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https://api.github.com/repos/huggingface/transformers/issues/1410 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1410/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1410/comments | https://api.github.com/repos/huggingface/transformers/issues/1410/events | https://github.com/huggingface/transformers/issues/1410 | 501,916,488 | MDU6SXNzdWU1MDE5MTY0ODg= | 1,410 | migrate BertForQuestionAnswering from pytorch-pretrained-bert not produce the same result | {
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"Hello! Have you put these models in `eval()` mode so as to deactivate the dropout modules?",
"For completeness sake: did you train both models with the same random seed? Or are you just trying to evaluate models that you trained?\r\n\r\nMy go-to method is:\r\n\r\n```python\r\ndef set_seed(seed):\r\n \"\"\" Se... | 1,570 | 1,576 | 1,576 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): retrained Bert
Language I am using the model on (English, Chinese....): multilingual - vietnamese
The tasks I am working on is:
* an official GLUE/SQUaD task: SQUaD
* my own task or dataset: same format as SQUaD
## To Reproduce... | {
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https://api.github.com/repos/huggingface/transformers/issues/1409 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1409/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1409/comments | https://api.github.com/repos/huggingface/transformers/issues/1409/events | https://github.com/huggingface/transformers/pull/1409 | 501,851,798 | MDExOlB1bGxSZXF1ZXN0MzI0MDQzOTMx | 1,409 | Evaluation result.txt path changing #1286 | {
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"Great, that looks good to me!",
"Ok, merging, thanks @brian41005 "
] | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | Here is the suggestion that I mention in issues #1286
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https://api.github.com/repos/huggingface/transformers/issues/1408 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1408/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1408/comments | https://api.github.com/repos/huggingface/transformers/issues/1408/events | https://github.com/huggingface/transformers/issues/1408 | 501,784,074 | MDU6SXNzdWU1MDE3ODQwNzQ= | 1,408 | Batched BertForNextSentencePrediction with variable length sentences | {
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"Hello! \r\n\r\n1 - Indeed, if you want to have several sequences of variable length in a single batch, you should pad the shorter sequences.\r\n\r\n\r\n2 - In the [`BertForNextSentencePrediction ` documentation](https://huggingface.co/transformers/model_doc/bert.html#bertfornextsentenceprediction) is written the f... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
What's the proper way to pad a batch of variable length sentences for the BertForNextSentencePrediction model?
I want to batch a list of sentences, and each sentence can have any length < max_seq_len. To fit them into a token tensor I assume I will need some form of padding?
Here's an... | {
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https://api.github.com/repos/huggingface/transformers/issues/1407 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1407/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1407/comments | https://api.github.com/repos/huggingface/transformers/issues/1407/events | https://github.com/huggingface/transformers/issues/1407 | 501,776,758 | MDU6SXNzdWU1MDE3NzY3NTg= | 1,407 | GPT-2 Training on non-english text | {
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"Hi! By \"GPT-2 training\" two different methods can be understood: training from scratch, and fine-tuning. \r\n\r\nIf you're looking at training GPT-2 on a different language such as Portuguese, then training from scratch seems necessary. You could use the [language modeling finetuning example](https://github.com/... | 1,570 | 1,615 | 1,597 | NONE | null | ## ❓ Questions & Help
I wish to train a GPT-2 in different languages, like Portuguese and maybe some programming languages like C++ (and play with token predictions).
But I could not find any examples of how to take an X dataset (like c++ source files), create the tokens from it and train a GPT-2 to predict new t... | {
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https://api.github.com/repos/huggingface/transformers/issues/1406 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1406/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1406/comments | https://api.github.com/repos/huggingface/transformers/issues/1406/events | https://github.com/huggingface/transformers/pull/1406 | 501,711,249 | MDExOlB1bGxSZXF1ZXN0MzIzOTMyNjIw | 1,406 | Distil update | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1406?src=pr&el=h1) Report\n> Merging [#1406](https://codecov.io/gh/huggingface/transformers/pull/1406?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/63ed224b7c550ead5f9599187e665ded57ce80d4?src=pr&el=desc) will **n... | 1,570 | 1,651 | 1,570 | MEMBER | null | Update Distil*
- update on distilbert weights
- add distilgpt2 weights
- link to the paper
- big update on code | {
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https://api.github.com/repos/huggingface/transformers/issues/1405 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1405/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1405/comments | https://api.github.com/repos/huggingface/transformers/issues/1405/events | https://github.com/huggingface/transformers/pull/1405 | 501,659,443 | MDExOlB1bGxSZXF1ZXN0MzIzODg5OTEz | 1,405 | Re-order XLNet attention head outputs for better perf | {
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"Remaining CI failures are valid, they look to assume a `ijbn` ordering for all attention-based things, which no longer holds.\r\n\r\nI'm happy to add additional functionality to get these tests passing, but I'd like input on how you'd like that done (I'd lean to passing an optional `expected_attention_size` which ... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | Significant performance boost over the original orderings. On an already somewhat optimised branch this gave me > 2x end-to-end throughput on a squad xlnet fine-tuning task (batch 8, seq-length 512, fp16, amp opt level = O2)
Justifying this is the contraction
```
attn_vec = torch.einsum('bnij,jbnd->ibnd', attn_pr... | {
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https://api.github.com/repos/huggingface/transformers/issues/1404 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1404/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1404/comments | https://api.github.com/repos/huggingface/transformers/issues/1404/events | https://github.com/huggingface/transformers/issues/1404 | 501,640,115 | MDU6SXNzdWU1MDE2NDAxMTU= | 1,404 | How to speedup BERT eval | {
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"Did you try using DistilBERT? Inference should be ~ 60% faster",
"Turns out I wasn't using the using the gpu correctly. I moved the model and the inputs to gpu by doing `.to(device)` and it became 100x faster. Thanks for the suggestion."
] | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
Is there a simple way to speedup `.eval()` when using the BERT model
Specifically I am using `BertForSequenceClassification`. I have finetuned a the model separately on my own data and I am trying to get hidden representations after doing `model.eval()` as follows:
`last_hidden_layer, all_hidde... | {
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https://api.github.com/repos/huggingface/transformers/issues/1403 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1403/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1403/comments | https://api.github.com/repos/huggingface/transformers/issues/1403/events | https://github.com/huggingface/transformers/issues/1403 | 501,626,602 | MDU6SXNzdWU1MDE2MjY2MDI= | 1,403 | Is it possible to modify the parameters in GPT-2? | {
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"Hi! GPT-2, like all models in this library, directly inherit from pytorch's `nn.Module`, so you're free to finetune them or modify their parameters as you wish.",
"> Hi! GPT-2, like all models in this library, directly inherit from pytorch's `nn.Module`, so you're free to finetune them or modify their parameters... | 1,570 | 1,570 | 1,570 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I wonder whether it's possible to modify the parameters in GPT-2? Since we can not train GPT-2, modifying the parameters and observing the changes in results will be helpful. Thank you in advance! | {
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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"
] | 1,570 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
Hi,
Thanks for the awesome library 😊
I saw the examples on fine-tuning the models. My question is, how could we get model definitions i.e the layered architectures (model.summary) in Keras.
Any example notebooks demonstrating how we could get the model definitions and extend the archi... | {
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https://api.github.com/repos/huggingface/transformers/issues/1401 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1401/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1401/comments | https://api.github.com/repos/huggingface/transformers/issues/1401/events | https://github.com/huggingface/transformers/issues/1401 | 501,555,845 | MDU6SXNzdWU1MDE1NTU4NDU= | 1,401 | XLM add new models | {
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"Hi! Those models actually are available, we just forgot to add them to the documentation :). Thanks for letting us know!"
] | 1,570 | 1,570 | 1,570 | NONE | null | hi
can u add to your libs new pretrained models by XLM, like `mlm_17_1280.pth` & `mlm_100_1280.pth`? | {
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https://api.github.com/repos/huggingface/transformers/issues/1400 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1400/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1400/comments | https://api.github.com/repos/huggingface/transformers/issues/1400/events | https://github.com/huggingface/transformers/pull/1400 | 501,547,727 | MDExOlB1bGxSZXF1ZXN0MzIzNzk5MjI4 | 1,400 | Fix typo: initialy -> initially | {
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"Great thanks!",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1400?src=pr&el=h1) Report\n> Merging [#1400](https://codecov.io/gh/huggingface/transformers/pull/1400?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/391db836ab7ed2ca61c51a7cf1b135b6ab92be58?src=p... | 1,570 | 1,570 | 1,570 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1399 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1399/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1399/comments | https://api.github.com/repos/huggingface/transformers/issues/1399/events | https://github.com/huggingface/transformers/issues/1399 | 501,210,201 | MDU6SXNzdWU1MDEyMTAyMDE= | 1,399 | Generate Variable Length Text With GPT2 | {
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"Hi! In Write With Transformer, we use the context to predict the following token. We then add that token to the initial context, to generate the following one. This way we can generate long sequences according to a given token.\r\n\r\nIn that app we stop generating tokens once we have reached a given time, or once... | 1,569 | 1,570 | 1,570 | NONE | null | This might be obviously explained in the documentation, but I've been browsing through the code for a while and can't seem to find a resolution, so thank you in advance for your help.
As demoed with Write with Transformers, it seems to generate variable length text suggestions. I was wondering how this would be poss... | {
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https://api.github.com/repos/huggingface/transformers/issues/1398 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1398/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1398/comments | https://api.github.com/repos/huggingface/transformers/issues/1398/events | https://github.com/huggingface/transformers/pull/1398 | 501,209,385 | MDExOlB1bGxSZXF1ZXN0MzIzNTI4NDM4 | 1,398 | Fixed typo in docs README | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1398?src=pr&el=h1) Report\n> Merging [#1398](https://codecov.io/gh/huggingface/transformers/pull/1398?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/391db836ab7ed2ca61c51a7cf1b135b6ab92be58?src=pr&el=desc) will **n... | 1,569 | 1,570 | 1,570 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1397 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1397/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1397/comments | https://api.github.com/repos/huggingface/transformers/issues/1397/events | https://github.com/huggingface/transformers/pull/1397 | 501,203,132 | MDExOlB1bGxSZXF1ZXN0MzIzNTIzMzcx | 1,397 | remove token type inputs from roberta - fix #1234 | {
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https://api.github.com/repos/huggingface/transformers/issues/1396 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1396/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1396/comments | https://api.github.com/repos/huggingface/transformers/issues/1396/events | https://github.com/huggingface/transformers/pull/1396 | 501,084,428 | MDExOlB1bGxSZXF1ZXN0MzIzNDI1OTkx | 1,396 | Fix syntax typo in README.md | {
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"Thanks :)",
"Your welcome, you are doing a great job!",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1396?src=pr&el=h1) Report\n> Merging [#1396](https://codecov.io/gh/huggingface/transformers/pull/1396?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5c3b... | 1,569 | 1,569 | 1,569 | NONE | null | 
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https://api.github.com/repos/huggingface/transformers/issues/1395 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1395/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1395/comments | https://api.github.com/repos/huggingface/transformers/issues/1395/events | https://github.com/huggingface/transformers/issues/1395 | 500,978,921 | MDU6SXNzdWU1MDA5Nzg5MjE= | 1,395 | Masking of special tokens in masked LM finetuning. | {
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"The same issue here, using 4 RTX 2080Ti with Ubuntu 18.04.",
"This issue exists as the `mask_tokens` function will sometimes replace `<s>` with a random word. Not sure whether `<s>` should be masked. A workaround would be adding a line \r\n```\r\nmasked_indices[:, 0] = 0 # tokenizer.bos_token_id\r\n```\r\nright... | 1,569 | 1,590 | 1,576 | NONE | null | ## 🐛 Bug
roBERTa throws repeated warnings about the absence of special tokens in masked LM fine-tuning with `run_lm_finetuning.py`:
```
WARNING - transformers.modeling_roberta - A sequence with no special tokens has been passed to the RoBERTa model. This model requires special tokens in order to work. Please... | {
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https://api.github.com/repos/huggingface/transformers/issues/1394 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1394/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1394/comments | https://api.github.com/repos/huggingface/transformers/issues/1394/events | https://github.com/huggingface/transformers/issues/1394 | 500,973,294 | MDU6SXNzdWU1MDA5NzMyOTQ= | 1,394 | Change gpt2 language model loss function | {
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"Afaik the \"default\" loss function that gets computed if you pass your labels to `GPT2LMHeadModel` is `torch.nn.CrossEntropyLoss`. If you want to use a different loss function, can't you just grab the logits from the model and apply your own?\r\n\r\nSource:\r\nhttps://github.com/huggingface/transformers/blob/391d... | 1,569 | 1,576 | 1,576 | NONE | null | Hi all,
I want to include a new loss term for the gpt2 training loss. I am using the script run_lm_finetuning from the examples. This is my command:
python examples/run_lm_finetuning.py --output_dir=output --model_type=gpt2 --model_name_or_path=gpt2 --do_train --train_data_file=$TRAIN_FILE --eval_data_file=$TEST_... | {
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https://api.github.com/repos/huggingface/transformers/issues/1393 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1393/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1393/comments | https://api.github.com/repos/huggingface/transformers/issues/1393/events | https://github.com/huggingface/transformers/issues/1393 | 500,868,377 | MDU6SXNzdWU1MDA4NjgzNzc= | 1,393 | With GPT-2 is it possible to get previous word prediction? | {
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"Hi! There is one big difference between BERT and GPT-2, in that BERT is trained using masked language modeling, whereas GPT-2 is trained using causal language modeling.\r\n\r\nDuring pre-training, BERT learns to predict masked words given a bi-directional context. GPT-2, on the other hand, learns to predict a word... | 1,569 | 1,575 | 1,570 | NONE | null |
Feature/Question: With GPT-2 is it possible to get previous word prediction?
Hi,
I say this after seeing this https://towardsdatascience.com/deconstructing-bert-distilling-6-patterns-from-100-million-parameters-b49113672f77
And wondering how I could maybe write a method that would allow me to predict the pre... | {
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https://api.github.com/repos/huggingface/transformers/issues/1392 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1392/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1392/comments | https://api.github.com/repos/huggingface/transformers/issues/1392/events | https://github.com/huggingface/transformers/issues/1392 | 500,789,373 | MDU6SXNzdWU1MDA3ODkzNzM= | 1,392 | Bert's keyword argument 'output_all_encoded_layers' does not exist anymore? | {
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"Hi! @thomwolf can correct me if I'm wrong, but I believe this keyword was changed to `output_hidden_states` in version 1.0.0.",
"I can confirm what @LysandreJik suggests. The output of the embeddings is now also included as the first element. ",
"Alright, thank you very much!"
] | 1,569 | 1,570 | 1,570 | NONE | null | ## 📚 Migration
Model I am using (Bert, XLNet....): Bert
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [ ] the official example scripts: (give details)
* [X] my own modified scripts: (give details)
The tasks I am working on is:
* [ ] an official GLUE/SQU... | {
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https://api.github.com/repos/huggingface/transformers/issues/1391 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1391/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1391/comments | https://api.github.com/repos/huggingface/transformers/issues/1391/events | https://github.com/huggingface/transformers/issues/1391 | 500,750,856 | MDU6SXNzdWU1MDA3NTA4NTY= | 1,391 | Built-in pretrained models location | {
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"I've found it. It's a binary file. It's in ~/.cache/torch/transformers"
] | 1,569 | 1,569 | 1,569 | NONE | null | My laptop was run out of disk space while loading built-in pre-trained model.
Now BertForTokenClassification.from_pretrained("bert-base-cased") gives me RuntimeError: unexpected EOF, expected 5896093 more bytes. The file might be corrupted.
Where can I find that incomplete model and delete it so I can download th... | {
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https://api.github.com/repos/huggingface/transformers/issues/1390 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1390/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1390/comments | https://api.github.com/repos/huggingface/transformers/issues/1390/events | https://github.com/huggingface/transformers/issues/1390 | 500,733,997 | MDU6SXNzdWU1MDA3MzM5OTc= | 1,390 | ❓ How to use cached hidden states in run_generation ? | {
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"Hi! Yes, you understood the gist of it. The self-attention related to already computed tokens is not computed again.\r\n\r\nIn order to use the past, you would get the past from the model pass (I'm using GPT-2 in this example, XLNet would have `mems` instead of `past`):\r\n\r\n```py\r\nlogits, past = model(**input... | 1,569 | 1,575 | 1,575 | CONTRIBUTOR | null | ## ❓ Questions & Help
https://github.com/huggingface/transformers/blob/5c3b32d44d0164aaa9b91405f48e53cf53a82b35/examples/run_generation.py#L124
This line states that we could use `cached hidden states`. Correct me if I'm wrong :
* **Without using `cached hidden states`** : every step, the next token is predict... | {
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https://api.github.com/repos/huggingface/transformers/issues/1389 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1389/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1389/comments | https://api.github.com/repos/huggingface/transformers/issues/1389/events | https://github.com/huggingface/transformers/pull/1389 | 500,721,745 | MDExOlB1bGxSZXF1ZXN0MzIzMTMxNTMx | 1,389 | Fix compatibility issue with PyTorch 1.2 | {
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"Hi,\r\nWe can accept this since it breaks lower versions of PyTorch.\r\nYou can just feed your mask as a FloatTensor (as indicated in the docstrings I think)."
] | 1,569 | 1,571 | 1,571 | CONTRIBUTOR | null | Using PyTorch 1.2.0 give an error when running XLNet.
We should use the new way to reverse mask : instead of using `1 - mask`, we should use `~mask` | {
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https://api.github.com/repos/huggingface/transformers/issues/1388 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1388/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1388/comments | https://api.github.com/repos/huggingface/transformers/issues/1388/events | https://github.com/huggingface/transformers/pull/1388 | 500,659,132 | MDExOlB1bGxSZXF1ZXN0MzIzMDgxMTI5 | 1,388 | Add Roberta SQuAD model | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1388?src=pr&el=h1) Report\n> Merging [#1388](https://codecov.io/gh/huggingface/transformers/pull/1388?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5c3b32d44d0164aaa9b91405f48e53cf53a82b35?src=pr&el=desc) will **d... | 1,569 | 1,576 | 1,576 | NONE | null | There is the realisation of a RoBERTa SQuAD finetuning.
On 2x1080Ti on RoBERTa Base it gives:
python3 run_squad.py \
--model_type roberta \
--model_name_or_path roberta-base \
--do_train \
--do_eval \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--per_gp... | {
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https://api.github.com/repos/huggingface/transformers/issues/1387 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1387/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1387/comments | https://api.github.com/repos/huggingface/transformers/issues/1387/events | https://github.com/huggingface/transformers/issues/1387 | 500,574,184 | MDU6SXNzdWU1MDA1NzQxODQ= | 1,387 | TFTransfoXLLMHeadModel doesn't accept lm_labels parameter | {
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"I see now that I missed something. The documentation uses the parameter 'lm_labels' but the correct parameter is just 'labels'. The documentation says that when this parameter is present, prediction logits will not be output, but this is incorrect. They are output regardless of the presence of 'labels'.",
"This ... | 1,569 | 1,575 | 1,575 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): TFTransfoXLLMHeadModel
Language I am using the model on (English, Chinese....): Other
The problem arise when using:
* [ ] the official example scripts: (give details)
* [ X ] my own modified scripts: I have a script that trains a... | {
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https://api.github.com/repos/huggingface/transformers/issues/1386 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1386/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1386/comments | https://api.github.com/repos/huggingface/transformers/issues/1386/events | https://github.com/huggingface/transformers/pull/1386 | 500,558,576 | MDExOlB1bGxSZXF1ZXN0MzIzMDAwOTcy | 1,386 | Add RoBERTa question answering & Update SQuAD runner to support RoBERTa | {
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"@thomwolf / @LysandreJik / @VictorSanh / @julien-c Could you help review this PR? Thanks!",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1386?src=pr&el=h1) Report\n> Merging [#1386](https://codecov.io/gh/huggingface/transformers/pull/1386?src=pr&el=desc) into [master](https://codecov.io/gh/hu... | 1,569 | 1,576 | 1,576 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1385 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1385/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1385/comments | https://api.github.com/repos/huggingface/transformers/issues/1385/events | https://github.com/huggingface/transformers/pull/1385 | 500,497,651 | MDExOlB1bGxSZXF1ZXN0MzIyOTUzNDYx | 1,385 | [multiple-choice] Simplify and use tokenizer.encode_plus | {
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"Great addition. I feel like using enums would be especially helpful for the truncating strategy, indeed.",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1385?src=pr&el=h1) Report\n> Merging [#1385](https://codecov.io/gh/huggingface/transformers/pull/1385?src=pr&el=desc) into [master](https://c... | 1,569 | 1,574 | 1,570 | MEMBER | null | Our base tokenizer `PreTrainedTokenizer` now has the ability to encode a sentence pair up to a `max_length`, adding special tokens for each model and returning a mask of `token_type_ids`.
In this PR we upgrade `run_multiple_choice` by adopting this factorized tokenizer API.
To ensure the results are strictly the ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1384 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1384/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1384/comments | https://api.github.com/repos/huggingface/transformers/issues/1384/events | https://github.com/huggingface/transformers/pull/1384 | 500,451,678 | MDExOlB1bGxSZXF1ZXN0MzIyOTIwMTE1 | 1,384 | Quality of life enhancements in encoding + patch MLM masking | {
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"I think we should drop the `always_truncate` param, and just set it to `True` iff `max_length is not None`",
"Other than that I like it.",
"As seen with @julien-c , `always_truncate` really should be enabled by default when a `max_length` is specified."
] | 1,569 | 1,578 | 1,570 | MEMBER | null | This PR aims to add quality of life features to the encoding mechanism and patches an issue with the masked language modeling masking function.
1 - ~It introduces an `always_truncate` argument to the `encode` method.~ The `always_truncate` argument is now used as default, with no option to set it to `False` when a `... | {
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https://api.github.com/repos/huggingface/transformers/issues/1383 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1383/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1383/comments | https://api.github.com/repos/huggingface/transformers/issues/1383/events | https://github.com/huggingface/transformers/pull/1383 | 500,442,605 | MDExOlB1bGxSZXF1ZXN0MzIyOTEyOTk5 | 1,383 | Adding CTRL | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1383?src=pr&el=h1) Report\n> Merging [#1383](https://codecov.io/gh/huggingface/transformers/pull/1383?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/1c5079952f5f10eeac4cb6801b4fd1f36b0eff73?src=pr&el=desc) will **i... | 1,569 | 1,570 | 1,570 | CONTRIBUTOR | null | EDIT 10/04
Almost complete (tests pass / generation makes sense).
Please comment with issues if you find them.
**Incomplete - Adding to facilitate collaboration**
This PR would add functionality to perform inference on CTRL (https://github.com/salesforce/ctrl) in the `🤗/transformers` repo.
Commits will be sq... | {
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https://api.github.com/repos/huggingface/transformers/issues/1382 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1382/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1382/comments | https://api.github.com/repos/huggingface/transformers/issues/1382/events | https://github.com/huggingface/transformers/issues/1382 | 500,308,414 | MDU6SXNzdWU1MDAzMDg0MTQ= | 1,382 | Issue with `decode` in the presence of special tokens | {
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"Can't reproduce this on master now. Seems to be fixed.",
"Thanks a lot. It seems to be fixed. Now I get `'[SEP]'` and `' [SEP]'` consecutively with the first and the second command above. So we can close this issue."
] | 1,569 | 1,570 | 1,570 | CONTRIBUTOR | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): GPT-2
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [ ] the official example scripts: (give details)
* [x] my own modified scripts: (give details)
The tasks I am working on is... | {
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https://api.github.com/repos/huggingface/transformers/issues/1381 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1381/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1381/comments | https://api.github.com/repos/huggingface/transformers/issues/1381/events | https://github.com/huggingface/transformers/issues/1381 | 500,302,590 | MDU6SXNzdWU1MDAzMDI1OTA= | 1,381 | how to train RoBERTa from scratch | {
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"https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.pretraining.md",
"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",
"You can now leave `--model_name_or_path` to ... | 1,569 | 1,582 | 1,576 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I want to train RoBERTa model from scratch on different language. Is there any implementation available here to do this? | {
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https://api.github.com/repos/huggingface/transformers/issues/1380 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1380/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1380/comments | https://api.github.com/repos/huggingface/transformers/issues/1380/events | https://github.com/huggingface/transformers/issues/1380 | 500,045,764 | MDU6SXNzdWU1MDAwNDU3NjQ= | 1,380 | Confusing tokenizer result on single word | {
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"Hey @malmaud I think this #1196 can help you. The Roberta/GPT2 tokenizer expect a space to start. Without that, it sounds like you'll get strange behaviors.\r\n\r\nTo get the same output, in your first example, change it to \r\n```\r\nt.tokenize(\"mystery\", add_prefix_space=True)\r\n['Ġmystery']\r\n```",
"That ... | 1,569 | 1,575 | 1,575 | NONE | null | Not sure if this is expected, but it seems confusing to me:
```python
import transformers
t=transformers.AutoTokenizer.from_pretrained('roberta-base')
t.tokenize("mystery")
```
yields two tokens, `['my', 'stery']`.
Yet
```
t.tokenize("a mystery")
```
*also* yields two tokens, `['a', 'Ġmystery']`.... | {
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https://api.github.com/repos/huggingface/transformers/issues/1379 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1379/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1379/comments | https://api.github.com/repos/huggingface/transformers/issues/1379/events | https://github.com/huggingface/transformers/issues/1379 | 499,994,566 | MDU6SXNzdWU0OTk5OTQ1NjY= | 1,379 | TransfoXLCorpus requires pytorch to tokenize files | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | ## 🐛 Bug
The current TransfoXLCorpus code requires pytorch, and fails if it is not installed.
Model I am using (Bert, XLNet....): Transformer-XL
Language I am using the model on (English, Chinese....): Other
The problem arise when using:
* [ X ] my own modified scripts: I'm using a very simple script to r... | {
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https://api.github.com/repos/huggingface/transformers/issues/1378 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1378/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1378/comments | https://api.github.com/repos/huggingface/transformers/issues/1378/events | https://github.com/huggingface/transformers/issues/1378 | 499,954,198 | MDU6SXNzdWU0OTk5NTQxOTg= | 1,378 | TFDistilBertForSequenceClassification - TypeError: len is not well defined for symbolic Tensors during model.fit() | {
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"so, how to solve this problem?",
"Should be solved on master and the latest release."
] | 1,569 | 1,571 | 1,570 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (TFDistilBertForSequenceClassification):
Language I am using the model on (English):
The problem arise when using: model.fit()
* [ ] the official example scripts:
* [x] my own modified scripts:
The tasks I am working on is:
* [ ] an official G... | {
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https://api.github.com/repos/huggingface/transformers/issues/1377 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1377/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1377/comments | https://api.github.com/repos/huggingface/transformers/issues/1377/events | https://github.com/huggingface/transformers/issues/1377 | 499,932,246 | MDU6SXNzdWU0OTk5MzIyNDY= | 1,377 | Error when calculate tokens_id and Mask LM | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (DistilBert):
Language I am using the model on (English):
The problem arise when using: Distiller.prepare_batch( )
Error when token_ids is masked by mask LM matrix
* the official example scripts:
_token_ids_real = token_ids[pred_mask]
* my ow... | {
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https://api.github.com/repos/huggingface/transformers/issues/1376 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1376/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1376/comments | https://api.github.com/repos/huggingface/transformers/issues/1376/events | https://github.com/huggingface/transformers/issues/1376 | 499,916,787 | MDU6SXNzdWU0OTk5MTY3ODc= | 1,376 | Is it save the best model when used example like run_glue? | {
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<!-- A clear and concise description of the question. -->
I read the code of `run_glue.py`, I think it just save model checkpoint and the last step.
Is it wrong for me, or do I have to do some other operations? | {
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https://api.github.com/repos/huggingface/transformers/issues/1375 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1375/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1375/comments | https://api.github.com/repos/huggingface/transformers/issues/1375/events | https://github.com/huggingface/transformers/issues/1375 | 499,912,208 | MDU6SXNzdWU0OTk5MTIyMDg= | 1,375 | cannot import name 'TFBertForSequenceClassification' | {
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"Hi! The TensorFlow components are only available when you have TF2 installed on your system. Could you please check that you have it in the environment in which you're running your code?",
"It worked. Thanks"
] | 1,569 | 1,569 | 1,569 | NONE | null | I am unable to import TFBertForSequenceClassification.
from transformers import TFBertForSequenceClassification shows an error of cannot import name 'TFBertForSequenceClassification' | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | In QNLI task, the ids should be truncated is the pair cuz that is the huge one. Or we can't load QNLI dataset successfully. | {
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https://api.github.com/repos/huggingface/transformers/issues/1373 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1373/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1373/comments | https://api.github.com/repos/huggingface/transformers/issues/1373/events | https://github.com/huggingface/transformers/pull/1373 | 499,906,047 | MDExOlB1bGxSZXF1ZXN0MzIyNDk4MTU0 | 1,373 | Fixed critical css font-family issues | {
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"Amazing!"
] | 1,569 | 1,570 | 1,570 | CONTRIBUTOR | null | Fixed critical css font-family issues to ensure compatibility with multiple web browsers | {
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https://api.github.com/repos/huggingface/transformers/issues/1372 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1372/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1372/comments | https://api.github.com/repos/huggingface/transformers/issues/1372/events | https://github.com/huggingface/transformers/pull/1372 | 499,880,746 | MDExOlB1bGxSZXF1ZXN0MzIyNDg0OTQ3 | 1,372 | Simplify code by using six.string_types | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1372?src=pr&el=h1) Report\n> Merging [#1372](https://codecov.io/gh/huggingface/transformers/pull/1372?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/fd97761c5a977fd22df789d2851cf57c7c9c0930?src=pr&el=desc) will **i... | 1,569 | 1,576 | 1,576 | NONE | null | https://six.readthedocs.io/#six.string_types | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1371?src=pr&el=h1) Report\n> Merging [#1371](https://codecov.io/gh/huggingface/transformers/pull/1371?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ae50ad91ea2fedb64ecd2e7c8e2d0d4778dc03aa?src=pr&el=desc) will **i... | 1,569 | 1,583 | 1,583 | COLLABORATOR | null | * This commit replaces references to PyTorch activation functions/modules by a dict of functions that lives in `modeling_utils`. This ensures that all activation functions are available to all modules, praticularly custom functions such as swish and new_gelu.
* In addition, when available (PT1.2) the native PyTorch ge... | {
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https://api.github.com/repos/huggingface/transformers/issues/1370 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1370/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1370/comments | https://api.github.com/repos/huggingface/transformers/issues/1370/events | https://github.com/huggingface/transformers/issues/1370 | 499,844,775 | MDU6SXNzdWU0OTk4NDQ3NzU= | 1,370 | considerd to add albert? | {
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"Would definitely love to see an implementation of ALBERT added to this repository. Just for completeness:\r\n\r\n* paper: https://arxiv.org/abs/1909.11942\r\n* reddit: https://www.reddit.com/r/MachineLearning/comments/d9tdfo/albert_a_lite_bert_for_selfsupervised_learning_of/\r\n* medium: https://medium.com/syncedr... | 1,569 | 1,578 | 1,578 | NONE | null | ## 🚀 Feature
<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->
## Motivation
<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is rel... | {
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https://api.github.com/repos/huggingface/transformers/issues/1369 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1369/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1369/comments | https://api.github.com/repos/huggingface/transformers/issues/1369/events | https://github.com/huggingface/transformers/pull/1369 | 499,832,540 | MDExOlB1bGxSZXF1ZXN0MzIyNDU0MTgw | 1,369 | Update README.md | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1369?src=pr&el=h1) Report\n> Merging [#1369](https://codecov.io/gh/huggingface/transformers/pull/1369?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ae50ad91ea2fedb64ecd2e7c8e2d0d4778dc03aa?src=pr&el=desc) will **i... | 1,569 | 1,569 | 1,569 | CONTRIBUTOR | null | Lines 183 - 200, fixed indentation. Line 198, replaced `tokenizer_class` with `BertTokenizer`, since `tokenizer_class` is not defined in the loop it belongs to. | {
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https://api.github.com/repos/huggingface/transformers/issues/1368 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1368/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1368/comments | https://api.github.com/repos/huggingface/transformers/issues/1368/events | https://github.com/huggingface/transformers/issues/1368 | 499,832,359 | MDU6SXNzdWU0OTk4MzIzNTk= | 1,368 | Tried to import TFBertForPreTraining in google colab | {
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"Hey @mandavachetana its not just a google colab thing. Take a look here #1375 You need to make sure you are using tensorflow 2.0 and it should work.",
"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 con... | 1,569 | 1,575 | 1,575 | NONE | null | Tried to import TFBertForPreTraining and received an error
from transformers import BertTokenizer, TFBertForPreTraining
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-24-91f8709e090f> in <module... | {
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"You can check the two migration guides, they explain all the differences:\r\n- https://github.com/huggingface/transformers#Migrating-from-pytorch-transformers-to-transformers\r\n- https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers ",
"This issue has been automatica... | 1,569 | 1,576 | 1,576 | NONE | null | ## 📚 Migration
I am currently working on using Transformers with Snorkel's classification library [https://github.com/snorkel-team/snorkel](https://github.com/snorkel-team/snorkel) (for MTL learning in the future). I currently am trying to troubleshoot why the model is not learning, and so have my experiment set up... | {
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https://api.github.com/repos/huggingface/transformers/issues/1366 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1366/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1366/comments | https://api.github.com/repos/huggingface/transformers/issues/1366/events | https://github.com/huggingface/transformers/pull/1366 | 499,792,138 | MDExOlB1bGxSZXF1ZXN0MzIyNDI2NTU4 | 1,366 | fix redundant initializations of Embeddings in RobertaEmbeddings | {
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"Sorry, I will fix this "
] | 1,569 | 1,569 | 1,569 | CONTRIBUTOR | null | Based on the discussion with @julien-c in #1258, this PR fixes the issue of redundant multiple initializations of the embeddings in the constructor of `RobertaEmbeddings` by removing the constructor call of its parent class (i.e., `BertEmbeddings`) and creating `token_type_embeddings`, `LayerNorm`, and `dropout` in the... | {
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https://api.github.com/repos/huggingface/transformers/issues/1365 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1365/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1365/comments | https://api.github.com/repos/huggingface/transformers/issues/1365/events | https://github.com/huggingface/transformers/issues/1365 | 499,774,508 | MDU6SXNzdWU0OTk3NzQ1MDg= | 1,365 | Why add the arguments 'head_mask' and when to use this arguments | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]... | {
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https://api.github.com/repos/huggingface/transformers/issues/1364 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1364/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1364/comments | https://api.github.com/repos/huggingface/transformers/issues/1364/events | https://github.com/huggingface/transformers/issues/1364 | 499,769,288 | MDU6SXNzdWU0OTk3NjkyODg= | 1,364 | Is there any plan for Roberta in SQuAD? | {
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<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->
Hello, thx for the RoBERTa implementation. But I want to know is there any plan for the RoBERTa in SQuAD, because it is complex. And I simple changed the run_squad code as ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1363 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1363/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1363/comments | https://api.github.com/repos/huggingface/transformers/issues/1363/events | https://github.com/huggingface/transformers/issues/1363 | 499,761,986 | MDU6SXNzdWU0OTk3NjE5ODY= | 1,363 | Why the RoBERTa's max_position_embeddings size is 512+2=514? | {
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"What's your precise question?",
"> What's your precise question?\r\n\r\nthe self.padding_idx's meaning in modeling_roberta.py",
"It's the position of the padding vector. It's not unique to RoBERTa but far more general, especially for embeddings. Take a look at [the PyTorch documentation](https://pytorch.org/do... | 1,569 | 1,615 | 1,575 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
When I see the code of Roberta, I have a question about the padding_idx = 1, I don't know very well. And the comment is still confused for me. | {
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https://api.github.com/repos/huggingface/transformers/issues/1362 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1362/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1362/comments | https://api.github.com/repos/huggingface/transformers/issues/1362/events | https://github.com/huggingface/transformers/pull/1362 | 499,742,934 | MDExOlB1bGxSZXF1ZXN0MzIyMzk1MTc1 | 1,362 | fix link | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1362?src=pr&el=h1) Report\n> Merging [#1362](https://codecov.io/gh/huggingface/transformers/pull/1362?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a6a6d9e6382961dc92a1a08d1bab05a52dc815f9?src=pr&el=desc) will **n... | 1,569 | 1,569 | 1,569 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1361 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1361/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1361/comments | https://api.github.com/repos/huggingface/transformers/issues/1361/events | https://github.com/huggingface/transformers/pull/1361 | 499,665,321 | MDExOlB1bGxSZXF1ZXN0MzIyMzM5MTYx | 1,361 | distil-finetuning in run_squad | {
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"cf https://github.com/huggingface/transformers/issues/1193#issuecomment-534740929",
"Ok, as discussed let's copy this script to the `examples/distillation` folder and keep `run_squad` barebone for now as it's going to evolve in the short term.",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/... | 1,569 | 1,592 | 1,570 | MEMBER | null | - Add the option for double loss: fine-tuning + distillation from a larger squad-finetune model.
- Fix `inputs` for `DistilBERT` (also see fix in `run_glue.py` 702f589848baba97ea4897aa3f0bb937e1ec3bcf) | {
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https://api.github.com/repos/huggingface/transformers/issues/1360 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1360/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1360/comments | https://api.github.com/repos/huggingface/transformers/issues/1360/events | https://github.com/huggingface/transformers/issues/1360 | 499,626,355 | MDU6SXNzdWU0OTk2MjYzNTU= | 1,360 | Chunking Long Documents for Classification Tasks | {
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"I'm not sure that I understand. As you say, you can see it implemented in the run_squad example. What else would you like? ",
"Hello Bram,\r\n\r\nI mean I want to apply it with a sequence classification task like BertForSequenceClassification, for example, versus what is being done in squad.\r\n\r\nI don't think... | 1,569 | 1,569 | 1,569 | NONE | null | ## 🚀 Feature
A way to process long documents for downstream classification tasks. One approach is to chunk long sequences with a specific stride similar to what is done in the run_squad example.
## Motivation
For classification tasks using datasets that are on average longer than 512 tokens, I believe it woul... | {
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https://api.github.com/repos/huggingface/transformers/issues/1359 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1359/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1359/comments | https://api.github.com/repos/huggingface/transformers/issues/1359/events | https://github.com/huggingface/transformers/pull/1359 | 499,583,050 | MDExOlB1bGxSZXF1ZXN0MzIyMjc0Nzc1 | 1,359 | Update run_lm_finetuning.py | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1359?src=pr&el=h1) Report\n> Merging [#1359](https://codecov.io/gh/huggingface/transformers/pull/1359?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ca559826c4188be8713e46f191ddf5f379c196e7?src=pr&el=desc) will **n... | 1,569 | 1,569 | 1,569 | CONTRIBUTOR | null | The previous method, just as phrased, did not exist in the class. | {
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https://api.github.com/repos/huggingface/transformers/issues/1358 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1358/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1358/comments | https://api.github.com/repos/huggingface/transformers/issues/1358/events | https://github.com/huggingface/transformers/issues/1358 | 499,574,334 | MDU6SXNzdWU0OTk1NzQzMzQ= | 1,358 | How to contribute to “Write with transformer”? | {
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"What is it that you can contribute? The only (yet impressive) thing that is going on is language modeling. Can you contribute a pre-trained French model for one of the frameworks? That's (as far as I know) the only way to contribute. ",
"Thanks Bram, I’m going to investigate what the cost could be for XLNet on c... | 1,569 | 1,580 | 1,580 | NONE | null | ## 🚀 I would like to contribute to a French version of this App
I’m French, I write short stories, and I’m also a software engineer
## Motivation
I’ll retire in 6 months and I wanted to build such an app before I stumbled on your demo.
## Additional context
https://www.linkedin.com/in/mauceri/
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https://api.github.com/repos/huggingface/transformers/issues/1357 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1357/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1357/comments | https://api.github.com/repos/huggingface/transformers/issues/1357/events | https://github.com/huggingface/transformers/issues/1357 | 499,564,031 | MDU6SXNzdWU0OTk1NjQwMzE= | 1,357 | Support for SuperGLUE fine-tune/eval? | {
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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",
"So is HuggingFace going to write the finetuning implementation for SuperGlue?",
"Hi @jiachangliu, did you have any news about support... | 1,569 | 1,605 | 1,575 | MEMBER | null | ## 🚀 Feature
https://super.gluebenchmark.com/
Current canonical implem is https://github.com/nyu-mll/jiant/
## Motivation
https://twitter.com/_florianmai/status/1177489945918722050
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https://api.github.com/repos/huggingface/transformers/issues/1356 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1356/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1356/comments | https://api.github.com/repos/huggingface/transformers/issues/1356/events | https://github.com/huggingface/transformers/issues/1356 | 499,526,622 | MDU6SXNzdWU0OTk1MjY2MjI= | 1,356 | GPT and BERT pretrained models in French | {
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"Pre-training is indeed a tough pill to swallow. First of all you need a good dataset (does such dataset exist for French?), second you need a lot of processing power. A lot. If a dataset is available (preprocessed, ready to train) then I'd be willing to look into training the model on hardware that I have availabl... | 1,569 | 1,606 | 1,586 | NONE | null | ## 🚀 Need for GPT and BERT pretrained models in French
All models are in English only and the multilingual models are quite poor
## Motivation
Applications like tools for writers and linguists need fully dedicated language support
## Additional context
The computation cost to pretrain models in French i... | {
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https://api.github.com/repos/huggingface/transformers/issues/1355 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1355/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1355/comments | https://api.github.com/repos/huggingface/transformers/issues/1355/events | https://github.com/huggingface/transformers/pull/1355 | 499,506,984 | MDExOlB1bGxSZXF1ZXN0MzIyMjEzNzI1 | 1,355 | Fix tensorflow_dataset glue support | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1355?src=pr&el=h1) Report\n> Merging [#1355](https://codecov.io/gh/huggingface/transformers/pull/1355?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ca559826c4188be8713e46f191ddf5f379c196e7?src=pr&el=desc) will **d... | 1,569 | 1,569 | 1,569 | NONE | null | This PR fixes issue #1354 .
`glue_convert_examples_to_features` assumed that tensorflow_dataset examples contains the features `'sentence1'` and `'sentence2'`. This commit encapsulates the choice of features in the glue processor and uses that to parse examples.
Built with @philipp-eisen . | {
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https://api.github.com/repos/huggingface/transformers/issues/1354 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1354/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1354/comments | https://api.github.com/repos/huggingface/transformers/issues/1354/events | https://github.com/huggingface/transformers/issues/1354 | 499,497,695 | MDU6SXNzdWU0OTk0OTc2OTU= | 1,354 | run_tf_glue.py breaks when changing to a glue dataset different from mrpc | {
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"Fixed with #1355"
] | 1,569 | 1,569 | 1,569 | NONE | null | ## 🐛 Bug - run_tf_glue.py breaks when changing to a glue dataset different from mrpc
<!-- Important information -->
[run_tf_glue.py](https://github.com/huggingface/transformers/blob/master/examples/run_tf_glue.py) breaks when changing to a glue dataset different from `mrpc`, where the features are not called `'s... | {
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"👍"
] | 1,569 | 1,569 | 1,569 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1352 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1352/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1352/comments | https://api.github.com/repos/huggingface/transformers/issues/1352/events | https://github.com/huggingface/transformers/issues/1352 | 499,465,519 | MDU6SXNzdWU0OTk0NjU1MTk= | 1,352 | wwm-bert lm_finetune | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | ## 🚀 Feature
in run_lm_finetuning.py
present how to finetune language model with dataset
## Motivation
But there isn't option to finetune whole word masking bert models
I suggest to add it | {
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https://api.github.com/repos/huggingface/transformers/issues/1351 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1351/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1351/comments | https://api.github.com/repos/huggingface/transformers/issues/1351/events | https://github.com/huggingface/transformers/issues/1351 | 499,451,961 | MDU6SXNzdWU0OTk0NTE5NjE= | 1,351 | SQUAD: V2 referenced at top of Readme; V1 referenced in usage instructions | {
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"Need to use the flag \r\n--version_2_with_negative"
] | 1,569 | 1,569 | 1,569 | NONE | null | ## ❓ Questions & Help
There seems to be an inconsistency in the README, namely that run_squad.py is cited to be trained on SQUAD v2 towards the top, but scrolling down to view the command shows that v1 is used. Running the command cited over a copy of the v2 dataset on my machine yields the following error:
```
... | {
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https://api.github.com/repos/huggingface/transformers/issues/1350 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1350/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1350/comments | https://api.github.com/repos/huggingface/transformers/issues/1350/events | https://github.com/huggingface/transformers/issues/1350 | 499,414,129 | MDU6SXNzdWU0OTk0MTQxMjk= | 1,350 | Custom models: MixUp Transformers with TF.Keras code | {
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"The main issue is at line 85 on the forward pass of `TFRobertaMainLayer`:\r\n\r\nhttps://github.com/huggingface/transformers/blob/ca559826c4188be8713e46f191ddf5f379c196e7/transformers/modeling_tf_roberta.py#L85\r\n\r\nIt seems that passing Input placeholders mess up this comparison:\r\n\r\n> OperatorNotAll... | 1,569 | 1,646 | 1,576 | NONE | null | Ideally I would like to use `TFRobertaModel` or any other model (BERT, XLNet) as parts (modules) of a bigger model. For example, it could be nice to start with Roberta as a document encoder and then build a multi-label classifier on top of that. Possibly there are ways to hack `TFRobertaForSequenceClassification` in or... | {
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https://api.github.com/repos/huggingface/transformers/issues/1349 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1349/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1349/comments | https://api.github.com/repos/huggingface/transformers/issues/1349/events | https://github.com/huggingface/transformers/pull/1349 | 499,358,473 | MDExOlB1bGxSZXF1ZXN0MzIyMDk0NDEw | 1,349 | Just some typos | {
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"👍 ",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1349?src=pr&el=h1) Report\n> Merging [#1349](https://codecov.io/gh/huggingface/transformers/pull/1349?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d83d295763b738aa0c071f8b63ad6e155b6cf515?src=pr&el=desc)... | 1,569 | 1,569 | 1,569 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1348 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1348/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1348/comments | https://api.github.com/repos/huggingface/transformers/issues/1348/events | https://github.com/huggingface/transformers/issues/1348 | 499,334,082 | MDU6SXNzdWU0OTkzMzQwODI= | 1,348 | Urgent: RoBERTa-Large-MNLI does not work for 2-way classification anymore | {
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"Please choose a better title for your post and specify (or remove) the first part of your post. \r\n\r\nAs far as I can tell this is an issue specific to the mnli model. As you say it's pre-trained with three final out features. When loading the state dict into the model, all weights from the pretrained model are ... | 1,569 | 1,657 | 1,581 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (RoBERTa):
Language I am using the model on (English):
The problem arise when using:
* [ ] the official example scripts: (give details)
* [ ] my own modified scripts: (give details)
The tasks I am working on is:
* [ ] an official GLUE/SQUaD tas... | {
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https://api.github.com/repos/huggingface/transformers/issues/1347 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1347/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1347/comments | https://api.github.com/repos/huggingface/transformers/issues/1347/events | https://github.com/huggingface/transformers/issues/1347 | 499,321,543 | MDU6SXNzdWU0OTkzMjE1NDM= | 1,347 | Use PyTorch's GELU activation | {
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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"
] | 1,569 | 1,575 | 1,575 | COLLABORATOR | null | ## 🚀 Feature
PyTorch 1.2 provides a built-in, GPU-accelerated GELU function at `torch.nn.functional.gelu`. Reading through the merged pull request (https://github.com/pytorch/pytorch/pull/20665) it seems that this is optimised for CUDA, too. Therefore I would propose trying to import the built-in gelu function firs... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1346?src=pr&el=h1) Report\n> Merging [#1346](https://codecov.io/gh/huggingface/transformers/pull/1346?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/da2e47ad15e552b84815da20daf3282b517103f7?src=pr&el=desc) will **n... | 1,569 | 1,569 | 1,569 | COLLABORATOR | null | Closes huggingface/transformers#1332 | {
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https://api.github.com/repos/huggingface/transformers/issues/1345 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1345/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1345/comments | https://api.github.com/repos/huggingface/transformers/issues/1345/events | https://github.com/huggingface/transformers/issues/1345 | 499,291,501 | MDU6SXNzdWU0OTkyOTE1MDE= | 1,345 | Ram utilisation of DistilBERT | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I was checking the memory consumption of RoBERTa and DistilBERT. I found there is no significant change in memory usage. Although Inference time is around 1sec for DistilBERT and for RoBERTa is 2sec.
Memory usage on CPU:
Port 9000: Di... | {
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https://api.github.com/repos/huggingface/transformers/issues/1344 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1344/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1344/comments | https://api.github.com/repos/huggingface/transformers/issues/1344/events | https://github.com/huggingface/transformers/issues/1344 | 499,173,502 | MDU6SXNzdWU0OTkxNzM1MDI= | 1,344 | Errors when using fp16 with traced models | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Has this ever been solved? I have the same issue",
"I think maybe the reason is `.half()` only change(cast) the data, but it is not t... | 1,569 | 1,599 | 1,575 | NONE | null | ## 🐛 Bug
When I run
```
roberta_model = RobertaForMaskedLM.from_pretrained("roberta-base", torchscript=True)
roberta_model.cuda()
roberta_model.half()
traced_model = torch.jit.trace(roberta_model, (r_input_ids))
```
I get the following error
`
Expected object of scalar type Float but got scalar type ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1343 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1343/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1343/comments | https://api.github.com/repos/huggingface/transformers/issues/1343/events | https://github.com/huggingface/transformers/issues/1343 | 499,092,393 | MDU6SXNzdWU0OTkwOTIzOTM= | 1,343 | RobertaTokenizer documentation is off with the new transformers library | {
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"You're right! Thanks for letting us know."
] | 1,569 | 1,569 | 1,569 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): Roberta
Language I am using the model on (English, Chinese....): NA
The problem arise when using:
* [ ] the official example scripts:
The tasks I am working on is:
NA
## To Reproduce
Steps to reproduce the behavior:
I... | {
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https://api.github.com/repos/huggingface/transformers/issues/1342 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1342/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1342/comments | https://api.github.com/repos/huggingface/transformers/issues/1342/events | https://github.com/huggingface/transformers/issues/1342 | 499,041,973 | MDU6SXNzdWU0OTkwNDE5NzM= | 1,342 | AttributeError: 'RobertaTokenizer' object has no attribute 'add_special_tokens_sentences_pair' | {
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"Hey @frankfka \r\n\r\nPerhaps you are looking for tokenizer.add_special_tokens_sequence_pair instead of tokenizer.add_special_tokens_sentences_pair?\r\n\r\n```\r\nfrom transformers import RobertaTokenizer\r\ntokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\r\n\r\ntokenizer.add_special_tokens_sequence... | 1,569 | 1,569 | 1,569 | NONE | null | With the latest update to `Transformers`, has the function been removed? I still see it in the code, but I run into the error:
`AttributeError: 'RobertaTokenizer' object has no attribute 'add_special_tokens_sentences_pair'` | {
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https://api.github.com/repos/huggingface/transformers/issues/1341 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1341/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1341/comments | https://api.github.com/repos/huggingface/transformers/issues/1341/events | https://github.com/huggingface/transformers/issues/1341 | 499,028,190 | MDU6SXNzdWU0OTkwMjgxOTA= | 1,341 | Examples in Colab | {
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"Why not simply run the example scripts in colab yourself?",
"I'm not exactly sure how to set it up , this is a pretty popular library so I was thinking their might be a blog post out there ",
"https://huggingface.co/transformers/notebooks.html",
"This issue has been automatically marked as stale because it h... | 1,569 | 1,575 | 1,575 | NONE | null | Hi all , does anyone have a Colab sample to share ? | {
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https://api.github.com/repos/huggingface/transformers/issues/1340 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1340/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1340/comments | https://api.github.com/repos/huggingface/transformers/issues/1340/events | https://github.com/huggingface/transformers/issues/1340 | 499,002,913 | MDU6SXNzdWU0OTkwMDI5MTM= | 1,340 | Size mismatch when loading pretrained model | {
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"I'm having the same problem with RoBERTa, it didn't happen until a few hours.",
"Hi, thanks for pointing it out, I made a mistake with a config object hosted on our S3. It should be fixed now.",
"Running the following snippet:\r\n`# Load the model in fairseq`\r\n`from fairseq.models.roberta import RobertaModel... | 1,569 | 1,629 | 1,569 | NONE | null | I'm seeing this:
```
In [1]: import pytorch_transformers
In [2]: m=pytorch_transformers.AutoModel.from_pretrained('roberta-base')
-------------------------------------------------------------... | {
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https://api.github.com/repos/huggingface/transformers/issues/1339 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1339/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1339/comments | https://api.github.com/repos/huggingface/transformers/issues/1339/events | https://github.com/huggingface/transformers/issues/1339 | 498,958,266 | MDU6SXNzdWU0OTg5NTgyNjY= | 1,339 | Why is the vocabulary of token_type_ids and input_ids shared? | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
https://github.com/huggingface/transformers/blob/17ea43cf985829634bd86b36b44e5410c6f83e36/transformers/modeling_gpt2.py#L421
In GPT2Model, forward method, it seems the vocabulary of token_type_ids and input_ids is shared. I checked... | {
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https://api.github.com/repos/huggingface/transformers/issues/1338 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1338/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1338/comments | https://api.github.com/repos/huggingface/transformers/issues/1338/events | https://github.com/huggingface/transformers/issues/1338 | 498,923,282 | MDU6SXNzdWU0OTg5MjMyODI= | 1,338 | Extending `examples/` to TensorFlow | {
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"Indeed, there is currently one example for tensorflow, `run_tf_glue` and it doesn't have command-line arguments. We'll update this one to make it as flexible as the PyTorch one and add other examples when we have the bandwidth.\r\n\r\nDo you want to help in this project? Happy to welcome a PR on this topic (for in... | 1,569 | 1,575 | 1,575 | NONE | null | ## 🚀 Feature
Hi, thanks for putting in the tremendous effort for TensorFlow-PyTorch interoperability! Would those scripts in the `examples/` be soon extended to Tensorflow as well?
## Motivation
I (and presumably many others) rely on the examples to quickly experiment with models and ideas. Extending the exam... | {
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https://api.github.com/repos/huggingface/transformers/issues/1337 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1337/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1337/comments | https://api.github.com/repos/huggingface/transformers/issues/1337/events | https://github.com/huggingface/transformers/pull/1337 | 498,902,034 | MDExOlB1bGxSZXF1ZXN0MzIxNzMzODI2 | 1,337 | faster dataset building | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1337?src=pr&el=h1) Report\n> Merging [#1337](https://codecov.io/gh/huggingface/transformers/pull/1337?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a3e0dbba9512866064c20e9bc99c62725f6c36fb?src=pr&el=desc) will **n... | 1,569 | 1,569 | 1,569 | CONTRIBUTOR | null | Now it takes around 1 minute to process 20mb and it takes forever for 200mb dataset (it's non-linear). This is a fix to make it linear. | {
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https://api.github.com/repos/huggingface/transformers/issues/1336 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1336/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1336/comments | https://api.github.com/repos/huggingface/transformers/issues/1336/events | https://github.com/huggingface/transformers/pull/1336 | 498,834,770 | MDExOlB1bGxSZXF1ZXN0MzIxNjc4ODM0 | 1,336 | Completed the documentation with TF2 | {
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https://api.github.com/repos/huggingface/transformers/issues/1335 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1335/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1335/comments | https://api.github.com/repos/huggingface/transformers/issues/1335/events | https://github.com/huggingface/transformers/issues/1335 | 498,514,263 | MDU6SXNzdWU0OTg1MTQyNjM= | 1,335 | Optimize XLNet model to generate embedding of long documents | {
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We got an average of 0.8 second per document with TransformerXL and 1.3 second per document with XLNet.
To optimize XLNet we found that using only 200 tokens per call is optimal.
A ratio around 35... | {
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https://api.github.com/repos/huggingface/transformers/issues/1334 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1334/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1334/comments | https://api.github.com/repos/huggingface/transformers/issues/1334/events | https://github.com/huggingface/transformers/issues/1334 | 498,283,195 | MDU6SXNzdWU0OTgyODMxOTU= | 1,334 | Typo in modeling_bert file | {
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"Hi! Indeed it is inconsistent, but it doesn't really change anything as the superclass `PreTrainedModel` assigns the config as one of its attributes: `self.config = config`. Referencing `config` or `self.config` therefore references the same object!"
] | 1,569 | 1,569 | 1,569 | NONE | null | I was looking at the code of BertModel adapted for different tasks here
https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/modeling_bert.py
I noticed a small typo in line 882
`self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)`
I think it should be either `s... | {
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https://api.github.com/repos/huggingface/transformers/issues/1333 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1333/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1333/comments | https://api.github.com/repos/huggingface/transformers/issues/1333/events | https://github.com/huggingface/transformers/pull/1333 | 498,269,342 | MDExOlB1bGxSZXF1ZXN0MzIxMjMyODMx | 1,333 | [FIX] fix run_generation.py to work with batch_size > 1 | {
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"@thomwolf\r\nI created this PR to deal with the `top p` generations.\r\nShould I have opened an issue first to check if it is needed?\r\nShould I deal with the conflicts?\r\n\r\nCheers.",
"Hi @mataney, thanks.\r\n\r\nThis was rebased, fixed by https://github.com/huggingface/transformers/commit/f96ce1c24151349251... | 1,569 | 1,572 | 1,572 | CONTRIBUTOR | null | I expended the `top_k_top_p_filtering` function, and by that the`run_generation.py` script to work with num_samples > 1.
This can be expended by scattering the sorted tensors.
First pull request in this repository, so let me know if I need to do anything else :)
Cheers, Matan. | {
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https://api.github.com/repos/huggingface/transformers/issues/1332 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1332/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1332/comments | https://api.github.com/repos/huggingface/transformers/issues/1332/events | https://github.com/huggingface/transformers/issues/1332 | 498,175,777 | MDU6SXNzdWU0OTgxNzU3Nzc= | 1,332 | pytorch-transformers returns output of 13 layers? | {
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"I am looking at this too and I believe (might be wrong) that the embedding layer sits in the last position. So I guess you should do [-2:-5] ",
"> I am looking at this too and I believe (might be wrong) that the embedding layer sits in the last position. So I guess you should do [-2:-5]\r\n\r\nHm, I don't think ... | 1,569 | 1,569 | 1,569 | COLLABORATOR | null | ## 📚 Migration
<!-- Important information -->
Model I am using (Bert, XLNet....): BertModel
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [x] my own modified scripts: (give details)
The tasks I am working on is:
* [x] my own task or dataset: (give det... | {
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https://api.github.com/repos/huggingface/transformers/issues/1331 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1331/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1331/comments | https://api.github.com/repos/huggingface/transformers/issues/1331/events | https://github.com/huggingface/transformers/issues/1331 | 498,038,255 | MDU6SXNzdWU0OTgwMzgyNTU= | 1,331 | Is the UI code for https://transformer.huggingface.co open source? | {
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"No we haven't open sourced the UI code.",
"Are there plans to open source the UI or there's no plan for it?",
"No short term plans to do it!"
] | 1,569 | 1,569 | 1,569 | NONE | null | ## ❓ Questions & Help
Is the UI code for https://transformer.huggingface.co open source? | {
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https://api.github.com/repos/huggingface/transformers/issues/1330 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1330/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1330/comments | https://api.github.com/repos/huggingface/transformers/issues/1330/events | https://github.com/huggingface/transformers/issues/1330 | 498,023,812 | MDU6SXNzdWU0OTgwMjM4MTI= | 1,330 | Loading errors for BERT base on GPU with PyTorch 0.4.1 | {
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https://api.github.com/repos/huggingface/transformers/issues/1329 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1329/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1329/comments | https://api.github.com/repos/huggingface/transformers/issues/1329/events | https://github.com/huggingface/transformers/pull/1329 | 498,007,097 | MDExOlB1bGxSZXF1ZXN0MzIxMDI4ODI4 | 1,329 | GLUE Script for Tensorflow | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1329?src=pr&el=h1) Report\n> Merging [#1329](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1329?src=pr&el=desc) into [tf2](https://codecov.io/gh/huggingface/pytorch-transformers/commit/e8e956dbb2a6df696d79e2f4dc154849a8e06611?src... | 1,569 | 1,651 | 1,569 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1328 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1328/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1328/comments | https://api.github.com/repos/huggingface/transformers/issues/1328/events | https://github.com/huggingface/transformers/issues/1328 | 497,908,347 | MDU6SXNzdWU0OTc5MDgzNDc= | 1,328 | Sequence Classification pooled output vs last hidden state | {
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"Both would probably work, but I agree that streamlining is a good idea. In their paper, BERT gets the best results by concatenating the last four layers, so what I always use is something like this (from the top of my head):\r\n\r\n```python\r\noutputs = self.bert(input_ids,\r\n attention_mask=a... | 1,569 | 1,682 | 1,569 | NONE | null | ## ❓ Questions & Help
Why in BertForSequenceClassification do we pass the pooled output to the classifier as below from the source code
```python
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=... | {
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https://api.github.com/repos/huggingface/transformers/issues/1327 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1327/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1327/comments | https://api.github.com/repos/huggingface/transformers/issues/1327/events | https://github.com/huggingface/transformers/pull/1327 | 497,869,766 | MDExOlB1bGxSZXF1ZXN0MzIwOTE4MzI4 | 1,327 | Pytorch/TF2 determinism | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1327?src=pr&el=h1) Report\n> Merging [#1327](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1327?src=pr&el=desc) into [tf2](https://codecov.io/gh/huggingface/pytorch-transformers/commit/128bdd4c3549e2a1401af87493ff6be467c79c14?src... | 1,569 | 1,576 | 1,569 | MEMBER | null | Check to see if the models have the same results when in eval mode (pt) or when training=False (tf) | {
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https://api.github.com/repos/huggingface/transformers/issues/1326 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1326/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1326/comments | https://api.github.com/repos/huggingface/transformers/issues/1326/events | https://github.com/huggingface/transformers/issues/1326 | 497,737,187 | MDU6SXNzdWU0OTc3MzcxODc= | 1,326 | RuntimeError: expected scalar type Half but found Float | {
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"I've encountered this problem as well",
"Seems like an apex error (apex should be converting the tensors to half).\r\nMaybe try to update or reinstall apex following carefully the required step for installation? ",
"This issue has been automatically marked as stale because it has not had recent activity. It wi... | 1,569 | 1,581 | 1,581 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....):
bert-large-uncased
Language I am using the model on (English, Chinese....):
English
The problem arise when using:
* [ ] the official example scripts: (give details)
* [x] my own modified scripts: (give details)
I am using a ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1325 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1325/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1325/comments | https://api.github.com/repos/huggingface/transformers/issues/1325/events | https://github.com/huggingface/transformers/pull/1325 | 497,700,800 | MDExOlB1bGxSZXF1ZXN0MzIwNzc5NzI1 | 1,325 | [Proposal] GLUE processors included in library | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1325?src=pr&el=h1) Report\n> Merging [#1325](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1325?src=pr&el=desc) into [glue-example](https://codecov.io/gh/huggingface/pytorch-transformers/commit/a6981076eca5494b9d230f13217c14b9344... | 1,569 | 1,578 | 1,569 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1324 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1324/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1324/comments | https://api.github.com/repos/huggingface/transformers/issues/1324/events | https://github.com/huggingface/transformers/issues/1324 | 497,644,703 | MDU6SXNzdWU0OTc2NDQ3MDM= | 1,324 | A Micro BERT | {
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"I am using a much smaller dataset with my project, but it doesn't mean I need a bert with lesser layers. Otherwise, I have no way to utilize the pre-trained model.\r\n\r\nWhat is the problem you have with the smaller dataset?",
"My dataset is very esoteric, in the sense that BERTs pretrained weights will almost ... | 1,569 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
Hello,
Has anyone solved a problem like this, or knows of a solution:
I want to pre-train BERT on a custom dataset, but this data is much smaller than the one used by Google.
So is it possible to train it on a "micro" bert with much lesser layers, etc.
Thanks in advance | {
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https://api.github.com/repos/huggingface/transformers/issues/1323 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1323/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1323/comments | https://api.github.com/repos/huggingface/transformers/issues/1323/events | https://github.com/huggingface/transformers/issues/1323 | 497,572,484 | MDU6SXNzdWU0OTc1NzI0ODQ= | 1,323 | How to build a Text-to-Feature Extractor based on Fine-Tuned BERT Model | {
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"The explanation for fine-tuning is in the README https://github.com/huggingface/pytorch-transformers#quick-tour-of-the-fine-tuningusage-scripts.",
"Thanks, but as far as i understands its about \"Fine-tuning on GLUE tasks for **sequence classification**\". I want to do \"Fine-tuning on My Data for **word-to-feat... | 1,569 | 1,659 | 1,569 | NONE | null | I have now tried for several days to solve an issue I have...
I need to make a feature extractor for a project I am doing, so I am able to translate a given sentence e.g. "My hat is blue" into a vector of a given length e.g. 768. That vector will then later on be combined with several other values for the final pred... | {
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https://api.github.com/repos/huggingface/transformers/issues/1322 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1322/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1322/comments | https://api.github.com/repos/huggingface/transformers/issues/1322/events | https://github.com/huggingface/transformers/issues/1322 | 497,547,516 | MDU6SXNzdWU0OTc1NDc1MTY= | 1,322 | parameter never_split not added in BasicTokenizer's tokenize | {
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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"
] | 1,569 | 1,575 | 1,575 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....):
Bert
Language I am using the model on (English, Chinese....):
English
The problem arise when using:
* [ ] my own modified scripts: I need to add some special tokens that will not been split during tokenizing. And my special tok... | {
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https://api.github.com/repos/huggingface/transformers/issues/1321 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1321/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1321/comments | https://api.github.com/repos/huggingface/transformers/issues/1321/events | https://github.com/huggingface/transformers/issues/1321 | 497,321,954 | MDU6SXNzdWU0OTczMjE5NTQ= | 1,321 | Using pytorch-transformer to reimplement the "Attention is all you need" paper | {
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"Hi, this repository's objective is mainly to host **pretrained** models, not really to build a model from scratch.\r\n\r\nYou could use some of this library's components though, like multi-headed attention, to help you in your endeavor."
] | 1,569 | 1,569 | 1,569 | NONE | null | ## ❓ Questions & Help
I use this repo for a long time but I realized even though the name is PyTorch transformers I can't find an easy way to re-implement the original paper of "Attention is all you need" with pretrained model. Can someone help me? | {
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https://api.github.com/repos/huggingface/transformers/issues/1320 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1320/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1320/comments | https://api.github.com/repos/huggingface/transformers/issues/1320/events | https://github.com/huggingface/transformers/issues/1320 | 497,268,694 | MDU6SXNzdWU0OTcyNjg2OTQ= | 1,320 | Why does padding affect the embedding results for XLNet? Pre-padding returns different embeddings than post-padding. Which one should be used? | {
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"I might be wrong, but intuitively I would say that that makes things easier. XLNet expects single sequences that look like this `tok1 tok2 ... SEP CLS`. So in contrast with BERT, the classification token is at the end of a sentence rather than beginning. This is before padding. So if you use post-padding, the posi... | 1,569 | 1,592 | 1,578 | NONE | null | ## ❓ Questions & Help
Hello, I am confused with different results of XLNet depending on padding. For Bert, padding doesn't affect the outputs, but for XLNet with **pre** padding (which I saw in https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py#L281), returns very different results ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1319 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1319/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1319/comments | https://api.github.com/repos/huggingface/transformers/issues/1319/events | https://github.com/huggingface/transformers/issues/1319 | 497,135,436 | MDU6SXNzdWU0OTcxMzU0MzY= | 1,319 | BertForQuestionAnswering output to predict text | {
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<!-- A clear and concise description of the question. -->
In predict mode, BertForQuestionAnswering model output a tuple like below, how to get the text answer interactively
```
tensor([[ 0.4691, 0.3912, -0.3447, 0.9756, 0.7171, 0.3746, 0.5273, 0.3756,
0.2083, 0.4130, 0.2145, 0.1327, 0.7265,... | {
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https://api.github.com/repos/huggingface/transformers/issues/1318 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1318/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1318/comments | https://api.github.com/repos/huggingface/transformers/issues/1318/events | https://github.com/huggingface/transformers/issues/1318 | 497,029,786 | MDU6SXNzdWU0OTcwMjk3ODY= | 1,318 | A sequence with no special tokens has been passed to the RoBERTa model. This model requires special tokens in order to work. Please specify add_special_tokens=True in your encoding. | {
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"@yaroslavvb @cynthia @myleott ",
"Hi, this error springs when you're passing an input to the model which doesn't have the special tokens it needs (cls token and sep token).\r\n\r\nThe `encode` method accepts the argument `add_special_tokens`, which will take care of adding the special tokens to your sequence.",
... | 1,569 | 1,579 | 1,576 | NONE | null | This is my code for Roberta:
```
# coding: utf-8
# In[4]:
import pandas as pd
import numpy as np
import json, re
from tqdm import tqdm_notebook
from uuid import uuid4
## Torch Modules
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch.auto... | {
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https://api.github.com/repos/huggingface/transformers/issues/1317 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1317/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1317/comments | https://api.github.com/repos/huggingface/transformers/issues/1317/events | https://github.com/huggingface/transformers/issues/1317 | 496,950,002 | MDU6SXNzdWU0OTY5NTAwMDI= | 1,317 | BertTokenizer provides wrong encode function for Japanese BERT | {
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"I had discovered that this phenomenon is due to function: _run_strip_accents(token) in class: BasicTokenizer. Perhaps, the authors should give an option to choose whether to remove accents or not because in some language such as Japanese, removing accents makes a new word",
"Hi, I have trained this Japanese BERT... | 1,569 | 1,569 | 1,569 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): BertTokenizer
Language I am using the model on (English, Chinese....): Japanese
I tried to load the tokenizer for Bert from pretrained [Bert for Japanese](http://nlp.ist.i.kyoto-u.ac.jp/index.php?BERT%E6%97%A5%E6%9C%AC%E8%AA%9EPre... | {
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https://api.github.com/repos/huggingface/transformers/issues/1316 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1316/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1316/comments | https://api.github.com/repos/huggingface/transformers/issues/1316/events | https://github.com/huggingface/transformers/issues/1316 | 496,870,631 | MDU6SXNzdWU0OTY4NzA2MzE= | 1,316 | How to predict missing word [MASK] using Robert | {
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"Basically, the problem is that the model is called masked language model, but it does not mask anything.\r\n\r\nIf i want to get token distribution for word \"dog\", but the model sees word dog, because its not masked, so it use the word in prediction. Input should not be \"Hello, my dog is cute\", but something ... | 1,569 | 1,581 | 1,569 | NONE | null | I am reading the docs and I still cannot figure out how to I predict missing word in a sentence using Robert. With bert this is described at https://huggingface.co/pytorch-transformers/quickstart.html
# Mask a token that we will try to predict back with `BertForMaskedLM`
masked_index = 8
tokenized_text... | {
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https://api.github.com/repos/huggingface/transformers/issues/1315 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1315/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1315/comments | https://api.github.com/repos/huggingface/transformers/issues/1315/events | https://github.com/huggingface/transformers/pull/1315 | 496,853,826 | MDExOlB1bGxSZXF1ZXN0MzIwMDk4MTM0 | 1,315 | Remove unnecessary use of FusedLayerNorm | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1315?src=pr&el=h1) Report\n> Merging [#1315](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1315?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/a2d4950f5c909f7bb4ea7c06afa6cdecde7e8750?... | 1,569 | 1,570 | 1,569 | CONTRIBUTOR | null | Fix #1172 | {
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https://api.github.com/repos/huggingface/transformers/issues/1314 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1314/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1314/comments | https://api.github.com/repos/huggingface/transformers/issues/1314/events | https://github.com/huggingface/transformers/issues/1314 | 496,817,334 | MDU6SXNzdWU0OTY4MTczMzQ= | 1,314 | How to preprocess my own data to use RoBERTa of Multiple GPUs | {
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"@spolu @cynthia @myleott ",
"Hi, you can follow the `run_glue` example which is better for text classification.\r\nBut you will have to modify it for your needs, it's not plug and play.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further ... | 1,569 | 1,575 | 1,575 | NONE | null | Hey,
I am bit naive using deep learning of text-classification, my data **(.csv)** consist of basically two columns:
- Text
- Labels
As per basic objective, model should take unseen text and predict label _(variable y)_ from the trained labels.
**I followed this tutorial to train RoBERTa algorithm:**
- [h... | {
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https://api.github.com/repos/huggingface/transformers/issues/1313 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1313/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1313/comments | https://api.github.com/repos/huggingface/transformers/issues/1313/events | https://github.com/huggingface/transformers/pull/1313 | 496,780,679 | MDExOlB1bGxSZXF1ZXN0MzIwMDQ2NDU1 | 1,313 | Add option to use a 'stop token' | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1313?src=pr&el=h1) Report\n> :exclamation: No coverage uploaded for pull request base (`master@ecc4f1b`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).\n> The diff coverage is `n/a`.\n... | 1,569 | 1,570 | 1,570 | CONTRIBUTOR | null | This will be used to truncate the output text to everything till right before the 'stop token'. If the 'stop token' is not found, then the whole text will be returned based on the specified 'length'. | {
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https://api.github.com/repos/huggingface/transformers/issues/1312 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1312/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1312/comments | https://api.github.com/repos/huggingface/transformers/issues/1312/events | https://github.com/huggingface/transformers/issues/1312 | 496,750,626 | MDU6SXNzdWU0OTY3NTA2MjY= | 1,312 | In BertForSequenceClassification, why is loss initialised in every forward? | {
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"Also it will be nice if the user gets to use the loss_func itself, Like currently i am using that class with slight modifications to match the pipeline with different losses rather than only CrossEntropy loss. (plus add class_weights etc as well to it)\r\n\r\nThough this is what i did actually to use a different l... | 1,569 | 1,586 | 1,586 | COLLABORATOR | null | Looking at [the source](https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/modeling_bert.py#L902-L910) I can see that the correct loss function is initialized in each call to forward.
https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/modeling_bert.... | {
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https://api.github.com/repos/huggingface/transformers/issues/1311 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1311/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1311/comments | https://api.github.com/repos/huggingface/transformers/issues/1311/events | https://github.com/huggingface/transformers/issues/1311 | 496,743,901 | MDU6SXNzdWU0OTY3NDM5MDE= | 1,311 | RoBERTa : add_special_tokens=True | {
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"I'm getting the same warning, also here: https://github.com/huggingface/pytorch-transformers/issues/1318",
"I think you should add < s > without spaces before as well as after sentences.",
"Please share a self contained script exhibiting the behavior and allthe information on the python/pytorch/pytorch-transf... | 1,569 | 1,575 | 1,575 | NONE | null | I set add_special_tokens=True
but I still get:
A sequence with no special tokens has been passed to the RoBERTa model. This model requires special tokens in order to work. Please specify add_special_tokens=True in your encoding. | {
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