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https://api.github.com/repos/huggingface/transformers/issues/2812 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2812/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2812/comments | https://api.github.com/repos/huggingface/transformers/issues/2812/events | https://github.com/huggingface/transformers/issues/2812 | 563,278,618 | MDU6SXNzdWU1NjMyNzg2MTg= | 2,812 | How can I finetune the BERTModel on my own corpus? | {
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"Take a look at the `resize_embeddings` function and `examples/run_language_modeling.py`.",
"sorry, where is the resize_embeddings function?",
"My bad, it's a method on `PretrainedModel` called `resize_token_embeddings`. There is a call in `run_language_modeling.py. "
] | 1,581 | 1,583 | 1,583 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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"I answered your question on stack overflow."
] | 1,581 | 1,581 | 1,581 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
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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"
] | 1,581 | 1,587 | 1,587 | NONE | null | #Question
https://stackoverflow.com/questions/60157959/transformers-summarization-with-python-pytorch-how-to-get-longer-output
Should i train it myself to get summary output longer than used in original training script?
:
python run_summarization.py \
--documents_dir $DATA_PATH \
--summari... | {
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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"
] | 1,581 | 1,587 | 1,587 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): Bert
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ x] the official example scripts: (give details below)
* [ x] my own modified scripts: (give details below)
The tasks I am working on is:
... | {
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https://api.github.com/repos/huggingface/transformers/issues/2807 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2807/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2807/comments | https://api.github.com/repos/huggingface/transformers/issues/2807/events | https://github.com/huggingface/transformers/pull/2807 | 562,976,299 | MDExOlB1bGxSZXF1ZXN0MzczNDcyMTk5 | 2,807 | get_activation('relu') provides a simple mapping from strings in configs to activation functions | {
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"Happy to do TF in a separate PR. I don't think worth breaking backwards compatibility over this.",
"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2807?src=pr&el=h1) Report\n> Merging [#2807](https://codecov.io/gh/huggingface/transformers/pull/2807?src=pr&el=desc) into [master](https://codecov.i... | 1,581 | 1,581 | 1,581 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/2806 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2806/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2806/comments | https://api.github.com/repos/huggingface/transformers/issues/2806/events | https://github.com/huggingface/transformers/issues/2806 | 562,886,631 | MDU6SXNzdWU1NjI4ODY2MzE= | 2,806 | TFBertModel.from_pretrained('neuralmind/bert-base-portuguese-cased') -> TypeError | {
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"`BertModel` is the pytorch model, and is therefore only available if you have torch installed. As you correctly said, `TFBertModel` is the TensorFlow equivalent.\r\n\r\nImporting with `from transformers import TFBertModel` raises the above error?",
"Loading the model gives me the error: `TFBertModel.from_pretrai... | 1,581 | 1,595 | 1,587 | NONE | null | I just installed the library on a TensorFlow environment (2.0.0-rc1) and there is no `BertModel` in `transformers`.
Is `TFBertModel` equivalent? If so, then I get the error `TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType` when loading the model with `model = TFBertModel.from_pret... | {
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https://api.github.com/repos/huggingface/transformers/issues/2805 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2805/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2805/comments | https://api.github.com/repos/huggingface/transformers/issues/2805/events | https://github.com/huggingface/transformers/pull/2805 | 562,866,185 | MDExOlB1bGxSZXF1ZXN0MzczMzg1MjIy | 2,805 | [model_cards] Add new German Europeana BERT models | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2805?src=pr&el=h1) Report\n> Merging [#2805](https://codecov.io/gh/huggingface/transformers/pull/2805?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/68ccc04ee6c762183ff2b34b8b85d139f77cbf14?src=pr&el=desc) will **n... | 1,581 | 1,581 | 1,581 | COLLABORATOR | null | Hi,
this PR adds the model cards for two new BERT models for Historic German.
The cased and uncased BERT models were trained on a huge corpus: newspapers from [Europeana](http://www.europeana-newspapers.eu/). Time period of these (noisy) OCRed newspapers is 18th - 20th century.
More information can be found [h... | {
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https://api.github.com/repos/huggingface/transformers/issues/2804 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2804/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2804/comments | https://api.github.com/repos/huggingface/transformers/issues/2804/events | https://github.com/huggingface/transformers/pull/2804 | 562,851,167 | MDExOlB1bGxSZXF1ZXN0MzczMzcyNzMx | 2,804 | Fix a few issues regarding the language modeling script | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2804?src=pr&el=h1) Report\n> Merging [#2804](https://codecov.io/gh/huggingface/transformers/pull/2804?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/539f601be712619dc8c428f0a0b5e8e15f82ac4c?src=pr&el=desc) will **n... | 1,581 | 1,581 | 1,581 | MEMBER | null | The language modeling script currently has a few issues.
- in the line by line dataset, no special tokens are added (that's due to the fact the `batch_encode_plus` has the `add_special_token` flag `False` by default, which is misleading).
- the max length is ill computed in that same dataset, as it doesn't take int... | {
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https://api.github.com/repos/huggingface/transformers/issues/2803 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2803/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2803/comments | https://api.github.com/repos/huggingface/transformers/issues/2803/events | https://github.com/huggingface/transformers/issues/2803 | 562,765,943 | MDU6SXNzdWU1NjI3NjU5NDM= | 2,803 | Support DeepSpeed for language modeling finetuning | {
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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"
] | 1,581 | 1,587 | 1,587 | NONE | null | # 🚀 Feature request
https://github.com/microsoft/DeepSpeed
This was just released, and given the code flow in `run_language_modeling.py` it seems like it would not be too difficult to drop-in, and it has a permissible license (MIT).
However, given the dependencies and difficulty installing them, it would like... | {
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https://api.github.com/repos/huggingface/transformers/issues/2801 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2801/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2801/comments | https://api.github.com/repos/huggingface/transformers/issues/2801/events | https://github.com/huggingface/transformers/issues/2801 | 562,678,742 | MDU6SXNzdWU1NjI2Nzg3NDI= | 2,801 | Can't load pre-trained Flaubert model | {
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"On the second issue, the CLI is Python 3.6+ only. We'll document this better in the future, cc @LysandreJik ",
"On the first issue, looks like your traceback might be truncated. Did you paste all of it?",
"> \r\n> \r\n> On the first issue, looks like your traceback might be truncated. Did you paste all of it?\... | 1,581 | 1,587 | 1,587 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): Flaubert
Language I am using the model on (English, Chinese ...): French
The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)
The tasks I am working on is... | {
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"When deploying on CircleCI, it runs the `build_and_test` job, which runs the [following suites](https://github.com/huggingface/transformers/blob/master/.circleci/config.yml#L126-L133).\r\n\r\nThe slow tests are ran by the `run_all_tests_torch_and_tf` suite, which only triggers [weekly](https://github.com/huggingfa... | 1,581 | 1,581 | 1,581 | CONTRIBUTOR | null | circle_ci.cfg says `RUN_SLOW: yes`, but all my circleci runs have the slow tests skipped.
Is this expected behavior?
@LysandreJik
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https://api.github.com/repos/huggingface/transformers/issues/2799 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2799/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2799/comments | https://api.github.com/repos/huggingface/transformers/issues/2799/events | https://github.com/huggingface/transformers/pull/2799 | 562,628,766 | MDExOlB1bGxSZXF1ZXN0MzczMTkwNTU3 | 2,799 | Add model readme for bert-base-german-cased | {
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https://api.github.com/repos/huggingface/transformers/issues/2798 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2798/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2798/comments | https://api.github.com/repos/huggingface/transformers/issues/2798/events | https://github.com/huggingface/transformers/issues/2798 | 562,614,410 | MDU6SXNzdWU1NjI2MTQ0MTA= | 2,798 | Reduce the CamemBERT dimensions | {
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"Hi @AlafateABULIMITI sounds like a question that's better suited for Stack Overflow. Thanks!"
] | 1,581 | 1,582 | 1,582 | NONE | null | I want to reduce the output dimension by adding a linear layer at the end of the camem model.
code :
```python
from transformers import CamembertTokenizer, CamembertModel
import torch
from torch.nn import Sequential, Linear
tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertMod... | {
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https://api.github.com/repos/huggingface/transformers/issues/2797 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2797/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2797/comments | https://api.github.com/repos/huggingface/transformers/issues/2797/events | https://github.com/huggingface/transformers/pull/2797 | 562,583,783 | MDExOlB1bGxSZXF1ZXN0MzczMTUzNjE5 | 2,797 | Add model readme for deepset/roberta-base-squad2 | {
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https://api.github.com/repos/huggingface/transformers/issues/2796 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2796/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2796/comments | https://api.github.com/repos/huggingface/transformers/issues/2796/events | https://github.com/huggingface/transformers/issues/2796 | 562,568,356 | MDU6SXNzdWU1NjI1NjgzNTY= | 2,796 | output padding different to zero in hidden layers with attention mask | {
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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"
] | 1,581 | 1,587 | 1,587 | NONE | null | # 🐛 Bug
On the last layer, the token corresponding to the padding does not return 0, even if attention masking is used.
## Information
Model I am using (Bert, XLNet ...):
roberta and roberta XLM
Language I am using the model on (English, Chinese ...):
english
The problem arises when using:
* [x] the offi... | {
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https://api.github.com/repos/huggingface/transformers/issues/2795 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2795/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2795/comments | https://api.github.com/repos/huggingface/transformers/issues/2795/events | https://github.com/huggingface/transformers/issues/2795 | 562,551,863 | MDU6SXNzdWU1NjI1NTE4NjM= | 2,795 | Probably a bug in XLMRobertaTokenizer | {
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"Hi, if I am not wrong the pad should be 2 ?\r\nat least the parameters tokenizer.pad_token_id for XLM-R \r\n\r\nEDIT: 2 is eos sorry.",
"Indeed, this is a bug that will be fixed when #3198 is merged. Thanks for letting us know."
] | 1,581 | 1,584 | 1,584 | NONE | null | (Everything goes perfect when I did experiment with MultilingualBert, but seems only the base-model is released.)
When using XLM-R, the according tokenizer (XLMRobertaTokenizer) converts \<unk\> and every OOV token into id = 1. However, 1 should be the number of \<pad\>. (And the tokenizer can convert 1 to \<pad\>, ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2794 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2794/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2794/comments | https://api.github.com/repos/huggingface/transformers/issues/2794/events | https://github.com/huggingface/transformers/issues/2794 | 562,544,518 | MDU6SXNzdWU1NjI1NDQ1MTg= | 2,794 | You must specify an aggregation method to update a MirroredVariable in Replica Context. | {
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"Tried executing the test case: **test_optimization_tf.py**\r\nThe test case also fails when on GPU.",
"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"
] | 1,581 | 1,587 | 1,587 | NONE | null | # You must specify an aggregation method to update a MirroredVariable in Replica Context.
##
<ipython-input-28-7cf32baaf070>:52 step_fn *
gradient_accumulator(grads)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/distribute/distribute_lib.py:763 experimental_run_v2
return self._exte... | {
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CircleCI machines are running without GPU hardware so, at initialisation, TensorFlow tests are looking for NVidia driver version but fails ... | {
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"You're correct! I updated it with 539f601. Thanks."
] | 1,581 | 1,581 | 1,581 | NONE | null | # 🐛 Bug (barely)
Discrepancy in variable names between docs and code:
I presume [``intermediate_size``](https://github.com/huggingface/transformers/blob/520e7f211926e07b2059bc8e21b668db4372e4db/src/transformers/configuration_distilbert.py#L63) refers to [``hidden_dim``](https://github.com/huggingface/transformer... | {
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https://api.github.com/repos/huggingface/transformers/issues/2791 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2791/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2791/comments | https://api.github.com/repos/huggingface/transformers/issues/2791/events | https://github.com/huggingface/transformers/pull/2791 | 562,279,825 | MDExOlB1bGxSZXF1ZXN0MzcyOTA0MDUx | 2,791 | Create BERT-of-Theseus model card | {
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https://api.github.com/repos/huggingface/transformers/issues/2790 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2790/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2790/comments | https://api.github.com/repos/huggingface/transformers/issues/2790/events | https://github.com/huggingface/transformers/issues/2790 | 562,223,245 | MDU6SXNzdWU1NjIyMjMyNDU= | 2,790 | Is there any way that I can directly feed the hidden output of the embedding layer into each of the transformer's layer? | {
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"How about manually handling the embeddings and attention layers?\r\n\r\n```py\r\nfrom transformers import GPT2LMHeadModel, GPT2Tokenizer\r\n\r\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\nmodel = GPT2LMHeadModel.from_pretrained(\"gpt2\")\r\n\r\nsequence = tokenizer.encode(\"Try this out\", return_tensor... | 1,581 | 1,592 | 1,582 | NONE | null | Hello,
For an original sequence ```X``` that has length of ```n```, I am interested in feeding the embedding of the original sequence ```X``` (```E```) as an input to the self-attention block of each layer of ```GPT2LMHeadModels``` (here, layer = self-attention block + feedforward block), and examine the layer outpu... | {
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https://api.github.com/repos/huggingface/transformers/issues/2789 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2789/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2789/comments | https://api.github.com/repos/huggingface/transformers/issues/2789/events | https://github.com/huggingface/transformers/issues/2789 | 562,220,249 | MDU6SXNzdWU1NjIyMjAyNDk= | 2,789 | Is there any way that I can extract the hidden output from the self-attention layer? | {
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"That would be the attentions, which you can output by specifying `output_attentions=True` in your configuration object.",
"Hello,\r\n\r\nThank you very much for your reply.\r\n\r\nWhat I want to obtain though, is not the individual attention weights themselves but rather the final product of the self-attention l... | 1,581 | 1,665 | 1,588 | NONE | null | Hello,
From my understanding, for the ```GPT2LMHeadModel```, the output ```past``` allows me to retrieve the key and value vectors that are used in the self-attention block (which is prior to the feedforward block).
Is there any way I can extract the output of the self-attention block **at a particular head of a... | {
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https://api.github.com/repos/huggingface/transformers/issues/2788 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2788/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2788/comments | https://api.github.com/repos/huggingface/transformers/issues/2788/events | https://github.com/huggingface/transformers/issues/2788 | 562,142,646 | MDU6SXNzdWU1NjIxNDI2NDY= | 2,788 | SQuAD preprocessing not working for roberta (wrong p_mask) | {
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"I think I have a problem that is related regarding training/evaluation using run_squad.py. \r\n\r\nI wanted to train a roberta model on my own Q&A dataset mixed with the SQuAD dataset by running:\r\n\r\n`python ./examples/run_squad.py --output_dir=/home/jupyter/sec_roberta/roberta-base-mixed-quad --model_type=ro... | 1,581 | 1,589 | 1,589 | CONTRIBUTOR | null | **Description**
The pipeline for QA crashes for roberta models.
It's loading the model and tokenizer correctly, but the SQuAD preprocessing produces a wrong `p_mask` leading to no possible prediction and the error message below.
The observed `p_mask` for a roberta model is
```[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1... | {
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https://api.github.com/repos/huggingface/transformers/issues/2787 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2787/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2787/comments | https://api.github.com/repos/huggingface/transformers/issues/2787/events | https://github.com/huggingface/transformers/issues/2787 | 562,124,488 | MDU6SXNzdWU1NjIxMjQ0ODg= | 2,787 | Distillation code loss functions | {
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"Hello @snaik2016,\r\nThe part of code you're referring to is not a distillation loss. It's the \"classic\" causal language modeling loss.\r\nVictor",
"Not referring to the \"distillation loss\" just the part of the code where loss is computed in distillation code. The exact same quantity is return by model outpu... | 1,581 | 1,587 | 1,587 | NONE | null | # ❓ Questions & Help
Why compute cross entropy loss from the hard labels in distillation code?
if self.alpha_clm > 0.0:
shift_logits = s_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[..., 1:].contiguous()
loss_clm = self.lm_loss_fct(shift_logits.view(-1, shift_logi... | {
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https://api.github.com/repos/huggingface/transformers/issues/2786 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2786/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2786/comments | https://api.github.com/repos/huggingface/transformers/issues/2786/events | https://github.com/huggingface/transformers/issues/2786 | 562,085,633 | MDU6SXNzdWU1NjIwODU2MzM= | 2,786 | SequenceSummary: config.summary_activation = 'relu' would be ignored | {
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"I think both approaches are reasonable. @LysandreJik @thomwolf?",
"I agree with both approaches as well. The second one would probably be the most useful.",
"Yes, like Lysandre"
] | 1,581 | 1,581 | 1,581 | CONTRIBUTOR | null | This isn't a bug, but merely an unintuitive argument name.
`summary_activation` sounds like it can be general, e.g. "relu" or "gelu" or something, but if its not "tanh" its ignored.
Since I assume it's annoying to go through all the configs and rename a field to use_tanh=True, I propose that we raise if summary_a... | {
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https://api.github.com/repos/huggingface/transformers/issues/2785 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2785/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2785/comments | https://api.github.com/repos/huggingface/transformers/issues/2785/events | https://github.com/huggingface/transformers/pull/2785 | 562,081,084 | MDExOlB1bGxSZXF1ZXN0MzcyNzYwNzg1 | 2,785 | Create README.md | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2785?src=pr&el=h1) Report\n> Merging [#2785](https://codecov.io/gh/huggingface/transformers/pull/2785?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/520e7f211926e07b2059bc8e21b668db4372e4db?src=pr&el=desc) will **n... | 1,581 | 1,581 | 1,581 | CONTRIBUTOR | null | Albert xxlarge version 1 language model fine-tuned on SQuAD2.0 with the following results:
```
{'exact': 85.65653162637918,
'f1': 89.260458954177,
'total': 11873,
'HasAns_exact': 82.6417004048583,
'HasAns_f1': 89.85989020967376,
'HasAns_total': 5928,
'NoAns_exact': 88.66274179983179,
'NoAns_f1': 88.662... | {
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https://api.github.com/repos/huggingface/transformers/issues/2784 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2784/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2784/comments | https://api.github.com/repos/huggingface/transformers/issues/2784/events | https://github.com/huggingface/transformers/issues/2784 | 561,999,599 | MDU6SXNzdWU1NjE5OTk1OTk= | 2,784 | ERROR:CUDA out of memory when using GPT2 tour | {
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"I've already tried to change batch_size to 1. This doesn't seem to be effective"
] | 1,581 | 1,581 | 1,581 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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https://api.github.com/repos/huggingface/transformers/issues/2783 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2783/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2783/comments | https://api.github.com/repos/huggingface/transformers/issues/2783/events | https://github.com/huggingface/transformers/issues/2783 | 561,998,838 | MDU6SXNzdWU1NjE5OTg4Mzg= | 2,783 | Features proposals to simplify training Tensorflow model | {
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"I think that we are trying to keep model logic and training logic very separate. So if you want to try to make the tf **examples** (where training happens) simpler, I'd recommend sending a PR that does that for `examples/run_tf_glue.py` or `example_run_tf_ner.py`, or even adding a new Tensorflow example for a task... | 1,581 | 1,590 | 1,590 | CONTRIBUTOR | null | # 🚀 Feature request
Hello,
On my side I thought to implement some features to simplify the way we can train Tensorflow models (I think it can certainly be adapted to Pytorch as well), and I wanted to know if it might be useful for you. Here a non exhaustive list of features I have in mind:
1. Augment the tranin... | {
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"@yuchenlin May I ask if you figured out the bug?"
] | 1,581 | 1,581 | 1,581 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...):
**roberta-large**
Language I am using the model on (English, Chinese ...):
English
The problem arises when using:
* [* ] the official example scripts:
https://github.com/huggingface/transformers/tree/6c1b23554f8bb5b5e1f6c80969acab764c755678/examp... | {
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https://api.github.com/repos/huggingface/transformers/issues/2781 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2781/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2781/comments | https://api.github.com/repos/huggingface/transformers/issues/2781/events | https://github.com/huggingface/transformers/issues/2781 | 561,938,129 | MDU6SXNzdWU1NjE5MzgxMjk= | 2,781 | Flaky TF pipelines test on CircleCI | {
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"Indeed, this is a recurring error. I have not yet found the time to dive into it yet, we should do so shortly.",
"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"
] | 1,581 | 1,587 | 1,587 | CONTRIBUTOR | null | Environment: CircleCI
Test: `tests/test_pipelines.py::MultiColumnInputTestCase::test_tf_question_answering`
Traceback: https://circleci.com/gh/huggingface/transformers/15691?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link
Diff where I changed nothing relevant and the test sta... | {
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https://api.github.com/repos/huggingface/transformers/issues/2780 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2780/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2780/comments | https://api.github.com/repos/huggingface/transformers/issues/2780/events | https://github.com/huggingface/transformers/issues/2780 | 561,926,066 | MDU6SXNzdWU1NjE5MjYwNjY= | 2,780 | Pipelines- if initial model download is interrupted, everything is ruined | {
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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"
] | 1,581 | 1,587 | 1,587 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): pipeline('ner') and pipeline('feature-extraction')
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
- [x] my own modified scripts: (give details below)
The tasks I am working on is:
- [x] (most... | {
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https://api.github.com/repos/huggingface/transformers/issues/2779 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2779/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2779/comments | https://api.github.com/repos/huggingface/transformers/issues/2779/events | https://github.com/huggingface/transformers/issues/2779 | 561,890,394 | MDU6SXNzdWU1NjE4OTAzOTQ= | 2,779 | configuration from custom config file not working | {
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"Did you try using `GPT2Config.from_json_file('xxx.json')`? `from_pretrained` should be used when pointing to a directory containing a `config.json` file.",
"yes it is working now ",
"Great to hear!"
] | 1,581 | 1,581 | 1,581 | NONE | null | I am trying to get the configuration from a custom config file by the following line :
`config = GPT2Config.from_pretrained("./lm/gpt2-xl/lm/my_config.json")`
This is similar to the example of this [page]. But I am getting the following error:
```
OSError: Model name './lm/gpt2-xl/lm/my_config.json' was not foun... | {
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https://api.github.com/repos/huggingface/transformers/issues/2778 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2778/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2778/comments | https://api.github.com/repos/huggingface/transformers/issues/2778/events | https://github.com/huggingface/transformers/pull/2778 | 561,854,929 | MDExOlB1bGxSZXF1ZXN0MzcyNTg3MTQx | 2,778 | Preserve spaces in GPT-2 tokenizers | {
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"You're correct about the GPT-2 tokenizer – I failed to consider that GPT2 doesn't have a BOS token. I've pushed an alternative solution that defines a base `prepare_for_tokenization` method which children can override to make changes to the text before tokenization.\r\n\r\nAs for your second point, the changes are... | 1,581 | 1,582 | 1,581 | CONTRIBUTOR | null | **The issue**: The GPT-2 and RoBERTa tokenizers are incorrectly stripping whitespace following special characters, preventing the BPE encoder from correctly encoding spaces in tokens following RoBERTa `<mask>` and `<unk>` tokens.
```
tokenizer.convert_ids_to_tokens(tokenizer.encode('She likes <mask> cats.'))
# out... | {
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https://api.github.com/repos/huggingface/transformers/issues/2777 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2777/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2777/comments | https://api.github.com/repos/huggingface/transformers/issues/2777/events | https://github.com/huggingface/transformers/pull/2777 | 561,824,178 | MDExOlB1bGxSZXF1ZXN0MzcyNTYyMTUy | 2,777 | distilbert-base-cased | {
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"do we want to actually change the model used in the pipeline?",
"> do we want to actually change the model used in the pipeline?\r\n\r\nI'm not sure to understand the rationale behind the question.\r\nPurely from a perf point of view, it's the same inf speed, while having better metrics than before.",
"Nevermi... | 1,581 | 1,581 | 1,581 | MEMBER | null | - Weights
- Readmes and docs
- Previous omissions
weights are uploaded on s3, along with modelcards.
@LysandreJik Could you make sure I didn't forget anything?
@mfuntowicz Could have a check on the pipeline part? | {
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https://api.github.com/repos/huggingface/transformers/issues/2776 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2776/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2776/comments | https://api.github.com/repos/huggingface/transformers/issues/2776/events | https://github.com/huggingface/transformers/issues/2776 | 561,766,372 | MDU6SXNzdWU1NjE3NjYzNzI= | 2,776 | Pipeline for text classification | {
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"Did you check the README?\r\n\r\ngrep `text-classification: Initialize a TextClassificationPipeline directly, or see sentiment-analysis for an example.\r\n`",
"Sorry, missed this somehow. Thanks for adding it!\n\nOn Fri, Feb 7, 2020 at 12:38 PM Julien Chaumond <notifications@github.com>\nwrote:\n\n> Did you che... | 1,581 | 1,615 | 1,588 | NONE | null | # 🚀 Feature request
Could you please add a text classification pipeline?
<!-- 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
rela... | {
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"Hi @AlecS12, \r\n\r\nI'm currently working on integrating tokenizers library inside transformers with pipelines support.\r\n\r\nIt should not be long now before it lang on master / get released.\r\n\r\nYou can track the development here: https://github.com/huggingface/transformers/pull/2674\r\nIf you want to check... | 1,581 | 1,596 | 1,596 | NONE | null | # 🚀 Feature request
Currently tokenizers are not working with QA pipeline, because they do not have the tokenize method implemented. Speeding up the tokenization would be really beneficial for my application.
<!-- A clear and concise description of the feature proposal.
Please provide a link to the paper and c... | {
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"Indeed, there was an error in the code, thank you for letting us know! I've patched it with fd639e5be31f83447c37cf79023fd98bac29f86c.\r\n\r\nIt is now [updated in the docs](https://huggingface.co/transformers/quickstart.html#using-the-past). Thanks!"
] | 1,581 | 1,581 | 1,581 | NONE | null | I am trying to run [hugginface][1] gpt2-xl model. I ran code from the [quickstart][2] page that load the small gpt2 model and generate text by the following code:
```
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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https://api.github.com/repos/huggingface/transformers/issues/2773 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2773/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2773/comments | https://api.github.com/repos/huggingface/transformers/issues/2773/events | https://github.com/huggingface/transformers/issues/2773 | 561,565,418 | MDU6SXNzdWU1NjE1NjU0MTg= | 2,773 | How to load a pretrained TF model using AutoModel? | {
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"Hi @erikchwang, you should use `TFAutoModel` instead",
"Is this TFAutoModel mentioned in the document? I cannot find it...",
"I'll add it to the model pages soon. Thanks!",
"when I load TF model by use AutoModel from your document, there are many errors, like this \r\n`model = AutoModel.from_pretrained(r'/Us... | 1,581 | 1,603 | 1,581 | NONE | null | Run the following code:
```
import tensorflow as tf
from transformers import AutoModel, TFBertModel
auto_model = AutoModel.from_pretrained("bert-base-uncased")
tfbert_model = TFBertModel.from_pretrained("bert-base-uncased")
print(auto_model.__class__)
print(tfbert_model.__class__)
```
Then the output is:
... | {
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https://api.github.com/repos/huggingface/transformers/issues/2772 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2772/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2772/comments | https://api.github.com/repos/huggingface/transformers/issues/2772/events | https://github.com/huggingface/transformers/issues/2772 | 561,506,277 | MDU6SXNzdWU1NjE1MDYyNzc= | 2,772 | How to generate different suggestions with GPT2 or XLNet like Write With Transformers? | {
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"I closed this issue since I found useful to set the `do_sample` argument to True, as mentioned in this issue: https://github.com/huggingface/transformers/issues/2415"
] | 1,581 | 1,581 | 1,581 | NONE | null | Hello,
I want to generate with run_generation more different suggestions of the next words, preferably with variable length and different terms or synonyms, like it is done in Write With Transformer.
Any suggestion or idea on how to achieve this?
Thanks | {
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https://api.github.com/repos/huggingface/transformers/issues/2771 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2771/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2771/comments | https://api.github.com/repos/huggingface/transformers/issues/2771/events | https://github.com/huggingface/transformers/issues/2771 | 561,455,322 | MDU6SXNzdWU1NjE0NTUzMjI= | 2,771 | export to onnx issue | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,581 | 1,586 | 1,586 | NONE | null | Hi experts,
I got an error when running the onnx model after conversion. Can anyone please help to take a look?
code:
`torch.onnx.export(model,
(input_ids, attention_mask, token_type_ids),
"bert.onnx",
input_names=['input_ids', 'attention_mask', 'token_type_ids'],
export_params=True, ver... | {
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https://api.github.com/repos/huggingface/transformers/issues/2770 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2770/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2770/comments | https://api.github.com/repos/huggingface/transformers/issues/2770/events | https://github.com/huggingface/transformers/issues/2770 | 561,425,701 | MDU6SXNzdWU1NjE0MjU3MDE= | 2,770 | The prediction output is random | {
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"Hi @Mozen, \r\nYou will need to train your model for sequence classification first.\r\nThe pre-trained models are not yet trained for the downstream task. So right now, you have an untrained sequence classification head on top of Bert. \r\n\r\nI could not find where it was mentioned in the docs, but have a look at... | 1,581 | 1,581 | 1,581 | NONE | null | When I use the official example scripts to predict a text sentence classification model, I found that the output is different every time。
```
from transformers import BertTokenizer, BertForSequenceClassification
import torch
import numpy as np
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
m... | {
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https://api.github.com/repos/huggingface/transformers/issues/2769 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2769/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2769/comments | https://api.github.com/repos/huggingface/transformers/issues/2769/events | https://github.com/huggingface/transformers/issues/2769 | 561,390,917 | MDU6SXNzdWU1NjEzOTA5MTc= | 2,769 | Model download: tf-xlm-roberta-large "tf_model.h5" file missing | {
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"same for roberta-base and roberta-large",
"cc @jplu :)",
"@jplu Renamed the file using `aws s3 mv s3://models.huggingface.co/bert/jplu/tf-xlm-roberta-large/xlm-roberta-large-tf_model.h5 s3://models.huggingface.co/bert/jplu/tf-xlm-roberta-large/tf_model.h5`\r\n\r\nDoes it work now @paradc2 ?\r\n\r\n@RichJackson... | 1,581 | 1,588 | 1,581 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): tf-xlm-roberta-large
The "tf_model.h5" file for tf-xlm-roberta-large appears to be missing as the following url from model hub is returning "NoSuchKey" errors: https://s3.amazonaws.com/models.huggingface.co/bert/jplu/tf-xlm-roberta-large/tf_model.h5
... | {
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https://api.github.com/repos/huggingface/transformers/issues/2768 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2768/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2768/comments | https://api.github.com/repos/huggingface/transformers/issues/2768/events | https://github.com/huggingface/transformers/issues/2768 | 561,387,126 | MDU6SXNzdWU1NjEzODcxMjY= | 2,768 | why take the first hidden state for sequence classification (DistilBertForSequenceClassification) | {
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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"
] | 1,581 | 1,586 | 1,586 | NONE | null | In the last few layers of sequence classification [here][1], the first hidden state of the sequence length of the transformer output to be used for classification.
hidden_state = distilbert_output[0] # (bs, seq_len, dim) <-- transformer output
pooled_output = hidden_state[:, 0] # (bs, dim) <-- ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2767 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2767/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2767/comments | https://api.github.com/repos/huggingface/transformers/issues/2767/events | https://github.com/huggingface/transformers/issues/2767 | 561,366,595 | MDU6SXNzdWU1NjEzNjY1OTU= | 2,767 | Adapter-BERT is missing in transformers library? | {
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"Up, I think this is an awesome idea",
"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"
] | 1,581 | 1,588 | 1,588 | NONE | null | Adapter BERT obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency. ( https://github.com/google-research/adapter-bert ) @thomwolf
I think, it will be useful if adapter-bert is also included in the library.
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https://api.github.com/repos/huggingface/transformers/issues/2766 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2766/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2766/comments | https://api.github.com/repos/huggingface/transformers/issues/2766/events | https://github.com/huggingface/transformers/pull/2766 | 561,360,496 | MDExOlB1bGxSZXF1ZXN0MzcyMTg2MzMx | 2,766 | Fix documentation in ProjectedAdaptiveLogSoftmax | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2766?src=pr&el=h1) Report\n> Merging [#2766](https://codecov.io/gh/huggingface/transformers/pull/2766?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/33d3072e1c54bcd235447b98c6dea1b4cb71234c?src=pr&el=desc) will **n... | 1,581 | 1,581 | 1,581 | CONTRIBUTOR | null | The shape of outputs for forward in ProjectedAdaptiveLogSoftmax is flipped in the documentation: it should be log probabilities when `labels` is `None` and NLLs otherwise. This is what the code does, but the docstring has them flipped. | {
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https://api.github.com/repos/huggingface/transformers/issues/2765 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2765/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2765/comments | https://api.github.com/repos/huggingface/transformers/issues/2765/events | https://github.com/huggingface/transformers/pull/2765 | 561,324,272 | MDExOlB1bGxSZXF1ZXN0MzcyMTU3MTIw | 2,765 | Add option to `cached_path` to automatically extract archives | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2765?src=pr&el=h1) Report\n> Merging [#2765](https://codecov.io/gh/huggingface/transformers/pull/2765?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2c12464a20160061a8b436b4939e8d5fa2437a15?src=pr&el=desc) will **d... | 1,581 | 1,581 | 1,581 | MEMBER | null | Slight modification to `cached_path` so that zip and tar archives can be automatically extracted.
- archives are extracted in the same directory than the (possibly downloaded) archive in a created extraction directory named from the archive.
- automatic extraction is activated by setting `extract_compressed_file=True... | {
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https://api.github.com/repos/huggingface/transformers/issues/2764 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2764/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2764/comments | https://api.github.com/repos/huggingface/transformers/issues/2764/events | https://github.com/huggingface/transformers/pull/2764 | 561,242,382 | MDExOlB1bGxSZXF1ZXN0MzcyMDg5MjM5 | 2,764 | [examples] rename run_lm_finetuning to run_language_modeling | {
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"Great!!"
] | 1,581 | 1,581 | 1,581 | MEMBER | null | And corresponding doc updates | {
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https://api.github.com/repos/huggingface/transformers/issues/2763 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2763/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2763/comments | https://api.github.com/repos/huggingface/transformers/issues/2763/events | https://github.com/huggingface/transformers/issues/2763 | 561,230,619 | MDU6SXNzdWU1NjEyMzA2MTk= | 2,763 | Add albert-base-v3 to pretrained models? | {
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"Hi, as mentioned in their changelog, the only difference between the v2 and v3 is the compatibility with TF 1.15 as they removed the `einsum` operation.\r\n\r\nIt won't change anything for the huggingface/transformers users as the models available here are only for TF2.\r\n\r\n. Could you please add it to the list of available pretrained models [here](https://huggingface.co/transformers/pretrained_models.html)?
## Motivation
Would provide the community with the most up-to-date... | {
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https://api.github.com/repos/huggingface/transformers/issues/2762 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2762/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2762/comments | https://api.github.com/repos/huggingface/transformers/issues/2762/events | https://github.com/huggingface/transformers/pull/2762 | 561,215,835 | MDExOlB1bGxSZXF1ZXN0MzcyMDY3MjM3 | 2,762 | Add contributors snapshot | {
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https://api.github.com/repos/huggingface/transformers/issues/2761 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2761/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2761/comments | https://api.github.com/repos/huggingface/transformers/issues/2761/events | https://github.com/huggingface/transformers/pull/2761 | 561,202,058 | MDExOlB1bGxSZXF1ZXN0MzcyMDU1NzI2 | 2,761 | [docs] Add menu w/ links to other pages on hf.co | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2761?src=pr&el=h1) Report\n> Merging [#2761](https://codecov.io/gh/huggingface/transformers/pull/2761?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/33d3072e1c54bcd235447b98c6dea1b4cb71234c?src=pr&el=desc) will **n... | 1,581 | 1,581 | 1,581 | MEMBER | null | {
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https://api.github.com/repos/huggingface/transformers/issues/2760 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2760/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2760/comments | https://api.github.com/repos/huggingface/transformers/issues/2760/events | https://github.com/huggingface/transformers/pull/2760 | 561,135,192 | MDExOlB1bGxSZXF1ZXN0MzcyMDAxMDM2 | 2,760 | build: add poetry, an alternative to setup.py with dependency versions tracked | {
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"Not sure why this breaks the CI?\r\n\r\nShouldn't we _not_ version control the .lock file?",
"To me one interesing is to track `.lock` that way you are always certain to have a working versions, i.e. dependencies version that match together.\r\n\r\nIndeed I am not sure why the CI fails :/",
"Pinging @aaugustin... | 1,581 | 1,588 | 1,588 | NONE | null | Hello,
I have written a `pyproject.toml` so your project can be setup using [poetry](https://github.com/python-poetry/poetry) .
That way requirements versions can easily be tracked for people wanting to use **poetry** (it is optional).
# Example
```bash
# first setup your virtual environment, then:
pip inst... | {
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https://api.github.com/repos/huggingface/transformers/issues/2759 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2759/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2759/comments | https://api.github.com/repos/huggingface/transformers/issues/2759/events | https://github.com/huggingface/transformers/issues/2759 | 561,083,113 | MDU6SXNzdWU1NjEwODMxMTM= | 2,759 | Loss is calculated on all tokens, including padding, in the LM fine-tuning example | {
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"Hello, `BertForMaskedLM` [does not use an ignore index set to -1](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py#L1018), nor does any other models.\r\n\r\nIt was updated in [2.4.0](https://github.com/huggingface/transformers/releases/tag/v2.4.0). The scripts should be run... | 1,581 | 1,581 | 1,581 | NONE | null | # 🐛 Bug
The BERT fine-tuning example uses a special index to mark ignored locations for the loss function:
`loss_fct = CrossEntropyLoss(ignore_index=-1)`
While in the same example, the masking function that samples locations to be included or excluded uses a different index: -100 (which is the default ignored index... | {
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https://api.github.com/repos/huggingface/transformers/issues/2758 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2758/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2758/comments | https://api.github.com/repos/huggingface/transformers/issues/2758/events | https://github.com/huggingface/transformers/issues/2758 | 561,080,134 | MDU6SXNzdWU1NjEwODAxMzQ= | 2,758 | TFRoberta output with attention_mask changes in version 2.3.0 vs 2.4.1 | {
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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"
] | 1,581 | 1,586 | 1,586 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): Roberta
Language I am using the model on (English, Chinese ...): not relevant
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)
```python
import tensorf... | {
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"1. What do you mean \"weird results\"?\r\n2. What do you mean \"v2 dataset it not works anymore\"?\r\n3. Please provide all the information required in the template, i.e. python version, transformers version, torch version etc",
"1. F1 & Exact match:~18 (should be 88/81 no ?)\r\n2. Squad 2.0\r\n3. Python 3.6, la... | 1,580 | 1,581 | 1,581 | NONE | null | I'm not able to reproduce the squad experimentation (via the example). I tried the command line;
python run_squad.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--do_train \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.... | {
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https://api.github.com/repos/huggingface/transformers/issues/2756 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2756/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2756/comments | https://api.github.com/repos/huggingface/transformers/issues/2756/events | https://github.com/huggingface/transformers/pull/2756 | 560,965,048 | MDExOlB1bGxSZXF1ZXN0MzcxODU5OTMy | 2,756 | BERT decoder: Fix failure with the default attention mask. | {
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"Thanks for the feedback! That's a valid concern. I made config handling consistent, at least for the BERT tests. But if you decide that it's too much change for such a trivial fix, I can revert the changes in tests.",
"(Seems like CircleCI tests failure is transient and unrelated to the PR)",
"That's one way o... | 1,580 | 1,581 | 1,581 | CONTRIBUTOR | null | PyTorch < 1.3 requires multiplication operands to be of the same type. This was violated when using default attention mask (i.e.., `attention_mask=None` in arguments) given BERT in the decoder mode.
In particular, this was breaking `Model2Mode`l and made a tutorial from quickstart.md failing.
A test is included, ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2755 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2755/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2755/comments | https://api.github.com/repos/huggingface/transformers/issues/2755/events | https://github.com/huggingface/transformers/issues/2755 | 560,874,868 | MDU6SXNzdWU1NjA4NzQ4Njg= | 2,755 | Multi-text files support for run_lm_finetuning | {
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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"
] | 1,580 | 1,586 | 1,586 | CONTRIBUTOR | null | # 🚀 Feature request
Support multi-text files for run_lm_finetuning.
## Motivation
Currently, you support training from scratch but it only supports a single file. Usually, when we train from scratch we train a model using multi-text files, not a single text file.
It will be great to support multi-text file a... | {
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https://api.github.com/repos/huggingface/transformers/issues/2754 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2754/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2754/comments | https://api.github.com/repos/huggingface/transformers/issues/2754/events | https://github.com/huggingface/transformers/pull/2754 | 560,870,986 | MDExOlB1bGxSZXF1ZXN0MzcxNzgzMjIx | 2,754 | Changed vocabulary save function. Variable name was inconsistent | {
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"Great, thank you for taking the time to fix it!"
] | 1,580 | 1,581 | 1,581 | NONE | null | Caused an error to be thrown when passing a file name instead of a directory.
UnboundLocalError: local variable 'vocab_file' referenced before assignment
Associated with issue #2753
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https://api.github.com/repos/huggingface/transformers/issues/2753 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2753/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2753/comments | https://api.github.com/repos/huggingface/transformers/issues/2753/events | https://github.com/huggingface/transformers/issues/2753 | 560,866,156 | MDU6SXNzdWU1NjA4NjYxNTY= | 2,753 | Saving tokenizer vocabulary throws error when passing file name instead of directory. | {
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"Closed by #2754 "
] | 1,580 | 1,581 | 1,581 | NONE | null | # 🐛 Bug
## Information
Using transfo-xl-wt103
UnboundLocalError: local variable 'vocab_file' referenced before assignment
## To reproduce
Steps to reproduce the behavior:
```
from transformers import TransfoXLTokenizer, TFTransfoXLLMHeadModel
tokenizer = TransfoXLTokenizer.from_pretrained('tran... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2752?src=pr&el=h1) Report\n> Merging [#2752](https://codecov.io/gh/huggingface/transformers/pull/2752?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9d87eafd118739a4c121d69d7cff425264f01e1c?src=pr&el=desc) will **n... | 1,580 | 1,581 | 1,581 | CONTRIBUTOR | null | Fix multi-gpu evaluation while training in `examples/run_glue.py` | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,580 | 1,586 | 1,586 | NONE | null | # ❓ Questions & Help
Hi,
I want to do sentence pair classification on Quora Questions Dataset by fine-tuning BERT. I am new to this and do not know where to start? Can anyone let me know how do i get started with this?
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"See https://huggingface.co/transformers/v1.2.0/_modules/pytorch_transformers/modeling_bert.html#BertModel\r\n\r\nI think this is your output[1]:\r\n\"Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear laye... | 1,580 | 1,587 | 1,587 | NONE | null | By default `output = BertModel.from_pretrained('bert-base-uncased')` is a 2-tuple where `output[0]` is the hidden states of the last layer, but how is `output[1]` computed? It doesn't seem to be average of the last layer hidden states vectors over multiple tokens. I am trying to leverage output as sentence embedding, n... | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2749?src=pr&el=h1) Report\n> Merging [#2749](https://codecov.io/gh/huggingface/transformers/pull/2749?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ada24def22199459d8c1decc311dfe8dae7a7d8c?src=pr&el=desc) will **d... | 1,580 | 1,651 | 1,582 | MEMBER | null | The script `run_generation` has a few issues that I aim to fix in this PR:
- [x] The XLNet and XLM generations are broken (crash)
- [x] An end of sequence token is added to all sequences, even when models don't have that token, and results in weird end of sequences.
- [x] No way to generate multiple sequences at a... | {
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"#2240 was an error with DistilBERT and was fixed with https://github.com/huggingface/transformers/commit/ea2600bd5f1d36f2fb61958be21db5b901e33884 \r\n\r\nDoes this error happen every time you run the test suite?",
"yes!",
"I'm running on Darwin 19.2, Python 3.7.5, torch 1.3.1, tensorflow 2.0.0 and transformers... | 1,580 | 1,587 | 1,587 | CONTRIBUTOR | null | Running the unit tests locally on mac, I get "Fatal Python error: Aborted"
To reproduce, try `pytest tests/test_modeling_tf_albert.py::TFAlbertModelTest::test_pt_tf_model_equivalence `
### Environment Info
- `transformers` version: 2.4.1
- Platform: Darwin-19.0.0-x86_64-i386-64bit
- Python version: 3.7.5
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The decoder is left-to-right, the encoder is biredictional. As such, the code only uses a causal attention mask in the decoder.
### TODO:
- [x] conversi... | {
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"@thomwolf can you give any insights regarding this?",
"how much lines in `test.txt`?",
"1,041,130 line",
"I have a similar issue finetuning the language model with bert. In the end, I had to scale down my training to ~200,000 lines to make it work, which is a very small proportion of my original dataset.",
... | 1,580 | 1,591 | 1,591 | NONE | null | # ❓ Questions & Help
## Information
Model I am using (Albert(all types)):
Language I am using the model on (English):
The problem arises when using:
* [ ] the official example scripts: (give details below)
the code returns memory allocation problems when run with any version from albert. i tried to reduce... | {
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"I'm using both \"bert-base-uncased\" for both encoder and decoder.",
"> I think I found the problem. I moved the code inside the train_step outside to the enclosing function and it seems to work.\r\n\r\nHi, I am having the same problem, what solved it for you?",
"> > I think I found the problem. I moved the co... | 1,580 | 1,582 | 1,580 | NONE | null | Hi, I am trying to use PreTrainedEncoderDecoder to train a seq2seq model. I have a working training code, but I'm not sure if I'm doing things correctly because the decoded token is always the same token during inference.
The data is paired input and target sentence pairs.
The dataset class looks like this:
```
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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",
"Well - is this realy a bug or just an improvement of the documentation?",
"In my opinion it's a bug as it's misleading. If you want t... | 1,580 | 1,596 | 1,586 | NONE | null | # 🐛 Bug
When calling BertTokenizer with do_lower_case=True, the tokenizer gets rid of accents, which is a misleading behavior not indicated in the name of the parameter. We suggest that you create another parameter which indicates whether or not to strip accents, separated from do_lower_case! This also happens in A... | {
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https://api.github.com/repos/huggingface/transformers/issues/2741 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2741/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2741/comments | https://api.github.com/repos/huggingface/transformers/issues/2741/events | https://github.com/huggingface/transformers/issues/2741 | 560,243,422 | MDU6SXNzdWU1NjAyNDM0MjI= | 2,741 | XLM-Roberta mask filling error | {
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"Found solution in [#2509](https://github.com/huggingface/transformers/pull/2509).",
"Hi, indeed this is an error. This will be fixed once #3198 is merged."
] | 1,580 | 1,584 | 1,584 | NONE | null | # XLM-Roberta mask token filling error
Hi! I am trying to use XLM-Roberta for Masked LM task, but the error occurs when the model fills masked token in a test sentence.
**The code is:**
```
config.model_name = 'xlm-roberta-base'
tokenizer: tr.XLMRobertaTokenizer = tr.XLMRobertaTokenizer.from_pretrained(con... | {
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https://api.github.com/repos/huggingface/transformers/issues/2740 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2740/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2740/comments | https://api.github.com/repos/huggingface/transformers/issues/2740/events | https://github.com/huggingface/transformers/issues/2740 | 560,205,858 | MDU6SXNzdWU1NjAyMDU4NTg= | 2,740 | T5 | {
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"Your attention_mask should be torch.tensor([[1,1,1,1,1,1]]",
"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"
] | 1,580 | 1,589 | 1,589 | NONE | null | the code is here
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5WithLMHeadModel.from_pretrained('t5-small')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
print(tokenizer._tokenize("Hello, my dog is cute"))
print(input_ids)
... | {
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https://api.github.com/repos/huggingface/transformers/issues/2739 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2739/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2739/comments | https://api.github.com/repos/huggingface/transformers/issues/2739/events | https://github.com/huggingface/transformers/issues/2739 | 560,154,550 | MDU6SXNzdWU1NjAxNTQ1NTA= | 2,739 | Development Infrastructure for ML Projects | {
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"This is an interesting subject but is way too broad for discussing here",
"@julien-c any forum you know for having this discussion?"
] | 1,580 | 1,580 | 1,580 | NONE | null | Hi guys, I am sorry to post an issue that is a bit outside the scope of this project. But I have been a consistent watcher of the transformers project and it is excellent on how I can collaborate and coordinate with people and develop something.
But I am a rookie when it comes to working with people and get going with... | {
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https://api.github.com/repos/huggingface/transformers/issues/2738 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2738/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2738/comments | https://api.github.com/repos/huggingface/transformers/issues/2738/events | https://github.com/huggingface/transformers/pull/2738 | 560,010,223 | MDExOlB1bGxSZXF1ZXN0MzcxMDc2MzY2 | 2,738 | Fix GPT2 config set to trainable | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2738?src=pr&el=h1) Report\n> Merging [#2738](https://codecov.io/gh/huggingface/transformers/pull/2738?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9e5b549b4d47678bdc74bc8f650e82cf25bfc245?src=pr&el=desc) will **n... | 1,580 | 1,580 | 1,580 | CONTRIBUTOR | null | There's currently a bug in the GPT2 model which prevents it from being saved. This is caused by setting the trainable parameter to the GPT2 config, which cannot be packaged later in the save pipeline. Gotta love python...
Here is a simple script which you can use to reproduce this bug (and check the fix):
```
from... | {
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https://api.github.com/repos/huggingface/transformers/issues/2737 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2737/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2737/comments | https://api.github.com/repos/huggingface/transformers/issues/2737/events | https://github.com/huggingface/transformers/issues/2737 | 560,001,464 | MDU6SXNzdWU1NjAwMDE0NjQ= | 2,737 | Version 2.4.1 breaks run_lm_finetuning.py, version 2.3.0 runs fine | {
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"Hi, can you check that the current version of `run_lm_finetuning` crashes on your side by pulling the latest repo version? The `run_lm_finetuning` script was updated 30 minutes ago in regard to that error.",
"Ah yes, it works, specifically switching into this line\r\n\r\n`labels[~masked_indices] = -1 `\r\n\r\nto... | 1,580 | 1,581 | 1,580 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): Bert
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [x] the official example scripts: (give details below)
Maksed language modeling
https://github.com/huggingface/transformers/blob/master/ex... | {
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https://api.github.com/repos/huggingface/transformers/issues/2736 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2736/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2736/comments | https://api.github.com/repos/huggingface/transformers/issues/2736/events | https://github.com/huggingface/transformers/pull/2736 | 559,958,435 | MDExOlB1bGxSZXF1ZXN0MzcxMDMzNjkx | 2,736 | TensorFlow XLM doesn't accept NumPy arrays for the attention mask | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2736?src=pr&el=h1) Report\n> Merging [#2736](https://codecov.io/gh/huggingface/transformers/pull/2736?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9c67196b83a824df577742d32d38e9121d8a9285?src=pr&el=desc) will **i... | 1,580 | 1,651 | 1,587 | MEMBER | null | Convert NumPy attention mask to a TensorFlow tensor so that the mask creation doesn't crash
closes #2729 | {
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https://api.github.com/repos/huggingface/transformers/issues/2735 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2735/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2735/comments | https://api.github.com/repos/huggingface/transformers/issues/2735/events | https://github.com/huggingface/transformers/pull/2735 | 559,916,187 | MDExOlB1bGxSZXF1ZXN0MzcwOTk4OTY0 | 2,735 | test_attention_weights cleanup | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2735?src=pr&el=h1) Report\n> Merging [#2735](https://codecov.io/gh/huggingface/transformers/pull/2735?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/86a0bb6e2117ad98141d92b700964aa0e73f8f49?src=pr&el=desc) will **d... | 1,580 | 1,580 | 1,580 | CONTRIBUTOR | null | No logic changes, just uses getattr to make code more readable. | {
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https://api.github.com/repos/huggingface/transformers/issues/2734 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2734/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2734/comments | https://api.github.com/repos/huggingface/transformers/issues/2734/events | https://github.com/huggingface/transformers/pull/2734 | 559,895,316 | MDExOlB1bGxSZXF1ZXN0MzcwOTgyMDM4 | 2,734 | pass langs parameter to certain XLM models | {
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"This seems to be failing a line length check, but my lines are not the longest in the file -- let me know if I should edit (the whole file) to conform.",
"Hi, thanks for opening this pull request! For the code quality to pass, you can check what's wrong with `make quality` at the root of the repo, and fix the bl... | 1,580 | 1,580 | 1,580 | CONTRIBUTOR | null | Adding an argument that specifies the language the SQuAD dataset is in so language-sensitive XLMs (e.g. `xlm-mlm-tlm-xnli15-1024`) don't default to language `0`.
Allows resolution of issue #1799 . | {
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https://api.github.com/repos/huggingface/transformers/issues/2733 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2733/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2733/comments | https://api.github.com/repos/huggingface/transformers/issues/2733/events | https://github.com/huggingface/transformers/issues/2733 | 559,868,745 | MDU6SXNzdWU1NTk4Njg3NDU= | 2,733 | Save model wrapped in Keras | {
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"Same problem.",
"On which version are you running? Is it possible that [this fix](https://github.com/huggingface/transformers/pull/3103) fixed your issue? Can you try installing from master to check?",
"This doesn't look like the same thing I was fixing in #3103 so I doubt that that helped.",
"In particular,... | 1,580 | 1,613 | 1,606 | NONE | null | Hi all,
Sorry for my naive question but I am trying to save my keras model (<class 'tensorflow.python.keras.engine.training.Model'>) in which I use TFBertModel() function as an hidden layer. To do that I use the save() function provided by the tf.keras package.
But I got this error:
```python
------------------... | {
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https://api.github.com/repos/huggingface/transformers/issues/2732 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2732/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2732/comments | https://api.github.com/repos/huggingface/transformers/issues/2732/events | https://github.com/huggingface/transformers/issues/2732 | 559,813,125 | MDU6SXNzdWU1NTk4MTMxMjU= | 2,732 | Error for run_lm_finetuning.py (CUDA error: device-side assert triggered) | {
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"Hi, thank you for your report. As discussed in #2719, 3bf5417 should have fixed it. Please let me know if it fixes your issue.",
"Yes, if I change `labels[~masked_indices] = -1` to `labels[~masked_indices] = -100`, then it works fine for both lm (GPT) and mlm (BERT-like).\r\nBut I'm worrying about 3bf5417 becaus... | 1,580 | 1,580 | 1,580 | NONE | null | ### Reporting Error
I updated transformers from 2.3.x to 2.4.1 today, and I'm facing a runtime error which is RuntimeError: CUDA error: device-side assert triggered.
I reviewed recent updates and found out the commits [Follow up 213] is causing the error.
Below are the changes from the commits:
- labels[~masked_i... | {
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https://api.github.com/repos/huggingface/transformers/issues/2731 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2731/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2731/comments | https://api.github.com/repos/huggingface/transformers/issues/2731/events | https://github.com/huggingface/transformers/issues/2731 | 559,795,559 | MDU6SXNzdWU1NTk3OTU1NTk= | 2,731 | Masked LM and 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"
] | 1,580 | 1,586 | 1,586 | NONE | null | Hello,
Is it correct to say that fine-tuning a TFBertForSequenceClassification model is the same as fine-tuning BERT's mlm and in additional a classification layer at the same time?
Thanks! | {
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https://api.github.com/repos/huggingface/transformers/issues/2730 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2730/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2730/comments | https://api.github.com/repos/huggingface/transformers/issues/2730/events | https://github.com/huggingface/transformers/issues/2730 | 559,789,093 | MDU6SXNzdWU1NTk3ODkwOTM= | 2,730 | QuickStart code error | {
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"Hi! Indeed this was an error, it should have been fixed with https://github.com/huggingface/transformers/commit/90ab15cb7a8fcf8bf58c05453ddf1aa6a4fa00c1.\r\n\r\nCould you try installing from source:\r\n\r\n```py\r\npip install git+https://github.com/huggingface/transformers\r\n```\r\n\r\nand let me know if it fixe... | 1,580 | 1,582 | 1,582 | NONE | null | In the model2model quickstart example, I'm getting an error here:
`outputs = model(question_tensor, answer_tensor, decoder_lm_labels=labels_tensor)`
With the following message:
`RuntimeError: The size of tensor a (8) must match the size of tensor b (768) at non-singleton dimension 3`
Any ideas? | {
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https://api.github.com/repos/huggingface/transformers/issues/2729 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2729/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2729/comments | https://api.github.com/repos/huggingface/transformers/issues/2729/events | https://github.com/huggingface/transformers/issues/2729 | 559,768,165 | MDU6SXNzdWU1NTk3NjgxNjU= | 2,729 | Attention Mask for TFXLM Model doesn't work | {
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"Hi! There seems to be an error in the current implementation where it doesn't accept NumPy arrays, only TensorFlow arrays. I'm working on it in [the branch fix-tf-xlm](https://github.com/huggingface/transformers/tree/fix-tf-xlm). In the meantime, you can use a tf.Tensor instead and it should work fine.\r\n\r\nPlea... | 1,580 | 1,655 | 1,589 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): XLM
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [x] the official example scripts: (give details below)
## To reproduce
Steps to reproduce the behavior:
```
from transformers import *
... | {
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https://api.github.com/repos/huggingface/transformers/issues/2728 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2728/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2728/comments | https://api.github.com/repos/huggingface/transformers/issues/2728/events | https://github.com/huggingface/transformers/issues/2728 | 559,667,477 | MDU6SXNzdWU1NTk2Njc0Nzc= | 2,728 | RuntimeError: expected dtype Float but got dtype Long - run_lm_finetuning.py | {
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"@paulthemagno thanks for creating this. My environment is exactly the same, except I am running the lm fine tuner on a Python 3.7.3 environment. @LysandreJik asked for more information. A couple of more inputs although I am not sure if this is going to help. This problem is not happening when I subset my dataset... | 1,580 | 1,580 | 1,580 | NONE | null | # 🐛 Bug
## Information
I'm using my Camembert-based language model on Italian language (built from scratch).
I'm trying to use [run_lm_finetuning.py](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py) to fine-tune my language model on a dataset.
@julien-c suggested my t... | {
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https://api.github.com/repos/huggingface/transformers/issues/2727 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2727/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2727/comments | https://api.github.com/repos/huggingface/transformers/issues/2727/events | https://github.com/huggingface/transformers/issues/2727 | 559,652,475 | MDU6SXNzdWU1NTk2NTI0NzU= | 2,727 | XLM Roberta token_type_ids bug with batch_encode_plus | {
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"# Note\r\n\r\n- No error occurs for other models (e.g. `bert-base-cased`)\r\n- I think the configuration of `xlm-roberta-base` is incorrect:\r\n\r\n```\r\n>>> cfg = XLMRobertaConfig.from_pretrained(\"xlm-roberta-base\")\r\n>>> cfg.type_vocab_size\r\n1 # 2 is correct?\r\n```",
"> * I think the conf... | 1,580 | 1,584 | 1,584 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): XLM Roberta
Language I am using the model on (English, Chinese ...): English
The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)
The tasks I am working on ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2726 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2726/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2726/comments | https://api.github.com/repos/huggingface/transformers/issues/2726/events | https://github.com/huggingface/transformers/issues/2726 | 559,597,672 | MDU6SXNzdWU1NTk1OTc2NzI= | 2,726 | convert_tokens_to_ids(self, tokens)中的ids.append(self.vocab[token])，KeyError | {
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"The token that you are trying to convert to ids doesn't exist. 'persuading' is a long word, so it's likely that it is not as such present in the vocab. Instead you'll have to tokenize it first into subword units.",
"This issue has been automatically marked as stale because it has not had recent activity. It will... | 1,580 | 1,586 | 1,586 | NONE | null | # ❓ Questions & Help
## Details
<!-- Description of your issue -->
KeyError Traceback (most recent call last)
<ipython-input-20-dfb0a32a8e67> in <module>()
19 for mask_pos in mask_positions:
20 candidates = options[num]
---> 21 candidates_ids = tokenizer.conve... | {
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https://api.github.com/repos/huggingface/transformers/issues/2725 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2725/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2725/comments | https://api.github.com/repos/huggingface/transformers/issues/2725/events | https://github.com/huggingface/transformers/issues/2725 | 559,441,283 | MDU6SXNzdWU1NTk0NDEyODM= | 2,725 | add TinyBERT? | {
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"tinybert just a normal bert with smaller parameters. I am not sure whether huggingface team will create new object called `TinyBert`. I think you can simply contact `huawei-noah` first to get permission to upload tinybert using your personal account.",
"Or you could ask them if they would create an [org account... | 1,580 | 1,590 | 1,590 | NONE | null | # 🌟 New model addition
## Model description
TinyBERT is a smaller version of the Base BERT model, it uses transformer distillation (a type of knowledge distillation) to transfer the plenty of knowledge encoded in a large “teacher” BERT to a small “student” TinyBERT. is empirically effective and achieves more than ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2724 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2724/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2724/comments | https://api.github.com/repos/huggingface/transformers/issues/2724/events | https://github.com/huggingface/transformers/issues/2724 | 559,406,630 | MDU6SXNzdWU1NTk0MDY2MzA= | 2,724 | sequence labeling for sentences and not tokens | {
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"Hi! You could leverage one of the `XXXForSequenceClassification` models for this. Their purpose is to classify sequences into a given number of labels. You would need to initialize a model from a pre-trained checkpoint:\r\n\r\n```py\r\nfrom transformers import BertForSequenceClassification\r\n\r\nmodel = BertForSe... | 1,580 | 1,587 | 1,587 | NONE | null | # ❓ Questions & Help
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
new models and benchmarks, and migration questions. For all other questions,
we direct you to Stack Overflow (SO) where a whole community of PyTorch and
Tensorflow enthusiast can help you out. Make s... | {
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https://api.github.com/repos/huggingface/transformers/issues/2723 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2723/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2723/comments | https://api.github.com/repos/huggingface/transformers/issues/2723/events | https://github.com/huggingface/transformers/pull/2723 | 559,398,248 | MDExOlB1bGxSZXF1ZXN0MzcwNTc0ODI0 | 2,723 | Improved testing | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2723?src=pr&el=h1) Report\n> Merging [#2723](https://codecov.io/gh/huggingface/transformers/pull/2723?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/6c1b23554f8bb5b5e1f6c80969acab764c755678?src=pr&el=desc) will **i... | 1,580 | 1,651 | 1,580 | MEMBER | null | Adding some tests for some models that were not tested. | {
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https://api.github.com/repos/huggingface/transformers/issues/2722 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2722/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2722/comments | https://api.github.com/repos/huggingface/transformers/issues/2722/events | https://github.com/huggingface/transformers/issues/2722 | 559,344,966 | MDU6SXNzdWU1NTkzNDQ5NjY= | 2,722 | Bert and Roberta models cannot be converted to TFLite | {
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"cc @Pierrci ",
"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"
] | 1,580 | 1,586 | 1,586 | CONTRIBUTOR | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...): Bert / Roberta
Language I am using the model on (English, Chinese ...): N/A
The problem arises when using:
* [ ] the official example scripts: (give details below)
Sort of. Using the tflite conversion script provided here:
https://github.com/hug... | {
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https://api.github.com/repos/huggingface/transformers/issues/2721 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2721/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2721/comments | https://api.github.com/repos/huggingface/transformers/issues/2721/events | https://github.com/huggingface/transformers/issues/2721 | 559,158,183 | MDU6SXNzdWU1NTkxNTgxODM= | 2,721 | Is transformers ovewriting tokenizer? | {
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"Hi, I believe your issue was solved with https://github.com/huggingface/tokenizers/issues/120",
"Sure. I will close this case."
] | 1,580 | 1,580 | 1,580 | NONE | null | Hello. I haven't been able to use tokenizer since friday.
It seems that if I install transformers via pip it overwrites tokenizer installation with a version that doesn't work.
If I get a new instance and do that:
`pip install transformers`
When, I do that:
`pip install tokenizers`
I got the following ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2720 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2720/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2720/comments | https://api.github.com/repos/huggingface/transformers/issues/2720/events | https://github.com/huggingface/transformers/pull/2720 | 559,052,143 | MDExOlB1bGxSZXF1ZXN0MzcwMjkyNzMw | 2,720 | Add READMEs to Tensorflow versions of CamemBERT and XLM-RoBERTa | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2720?src=pr&el=h1) Report\n> Merging [#2720](https://codecov.io/gh/huggingface/transformers/pull/2720?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2ba147ecffa28e5a4f96eebd09dcd642117dedae?src=pr&el=desc) will **d... | 1,580 | 1,600 | 1,580 | CONTRIBUTOR | null | Add model cards. | {
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https://api.github.com/repos/huggingface/transformers/issues/2719 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2719/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2719/comments | https://api.github.com/repos/huggingface/transformers/issues/2719/events | https://github.com/huggingface/transformers/issues/2719 | 558,964,825 | MDU6SXNzdWU1NTg5NjQ4MjU= | 2,719 | Error when running run_lm_finetuning.py | {
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"Hi, we would need more information to help you (all the information required in the bug template): transformers version, the full error trace, the script version.\r\n\r\nThis error is probably due to a version mismatch between your script and the transformers version you have installed.",
"I'm having a similar i... | 1,580 | 1,580 | 1,580 | NONE | null | I'm getting the following error when trying to finetune BERT for armenian language
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed. at C:\w\1\s\windows\pytorch\aten\src\THNN/generic/ClassNLLCriterion.c:97
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https://api.github.com/repos/huggingface/transformers/issues/2718 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2718/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2718/comments | https://api.github.com/repos/huggingface/transformers/issues/2718/events | https://github.com/huggingface/transformers/issues/2718 | 558,964,258 | MDU6SXNzdWU1NTg5NjQyNTg= | 2,718 | DistilBertForMaskedLM is not passing ignore_index to loss fct nn.CrossEntropyLoss | {
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"You're absolutely correct, this was a bug. I've updated it in 239dd23.",
"Thank you :). Will close this bug. "
] | 1,580 | 1,580 | 1,580 | NONE | null | # 🐛 Bug
I'm running `run_lm_finetuning.py` and got the error below:
```
/pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [6,0,0]... | {
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https://api.github.com/repos/huggingface/transformers/issues/2717 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2717/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2717/comments | https://api.github.com/repos/huggingface/transformers/issues/2717/events | https://github.com/huggingface/transformers/issues/2717 | 558,872,938 | MDU6SXNzdWU1NTg4NzI5Mzg= | 2,717 | error while training distilbert multilingual model | {
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"pls reply to above",
"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"
] | 1,580 | 1,587 | 1,587 | NONE | null | hi,
I am trying to finetune distilbert multilingual cased model, but i am getting error while training the model:
while with the same code using distilbert uncased , there is no such error.
Can you please check if there is some problem with distilbert multilingual cased model?
error is:
ValueError: Error when ... | {
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https://api.github.com/repos/huggingface/transformers/issues/2716 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2716/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2716/comments | https://api.github.com/repos/huggingface/transformers/issues/2716/events | https://github.com/huggingface/transformers/pull/2716 | 558,766,640 | MDExOlB1bGxSZXF1ZXN0MzcwMDYyMzQ0 | 2,716 | Added README.md to Swedish BERT models from National Library of Sweden | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2716?src=pr&el=h1) Report\n> Merging [#2716](https://codecov.io/gh/huggingface/transformers/pull/2716?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/2ba147ecffa28e5a4f96eebd09dcd642117dedae?src=pr&el=desc) will **n... | 1,580 | 1,580 | 1,580 | CONTRIBUTOR | null | Following the lead of others these are not actual model cards but rather the README.md-files from https://github.com/Kungbib/swedish-bert-models | {
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https://api.github.com/repos/huggingface/transformers/issues/2715 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2715/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2715/comments | https://api.github.com/repos/huggingface/transformers/issues/2715/events | https://github.com/huggingface/transformers/pull/2715 | 558,758,519 | MDExOlB1bGxSZXF1ZXN0MzcwMDU2Mzg1 | 2,715 | Optimize causal mask using torch.where | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/2715?src=pr&el=h1) Report\n> Merging [#2715](https://codecov.io/gh/huggingface/transformers/pull/2715?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/33ef7002e17fe42b276dc6d36c07a3c39b1f09ed?src=pr&el=desc) will **d... | 1,580 | 1,586 | 1,586 | CONTRIBUTOR | null | Instead of multiplying by 1.0 float mask, use torch.where with a bool mask for increased performance. | {
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https://api.github.com/repos/huggingface/transformers/issues/2714 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2714/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2714/comments | https://api.github.com/repos/huggingface/transformers/issues/2714/events | https://github.com/huggingface/transformers/issues/2714 | 558,751,600 | MDU6SXNzdWU1NTg3NTE2MDA= | 2,714 | How to add Dense layer on top of TFBertForSequenceClassification model? | {
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"Found the solution on [1936](https://github.com/huggingface/transformers/issues/1936). Closing.",
"Can you write down the solution here? @sainimohit23 "
] | 1,580 | 1,597 | 1,580 | NONE | null | I am having a really hard time adding the dense layers on the top of this model. I have tried to add the layers of `TFBertForSequenceClassification` in a sequential model with some dense layers like this:
```
bert_model = TFBertForSequenceClassification.from_pretrained("bert-base-cased", config=config)
model = k... | {
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https://api.github.com/repos/huggingface/transformers/issues/2713 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/2713/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/2713/comments | https://api.github.com/repos/huggingface/transformers/issues/2713/events | https://github.com/huggingface/transformers/issues/2713 | 558,713,444 | MDU6SXNzdWU1NTg3MTM0NDQ= | 2,713 | Weights of FlaubertForQuestionAnswering not initialized from pretrained model | {
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"The Flaubert checkpoints contain the base transformer model, not the weights for question answering (similar to most checkpoints). The point of the `run_squad` script is to fine-tune the weights of the additional question answering head to the specific task (french squad in your case).",
"Hi @LysandreJik \r\n\r\... | 1,580 | 1,581 | 1,581 | NONE | null | # 🐛 Bug
## Information
Model I am using (Bert, XLNet ...):
`Flaubert`
Language I am using the model on (English, Chinese ...):
`French`
The tasks I am working on is: **Fine-tune Flaubert on French-translated SQuAD**
The problem arises when using:
```
python3 ./examples/run_squad.py \
--model_... | {
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