger right now it's not ignored, so the slow test will fail because the output isn't the same. I don't think it's too big of a deal though, we can fix that after the release with only partial testing of the file.",
... | 1,593 | 1,593 | 1,593 | MEMBER | null | This PR updates the docs for benchmarks and adds a README.md where the community can post their benchmark results.
Would be happy about Feedback from @sgugger and @LysandreJik .
@LysandreJik - I deleted the part about "This work was done by [Timothy Liu](https://github.com/tlkh)." because the links were broken. | {
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"Hi, I believe this error may be due to [the sentencepiece version 0.1.92 which causes a segmentation fault.](https://github.com/huggingface/transformers/issues/4857).",
"> Hi, I believe this error may be due to [the sentencepiece version 0.1.92 which causes a segmentation fault.](https://github.com/huggingface/t... | 1,593 | 1,607 | 1,593 | NONE | null | # 🐛 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)
* [x] my own modified scripts: (give details below)
The tasks I am working on is:
* ... | {
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"\r\nHi @guotong1988 It's possible to do these tasks with text-to-text approach.\r\n\r\nfor predicting next sentence you can process input like this\r\n`sentence1: sentence1_text sentence2: sentence2_text`\r\nand ask the model to predict `true` if sentence2 is the next sentence else `false`\r\n\r\nand for genera... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Input the previous sentence and predict/generate the next sentence.
Thank you very much. | {
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"Pretty cool!",
"Thank you @julien-c"
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https://api.github.com/repos/huggingface/transformers/issues/5355 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5355/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5355/comments | https://api.github.com/repos/huggingface/transformers/issues/5355/events | https://github.com/huggingface/transformers/pull/5355 | 647,223,357 | MDExOlB1bGxSZXF1ZXN0NDQxMjg1NjQw | 5,355 | Update Bertabs example to work again | {
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"There are `remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization` and also `remi/bertabs-finetuned-extractive-abstractive-summarization`. Which should be used here? @sshleifer ",
"Also #5234",
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https://api.github.com/repos/huggingface/transformers/issues/5350 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5350/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5350/comments | https://api.github.com/repos/huggingface/transformers/issues/5350/events | https://github.com/huggingface/transformers/pull/5350 | 647,036,121 | MDExOlB1bGxSZXF1ZXN0NDQxMTM3MzQw | 5,350 | Move tests/utils.py -> transformers/testing_utils.py | {
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"CI failure is spurious",
"no objection",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5350?src=pr&el=h1) Report\n> Merging [#5350](https://codecov.io/gh/huggingface/transformers/pull/5350?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/28a690a80e6c8dbcb50... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Fixes #5350
The motivation is to allow examples/ tests to use these utilities.
for both groups of tests, the import is
```python
from transformers.testing_utils import slow
```
Motivation: I was about to rewrite the @slow decorator today and felt that this was cleaner. | {
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https://api.github.com/repos/huggingface/transformers/issues/5349 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5349/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5349/comments | https://api.github.com/repos/huggingface/transformers/issues/5349/events | https://github.com/huggingface/transformers/issues/5349 | 647,033,435 | MDU6SXNzdWU2NDcwMzM0MzU= | 5,349 | T5 FP16: bad generations from my converted checkpoint | {
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"Not sure whether I can help you here. I don't think fp16 is really stable for T5 no? ",
"Definitely not stable. But you had an idea for how to fix related to layernorm if i recall?\r\n",
"As it is now layer norm is always done in fp32: https://github.com/huggingface/transformers/blob/9a473f1e43221348334b9e7f95... | 1,593 | 1,599 | 1,599 | CONTRIBUTOR | null | `t5-base` can generate reasonable summaries in fp16, but my checkpoint `sshleifer/t5-base-cnn` cannot. Is there something besides the conversion script I need to do to make fp16 work?
[Colab](https://colab.research.google.com/drive/1k8agNDPdzfF38aTrz5o1kcqvWDHUxAnD?usp=sharing) showing good generations for t5-base,... | {
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"This [comment](https://github.com/huggingface/transformers/issues/3553#issuecomment-624306027) answers this issue."
] | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | ```python
from transformers import *
model = T5ForConditionalGeneration.from_pretrained("t5-base")
```
Is this concerning?
```
Some weights of T5ForConditionalGeneration were not initialized from the model checkpoint at t5-base and are newly initialized: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.w... | {
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"Hi, can you share more details, (all the details asked in the issue template for instance).\r\nIn particular, the exact command line you are using, the full error message, etc...",
"Hi! Thank you for your reply. My apologies for not using the template.\r\n\r\nI'm following the tutorial on Colab and switched Line... | 1,593 | 1,598 | 1,593 | NONE | null | I'm currently following the tutorial on how to train a new language model ([here](https://huggingface.co/blog/how-to-train)) and facing some issues on Colab because of my large training corpus (+40 mil lines, 5 GB).
I tried to use IterableDataset so I can load data on the fly, and I'm getting this error when I try ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5346 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5346/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5346/comments | https://api.github.com/repos/huggingface/transformers/issues/5346/events | https://github.com/huggingface/transformers/issues/5346 | 647,023,878 | MDU6SXNzdWU2NDcwMjM4Nzg= | 5,346 | Upload model card with the CLI | {
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"This is not currently documented (cc @sgugger) but it's already possible, see for instance https://huggingface.co/Helsinki-NLP/opus-mt-fr-en\r\n\r\nAt some point it might become the preferred way of publishing a model card too.",
"Will update the tutorial today.",
"Thanks, this is great!"
] | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | 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. -->
Upload model card README.md with transformers CLI.
## Motivation
Some applications may require to upload multiple models. It would be nice to be able t... | {
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"Thanks for the notebook - I can reproduce the results both on a local GPU and on colab. You are right, this slowdown is disproportionate! \r\nI'm suspecting the double for loop to be the reason for the heavy slow down. It might be possible to replace the two for loops by some smart matrix operations, but not sure.... | 1,593 | 1,600 | 1,600 | NONE | null | # 🐛 Bug
## Information
Text generation when using the `repetition_penalty` takes about 2x-10x longer on a GPU, which is disproportionate and implies the GPU may not be used in that instance.
Reported from https://github.com/minimaxir/aitextgen/issues/34
The `enforce_repetition_penalty_` function at
htt... | {
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Regarding issue #5309
@sshleifer | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5343?src=pr&el=h1) Report\n> Merging [#5343](https://codecov.io/gh/huggingface/transformers/pull/5343?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9fe09cec76efa1e221c3fd6eb8520ba0a911f092&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | MEMBER | null | After reviewing code of similar PyTorch implementation: https://github.com/lucidrains/reformer-pytorch/pull/104 and original trax code again: https://github.com/google/trax/blob/master/trax/layers/research/efficient_attention.py#L1265-L1266, there is a simpler way to implement the backward reverse sort function.
The ... | {
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https://api.github.com/repos/huggingface/transformers/issues/5342 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5342/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5342/comments | https://api.github.com/repos/huggingface/transformers/issues/5342/events | https://github.com/huggingface/transformers/pull/5342 | 646,879,121 | MDExOlB1bGxSZXF1ZXN0NDQxMDI4NDc5 | 5,342 | Added support for XLNet language modelling training in examples | {
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"CircleCI says:\r\nexamples/language-modeling/run_language_modeling.py:29: in <module>\r\n from transformers import (\r\nE ImportError: cannot import name 'DataCollatorForXLNetLanguageModeling'\r\n\r\nHow can I fix this?",
"`DataCollatorForXLNetLanguageModeling` needs to be imported into `src/transformers/__... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Updated src/transformers/data/data_collator.py with a new XLNet-specific collator that applies masking and generates revelant tensors (input_ids, perm_mask, target_mask, labels) as per https://github.com/zihangdai/xlnet/blob/master/data_utils.py. Also added relevant calls and imports in examples/language-modeling/run_l... | {
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"I don't get it"
] | 1,593 | 1,594 | 1,594 | NONE | null | # ❓ I'm using the gpt2 model, In Tensorflow the serving is very slow, Tensorflow is much faster.
## Details
I use tf.saved_model save model
`tf.saved_model.save(model, export_dir="/model/2/")`
Tensorflow reads the model
```
imported = tf.saved_model.load("/model/2/")
f = imported.signatures["serving_default"... | {
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https://api.github.com/repos/huggingface/transformers/issues/5340 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5340/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5340/comments | https://api.github.com/repos/huggingface/transformers/issues/5340/events | https://github.com/huggingface/transformers/issues/5340 | 646,859,546 | MDU6SXNzdWU2NDY4NTk1NDY= | 5,340 | GPT2Tokenizer remove the ' ' (space) if it is at the end of text? | {
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"Hi @carter54,\r\n\r\nThanks for your issue! I think you're correct. This was a bug in a previous version.\r\nNote that for v3.0.0 we have done a major refactoring that corrected this error as far as I can see. \r\n\r\nIf you take a look at this line in current master in this line: https://github.com/huggingface/tr... | 1,593 | 1,594 | 1,594 | NONE | null | # ❓ Questions & Help
GPT2Tokenizer remove the ' ' (space) if it is at the end of text?
## Details
<!-- Description of your issue -->
Hello~ I'm using transformers v2.11.0 to run some GPT2 model.
When I tested the code, I found that the token id list generated by GPT2Tokenizer had confusing result when I input a t... | {
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https://api.github.com/repos/huggingface/transformers/issues/5339 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5339/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5339/comments | https://api.github.com/repos/huggingface/transformers/issues/5339/events | https://github.com/huggingface/transformers/issues/5339 | 646,856,604 | MDU6SXNzdWU2NDY4NTY2MDQ= | 5,339 | Predefined tasks in T5 | {
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"You can take a look the paper to find out other tasks. AFAIK it includes GLUE tasks, and SQuAD QA as well.",
"https://arxiv.org/pdf/1910.10683\r\n\r\nOn page 45 the examples for many tasks are starting and ending on page 52",
"Thanks for the help. I will refer the paper."
] | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | ```
"task_specific_params": {
"summarization": {
"early_stopping": true,
"length_penalty": 2.0,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"translation_en_to_de": {
"early_stopping":... | {
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"I've got the same problem here. Have you solved it?",
"Same here. Any solution to solve? Or is this just a warning message that can be ignored?",
"Pinging @sshleifer ",
"Ignore it. I will try to get it out of the code. I think the lr starts at 0 and climbs up for `warmup_steps` iterations."
] | 1,593 | 1,597 | 1,593 | NONE | null | transformers/examples/seq2seq/finetune.py 219row :
if max(scheduler.get_last_lr()) > 0:
warnings.warn("All learning rates are 0")
why, is there something i left?
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https://api.github.com/repos/huggingface/transformers/issues/5337 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5337/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5337/comments | https://api.github.com/repos/huggingface/transformers/issues/5337/events | https://github.com/huggingface/transformers/pull/5337 | 646,796,514 | MDExOlB1bGxSZXF1ZXN0NDQwOTc4Mjkw | 5,337 | arxiv-ai-gpt2 model card | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5337?src=pr&el=h1) Report\n> Merging [#5337](https://codecov.io/gh/huggingface/transformers/pull/5337?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/efae6645e223f29cf05eeafe95105a9f869b66dd&el=desc) will **decrease... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Add model card and generation script for model arxiv_ai_gpt2. | {
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https://api.github.com/repos/huggingface/transformers/issues/5336 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5336/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5336/comments | https://api.github.com/repos/huggingface/transformers/issues/5336/events | https://github.com/huggingface/transformers/issues/5336 | 646,765,713 | MDU6SXNzdWU2NDY3NjU3MTM= | 5,336 | BillSum dataset finetuning | {
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"Interesting! \r\nI wonder what you get starting from bart-large. \r\nIf you use the new finetune.py with --logger wandb I could more easily see your logs and suggest hyperparameters.\r\n",
"Thanks! and Sure, let me do that and share the results soon!",
"Took me a while to get to this :/ the numbers look as exp... | 1,593 | 1,603 | 1,603 | CONTRIBUTOR | null | (from distilbart issue).
Hey @sshleifer , thanks for the distilled BART version I was able to fine tune it with the same script on BillSum dataset as T5 but the numbers are way different between the two. I just wanted to understand if I might be doing something wrong with regards to fine tuning distilBART, does it r... | {
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https://api.github.com/repos/huggingface/transformers/issues/5335 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5335/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5335/comments | https://api.github.com/repos/huggingface/transformers/issues/5335/events | https://github.com/huggingface/transformers/issues/5335 | 646,691,111 | MDU6SXNzdWU2NDY2OTExMTE= | 5,335 | BertForPreTraining and BertModel when loading TF checkpoints | {
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"Hi! What is your library version? The `output_hidden_states` as argument to the call method was only added in the v3.0.0 version, which was released this morning."
] | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Hello,
I want to load tensorflow checkpoints and when I run code below
```python
config = BertConfig.from_json_file('./bert_config.json')
config.output_hidden_states = True
bert_model = BertForPreTraining.from_pretrained('./model.ckpt', from_tf=True, config=config)
......
self.bert = bert_model
.......
bert... | {
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https://api.github.com/repos/huggingface/transformers/issues/5334 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5334/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5334/comments | https://api.github.com/repos/huggingface/transformers/issues/5334/events | https://github.com/huggingface/transformers/issues/5334 | 646,688,681 | MDU6SXNzdWU2NDY2ODg2ODE= | 5,334 | Add "labels" functionality for all TF Causal LM and Masked LM models | {
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"Awesome! I'm in!!! ",
"Yes please! Happy to help if you need it."
] | 1,593 | 1,594 | 1,594 | MEMBER | null | # 🚀 Feature request
Currently, it is not possible to calculate the loss of a CLM or MLM model using TF:
```python
from transformers import TFBertForMaskedLM, BertTokenizerFast
model = TFBertForMaskedLM.from_pretrained("bert-base-uncased")
tok = BertTokenizerFast.from_pretrained("bert-base-uncased")
input... | {
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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 | The same issue as these two:
https://github.com/huggingface/transformers/issues/1722#issue-517212978
https://github.com/huggingface/transformers/issues/1529#issue-507567684 | {
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https://api.github.com/repos/huggingface/transformers/issues/5332 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5332/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5332/comments | https://api.github.com/repos/huggingface/transformers/issues/5332/events | https://github.com/huggingface/transformers/issues/5332 | 646,655,857 | MDU6SXNzdWU2NDY2NTU4NTc= | 5,332 | Link to the example/summarization in doc is broken | {
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"Duplicate of #5309"
] | 1,593 | 1,593 | 1,593 | NONE | null | The link to the example/summarization folder in [the-big-table-of-tasks](https://huggingface.co/transformers/examples.html#the-big-table-of-tasks) is broken because the folder's name has been changed to seq2seq | {
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https://api.github.com/repos/huggingface/transformers/issues/5331 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5331/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5331/comments | https://api.github.com/repos/huggingface/transformers/issues/5331/events | https://github.com/huggingface/transformers/pull/5331 | 646,604,169 | MDExOlB1bGxSZXF1ZXN0NDQwODU3NTQ4 | 5,331 | Adds train_batch_size, eval_batch_size, and n_gpu to to_sanitized_dict output for logging. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5331?src=pr&el=h1) Report\n> Merging [#5331](https://codecov.io/gh/huggingface/transformers/pull/5331?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0267668c3d648c6e41afda97f5df8671ee880ac3&el=desc) will **decrease... | 1,593 | 1,596 | 1,596 | CONTRIBUTOR | null | Closes #5330
```
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("dir")
>>> args.to_sanitized_dict()
{
"output_dir": "dir",
"overwrite_output_dir": False,
"do_train": False,
...
"train_batch_size": 8,
"eval_batch_size": 8,
"n_gpu": 0,
}
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/5330 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/5330/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/5330/comments | https://api.github.com/repos/huggingface/transformers/issues/5330/events | https://github.com/huggingface/transformers/issues/5330 | 646,603,944 | MDU6SXNzdWU2NDY2MDM5NDQ= | 5,330 | Better hyperparameter tensorboard logging in Trainer. | {
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`Trainer` should write `train_batch_size` and `eval_batch_size` to tensorboard. Currently only `per_device_` batch sizes are logged as hyperparamters, which means unless you know how many gpus you trained on (which is also not logged), you can't know the actual batch sizes used in training.
#... | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,593 | 1,599 | 1,599 | CONTRIBUTOR | null | Currently, `Trainer` automatically closes the tensorboard writer `tb_writer` after the training loop is done. Often I'll want to add extra observations, metrics, data, etc ... to the tensorboard event that requires the trained model (ie after the training loop is done). For example.
```
tb_writer = SummaryWriter(lo... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5328?src=pr&el=h1) Report\n> Merging [#5328](https://codecov.io/gh/huggingface/transformers/pull/5328?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/393b8dc09a97197df1937a7e86c0c6b4ce69c7e9&el=desc) will **increase... | 1,593 | 1,604 | 1,604 | CONTRIBUTOR | null | Set the `close_tb_writer` parameter to `False` in `Trainer.train()` to keep the tensorboard writer open.
```
tb_writer = SummaryWriter(log_dir="some_dir")
# do some stuff with tb_writer before training
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_da... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5327?src=pr&el=h1) Report\n> Merging [#5327](https://codecov.io/gh/huggingface/transformers/pull/5327?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/efae6645e223f29cf05eeafe95105a9f869b66dd&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | (1) I fixed the config of mbart to go from BLEU score 12 to BLEU score 26. This requires that the `decoder_start_token_id=lang_code['ro']` but that we filter that out when we decode.
(2) I added another, harder, integration test case. The expected result is only 1 word different than GT. This also adds implicit covera... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5326?src=pr&el=h1) Report\n> Merging [#5326](https://codecov.io/gh/huggingface/transformers/pull/5326?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5543b30aa6b52da3c8f7d9e525b0edc26226d717&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | CONTRIBUTOR | null | Otherwise, if label is not specified, the following error occurs:
Traceback (most recent call last):
File "run_ner.py", line 303, in <module>
main()
File "run_ner.py", line 101, in main
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5324?src=pr&el=h1) Report\n> Merging [#5324](https://codecov.io/gh/huggingface/transformers/pull/5324?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5543b30aa6b52da3c8f7d9e525b0edc26226d717&el=desc) will **increase... | 1,593 | 1,593 | 1,593 | COLLABORATOR | null | {
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Also it adds a link to the trainer tutorial recently merged in the quicktour.
Preview is [here](https://53867-15... | {
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"also\r\n```bash\r\nSome weights of the model checkpoint at facebook/mbart-large-en-ro were not used when initializing BartForConditionalGeneration: ['lm_head.weight']\r\n```",
"Hey @sshleifer \r\nWant me to pick this one up?",
"It's non trivial. You probably have to update S3. I would pick another.",
"Maybe ... | 1,593 | 1,595 | 1,595 | CONTRIBUTOR | null | This warning is safe to ignore, but can be easily fixed.
```bash
Some weights of BartForConditionalGeneration were not initialized from the model checkpoint at facebook/mbart-large-en-ro and are newly initialized: ['final_logits_bias']
You should probably TRAIN this model on a down-stream task to be able to use it... | {
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