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https://api.github.com/repos/huggingface/transformers/issues/4619 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4619/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4619/comments | https://api.github.com/repos/huggingface/transformers/issues/4619/events | https://github.com/huggingface/transformers/pull/4619 | 625,781,893 | MDExOlB1bGxSZXF1ZXN0NDIzOTIwODcx | 4,619 | removed deprecated use of Variable API from pplm example | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4619?src=pr&el=h1) Report\n> Merging [#4619](https://codecov.io/gh/huggingface/transformers/pull/4619?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/842588c12ffbbe3502c5ab4a18646ad31d9c1e34&el=desc) will **increase... | 1,590 | 1,591 | 1,591 | CONTRIBUTOR | null | This is same as previous [PR](https://github.com/huggingface/transformers/pull/4156) which I closed due to code styling issue. Didn't know that specific isort version was supposed to be used. | {
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https://api.github.com/repos/huggingface/transformers/issues/4618 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4618/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4618/comments | https://api.github.com/repos/huggingface/transformers/issues/4618/events | https://github.com/huggingface/transformers/pull/4618 | 625,739,652 | MDExOlB1bGxSZXF1ZXN0NDIzODg2NzEz | 4,618 | per_device instead of per_gpu/error thrown when argument unknown | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4618?src=pr&el=h1) Report\n> Merging [#4618](https://codecov.io/gh/huggingface/transformers/pull/4618?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/842588c12ffbbe3502c5ab4a18646ad31d9c1e34&el=desc) will **decrease... | 1,590 | 1,590 | 1,590 | MEMBER | null | Modified the trainer argument so that `per_device_train_batch_size` and `per_device_eval_batch_size` are preferred over `per_gpu_*`.
`per_gpu_*` still works when `per_device_*` isn't used, but is deprecated.
The trainer argument parser now throws an error if an argument is unknown, only if the `return_remaining_s... | {
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https://api.github.com/repos/huggingface/transformers/issues/4617 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4617/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4617/comments | https://api.github.com/repos/huggingface/transformers/issues/4617/events | https://github.com/huggingface/transformers/issues/4617 | 625,729,545 | MDU6SXNzdWU2MjU3Mjk1NDU= | 4,617 | run evalation after every epoch in Trainer | {
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"You should use `--evaluate_during_training` which should do mostly what you're looking for",
"@prajjwal1 , you should be able to achieve this with `--evaluate_during_training` provided you set `--save_steps` to `number_of_samples/batch_size`. However, I'm currently having trouble achieving this with that option... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | # 🚀 Feature request
With the current Trainer implementation:
`trainer.train(..)` is called first followed by `trainer.evaluate(..)`. It would be nice if the user can pass the flag `--run_eval` (something similar) to run evaluation after every epoch. It would be nice for users who want to see how model performs on ... | {
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https://api.github.com/repos/huggingface/transformers/issues/4616 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4616/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4616/comments | https://api.github.com/repos/huggingface/transformers/issues/4616/events | https://github.com/huggingface/transformers/pull/4616 | 625,678,877 | MDExOlB1bGxSZXF1ZXN0NDIzODM4MDk2 | 4,616 | [testing] LanguageModelGenerationTests require_tf or require_torch | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4616?src=pr&el=h1) Report\n> Merging [#4616](https://codecov.io/gh/huggingface/transformers/pull/4616?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a9aa7456ac824c9027385b149f405e4f5649273f&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/4615 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4615/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4615/comments | https://api.github.com/repos/huggingface/transformers/issues/4615/events | https://github.com/huggingface/transformers/pull/4615 | 625,671,611 | MDExOlB1bGxSZXF1ZXN0NDIzODMyMzg2 | 4,615 | [Longformer] longformer in question-answering pipeline | {
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"This works well however I noticed some discrepancy in answers generated with pipeline and without pipeline\r\n\r\nfor this example\r\n```\r\nquestion = 'Who was Jim Henson?'\r\ntext = 'Jim Henson was a nice puppet.'\r\n```\r\n\r\npipeline produces `nice puppet.`\r\n without pipeline `a nice puppet`\r\n\r\n@patric... | 1,590 | 1,601 | 1,599 | MEMBER | null | This PR adds `LongformerForQuestionAnswering` in `QuestionAnsweringPipeline`
@patrickvonplaten @ibeltagy | {
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https://api.github.com/repos/huggingface/transformers/issues/4614 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4614/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4614/comments | https://api.github.com/repos/huggingface/transformers/issues/4614/events | https://github.com/huggingface/transformers/pull/4614 | 625,574,625 | MDExOlB1bGxSZXF1ZXN0NDIzNzU2NzQz | 4,614 | [Contributing Doc] Update version command when contributing | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4614?src=pr&el=h1) Report\n> Merging [#4614](https://codecov.io/gh/huggingface/transformers/pull/4614?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a9aa7456ac824c9027385b149f405e4f5649273f&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | MEMBER | null | According to PR: #4131, the `CONTRIBUTING.md` should be updated a bit. @BramVanroy | {
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https://api.github.com/repos/huggingface/transformers/issues/4613 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4613/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4613/comments | https://api.github.com/repos/huggingface/transformers/issues/4613/events | https://github.com/huggingface/transformers/issues/4613 | 625,535,809 | MDU6SXNzdWU2MjU1MzU4MDk= | 4,613 | What does the output of feature-extraction pipeline represent? | {
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"They are embeddings generated from the model. (Bert -Base Model I guess. cause it has a hidden representation of 768 dim). You get 9 elements:- one contextual embedding for each word in your sequence. These values of embeddings represent some hidden features that are not easy to interpret.",
"So the pipeline wil... | 1,590 | 1,621 | 1,596 | NONE | null | I am using the feature-extraction pipeline:
```
nlp_fe = pipeline('feature-extraction')
nlp_fe('there is a book on the desk')
```
As an output I get a list with one element - that is a list with 9 elements - that is a list of 768 features (floats).
What is the output represent? What is every element of the lists,... | {
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https://api.github.com/repos/huggingface/transformers/issues/4612 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4612/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4612/comments | https://api.github.com/repos/huggingface/transformers/issues/4612/events | https://github.com/huggingface/transformers/issues/4612 | 625,532,857 | MDU6SXNzdWU2MjU1MzI4NTc= | 4,612 | Use fill-mask pipeline to get probability of specific token | {
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"Hi, the pipeline doesn't offer such a functionality yet. You're better off using the model directly. Here's an example of how you would replicate the pipeline's behavior, and get a token score at the end:\r\n\r\n```py\r\nfrom transformers import AutoModelWithLMHead, AutoTokenizer\r\nimport torch\r\n\r\ntokenizer =... | 1,590 | 1,703 | 1,596 | NONE | null | Hi,
I am trying to use the fill-mask pipeline:
```
nlp_fm = pipeline('fill-mask')
nlp_fm('Hugging Face is a French company based in <mask>')
```
And get the output:
```
[{'sequence': '<s> Hugging Face is a French company based in Paris</s>',
'score': 0.23106734454631805,
'token': 2201},
{'sequence': '... | {
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https://api.github.com/repos/huggingface/transformers/issues/4611 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4611/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4611/comments | https://api.github.com/repos/huggingface/transformers/issues/4611/events | https://github.com/huggingface/transformers/issues/4611 | 625,521,391 | MDU6SXNzdWU2MjU1MjEzOTE= | 4,611 | Key error while evaluating the Language Model finetuning | {
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"Looks like your training script is out of sync with the library. Can you install the library from source, as documented in https://github.com/huggingface/transformers/tree/master/examples#important-note ?",
"Thanks, @julien-c building from source, solves the issue. ",
"I also find a problem about this......\r\... | 1,590 | 1,606 | 1,590 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): DistilBert
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on i... | {
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https://api.github.com/repos/huggingface/transformers/issues/4610 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4610/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4610/comments | https://api.github.com/repos/huggingface/transformers/issues/4610/events | https://github.com/huggingface/transformers/pull/4610 | 625,466,809 | MDExOlB1bGxSZXF1ZXN0NDIzNjc0Nzg0 | 4,610 | README for HooshvareLab | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4610?src=pr&el=h1) Report\n> Merging [#4610](https://codecov.io/gh/huggingface/transformers/pull/4610?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a9aa7456ac824c9027385b149f405e4f5649273f&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | HooshvareLab/bert-base-parsbert-uncased | {
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"Usually, the input is simply cut in this case. Bart cuts the input to 1024 tokens when training on CNN Daily Mail. T5 cuts the input to 512 tokens when training on CNN Daily Mail.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity... | 1,590 | 1,596 | 1,596 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4607?src=pr&el=h1) Report\n> Merging [#4607](https://codecov.io/gh/huggingface/transformers/pull/4607?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a9aa7456ac824c9027385b149f405e4f5649273f&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | Model card for cased model | {
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https://api.github.com/repos/huggingface/transformers/issues/4606 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4606/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4606/comments | https://api.github.com/repos/huggingface/transformers/issues/4606/events | https://github.com/huggingface/transformers/issues/4606 | 625,337,409 | MDU6SXNzdWU2MjUzMzc0MDk= | 4,606 | Inconsistency in how Electra doing sentence level prediction | {
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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",
"Are there any updates on this issue?"
] | 1,590 | 1,617 | 1,596 | NONE | null | In `ElectraForSequenceClassification`:
We have docstring says, `ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks.`
This is also what I observed in the official repository.
https://github.com/google-research/electra/b... | {
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https://api.github.com/repos/huggingface/transformers/issues/4605 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4605/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4605/comments | https://api.github.com/repos/huggingface/transformers/issues/4605/events | https://github.com/huggingface/transformers/pull/4605 | 625,266,179 | MDExOlB1bGxSZXF1ZXN0NDIzNTE4OTc5 | 4,605 | Glue task cleanup | {
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filesystem
* Differentiate match vs mismatch for MNLI metrics
* Manually flush tensorboard writer to avoid missing metrics. | {
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https://api.github.com/repos/huggingface/transformers/issues/4604 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4604/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4604/comments | https://api.github.com/repos/huggingface/transformers/issues/4604/events | https://github.com/huggingface/transformers/pull/4604 | 625,143,527 | MDExOlB1bGxSZXF1ZXN0NDIzNDE5MDMx | 4,604 | updated model cards for both models at aubmindlab | {
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"Great logo!",
"link seems broken on huggingface.co but I'll fix directly",
"Thank you Julien!"
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | - added AraBERT image.
- updated usage examples
- updated results | {
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https://api.github.com/repos/huggingface/transformers/issues/4603 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4603/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4603/comments | https://api.github.com/repos/huggingface/transformers/issues/4603/events | https://github.com/huggingface/transformers/pull/4603 | 624,972,134 | MDExOlB1bGxSZXF1ZXN0NDIzMjc4MTcx | 4,603 | Creating a readme for ALBERT in Mongolian | {
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"That is awesome, thank you"
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | Here I am uploading Mongolian masked language model (ALBERT) on your platform.
https://en.wikipedia.org/wiki/Mongolia | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4602?src=pr&el=h1) Report\n> Merging [#4602](https://codecov.io/gh/huggingface/transformers/pull/4602?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5ddd8d6531c8c49fdd281b55b93f6c81c9826f4b&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/4601 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4601/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4601/comments | https://api.github.com/repos/huggingface/transformers/issues/4601/events | https://github.com/huggingface/transformers/issues/4601 | 624,953,220 | MDU6SXNzdWU2MjQ5NTMyMjA= | 4,601 | Which models can be using for encoder-decoder? | {
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"@blizda did you find an answer to your query - \"Only Bert can be using as encoder and decoder? If so, can you add list of available models for encoder-decoder in documentation?\"? ",
"There's an error-message saying which models can be used now; \r\n\"... Model type should be one of CamembertConfig, XLMRobertaC... | 1,590 | 1,619 | 1,590 | NONE | null | Hi, I trying to use EncoderDecoderModel. I tried google/electra-base-discriminator, google/electra-small-discriminator, albert-base-v2 as encoder and decoder:
```python
from transformers import EncoderDecoderModel
model = EncoderDecoderModel.from_encoder_decoder_pretrained('google/electra-small-discriminator', '... | {
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https://api.github.com/repos/huggingface/transformers/issues/4599 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4599/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4599/comments | https://api.github.com/repos/huggingface/transformers/issues/4599/events | https://github.com/huggingface/transformers/issues/4599 | 624,840,655 | MDU6SXNzdWU2MjQ4NDA2NTU= | 4,599 | ImportError: cannot import name 'AutoModelForQuestionAnswering' from 'transformers | {
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"What is your `transformers` version? Do you have PyTorch installed?",
"Thank you. I solved.",
"Great to hear!",
"> Thank you. I solved.\r\n\r\nHow did you solve it?",
"I installed pytorch library. "
] | 1,590 | 1,592 | 1,590 | NONE | null | Hi friends,
I would like to use transformers library. But while import I received this error.
This code:
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
import torch
# LOAD MODEL
tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-squad")
model = Auto... | {
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https://api.github.com/repos/huggingface/transformers/issues/4598 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4598/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4598/comments | https://api.github.com/repos/huggingface/transformers/issues/4598/events | https://github.com/huggingface/transformers/pull/4598 | 624,835,267 | MDExOlB1bGxSZXF1ZXN0NDIzMTY2ODA3 | 4,598 | [Reformer] automate axial_pos_shape | {
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"Hi @flozi00,\r\n\r\nThanks for the PR! To be honest I don't think we should merge this. A couple of reasons:\r\n\r\n1. In contrast to `num_buckets` which is said in the paper to be always around ~ `2 * sequence_length / chunk_length` `axial_pos_shape` can be freely set by the user. \r\n2. We are trying to add as l... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | This PR automates the calculation of the axial_pos_shape.
checked that every combination of 2**n works using this sheet: https://docs.google.com/spreadsheets/d/19gnP1ve2fT2F59LNiky44SPpmtmOegfIlk5_3OFXjyU/edit?usp=sharing
@patrickvonplaten
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https://api.github.com/repos/huggingface/transformers/issues/4597 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4597/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4597/comments | https://api.github.com/repos/huggingface/transformers/issues/4597/events | https://github.com/huggingface/transformers/pull/4597 | 624,830,156 | MDExOlB1bGxSZXF1ZXN0NDIzMTYyNjY0 | 4,597 | [Draft] Bharaax outputattentions | {
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https://api.github.com/repos/huggingface/transformers/issues/4596 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4596/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4596/comments | https://api.github.com/repos/huggingface/transformers/issues/4596/events | https://github.com/huggingface/transformers/issues/4596 | 624,805,729 | MDU6SXNzdWU2MjQ4MDU3Mjk= | 4,596 | AttributeError: 'Namespace' object has no attribute 'to_json_string' | {
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"I'm not sure I understand what you're trying to do. Do you mind explaining? Showing the code you're using would help as well.",
"```\r\ntraining_args = dict( num_cores= 8, model_name_or_path= 't5-base',\r\n max_len= 512 ,target_max_len= 2, output_dir= './models',\r\n overwrite_output_dir= True,\r\n per_gpu_t... | 1,590 | 1,590 | 1,590 | NONE | null | trainer.train()
don't know how to set parameters self.args.to_json_string()
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"Hi @gustavscholin, \r\n\r\nThanks for you issue!\r\nCould you please provide a code example so that I can reproduce the error? ",
"Hi, @patrickvonplaten @gustavscholin \r\nFor me, setting \"base_model_prefix\" in modeling_encoder_decoder.py fixed this problem, as finding params is based on self.base_model_prefix... | 1,590 | 1,591 | 1,591 | NONE | null | # 🐛 Bug
## Information
Error when loading a trained EncoderDecoder model. When loading the config the in `configuration_auto.py` the `model_type` is expected on the form `encoder-decoder` but in `configuration_encoder_decoder.py` `model_type` is on the form `encoder_decoder` which raises a KeyError.
The hyp... | {
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"hi, i tried again, and write the class of ClsNerModel to model_tf_bert.py, and then reinstall transformers again. it works!!\r\nso, if i create my class outside the lib of transformers, what else should i do to make it work, and avoid the mistakes above\r\nthanks a lot!"
] | 1,590 | 1,590 | 1,590 | NONE | null | hi, i have create a new class named ClsNerModel inherited from TFBertPreTrainedModel
i have trained it successfully and save the model using model.save_pretrained to some dir
however, when i loaded from that dir using ClsNerModel.from_pretrained, it fails and report some error below.
sorry to report this, and any... | {
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https://api.github.com/repos/huggingface/transformers/issues/4593 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4593/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4593/comments | https://api.github.com/repos/huggingface/transformers/issues/4593/events | https://github.com/huggingface/transformers/pull/4593 | 624,739,183 | MDExOlB1bGxSZXF1ZXN0NDIzMDkwNzAz | 4,593 | [Longformer For Question Answering] Conversion script, doc, small fixes | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4593?src=pr&el=h1) Report\n> Merging [#4593](https://codecov.io/gh/huggingface/transformers/pull/4593?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b86e42e0ac1b59f21f0eccf351d3346bbe3ed4eb&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | MEMBER | null | This PR adds
- Longformer For Question Answering doc
- Adds the link to the (official) uploaded model: https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa
- Some minor refactoring
- Conversion script
@ibeltagy @patil-suraj | {
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"Hi, what's your transformers and pytorch version? Is that the entire code you're using?\r\n\r\nYour code doesn't crash here but crashes at the line\r\n\r\n```py\r\nanswers = nlp(question=ques0, context=abstract, topk = 10)\r\n```\r\n\r\nDo you mind providing the question and the context you're using?",
"Sorry, i... | 1,590 | 1,590 | 1,590 | NONE | null | # 🐛 Bug
I am running Huggingface's nlp pipeline. The code is below:
```
nlp = pipeline("question-answering",
model = 'distilbert-base-cased-distilled-squad',
tokenizer='distilbert-base-cased-distilled-squad')
```
Model I am using is pipeline.
I try run an example using doc... | {
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] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Hi guys!
If you navigate to https://github.com/huggingface/transformers/blob/master/model_cards/mrm8488/bert-italian-finedtuned-squadv1-it-alfa/README.md
You will see the emojis without problem. But if you go to its HTML page on the model hub: https://huggingface.co/mrm8488/bert-italia... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4589?src=pr&el=h1) Report\n> Merging [#4589](https://codecov.io/gh/huggingface/transformers/pull/4589?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b86e42e0ac1b59f21f0eccf351d3346bbe3ed4eb&el=desc) will **not chan... | 1,590 | 1,590 | 1,590 | MEMBER | null | @patrickvonplaten | {
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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"
] | 1,590 | 1,596 | 1,596 | NONE | null | Is it possible to change this in order to predct the next two tokens?
```
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch.nn.functional as F
import re
def grow_branches(sentence_so_far, probs, input_probability,past, h):
#recursive function to find all sentence completio... | {
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https://api.github.com/repos/huggingface/transformers/issues/4587 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4587/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4587/comments | https://api.github.com/repos/huggingface/transformers/issues/4587/events | https://github.com/huggingface/transformers/pull/4587 | 624,557,258 | MDExOlB1bGxSZXF1ZXN0NDIyOTUyMDIw | 4,587 | ensure_ascii=False | {
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https://api.github.com/repos/huggingface/transformers/issues/4586 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4586/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4586/comments | https://api.github.com/repos/huggingface/transformers/issues/4586/events | https://github.com/huggingface/transformers/issues/4586 | 624,507,513 | MDU6SXNzdWU2MjQ1MDc1MTM= | 4,586 | T5Model in fp16 still yield nan with more complex examples | {
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"I got the same issue - seems to happen with the larger models (t5 small is fine)",
"I can reproduce the error - will investigate :-) ",
"Okey this took me quite some time to figure out...\r\n\r\nSo what happens is the following. When setting **all** modules in half as is done in the code snippet above, the fol... | 1,590 | 1,696 | 1,619 | NONE | null | # 🐛 Bug
Hello, thank you for the recent [PR](https://github.com/huggingface/transformers/pull/4436) with fp16 fixes. It seems to work well with short inputs, but once the model is fed with some more complex data it still yields nans.
## Information
Model I am using: T5
Language I am using the model on: Eng... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4585?src=pr&el=h1) Report\n> Merging [#4585](https://codecov.io/gh/huggingface/transformers/pull/4585?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3e3e552125e86824239e445dd3c659df0aea4db9&el=desc) will **decrease... | 1,590 | 1,591 | 1,591 | MEMBER | null | Two changes in this PR:
- As we're introducing more than two tensor backend alternatives I created an enum `TensorType` listing all the possible tensor we can create `TensorType.TENSORFLOW`, `TensorType.PYTORCH`, `TensorType.NUMPY`. This might help newcomers who don't know about `"tf"`, `"pt"`.
_->Note: Tensor... | {
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https://api.github.com/repos/huggingface/transformers/issues/4584 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4584/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4584/comments | https://api.github.com/repos/huggingface/transformers/issues/4584/events | https://github.com/huggingface/transformers/pull/4584 | 624,502,888 | MDExOlB1bGxSZXF1ZXN0NDIyOTEwNTE3 | 4,584 | [ci] fix 3 remaining slow GPU failures | {
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"Failure is `tests/test_hf_api.py::HfApiEndpointsTest::test_presign_and_upload`, which seems unrelated, so going to merge."
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/4583 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4583/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4583/comments | https://api.github.com/repos/huggingface/transformers/issues/4583/events | https://github.com/huggingface/transformers/issues/4583 | 624,493,156 | MDU6SXNzdWU2MjQ0OTMxNTY= | 4,583 | Provide simple way to train a new translation model from scratch | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"unstale",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further ... | 1,590 | 1,675 | 1,621 | NONE | null | # 🚀 Feature request
<!-- A clear and concise description of the feature proposal.
Please provide a link to the paper and code in case they exist. -->
## Motivation
Huggingface just released a huge pile of pretrained translation models. I just want to train a completely custom model on a custom language ... | {
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Add example output. Fix inconsistent use of quotes. | {
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https://api.github.com/repos/huggingface/transformers/issues/4581 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4581/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4581/comments | https://api.github.com/repos/huggingface/transformers/issues/4581/events | https://github.com/huggingface/transformers/pull/4581 | 624,459,316 | MDExOlB1bGxSZXF1ZXN0NDIyODc2NTkz | 4,581 | [GPT2, CTRL] Allow input of input_ids and past of variable length | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4581?src=pr&el=h1) Report\n> Merging [#4581](https://codecov.io/gh/huggingface/transformers/pull/4581?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/4c6b21805647f3a96737a50390a4c3e9463d8ef7&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | MEMBER | null | ## Description
This PR reverts the automatic cutting of input ids as introduced in PR: https://github.com/huggingface/transformers/pull/3734 and fixes issue https://github.com/huggingface/transformers/issues/4368 .
Currently, when `past` is used in combination with `input_ids`, the `input_ids` are cut to just the... | {
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https://api.github.com/repos/huggingface/transformers/issues/4580 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4580/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4580/comments | https://api.github.com/repos/huggingface/transformers/issues/4580/events | https://github.com/huggingface/transformers/pull/4580 | 624,432,748 | MDExOlB1bGxSZXF1ZXN0NDIyODU2MTU5 | 4,580 | LongformerForSequenceClassification | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4580?src=pr&el=h1) Report\n> Merging [#4580](https://codecov.io/gh/huggingface/transformers/pull/4580?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8cc6807e8997b8b7404c07037bd02c578da98baf&el=desc) will **increase... | 1,590 | 1,683 | 1,590 | MEMBER | null | This PR adds `LongformerForSequenceClassification`
@patrickvonplaten @ibeltagy
All the changes here are as per we discussed in `LongformerForQuestionAnswering`.
`forward` method automatically sets global attention on cls token. | {
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https://api.github.com/repos/huggingface/transformers/issues/4579 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4579/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4579/comments | https://api.github.com/repos/huggingface/transformers/issues/4579/events | https://github.com/huggingface/transformers/issues/4579 | 624,409,674 | MDU6SXNzdWU2MjQ0MDk2NzQ= | 4,579 | How to save tokenize data when training from scratch | {
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"There is a method to save tokenizer. Check this notebook: https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb\r\n\r\n",
"Thats what I am using.\r\nits saving it in the `dataset` variable not in any file. ByTokenize data I mean pretraining data.",
"You can look at serialization pract... | 1,590 | 1,596 | 1,596 | NONE | null | # ❓ Questions & Help
I am training Allbert from scratch following the blog post by hugging face. As it mentions that :
> If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step.
How this can be done any suggestions?
As of now , using the method ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4578?src=pr&el=h1) Report\n> Merging [#4578](https://codecov.io/gh/huggingface/transformers/pull/4578?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **decrease... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/4577 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4577/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4577/comments | https://api.github.com/repos/huggingface/transformers/issues/4577/events | https://github.com/huggingface/transformers/issues/4577 | 624,336,072 | MDU6SXNzdWU2MjQzMzYwNzI= | 4,577 | Using whole word masking on training LM from scratch | {
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"I think it's not implemented yet.\r\n\r\n@julien-c any suggestion/thoughts for pretraining with wwm?",
"NVIDIA/Megatron-LM does wwm on the fly in __ getitem __\r\n\r\nWe can do something similar in DataCollatorForLanguageModeling or in the dataset \r\n\r\nhttps://github.com/NVIDIA/Megatron-LM/blob/22c0e300670672... | 1,590 | 1,604 | 1,604 | NONE | null | # ❓ Questions & Help
## Details
Hello everyone,
I wanted to use _whole-word-masking_ in training LM from scratch. I could not have found how to apply this option using Trainer.
I thought this option should be managed in "class DataCollatorForLanguageModeling", but I could not find options for _whole-word-masking._... | {
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https://api.github.com/repos/huggingface/transformers/issues/4576 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4576/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4576/comments | https://api.github.com/repos/huggingface/transformers/issues/4576/events | https://github.com/huggingface/transformers/issues/4576 | 624,330,916 | MDU6SXNzdWU2MjQzMzA5MTY= | 4,576 | OSError: Model name 'transfo-xl-wt103' was not found in tokenizers model name list (transfo-xl-wt103). We assumed 'transfo-xl-wt103' was a path, a model identifier, or url to a directory containing vocabulary files named ['vocab.bin', 'vocab.txt'] but couldn't find such vocabulary files at this path or url. | {
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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"
] | 1,590 | 1,596 | 1,596 | NONE | null | # 🐛 Bug
## 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/4575 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4575/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4575/comments | https://api.github.com/repos/huggingface/transformers/issues/4575/events | https://github.com/huggingface/transformers/issues/4575 | 624,329,536 | MDU6SXNzdWU2MjQzMjk1MzY= | 4,575 | Onnx notebook problem | {
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"You can try newer version of PyTorch (like 1.3~1.5). The problem shall be resolved.",
"Hi thanks for the reply\r\nI tried with pytorch 1.4 but I got another error on the below\r\n\r\ndo you have any idea... | 1,590 | 1,590 | 1,590 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4574?src=pr&el=h1) Report\n> Merging [#4574](https://codecov.io/gh/huggingface/transformers/pull/4574?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/b86e42e0ac1b59f21f0eccf351d3346bbe3ed4eb&el=desc) will **decrease... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | Fix for issue [#4525](https://github.com/huggingface/transformers/issues/4525). | {
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https://api.github.com/repos/huggingface/transformers/issues/4573 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4573/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4573/comments | https://api.github.com/repos/huggingface/transformers/issues/4573/events | https://github.com/huggingface/transformers/issues/4573 | 624,269,517 | MDU6SXNzdWU2MjQyNjk1MTc= | 4,573 | Transformers' trainer sequence classification problem | {
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"I ended up using the vanilla way to train",
"I think compute_metrics should return a dictionary string to metric values. That is how it is written in the docstring of the train function"
] | 1,590 | 1,602 | 1,593 | CONTRIBUTOR | null | # ❓ Transformers' trainer sequence classification problem
## Details
I wanted to use `XLMRobertaForSequenceClassification` to classify a sequence into `1` or `0`.
```python
MODEL_NAME = 'xlm-roberta-base'
def multilingual_model(max_seq_length=SEQUENCE_LENGTH, trainable=False):
"""Build and return a mult... | {
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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"
] | 1,590 | 1,596 | 1,596 | NONE | null | # 🐛 Bug
In the GPT2 documentation [page](https://huggingface.co/transformers/model_doc/gpt2.html#gpt2lmheadmodel), in the parameters of both LMHead and DoubleHeads model, 'inputs_embeds' is registered as 'input_embeds' which leads to an error upon implementation.
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"Hello !\r\n\r\nWhich version of the lib do you use?",
"Hello!\r\n\r\nI think it's a problem with your GPU rather than from transformers. Check your TensorFlow whether \"Failed to load the native TensorFlow runtime.\" appears once it is imported.",
"That's probably because you don't have `tensorflow>=2.0` insta... | 1,590 | 1,596 | 1,596 | NONE | null | # 🐛 Bug
hi, thanks for your nice tool for NLP.
However, when I use transformers on MacOs to load Electra model I get an import error
> ImportError: cannot import name 'TFElectraModel' from 'transformers'
How can I fix this issue?
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https://api.github.com/repos/huggingface/transformers/issues/4570 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4570/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4570/comments | https://api.github.com/repos/huggingface/transformers/issues/4570/events | https://github.com/huggingface/transformers/pull/4570 | 624,134,115 | MDExOlB1bGxSZXF1ZXN0NDIyNjE2MTI3 | 4,570 | Model card: Updated the link to the paper | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4570?src=pr&el=h1) Report\n> Merging [#4570](https://codecov.io/gh/huggingface/transformers/pull/4570?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **decrease... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | The conference has changed the link to the paper, so I updated it. | {
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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"
] | 1,590 | 1,596 | 1,596 | NONE | null | # ❓ Questions & Help
## Details
hi, i made item collaborative filtering model by albert model.
i set vocab size to 600k, it made OOM.
(after initialized model, memory used 3 GB. but During initialization, it takes 11 GB)
i tracked source code and i found this:
albert model initialize albert word embedding... | {
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https://api.github.com/repos/huggingface/transformers/issues/4568 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4568/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4568/comments | https://api.github.com/repos/huggingface/transformers/issues/4568/events | https://github.com/huggingface/transformers/issues/4568 | 624,101,901 | MDU6SXNzdWU2MjQxMDE5MDE= | 4,568 | ❓ [BART] Different embedding sizes between pre-trained / fine-tuned checkpoint | {
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"Good catch. There is no mask token in the second checkpoint. I believe that is the same as the authors' implementation.\r\n\r\nCompletely off topic: if you still have the xsum data you used I would love a copy. I'm sam [at] huggingface.co . ",
"Thanks for your fast answer ! \r\n\r\nDo you know why there is no ma... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | # ❓ Questions & Help
Running this code :
```python
from transformers import BartModel
x = BartModel.from_pretrained('bart-large')
x2 = BartModel.from_pretrained('bart-large-cnn')
print(x.shared)
print(x2.shared)
```
Gives :
>Embedding(50265, 1024, padding_idx=1)
Embedding(50264, 1024, padding_idx=1... | {
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https://api.github.com/repos/huggingface/transformers/issues/4567 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4567/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4567/comments | https://api.github.com/repos/huggingface/transformers/issues/4567/events | https://github.com/huggingface/transformers/issues/4567 | 624,058,797 | MDU6SXNzdWU2MjQwNTg3OTc= | 4,567 | ❓ [BART] Why using bias for LM head if not trained ? | {
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"Great Q! The point of using them is that `MarianMTModel`, which inherits from `BartForConditionalGeneration` uses them. You're correct for the bart checkpoints they stay 0. If you think there is a comment or different approach that would be clearer, I'm very open to PR/other ideas."
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | # ❓ Questions & Help
As I understood, BART is not using a regular Linear layer as LM head, but instead reuse the weights of the shared embeddings.
As show here, biases are added :
https://github.com/huggingface/transformers/blob/a34a9896ac2a4a33ff9cd805c76eed914c8d8965/src/transformers/modeling_bart.py#L876
B... | {
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https://api.github.com/repos/huggingface/transformers/issues/4566 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4566/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4566/comments | https://api.github.com/repos/huggingface/transformers/issues/4566/events | https://github.com/huggingface/transformers/pull/4566 | 624,053,746 | MDExOlB1bGxSZXF1ZXN0NDIyNTUxNzg4 | 4,566 | variable name changes for Issue #4141 | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4566?src=pr&el=h1) Report\n> Merging [#4566](https://codecov.io/gh/huggingface/transformers/pull/4566?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **increase... | 1,590 | 1,596 | 1,596 | NONE | null | Hi
Let me know if additional changes are required for Issue #4141. Thank you for this awesome repository. | {
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https://api.github.com/repos/huggingface/transformers/issues/4565 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4565/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4565/comments | https://api.github.com/repos/huggingface/transformers/issues/4565/events | https://github.com/huggingface/transformers/issues/4565 | 624,006,767 | MDU6SXNzdWU2MjQwMDY3Njc= | 4,565 | changing config.axial_pos_shape for 'ReformerModelWithLMHead' when fine-tuning | {
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"I would not recommend to set `axial_pos_shape` to (512 * 1024). In the notebook I just used that to demonstrate how far the limits can be pushed for Reformer. Half a million token is extremely long and usually unnecessary. \r\n\r\nMake sure you have read and understood how AxialPostionEmbeddings work: https://hugg... | 1,590 | 1,628 | 1,598 | NONE | null | # ❓ Questions & Help
I'm trying to fine-tune the Reformer for the language generation task and I padded the sequence lengths to be a multiple of least common multiple chunk_length 64, and now I'm asked to pad the sequence to 524288(512 * 1024), which will give me an out of memory error.
I would like to know a wo... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4564?src=pr&el=h1) Report\n> Merging [#4564](https://codecov.io/gh/huggingface/transformers/pull/4564?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **decrease... | 1,590 | 1,590 | 1,590 | MEMBER | null | Fix automatic setting of `num_buckets` by making sure `num_buckets` is always a power of 2 and set as default.
The idea behind this whole function is that `num_buckets` should not be set by the user, but calculated on the fly to a good value before training (`num_buckets` ~ 2 * sequence length / chunk length as reco... | {
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https://api.github.com/repos/huggingface/transformers/issues/4563 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4563/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4563/comments | https://api.github.com/repos/huggingface/transformers/issues/4563/events | https://github.com/huggingface/transformers/issues/4563 | 623,919,777 | MDU6SXNzdWU2MjM5MTk3Nzc= | 4,563 | Decoding with DistilmBERT to generate text in different languages | {
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"So I can get the generation working well with distilgpt2, the thing is that I would like to do it multilingual using the light multilingual model DistilmBERT (distilbert-base-multilingual-cased), any tips? thank you :)\r\n\r\n```py\r\nimport torch\r\nfrom transformers import *\r\nfrom transformers import TFGPT2LM... | 1,590 | 1,596 | 1,596 | NONE | null | Good day and congrats for your great library
If I want to decode and get new generated text with the GPT2 heads, that works great like you suggest:
```py
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
input_ids = torch.tensor(tokenizer.encode("Once upon a time there was")).unsqueeze(0)
model = GPT2LMHeadMod... | {
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https://api.github.com/repos/huggingface/transformers/issues/4562 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4562/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4562/comments | https://api.github.com/repos/huggingface/transformers/issues/4562/events | https://github.com/huggingface/transformers/issues/4562 | 623,908,303 | MDU6SXNzdWU2MjM5MDgzMDM= | 4,562 | implementation of transformers for abstractive summarization task | {
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"@sshleifer might be able to help you here. @sshleifer - hope it's fine that I link you here :-) ",
"This might also help: https://github.com/huggingface/transformers/pull/4539/files?short_path=3a2ba7b#diff-3a2ba7b492f00029d14cec3994b73ac7",
"> This might also help: https://github.com/huggingface/transformers/p... | 1,590 | 1,590 | 1,590 | NONE | null | Hello, I am new to whole NLP world and PyTorch. I am trying to learn the concepts and that is taking some time for a rookie. I have a project to finish and I want to implement transformers & BERT on my abstractive summarization project. I tried to find some implementation tutorial on this topic but I could not find any... | {
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"Does this also happen when you use the slow tokenizers?\r\n\r\n```python\r\ntokenizer = AlbertTokenizer.from_pretrained(\"albert-base-v2\", use_fast=False)\r\n```",
"Thanks for the suggestion @BramVanroy . I just tried this and the tokenizer has now been running for 70 minutes, so I think that's a yes, it also h... | 1,590 | 1,598 | 1,598 | NONE | null | # 🐛 Bug
## Information
I am following the language modeling tutorial to train a LM on a simple wikipedia corpus from scratch. I am trying to use Albert instead of Roberta. As I couldn't find information on how to train an Albert Tokenizer from scratch, I'm loading the albert-base-v2 tokenizer. The Dataset creation... | {
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https://api.github.com/repos/huggingface/transformers/issues/4559 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4559/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4559/comments | https://api.github.com/repos/huggingface/transformers/issues/4559/events | https://github.com/huggingface/transformers/issues/4559 | 623,876,175 | MDU6SXNzdWU2MjM4NzYxNzU= | 4,559 | XLnet loss and accuracy not decreasing | {
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"I recommend to change your preproccessing to:\r\n\r\n```python\r\nfrom transformers import XLNetTokenizer \r\nfrom keras.preprocessing.sequence import pad_sequences\r\ntokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')\r\n\r\nencoded_sent = tokenizer.encode_plus(\r\n text, # Sentence to enc... | 1,590 | 1,590 | 1,590 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): XLnet base cased
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)
Can check the notebook ... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4558?src=pr&el=h1) Report\n> Merging [#4558](https://codecov.io/gh/huggingface/transformers/pull/4558?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **decrease... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | As per the code, distilbert is indeed supported by the `run_language_modeling.py` script, even though the README states otherwise. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4557?src=pr&el=h1) Report\n> Merging [#4557](https://codecov.io/gh/huggingface/transformers/pull/4557?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **increase... | 1,590 | 1,592 | 1,592 | MEMBER | null | Give more explicit logging messages when using the various `from_pretrained` methods in the lib.
Also makes these messages as `logging.warning` because it's a common source of silent mistakes.
cc @BramVanroy
Happy to improve the language further if people have advice. | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4556?src=pr&el=h1) Report\n> Merging [#4556](https://codecov.io/gh/huggingface/transformers/pull/4556?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/4549 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4549/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4549/comments | https://api.github.com/repos/huggingface/transformers/issues/4549/events | https://github.com/huggingface/transformers/issues/4549 | 623,818,001 | MDU6SXNzdWU2MjM4MTgwMDE= | 4,549 | Example script for SQuAD question answering unable to reproduce the claimed performance | {
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"My results are different as well: \r\n\r\n\"exact_match\": 71.92999053926206\r\n\"f1\": 80.70949484221217\r\n\r\nMy guess is that this occurs because we are not using a fixed seed. The runs are not deterministic so difference _will_ occur.",
"Possibly but the difference of 7~8 points in f1 and EM scores is way a... | 1,590 | 1,590 | 1,590 | NONE | null | # 🐛 Bug
## Information
The example script for SQuAD question answering (`examples/question-answering/run-squad.py`) fails to produce the correct results as claimed in the tutorial.
The correct performance is around f1 = 88.52, exact_match = 81.22 on SQuAD v1.1, but the script produces f1 = 81.97 and exact match... | {
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"Hey @drjosephliu - thanks so much for opening this PR! \r\nSorry for being lazy here - could you check out the comments I added in PR #4538 - I think they apply 1-to-1 the same way here.",
"No problem. I'm a bit busy this week, but will aim to get it done by the end of the week.",
"> No problem. I'm a bit busy... | 1,590 | 1,592 | 1,592 | CONTRIBUTOR | null | Attempts to close #3879 by refactoring `config.output_hidden_states` as an argument,`output_hidden_states`, to functions `forward()` and `call()`. Affects all PT and TF models that output hidden states.
Currently it is failing the following tests: `run_tests_tf`, `run_tests_torch` and `run_tests_torch_and_tf` becaus... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4547?src=pr&el=h1) Report\n> Merging [#4547](https://codecov.io/gh/huggingface/transformers/pull/4547?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | MEMBER | null | This PR adds `LongformerTokenizerFast` by sub-classing `RobertaTokenizerFast`.
@patrickvonplaten | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4546?src=pr&el=h1) Report\n> Merging [#4546](https://codecov.io/gh/huggingface/transformers/pull/4546?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a34a9896ac2a4a33ff9cd805c76eed914c8d8965&el=desc) will **decrease... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | The text index of test data of SST-2 are 1 rather than 0.
The label of MNLI task has a tricky swap on MNLI dataset, which should also be involved in get_labels() of dataset for correctness. | {
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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"
] | 1,590 | 1,596 | 1,596 | CONTRIBUTOR | null | https://github.com/huggingface/transformers/blob/a34a9896ac2a4a33ff9cd805c76eed914c8d8965/src/transformers/tokenization_gpt2.py#L338-L343
only passes four parameters without `lowercase` to
https://github.com/huggingface/tokenizers/blob/704cf3fdd2f607ead58a561b892b510b49c301db/bindings/python/tokenizers/implementation... | {
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https://api.github.com/repos/huggingface/transformers/issues/4544 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4544/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4544/comments | https://api.github.com/repos/huggingface/transformers/issues/4544/events | https://github.com/huggingface/transformers/issues/4544 | 623,786,946 | MDU6SXNzdWU2MjM3ODY5NDY= | 4,544 | seems that run_ner.py cannot handle the situation when example length exceed max_length? | {
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"Hi! Do you mind filling-in the template? It will help us help you better!",
"> Hi! Do you mind filling-in the template? It will help us help you better!\r\n\r\nOK!I think this will be a great practice for me",
"Great, thanks"
] | 1,590 | 1,592 | 1,592 | NONE | null | # 🐛 Bug
## 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)
* [x] my own modified scripts: (give details below)
The tasks I am working on is:
* [ ] an offici... | {
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"Hi @erickrf, \r\n\r\nThanks a lot for catching the error. The linked PR should solve it by making sure `num_buckets` is always a power of 2."
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): Reformer
transformers version: 2.9.0
When using a Reformer model such that `config.num_buckets` is set to `None` (as recommended), the model automatically determines the number of necessary buckets. However, depending on some hyperparameters, it may ... | {
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https://api.github.com/repos/huggingface/transformers/issues/4542 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4542/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4542/comments | https://api.github.com/repos/huggingface/transformers/issues/4542/events | https://github.com/huggingface/transformers/issues/4542 | 623,785,492 | MDU6SXNzdWU2MjM3ODU0OTI= | 4,542 | RuntimeError: The size of tensor a (1025) must match the size of tensor b (1024) at non-singleton dimension 3 | {
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"Length of 2000 is too long for GPT2, this won't be possible. \r\nYou can do two things here:\r\n\r\n1) You chunk your generation which means that you first produce a length of up to 1000 and then use a bit of that (100 or so tokens) as context to generate the next 900 tokens and the same again until you hit 2000.\... | 1,590 | 1,590 | 1,590 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): gpt2
Language I am using the model on (English, Chinese ...): english
The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on... | {
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"Hi @lingdoc, \r\n\r\nThanks for reporting this. Unfortunately, I'm not able to reproduce currently ... Loading, then training and finally saving works as espected on my side, with various tokenizers.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n# Some tra... | 1,590 | 1,596 | 1,596 | NONE | null | # 🐛 Bug
## Information
Model I am using: all/any
Language I am using the model on: English
The problem arises when using:
* [x] the official example scripts: `AutoTokenizer.from_pretrained([model], use_fast=True)`
After updating to Transformers v2.10.0, when setting `use_fast=True` as in `tokenizer = A... | {
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"This is not enough information to help us. Can you post a minimal reproducible example, or at least how you construct the model. Also show where the error is triggered. (And just my two cents, but adding RNNs on top of transformer-based models seems redundant... But I guess you can try it out!)",
"Hi @BramVanroy... | 1,590 | 1,596 | 1,596 | CONTRIBUTOR | null | **System information**
- TensorFlow version `2.1.0`
- Python version: `3`
- GPU model and memory: `NVIDIA Tesla P100`
- CUDA Version: `10.1`
- Environment: This happens both on Kaggle and Colab
**Describe the current behavior**
I'm trying to train a Hugging face transformer model (roBERTa base) with a custom t... | {
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"Also removed the tests for `output_attentions` since all of them were fetching values from the config",
"@Bharat123rox - that's awesome, thanks a lot! It's a lot of manual work, but it will be super useful once it's merged :-)\r\n\r\nI added a bunch of comments - let me know if something is unclear.\r\n\r\nRegar... | 1,590 | 1,591 | 1,591 | CONTRIBUTOR | null | Attempts to close #3880 by refactoring `config.output_attentions` => `output_attentions` in `forward()`/`call()` functions
**UPDATE** from Patrick
@thomwolf @LysandreJik
This PR adds the argument `output_attentions` to every forward function for more flexibility. This PR makes it possible to easily switch the o... | {
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```
pytorch_model = BertForSequenceClassification.from_pretrained('./models/', from_tf=True)
NameError: name 'BertForSequenceCla... | {
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"Hi @virginianegri, \r\n\r\nCould you specific for which model the method `resize_token_embeddings` does not work? \r\nCan you add a code snippet that reproduces the error?",
"The model is the TFGPT2LMHeadModel. This is my code:\r\n```\r\nfrom transformers import GPT2Tokenizer, TFGPT2LMHeadModel \r\n\r\ntokenizer... | 1,590 | 1,596 | 1,596 | NONE | null | I am using a TFGPT2LMHeadModel pretrained model and new special tokens to the gpt tokenizer.
However, the method resize_token_embeddings is not implemented in all gpt2 tf models.
Will it be added? Or are there any workarounds?
Thank you! | {
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https://api.github.com/repos/huggingface/transformers/issues/4535 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4535/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4535/comments | https://api.github.com/repos/huggingface/transformers/issues/4535/events | https://github.com/huggingface/transformers/issues/4535 | 623,605,657 | MDU6SXNzdWU2MjM2MDU2NTc= | 4,535 | How to speed up inference step in BertQuestionAnswering? | {
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"To improve inference speed you can use ONNX (also see here: https://github.com/huggingface/transformers/issues/260). In addition, you can opt for a distilled model rather than the full model. ",
"Hi! I've used this:\r\n\r\n\r\ndistilbert-base-uncased-distilled-squad | \r\n-- | --\r\nor \r\n\r\ndistilbert-base-c... | 1,590 | 1,590 | 1,590 | NONE | null | I'm working on a QA system which uses a pre-trained BertQA model. At this point, even if I use GPU, the step generating start_scores and end_scores for a set of 20 candidate passages till takes a few seconds which is the bottleneck of my application.
I just wonder do we have any strategies/tricks to speed up this s... | {
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https://api.github.com/repos/huggingface/transformers/issues/4533 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4533/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4533/comments | https://api.github.com/repos/huggingface/transformers/issues/4533/events | https://github.com/huggingface/transformers/pull/4533 | 623,580,180 | MDExOlB1bGxSZXF1ZXN0NDIyMjIwNjQ4 | 4,533 | Add nn.Module as superclass | {
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"Are there upstream issues? I can't see how my one-liner is breaking these tests",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4533?src=pr&el=h1) Report\n> Merging [#4533](https://codecov.io/gh/huggingface/transformers/pull/4533?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | Add nn.Module as superclass of `MMBTModel` to fix #4532 | {
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## Information
Model I am using (Bert, XLNet ...): MMBT
Language I am using the model on (English, Chinese ...): not related
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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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4531?src=pr&el=h1) Report\n> Merging [#4531](https://codecov.io/gh/huggingface/transformers/pull/4531?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e19b978151419fe0756ba852b145fccfc96dbeb4&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | MEMBER | null | Fix #4457
By using `flatten`, the following
```
dict_values(['[EOS]', '[BOS]'])
```
was being transformed into this:
```
['[', 'E', 'O', 'S', ']', '[', 'B', 'O', 'S', ']']
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/4530 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4530/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4530/comments | https://api.github.com/repos/huggingface/transformers/issues/4530/events | https://github.com/huggingface/transformers/pull/4530 | 623,509,197 | MDExOlB1bGxSZXF1ZXN0NDIyMTYzODMz | 4,530 | Tensorflow improvements | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4530?src=pr&el=h1) Report\n> Merging [#4530](https://codecov.io/gh/huggingface/transformers/pull/4530?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d976ef262e0b2c52363d201b2e14e5ecc42abbb3&el=desc) will **increase... | 1,590 | 1,591 | 1,591 | CONTRIBUTOR | null | Hello,
Here a quite big PR that propose the following updates:
- Loss computation is now attached to their respective class, such as PyTorch.
- Remove useless `mode` and `loss_name` parameters for the TF Trainer.
- Add missing task models to different Transformers
- Bugfix on T5 keras serialization + tests
- ... | {
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"You're absolutely right. Feel free to submit a PR to rectify this!",
"> You're absolutely right. RoBERTa swaps NSP for sentence order prediction (SOP). \r\n\r\nI think SOP was introduced into Albert \r\n\r\n>To further improve the performance of ALBERT, we also introduce a self-supervised loss for\r\nsentence-or... | 1,590 | 1,607 | 1,607 | CONTRIBUTOR | null |
In the roberta model docs
https://huggingface.co/transformers/model_doc/roberta.html
>pooler_output (tf.Tensor of shape (batch_size, hidden_size)):
Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear la... | {
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https://github.com/huggingface/transformers/issues/4527 | {
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"Additional info: I ran git-blame, and determined that the change was introduced in PR [#3706](https://github.com/huggingface/transformers/pull/3706). ",
"Hi @soldni, thanks for reporting the issue.\r\n\r\nThe behavior you mention can now be achieved through: \r\n\r\n```python\r\n>>> tok.encode('This is a sentenc... | 1,590 | 1,590 | 1,590 | NONE | null | # 🐛 Bug
## Information
Hello! I've just upgraded from Transformers 2.8 to Transformers 2.10, and noticed that parameter `max_len` is not properly honored when instantiating a pretrained model. For example, in Transformer 2.8.0, I was able to limit the length of a tokenized sequence as follows:
```python
impo... | {
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"Thanks!"
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | changed from https://https://arxiv.org/abs/2001.04451.pdf to https://arxiv.org/abs/2001.04451.pdf | {
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"Good catch! I guess that soon you'd want to move over PyTorch's built-in AMP which takes care of this automatically (I _think_), but for the time being your suggestion is a good fix. You can submit a PR if you want!",
"I think this is solved with the PR #4574 no?"
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): Longformer
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 i... | {
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https://api.github.com/repos/huggingface/transformers/issues/4524 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4524/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4524/comments | https://api.github.com/repos/huggingface/transformers/issues/4524/events | https://github.com/huggingface/transformers/issues/4524 | 623,397,496 | MDU6SXNzdWU2MjMzOTc0OTY= | 4,524 | Codecov migration to marketplace app | {
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"cc @LysandreJik @thomwolf ",
"Hi @thomasrockhu, indeed we've faced such issues before. We'll take a look, thanks!",
"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"
] | 1,590 | 1,596 | 1,596 | NONE | null | Hi, Tom from Codecov here.
We noticed that you are using our app with high frequency, and we’re so excited to see that! However, because you are not using our app, you may have experienced issues with uploading reports or viewing coverage information. This is due to rate-limiting from GitHub.
**In order to preven... | {
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https://api.github.com/repos/huggingface/transformers/issues/4523 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4523/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4523/comments | https://api.github.com/repos/huggingface/transformers/issues/4523/events | https://github.com/huggingface/transformers/issues/4523 | 623,282,683 | MDU6SXNzdWU2MjMyODI2ODM= | 4,523 | Can't reproduce export to onnx with custom bert model | {
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"Pinging @mfuntowicz, chief onnx officer",
"Hi @RensDimmendaal,\r\n\r\nThanks for reporting this 👍.\r\nCan you share the shape of the input you're feeding to the ONNX model? ",
"Thanks for investigating!\r\n\r\n```python\r\nfor k,v in inputs_onnx.items():\r\n print(f\"{k}: shape: {v.shape}\")\r\n```\r\n```\r\... | 1,590 | 1,591 | 1,591 | CONTRIBUTOR | null | # 🐛 Bug
I try to run onnx export on a custom bert model, but during inference I get the following error.
I share a google colab with the minimum changes to reproduce. All changes are marked with a `# CHANGE` comment. https://colab.research.google.com/drive/1eiqyQmvhwGih6IHrOg7MkLSc2q0zMHmH?usp=sharing
```
Inva... | {
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https://api.github.com/repos/huggingface/transformers/issues/4522 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4522/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4522/comments | https://api.github.com/repos/huggingface/transformers/issues/4522/events | https://github.com/huggingface/transformers/pull/4522 | 623,224,177 | MDExOlB1bGxSZXF1ZXN0NDIxOTMxNjA3 | 4,522 | Added huseinzol05/t5-small-bahasa-cased README.md | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4522?src=pr&el=h1) Report\n> Merging [#4522](https://codecov.io/gh/huggingface/transformers/pull/4522?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/bd6e3018322766b3a71ae6675552607923c02636&el=desc) will **increase... | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | {
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"I guess you can, since the architecture of distilbert is more or less the same as BERT (just half the layers), but I would not expect great performance. The power of distilling lies (next to the training objective) in having a teacher model (e.g.. a full BERT model) and initializing the distilled student network w... | 1,590 | 1,596 | 1,596 | NONE | null | # ❓ Questions & Help
Is it a right thing to use 'distilbert-base-cased' for training that with more data for some language ( which exists in the list of multilingual languages , and doesn't have a separate model still)
Will we reach good results as good as doing that with 6 layer BERT ?
Is there any differences... | {
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https://api.github.com/repos/huggingface/transformers/issues/4520 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4520/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4520/comments | https://api.github.com/repos/huggingface/transformers/issues/4520/events | https://github.com/huggingface/transformers/issues/4520 | 623,112,792 | MDU6SXNzdWU2MjMxMTI3OTI= | 4,520 | How to use its own custom Optimizer (GLUE Example) | {
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"Hi, the Trainer takes an optional `optimizers` arg which is a two-tuple of (optimizer, scheduler):\r\n\r\nhttps://github.com/huggingface/transformers/blob/95a26fcf2d8d7072e4e63129cea8605f756bba1d/src/transformers/trainer.py#L152-L181",
"I created the optimizers using the same way. But the model is not getting tr... | 1,590 | 1,685 | 1,590 | NONE | null | # ❓ Questions & Help
I am referring to the example on GLUE text-classification/run_glue.py.
I would like to change the default optimizer (which I believe it to be ADAMW) to my own one.
Should be in the imported Trainer or TrainingArguments, but I have not found an example doing so.
Is it possible with any optimiz... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4519?src=pr&el=h1) Report\n> Merging [#4519](https://codecov.io/gh/huggingface/transformers/pull/4519?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a08652772791fdaeed6f263b1a99926ca64be5dc&el=desc) will **decrease... | 1,590 | 1,651 | 1,623 | MEMBER | null | By setting `device` parameter in `DataCollator` we're able to allocate tensors directly on the right device at tensor creation and avoid moving data around afterwards.
This effectively avoid some snippet like this in the Trainer:
```python
for k, v in some_dict.items():
v.to(device)
``` | {
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"ooook.... this is embarrassing. I just realized that I had to detach the variable, so GPU memory could be freed. \r\nThis does the trick:\r\n```\r\nencoded_sentences.append( [x.detach().to('cpu') for x in model_outputs[4]+model_outputs[1]] )\r\n```\r\n\r\nsorry for the trouble ;) and thanks for the repo and all y... | 1,590 | 1,590 | 1,590 | NONE | null | # 🐛 Bug
## Information
I am extracting the internal representations of some of the Marian models.
There seems to be a memory leak problem. In this issue, you will find code for running the model sentence by sentence (bsz = 1) just to keep it simple. When I use batching, the problem persists and arises earlier.
... | {
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"Hi @chenjunweii - thanks for your issue! I will take a deeper look at the EncoderDecoder framework at the end of this week and should add a google colab on how to fine-tune it.",
"Using Bert - Bert model for seq2seq task should work using simpletransformers library, there is an working code.\r\nBut there is one ... | 1,590 | 1,662 | 1,591 | NONE | null | How to train a custom seq2seq model with `BertModel`,
I would like to use some Chinese pretrained model base on `BertModel`
so I've tried using `Encoder-Decoder Model`, but it seems the`Encoder-Decoder Model` is not used for conditional text generation
and I saw that BartModel seems to be the model I need, but... | {
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"I ran your script five times and I cannot reproduce this so it's very hard to debug. Can you clear the cache created by convert_examples and try again? And if that does not work, try updating tensorflow and tensorflow_datasets to the latest versions.",
"Thanks for your attention. As i said \"sometimes\", the scr... | 1,590 | 1,590 | 1,590 | NONE | null | # 🐛 Bug
## Information
The problem arises when using:
* [x] the official example scripts: (give details below)
"Quick tour TF 2.0 training and PyTorch interoperability"
## To reproduce
Steps to reproduce the behavior:
1.run the code:
```
import tensorflow as tf
import tensorflow_datasets
from transf... | {
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https://api.github.com/repos/huggingface/transformers/issues/4515 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4515/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4515/comments | https://api.github.com/repos/huggingface/transformers/issues/4515/events | https://github.com/huggingface/transformers/pull/4515 | 623,075,669 | MDExOlB1bGxSZXF1ZXN0NDIxODEyODg0 | 4,515 | Allow BatchEncoding to be pickled | {
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"Issues seems related to pretokenized input. Checking with @n1t0 what changed in-between 👍 ",
"Indeed, I think we'll have to merge https://github.com/huggingface/transformers/pull/4510 before this.",
"Closing in favor of #5039"
] | 1,590 | 1,651 | 1,592 | MEMBER | null | Overrides the methods ___get_state()___ & ___set_state()___ to (respectively) export the content of the underlying `data` dictionnary and - if defined - the content of `encodings`.
Unittests added to covert the serialization & deserialization of all the exported properties.
Block until new release of **tokenizer... | {
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https://api.github.com/repos/huggingface/transformers/issues/4514 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/4514/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/4514/comments | https://api.github.com/repos/huggingface/transformers/issues/4514/events | https://github.com/huggingface/transformers/issues/4514 | 623,022,855 | MDU6SXNzdWU2MjMwMjI4NTU= | 4,514 | ❓ How Linear layer difference between TF2 and PT are handled ? | {
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"[Here's an example when loading a TF BERT model in PyTorch.](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py#L122)"
] | 1,590 | 1,590 | 1,590 | CONTRIBUTOR | null | # ❓ Questions & Help
There is a difference between TF2 and Pytorch on how to store the weights of a Linear Layer.
As shown in [this Colab notebook](https://colab.research.google.com/drive/1zLWONO3wo09-PImo0kg1bwARsh2jKnpQ?usp=sharing), in order to get the same output for both TF2 and PT when using `torch.nn.Linea... | {
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