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https://api.github.com/repos/huggingface/transformers/issues/1209 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1209/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1209/comments | https://api.github.com/repos/huggingface/transformers/issues/1209/events | https://github.com/huggingface/transformers/issues/1209 | 490,093,302 | MDU6SXNzdWU0OTAwOTMzMDI= | 1,209 | run_squad.py predictions | {
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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",
"The comments say this situation is when the answer is a single null , but I don't see any conditionals to filter for this. hmmm did you... | 1,567 | 1,574 | 1,573 | NONE | null | ## ❓ Can anyone explain how start_logit and end_logit value is been determined ? And how it can used to measure the reliability of the answer span
In some issues I saw that if start_logit and end_logit = -1 then it is concluded to be unanswerable question.
There are some cases where I get the
> "1": [
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https://api.github.com/repos/huggingface/transformers/issues/1208 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1208/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1208/comments | https://api.github.com/repos/huggingface/transformers/issues/1208/events | https://github.com/huggingface/transformers/issues/1208 | 489,816,991 | MDU6SXNzdWU0ODk4MTY5OTE= | 1,208 | How to set the token_type_ids in XLNet correctly? | {
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"Hello! We have an example using `token_type_ids` in our `run_glue` script. You can look at how we build the features in the [`utils_glue`, especially concerning the `segment_ids`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/utils_glue.py#L456-L484) which are the `token_type_ids` that ... | 1,567 | 1,568 | 1,568 | NONE | null | Hi, I am fine-tuning an XLNet, and I want to use type embedding to indicate different parts of a sequence. I am facing a difficulty that `“indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices)”` which is the description of `token_type_ids` in the official document. Doe... | {
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https://api.github.com/repos/huggingface/transformers/issues/1207 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1207/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1207/comments | https://api.github.com/repos/huggingface/transformers/issues/1207/events | https://github.com/huggingface/transformers/issues/1207 | 489,742,623 | MDU6SXNzdWU0ODk3NDI2MjM= | 1,207 | convert_roberta_checkpoint_to_pytorch.py 514 max position? | {
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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"
] | 1,567 | 1,573 | 1,573 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I m not sure if this is intentional but why is max position 514. I m assuming the origingal roberta model is 512 like bert or is this incorrect? This is the only place i find this reference as well. | {
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https://api.github.com/repos/huggingface/transformers/issues/1206 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1206/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1206/comments | https://api.github.com/repos/huggingface/transformers/issues/1206/events | https://github.com/huggingface/transformers/issues/1206 | 489,672,228 | MDU6SXNzdWU0ODk2NzIyMjg= | 1,206 | the best way to cut the upper layers | {
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"Hi, don't know which model you are using so I can't answer precisely but here is the general workflow:\r\n1. load the relevant pretrained configuration with `config = config_class.from_pretrained('your-model-of-interest')`\r\n2. Reduce the number of layers in the configuration with for example: `config.num_hidden_... | 1,567 | 1,626 | 1,570 | NONE | null | Salut,
what would be the best way to cut the upper layers of transformers (from 12 cutting 5 upper layer results 7 layers model for the use)?
Best regards | {
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https://api.github.com/repos/huggingface/transformers/issues/1205 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1205/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1205/comments | https://api.github.com/repos/huggingface/transformers/issues/1205/events | https://github.com/huggingface/transformers/pull/1205 | 489,665,253 | MDExOlB1bGxSZXF1ZXN0MzE0NDMxNDI4 | 1,205 | Fix typo | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1205?src=pr&el=h1) Report\n> Merging [#1205](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1205?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/0b52642d379bed155e8aa4f4088588bfd8ceaa88?... | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1204 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1204/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1204/comments | https://api.github.com/repos/huggingface/transformers/issues/1204/events | https://github.com/huggingface/transformers/issues/1204 | 489,632,828 | MDU6SXNzdWU0ODk2MzI4Mjg= | 1,204 | Can't trace any model with pytorch-transformers 1.2 | {
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"Hi, thank you for reporting this. I can reproduce it on my side. It seems to be a problem relative to the model being on `cuda`, as it doesn't fail if you don't put the model/ids on `cuda`.\r\n\r\nThis doesn't fail:\r\n\r\n```py\r\nfrom pytorch_transformers import BertModel\r\nimport torch\r\n\r\nmodel = BertModel... | 1,567 | 1,574 | 1,573 | NONE | null | ## 🐛 Bug
I get an error whenever I try to trace any model from pytorch-transformers 1.2.0.
When I roll back to 1.1 everything is fine.
## To Reproduce
```python
from pytorch_transformers import BertModel
import torch
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
model.to('cud... | {
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https://api.github.com/repos/huggingface/transformers/issues/1203 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1203/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1203/comments | https://api.github.com/repos/huggingface/transformers/issues/1203/events | https://github.com/huggingface/transformers/pull/1203 | 489,602,124 | MDExOlB1bGxSZXF1ZXN0MzE0MzgyMzE2 | 1,203 | [2.0] TF 2.0 support | {
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"# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/1203?src=pr&el=h1) Report\n> Merging [#1203](https://codecov.io/gh/huggingface/transformers/pull/1203?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/4a233e5b2c18f0cf508f6b917cd1e02954764699?src=pr&el=desc) will **i... | 1,567 | 1,574 | 1,569 | MEMBER | null | Currently converted models:
- [x] BERT
- [x] GPT-2
- [x] XLNet
- [x] XLM
- [x] Transformer-XL
- [x] GPT
- [x] RoBERTa
- [x] DistilBert
With TF 2.0 Keras imperative interface and Eager, the workflow and models are suprisingly similar:
```python
import numpy
import torch
import tensorflow as tf
from pytor... | {
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https://api.github.com/repos/huggingface/transformers/issues/1202 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1202/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1202/comments | https://api.github.com/repos/huggingface/transformers/issues/1202/events | https://github.com/huggingface/transformers/issues/1202 | 489,557,326 | MDU6SXNzdWU0ODk1NTczMjY= | 1,202 | Learning word-pieces garble the predictions | {
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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"
] | 1,567 | 1,573 | 1,573 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): Bert
Language I am using the model on (English, Chinese....): English
The tasks I am working on is:
* [ x] my own task or dataset: Sentiment Analysis on chatbot conversations
Word-pieces change the prediction completely, I fail... | {
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https://api.github.com/repos/huggingface/transformers/issues/1201 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1201/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1201/comments | https://api.github.com/repos/huggingface/transformers/issues/1201/events | https://github.com/huggingface/transformers/pull/1201 | 489,434,510 | MDExOlB1bGxSZXF1ZXN0MzE0MjUzNjIx | 1,201 | [2.0] - Split configuration and modeling files | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1201?src=pr&el=h1) Report\n> Merging [#1201](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1201?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/0b52642d379bed155e8aa4f4088588bfd8ceaa88?... | 1,567 | 1,578 | 1,567 | MEMBER | null | Refactor to split configuration and modeling files so we can share configuration easily between various frameworks.
This PR is quite annoying to rebase so we should probably merge it pretty soon. | {
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https://api.github.com/repos/huggingface/transformers/issues/1200 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1200/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1200/comments | https://api.github.com/repos/huggingface/transformers/issues/1200/events | https://github.com/huggingface/transformers/issues/1200 | 489,430,102 | MDU6SXNzdWU0ODk0MzAxMDI= | 1,200 | Distributed device ordinal question | {
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"No, `local_rank` is only the local rank on each node.",
"Ah I see, thanks! A tangential question, considering the distributed setting only, would it be the same if we simply call `.cuda()` for the model and tensors instead of passing around the device?",
"This issue has been automatically marked as stale becau... | 1,567 | 1,573 | 1,573 | NONE | null | ## ❓ Questions & Help
In the following line, we set the device ordinal to be local rank. However, suppose we have four independent nodes each with only one GPU. Then the 4th node (rank 3) will execute this line with the device ordinal 3, but it really only has 1 GPU, so it'll be invalid to ask for the GPU 3. So won'... | {
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https://api.github.com/repos/huggingface/transformers/issues/1199 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1199/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1199/comments | https://api.github.com/repos/huggingface/transformers/issues/1199/events | https://github.com/huggingface/transformers/pull/1199 | 489,386,911 | MDExOlB1bGxSZXF1ZXN0MzE0MjE4ODkw | 1,199 | Fixing TransformerXL bool issue #1169 | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1199?src=pr&el=h1) Report\n> Merging [#1199](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1199?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/0b52642d379bed155e8aa4f4088588bfd8ceaa88?... | 1,567 | 1,578 | 1,567 | MEMBER | null | Fixing #1169 regarding using uint or bool masks in Transformer-XL and PyTorch 1.1.0 and 1.2.0.
Hopefully, this solution will be compatible upward with the future PyTorch releases. | {
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https://api.github.com/repos/huggingface/transformers/issues/1198 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1198/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1198/comments | https://api.github.com/repos/huggingface/transformers/issues/1198/events | https://github.com/huggingface/transformers/issues/1198 | 489,356,667 | MDU6SXNzdWU0ODkzNTY2Njc= | 1,198 | How to fine-tune xlnet on SQuAD with the parameter setting provided in the paper? | {
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"Here is my attempt to do layer-wise lr decay. It didn't help with the model performance though. Fixing the preprocessing code helped a lot, but still a few points lower than what they reported in the paper and lower than BERT large WWM model. See my comment in #947 \r\n```\r\nlr_layer_decay = 0.75\r\nn_layers = 24... | 1,567 | 1,575 | 1,575 | NONE | null | From [here](https://arxiv.org/pdf/1906.08237.pdf) on page 16, it seems we should set Layer-wise lr decay to 0.75. However, I didn't find a way to do so in `run_squad.py`. Could someone provide a sample command line that could run this fine-tune task with the given parameters?
Thanks! | {
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https://api.github.com/repos/huggingface/transformers/issues/1197 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1197/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1197/comments | https://api.github.com/repos/huggingface/transformers/issues/1197/events | https://github.com/huggingface/transformers/pull/1197 | 489,158,606 | MDExOlB1bGxSZXF1ZXN0MzE0MDM0MjAz | 1,197 | Fix loading of question answering bert from tf weights. | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1197?src=pr&el=h1) Report\n> Merging [#1197](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1197?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/89fd3450a61b5efd76d2524df2454e0a0e4ca070?... | 1,567 | 1,575 | 1,575 | NONE | null | I've got an attribute error when loading pretrained tf weights for question answering (bert) in `load_tf_weights_in_bert` at:
```
elif l[0] == 'squad':
pointer = getattr(pointer, 'classifier')
```
since `BertForQuestionAnswering` does not have a 'classifier' attribute but `qa_outputs`. I've added a try except... | {
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https://api.github.com/repos/huggingface/transformers/issues/1196 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1196/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1196/comments | https://api.github.com/repos/huggingface/transformers/issues/1196/events | https://github.com/huggingface/transformers/issues/1196 | 489,137,849 | MDU6SXNzdWU0ODkxMzc4NDk= | 1,196 | RoBERTa/GPT2 tokenization | {
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"This is a more complex question than it may seem but in general, I think both will be pretty similar in practice.\r\n\r\nThis is related to the fact that the GPT-2 tokenizer (also used by RoBERTa) requires a space before all the words (see [this wise note](https://github.com/pytorch/fairseq/blob/master/fairseq/mod... | 1,567 | 1,569 | 1,569 | COLLABORATOR | null | Hi,
I've one question regarding to the tokenization logic.
I'm using the RoBERTa tokenizer from `fairseq`:
```python
In [15]: tokens = roberta.encode("Berlin and Munich have a lot of puppeteer to see .") ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1195 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1195/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1195/comments | https://api.github.com/repos/huggingface/transformers/issues/1195/events | https://github.com/huggingface/transformers/pull/1195 | 489,085,648 | MDExOlB1bGxSZXF1ZXN0MzEzOTc0MjEz | 1,195 | [2.0] Reodering arguments for torch jit #1010 and future TF2.0 compatibility | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1195?src=pr&el=h1) Report\n> Merging [#1195](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1195?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/0b52642d379bed155e8aa4f4088588bfd8ceaa88?... | 1,567 | 1,576 | 1,568 | MEMBER | null | Torch jit (cf #1010) and TF 2.0 (cf #1104) are more strict than PyTorch on having a specific order of arguments for easy use.
This PR refactor the order of the keyword arguments to make them as natural as possible.
This will be a breaking change for people using positional order to input keyword arguments in the ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1194 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1194/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1194/comments | https://api.github.com/repos/huggingface/transformers/issues/1194/events | https://github.com/huggingface/transformers/issues/1194 | 489,082,380 | MDU6SXNzdWU0ODkwODIzODA= | 1,194 | How to finetune DistilBERT on custom data? | {
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"Hello @008karan,\r\nThere is the class `DistilBertForSequenceClassification` for classification tasks. Its used is really similar to `BertForSequenceClassification`: the main difference is that `DistilBertForSequenceClassification` does not need `token_type_ids` as inputs.",
"This issue has been automatically ma... | 1,567 | 1,573 | 1,573 | NONE | null | ## ❓ Questions & Help
I want to build classifier by using DistillBERT. I would like to know how to finetune it on the custom dataset and build classifier on it.
Thansk! | {
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https://api.github.com/repos/huggingface/transformers/issues/1193 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1193/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1193/comments | https://api.github.com/repos/huggingface/transformers/issues/1193/events | https://github.com/huggingface/transformers/issues/1193 | 489,055,216 | MDU6SXNzdWU0ODkwNTUyMTY= | 1,193 | how to get distilbert-base-uncased-distilled-squad? | {
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"Hello @RyanHuangNLP,\r\nI am not sure to get your question.\r\nDo you mean that you want to play with the model (do inferences)? If so, did you try `qa_model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad')`?",
"@VictorSanh I wonder how to get the 'distilbert-base-uncas... | 1,567 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I think it is initialize a six layer bert and distill with a 12 layer bert, then save the checkpoint file, is it right? | {
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https://api.github.com/repos/huggingface/transformers/issues/1192 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1192/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1192/comments | https://api.github.com/repos/huggingface/transformers/issues/1192/events | https://github.com/huggingface/transformers/issues/1192 | 489,047,515 | MDU6SXNzdWU0ODkwNDc1MTU= | 1,192 | Finetuning BertModel to extract textual features for VQA shows bad results | {
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"It turned out the learning rate was too high (1e-4) using 3e-5 showed good results."
] | 1,567 | 1,567 | 1,567 | NONE | null | ## ❓ Questions & Help
I am trying to use Bert as a textual feature extractor for VQA.
This is the code for tokenizing question text in VQA.
```
self.tokenizer = pytorch_transformers.BertTokenizer.from_pretrained(
'bert-base-uncased')
q = self.tokenizer.encode(q_text, add_special_tokens=True)... | {
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https://api.github.com/repos/huggingface/transformers/issues/1191 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1191/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1191/comments | https://api.github.com/repos/huggingface/transformers/issues/1191/events | https://github.com/huggingface/transformers/issues/1191 | 488,984,793 | MDU6SXNzdWU0ODg5ODQ3OTM= | 1,191 | how to use 'spiece.model' to create the xlnet_tokenizer | {
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"First, note that you can give proxies to the `from_pretrained` method if your connection problem comes from a proxy (see the doc/docstring for an example).\r\n\r\nYou can also download the sentence piece model from our S3 bucket (see the top of the `tokenization_xlnet.py` file for the url) and save it in a folder ... | 1,567 | 1,573 | 1,573 | NONE | null | ## ❓ Questions & Help
For some reason, my computer can not connect to Internet, which means I can not use the code "tokenizer = tokenizer_class.from_pretrained('xlnet-base-cased', do_lower_case = True)" to create the tokenizer.
The Piece model (spiece.model) is used for (de)tokenization, how can I use it to create t... | {
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https://api.github.com/repos/huggingface/transformers/issues/1190 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1190/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1190/comments | https://api.github.com/repos/huggingface/transformers/issues/1190/events | https://github.com/huggingface/transformers/pull/1190 | 488,898,834 | MDExOlB1bGxSZXF1ZXN0MzEzODI2MzEy | 1,190 | Fix reference of import in XLM tokenization | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1190?src=pr&el=h1) Report\n> Merging [#1190](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1190?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/0287d264e913e10018a95a2723115dc9121e5fc6?... | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1189 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1189/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1189/comments | https://api.github.com/repos/huggingface/transformers/issues/1189/events | https://github.com/huggingface/transformers/issues/1189 | 488,887,031 | MDU6SXNzdWU0ODg4ODcwMzE= | 1,189 | Roberta tokenizer fails on certain unicode characters | {
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"Does `fairseq` exhibit the same behavior?\r\n\r\nIf it does, I would ask upstream. But in any case, I'm not sure it's a bug (it's just the internal encoding used by the neural net)",
"I think you need to tokenize by XLM-Roberta-Tokenizer.\r\nI tokenized for Korean."
] | 1,567 | 1,583 | 1,567 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using: `Roberta`:
Language I am using the model on (English, Chinese....): English
The problem arise when using:
- The `roberta-base` tokenizer and tokenizing unicode accents
```
from pytorch_transformers import *
tokenizer = RobertaTokenizer.from_pr... | {
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https://api.github.com/repos/huggingface/transformers/issues/1188 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1188/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1188/comments | https://api.github.com/repos/huggingface/transformers/issues/1188/events | https://github.com/huggingface/transformers/issues/1188 | 488,875,048 | MDU6SXNzdWU0ODg4NzUwNDg= | 1,188 | BertEncoder head_mask not subscript-able error when not passed | {
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"Got same issue. If no `head_mask` is given to `.forward` method of `BertEncoder`, then the following code will cause `TypeError: 'NoneType' object is not subscriptable`.\r\n\r\nhttps://github.com/huggingface/transformers/blob/a701c9b32126f1e6974d9fcb3a5c3700527d8559/transformers/modeling_bert.py#L348\r\n\r\nI wond... | 1,567 | 1,588 | 1,576 | CONTRIBUTOR | null | ## 🐛 Bug
BertEncoder takes in head_mask parameters with default value of None but on https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/modeling_bert.py#L431, ith index is checked without checking if head_mask is None. If by default nothing is passed this results in error.
## Fi... | {
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https://api.github.com/repos/huggingface/transformers/issues/1187 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1187/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1187/comments | https://api.github.com/repos/huggingface/transformers/issues/1187/events | https://github.com/huggingface/transformers/issues/1187 | 488,669,256 | MDU6SXNzdWU0ODg2NjkyNTY= | 1,187 | Using do_eval from run_glue.py uses the cached result | {
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"If you simply remove the cached file or move it elsewhere, it won't be used by run_glue.py. That's what I have been doing.",
"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"
] | 1,567 | 1,573 | 1,573 | NONE | null | ## ❓ Questions & Help
Using do_eval from run_glue.py uses the cached result. I want to evaluate my fine-tuned models and I can't find any guide on how to do so. Anybody that can point me in the right direction? | {
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https://api.github.com/repos/huggingface/transformers/issues/1186 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1186/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1186/comments | https://api.github.com/repos/huggingface/transformers/issues/1186/events | https://github.com/huggingface/transformers/pull/1186 | 488,641,242 | MDExOlB1bGxSZXF1ZXN0MzEzNjIxMTEz | 1,186 | [README] link to Write With Transformer | {
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- experiment with different models (gpt2, xlnet) and/or model checkpoints (example for gpt2: small, large, arxiv)
- Share links to your documents.
With those two changes transfomer.huggingface.co is graduating to being an official... | {
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https://api.github.com/repos/huggingface/transformers/issues/1185 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1185/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1185/comments | https://api.github.com/repos/huggingface/transformers/issues/1185/events | https://github.com/huggingface/transformers/issues/1185 | 488,550,636 | MDU6SXNzdWU0ODg1NTA2MzY= | 1,185 | XLnet output attentions doesn't work | {
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"why did you close it?",
"I'm checking, it might be my own but."
] | 1,567 | 1,574 | 1,567 | NONE | null | ## 🐛 Bug
Model I am using - XLNet.
Language I am using the model on English.
The problem arise when using the flag `output_attentions=True`
## To Reproduce
## Expected behavior
When executing
` outputs= self.model(input_ids)
`
I would expect to have a tuple with outputs and attentions but it... | {
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https://api.github.com/repos/huggingface/transformers/issues/1184 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1184/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1184/comments | https://api.github.com/repos/huggingface/transformers/issues/1184/events | https://github.com/huggingface/transformers/issues/1184 | 488,422,837 | MDU6SXNzdWU0ODg0MjI4Mzc= | 1,184 | Convert RoBERTa to TF checkpoint | {
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"Hello, I believe that unfortunately the script currently only works for the `BertModel` base class. You would have to create a similar script for RoBERTa, it shouldn't be too different as both models have very similar architectures!",
"This issue has been automatically marked as stale because it has not had rece... | 1,567 | 1,573 | 1,573 | NONE | null | Can we use the "convert_pytorch_checkpoint_to_tf" script to convert the RoBERTa checkpoint to the Tensorflow ckpt? Thanks. | {
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https://api.github.com/repos/huggingface/transformers/issues/1183 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1183/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1183/comments | https://api.github.com/repos/huggingface/transformers/issues/1183/events | https://github.com/huggingface/transformers/issues/1183 | 488,413,118 | MDU6SXNzdWU0ODg0MTMxMTg= | 1,183 | 'DistilBertModel' object has no attribute 'init_weights' | {
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"Hello, and thank you for the bug report! It would seem you are using an outdated version of the master branch. Could you update it to the latest and tell me if the error remains?",
"Looks good! I pulled the latest pip package and that worked as well. Thanks."
] | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): DistilBertModel
Language I am using the model on (English, Chinese....): English
The problem arise when using:
* [x] the official example scripts: I am trying to run the sample in the given examples within a notebook. Specifically... | {
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https://api.github.com/repos/huggingface/transformers/issues/1182 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1182/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1182/comments | https://api.github.com/repos/huggingface/transformers/issues/1182/events | https://github.com/huggingface/transformers/pull/1182 | 488,357,720 | MDExOlB1bGxSZXF1ZXN0MzEzMzk1MjQ3 | 1,182 | Updated GLUE script. New feature: Binary mask creation from the tokenizer's encoding. | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1182?src=pr&el=h1) Report\n> Merging [#1182](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1182?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/0d1dad6d5323cf627cb8d7ddd428856ab8475f6b?... | 1,567 | 1,578 | 1,569 | MEMBER | null | The new `tokenizer.encode(seq_0, seq_1, add_special_tokens=True)` method makes life easier when building sequences. However, it makes it harder to create binary masks as the different sequence lengths are unknown. As a feature, I have therefore added a flag to the encode function so that it can output binary masks.
... | {
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https://api.github.com/repos/huggingface/transformers/issues/1181 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1181/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1181/comments | https://api.github.com/repos/huggingface/transformers/issues/1181/events | https://github.com/huggingface/transformers/issues/1181 | 488,227,590 | MDU6SXNzdWU0ODgyMjc1OTA= | 1,181 | DistilBERT Loss Function Choice and further query on extending to GPT2. | {
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"Hello @sai-prasanna,\r\nI believe that in the original implementation we release, the Knowledge Distillation loss is batch-averaged meaning that it should not be sensible to the sequence lenghts: `self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean')`. But anyways, you should just make sure that at the end, if yo... | 1,567 | 1,568 | 1,568 | NONE | null | ## ❓ Questions & Help
Can you describe the motiavtion behind scaling the KLDivLoss by squared temperature ?
https://github.com/huggingface/pytorch-transformers/blob/50792dbdcccd64f61483ec535ff23ee2e4f9e18d/examples/distillation/distiller.py#L331
When applying the same logic for GPT-2 distillation, I did the fo... | {
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https://api.github.com/repos/huggingface/transformers/issues/1180 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1180/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1180/comments | https://api.github.com/repos/huggingface/transformers/issues/1180/events | https://github.com/huggingface/transformers/issues/1180 | 488,216,197 | MDU6SXNzdWU0ODgyMTYxOTc= | 1,180 | DistilBERT baseline | {
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"Hello @jeremyasapp,\r\nThat's a good question!\r\n\r\nHere are the results on the pre-training solely using MLM training signal (the small model is initialized from the teacher though).\r\n\r\n\r\nThis is ... | 1,567 | 1,567 | 1,567 | NONE | null | Nice work on DistillBert! I was wondering if you had baseline numbers for the student model trained without the distillation objective? Seems like a very important baseline to understand if distillation was useful. | {
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https://api.github.com/repos/huggingface/transformers/issues/1179 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1179/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1179/comments | https://api.github.com/repos/huggingface/transformers/issues/1179/events | https://github.com/huggingface/transformers/issues/1179 | 488,198,509 | MDU6SXNzdWU0ODgxOTg1MDk= | 1,179 | DistilBERT training is killed because OOM | {
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"@tomohideshibata Could you paste the complete error message here 🤗\r\n\r\nI'm currently distilling a model and my RAM usage (system) is ~ 20 GB. GPU usage is ~8 GB on a V-100. If the OOM was caused for your GPU then I would recommend decreasing the batch size (which is 5 by default) :)",
"I'm trying to train di... | 1,567 | 1,580 | 1,570 | CONTRIBUTOR | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I am trying DistilBERT training. The training script (train.py) had gradually consumed CPU memory, and the training was killed because OOM in about one day (the available CPU memory is 96GB).
I used one GPU for the training.
Do yo... | {
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https://api.github.com/repos/huggingface/transformers/issues/1178 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1178/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1178/comments | https://api.github.com/repos/huggingface/transformers/issues/1178/events | https://github.com/huggingface/transformers/pull/1178 | 488,189,298 | MDExOlB1bGxSZXF1ZXN0MzEzMjY0MjA4 | 1,178 | added tokens may split a normal token into halves | {
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"This looks great, thanks a lot @askerlee.\r\n\r\nCan you add a docstring for the new argument?\r\n\r\nIdeally also a test in [tokenization_tests_commons.py](https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/tests/tokenization_tests_commons.py)",
"Thanks @thomwolf . I updated my... | 1,567 | 1,567 | 1,567 | NONE | null | In the tokenizer base class, split_on_token() attempts to split input text by each of the added tokens. Because it uses text.split(tok), it may accidentally split a token at the middle.
For example a new token "ht" is added to the vocabulary. Then "light" will be split into "lig" and "". But as "light" is in the or... | {
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https://api.github.com/repos/huggingface/transformers/issues/1177 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1177/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1177/comments | https://api.github.com/repos/huggingface/transformers/issues/1177/events | https://github.com/huggingface/transformers/issues/1177 | 488,114,298 | MDU6SXNzdWU0ODgxMTQyOTg= | 1,177 | How to install previous versions of pytorch-transformers | {
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"You can install an older version using the standard pypi procedure: `pip install pytorch-transformers==$VERSION`. The examples showcased on this repository probably won't work on older versions though.",
"HI\nThanks a lot, I need though a version which I think was called\npretrained_bert , thanks for your help.\... | 1,567 | 1,573 | 1,573 | NONE | null | Hi
I am using some codes requiring old versions of your work, do you mind telling me
how to install old version of this repository?
thanks
Best
Julia | {
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https://api.github.com/repos/huggingface/transformers/issues/1176 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1176/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1176/comments | https://api.github.com/repos/huggingface/transformers/issues/1176/events | https://github.com/huggingface/transformers/pull/1176 | 487,984,730 | MDExOlB1bGxSZXF1ZXN0MzEzMTAzMzIw | 1,176 | merge | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1176?src=pr&el=h1) Report\n> Merging [#1176](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1176?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/b6cd856b08e3860e59cc126be86b901ccab4f193?... | 1,567 | 1,567 | 1,567 | NONE | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1175 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1175/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1175/comments | https://api.github.com/repos/huggingface/transformers/issues/1175/events | https://github.com/huggingface/transformers/issues/1175 | 487,974,672 | MDU6SXNzdWU0ODc5NzQ2NzI= | 1,175 | May I get the details of Bert pre-train procedure? | {
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"Hi, you should refer to the original Bert repository and papers for details on the pretraining: https://github.com/google-research/bert",
"Thank you! Get it."
] | 1,567 | 1,567 | 1,567 | NONE | null | ## ❓ Questions & Help
I want to measure the time consume in BERT pre-train procedure. May I ask some questions here:
1. What is the pre-train data? Is that same in paper : BooksCorpus & English Wiki.
2. Is there some code for pre-train can I utilize?
3. May I know how much epochs should I train when pre-train 'base... | {
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https://api.github.com/repos/huggingface/transformers/issues/1174 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1174/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1174/comments | https://api.github.com/repos/huggingface/transformers/issues/1174/events | https://github.com/huggingface/transformers/pull/1174 | 487,948,451 | MDExOlB1bGxSZXF1ZXN0MzEzMDc3MTA0 | 1,174 | Fix byte-level BPE decoding error when using added tokens | {
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"Great addition! There was just a small issue regarding the way the special tokens were joined if they were not at the beginning of the sentence. I fixed it with my commit.\r\n\r\nBefore, the following code:\r\n```py\r\ntok = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\ntok.add_tokens([\"there my\", \"name is\"])\r\n... | 1,567 | 1,578 | 1,567 | MEMBER | null | This PR fixes a mismatch between regular unicode added tokens and byte-level BPE tokens when doing decoding.
Wrong behavior reported in #1133.
Also adds regression tests. | {
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https://api.github.com/repos/huggingface/transformers/issues/1173 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1173/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1173/comments | https://api.github.com/repos/huggingface/transformers/issues/1173/events | https://github.com/huggingface/transformers/issues/1173 | 487,892,350 | MDU6SXNzdWU0ODc4OTIzNTA= | 1,173 | Write with Transformer doesn't show 774M model? | {
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"(nevermind: refreshing the page fixed it... it's weird though because I've been checking every day!)",
"Hi! Your browser cache was probably playing tricks on you :)",
"That's exactly what I figured! Forgot the term for it. Kinda sucks that it did so for several days! :(\r\n\r\nAnyways, I'm having fun with it n... | 1,567 | 1,567 | 1,567 | NONE | null | According to [this twitter post](https://twitter.com/huggingface/status/1166368535221870592), Write With Transformer has been updated to include the Large model.
However, the Model & Decoder Settings only shows Small and Medium options for model size. | {
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https://api.github.com/repos/huggingface/transformers/issues/1172 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1172/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1172/comments | https://api.github.com/repos/huggingface/transformers/issues/1172/events | https://github.com/huggingface/transformers/issues/1172 | 487,876,148 | MDU6SXNzdWU0ODc4NzYxNDg= | 1,172 | apex fp16 FusedLayerNorm type issues | {
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"Yes, that's what we do now on master since #1089 (switching back to `torch.nn.LayerNorm`).\r\n\r\nThanks for reporting",
"@thomwolf yes, thank you for your response! I wanted to clarify; if I do fp16 I still see that master is doing \r\n\r\n```\r\ntry:\r\n from apex.normalization.fused_layer_norm import Fused... | 1,567 | 1,570 | 1,567 | NONE | null | #564 🐛 Bug
I seem to be getting the following error each time I try to train with APEX/fp16 with BERT finetuning. It happened with my own scripts and I also see this with repository's standard `finetune_on_pregenerated.py` which was recently updated. The error diagnostics seem to indicate an issue with the `FusedL... | {
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https://api.github.com/repos/huggingface/transformers/issues/1171 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1171/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1171/comments | https://api.github.com/repos/huggingface/transformers/issues/1171/events | https://github.com/huggingface/transformers/issues/1171 | 487,849,961 | MDU6SXNzdWU0ODc4NDk5NjE= | 1,171 | Can't get GPT2tokenizer to load correctly | {
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"Hello! The `GPT2Tokenizer` attribute `max_len_single_sentence` is a very new attribute. If you have installed the library prior to [this commit](https://github.com/huggingface/pytorch-transformers/commit/3bcbebd440c220adbaab657f2d13dac7c89f6453#diff-b1c89c3ce1d15ed636ed89d250f8f26a), 9 days ago, then you indeed wo... | 1,567 | 1,567 | 1,567 | NONE | null | ## ❓ Questions & Help
Hi, I'm a fairly new coder and I'm hitting a roadblock that I just don't understand – maybe someone here can help me, but I figure it's worth asking the community.
I'm trying to run run_lm_finetuning.py, and the tokenizer doesn't seem to be loading correctly. I'm getting this error: `Attribu... | {
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https://api.github.com/repos/huggingface/transformers/issues/1170 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1170/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1170/comments | https://api.github.com/repos/huggingface/transformers/issues/1170/events | https://github.com/huggingface/transformers/issues/1170 | 487,820,716 | MDU6SXNzdWU0ODc4MjA3MTY= | 1,170 | How to use BERT or word embedding for e-commerce product classification. | {
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"you solve this problem?",
"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"
] | 1,567 | 1,575 | 1,575 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I want to classify products on an e-commerce site. For example, if we take Amazon for example if a product name is iPhone XS then it should be categorized to Electronics -> mobile, very straight forward, however, the problem comes when ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1169 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1169/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1169/comments | https://api.github.com/repos/huggingface/transformers/issues/1169/events | https://github.com/huggingface/transformers/issues/1169 | 487,809,210 | MDU6SXNzdWU0ODc4MDkyMTA= | 1,169 | Attribute errors with pytorch_transformers tests | {
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"Hi, could you try updating your pytorch version to 1.2.0 ?",
"Same issue here. Pytorch==1.2.0, python==3.6.2",
"What exactly is your issue @ukliu ?",
"> What exactly is your issue @ukliu ?\r\n\r\nI was going through the pytorch-transformers tutorial at https://github.com/ukliu/pytorch-transformers \r\n\r\n<i... | 1,567 | 1,575 | 1,575 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (from the official repo):
Language I am using the model on (English, Yoruba,Igbo, Hausa etc):
The problem arise when using:
* [ ] the official example scripts: (give details): the test run script
The tasks I am working on is:
* [ ] an official... | {
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https://api.github.com/repos/huggingface/transformers/issues/1168 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1168/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1168/comments | https://api.github.com/repos/huggingface/transformers/issues/1168/events | https://github.com/huggingface/transformers/issues/1168 | 487,807,049 | MDU6SXNzdWU0ODc4MDcwNDk= | 1,168 | How to add new pre-trained model pytorch-transformers | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 1,567 | 1,573 | 1,573 | NONE | null | ## ❓ Questions & Help
Pytorch-transformers is a great library. I like that it does one thing, give access to the pre-trained SOTA models for NLP.
I and my team we want to help and start contributing:
- at first, want to add Polish BERT model like https://github.com/huggingface/pytorch-transformers/pull/688
Bu... | {
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https://api.github.com/repos/huggingface/transformers/issues/1167 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1167/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1167/comments | https://api.github.com/repos/huggingface/transformers/issues/1167/events | https://github.com/huggingface/transformers/issues/1167 | 487,799,928 | MDU6SXNzdWU0ODc3OTk5Mjg= | 1,167 | ImportError: cannot import name 'DistilBertModel' | {
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"Yes, you should install it from source right now if you want to use DistilBERT. We're planning a new release 1.2.0 that includes DistilBERT + GPT-2 Large + XLM 100/17 sometimes this week :)."
] | 1,567 | 1,567 | 1,567 | NONE | null | ## 🐛 Bug
Can you update the pypi package? I cannot import DistilBERT on pytorch_transformers==1.1.0 | {
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https://api.github.com/repos/huggingface/transformers/issues/1166 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1166/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1166/comments | https://api.github.com/repos/huggingface/transformers/issues/1166/events | https://github.com/huggingface/transformers/issues/1166 | 487,796,134 | MDU6SXNzdWU0ODc3OTYxMzQ= | 1,166 | Roberta for NER task | {
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"Hi @militu you should take a look at this long thread discussing NER for BERT (should be the same for RoBERTa): https://github.com/huggingface/pytorch-transformers/issues/64",
"But there is not RobertaForTokenClassification and TFRobertaForTokenClassification like BertForTokenClassification and TFBertForTokenCla... | 1,567 | 1,577 | 1,577 | NONE | null | ## ❓ Questions & Help
Hello, is there a way to use Roberta model for NER task? Is there a script somewhere?
Thank you. | {
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https://api.github.com/repos/huggingface/transformers/issues/1165 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1165/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1165/comments | https://api.github.com/repos/huggingface/transformers/issues/1165/events | https://github.com/huggingface/transformers/issues/1165 | 487,755,884 | MDU6SXNzdWU0ODc3NTU4ODQ= | 1,165 | Dependency errors when trying to use gpt2 using pytorch hub. | {
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"I found that https://github.com/huggingface/pytorch-transformers/commit/256086bc6908448fc6aff9b1e19d95c4f6019bee is the source of the issue. Reading the changes I could guess that the new way for retrieving the tokenizer and model is as follows:\r\n\r\n```python\r\ntokenizer = torch.hub.load('huggingface/pytorch-t... | 1,567 | 1,567 | 1,567 | NONE | null | It started today, yesterday it was working fine. When I try to download `gpt2` model from pytorch hub repository, as follows:
```python
torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Tokenizer', 'gpt2')
```
I get the following error: `ModuleNotFoundError: No module named 'sacremoses'`. If I add th... | {
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https://api.github.com/repos/huggingface/transformers/issues/1164 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1164/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1164/comments | https://api.github.com/repos/huggingface/transformers/issues/1164/events | https://github.com/huggingface/transformers/pull/1164 | 487,755,616 | MDExOlB1bGxSZXF1ZXN0MzEyOTQ2NDEx | 1,164 | distillation: fix ModuleNotFoundError error in token counts script | {
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"Great, thanks @stefan-it!"
] | 1,567 | 1,567 | 1,567 | COLLABORATOR | null | Hi,
I'm currently trying out "distillation" 😅
This PR a `ModuleNotFoundError` message in the `token_counts.py` script (same error was recently fixed in 803c1cc4eacd38f1b854578d7d717b5e4a1ada47 🤗 | {
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https://api.github.com/repos/huggingface/transformers/issues/1163 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1163/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1163/comments | https://api.github.com/repos/huggingface/transformers/issues/1163/events | https://github.com/huggingface/transformers/issues/1163 | 487,743,738 | MDU6SXNzdWU0ODc3NDM3Mzg= | 1,163 | [Help] how to make a constrained text generation | {
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"Yes, I think the sockeye paper and code is the right place to start even if it may look complicated at first sight. Try to combine it with the `run_generation.py` example.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs.... | 1,567 | 1,589 | 1,573 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
What I need is to make a constrained text generation via XLNet or GPT-2:
Input: No one has the intention of building a wall.
Constraint: the output should include two pre-defined key words: 'No one' and 'construct'.
Expected outpu... | {
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https://api.github.com/repos/huggingface/transformers/issues/1162 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1162/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1162/comments | https://api.github.com/repos/huggingface/transformers/issues/1162/events | https://github.com/huggingface/transformers/pull/1162 | 487,730,302 | MDExOlB1bGxSZXF1ZXN0MzEyOTI5ODY1 | 1,162 | XLNet bias fix on resize embeddings (cf #1124) | {
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"followers_url": "https://api.github.com/... | [] | closed | false | null | [] | [] | 1,567 | 1,576 | 1,567 | MEMBER | null | Fixed an issue where the linear layer bias wouldn't be resized along the weight resize when there was an embedding matrix resize with XLNet (cf #1124).
This fix works for any model that needs to tie its weights between an embedding layer & a linear layer if . that linear layer has a bias. | {
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https://api.github.com/repos/huggingface/transformers/issues/1161 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1161/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1161/comments | https://api.github.com/repos/huggingface/transformers/issues/1161/events | https://github.com/huggingface/transformers/issues/1161 | 487,591,607 | MDU6SXNzdWU0ODc1OTE2MDc= | 1,161 | Large Memory Layers | {
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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"
] | 1,567 | 1,572 | 1,572 | NONE | null | ## 🚀 Feature
Implement models with Large Memory Layers from this paper: https://arxiv.org/pdf/1907.05242.pdf
## Motivation
These models seem very promising. | {
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https://api.github.com/repos/huggingface/transformers/issues/1160 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1160/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1160/comments | https://api.github.com/repos/huggingface/transformers/issues/1160/events | https://github.com/huggingface/transformers/issues/1160 | 487,586,422 | MDU6SXNzdWU0ODc1ODY0MjI= | 1,160 | --seed does not change the fintuning results of the xlnet model | {
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"In the command line you are showing, you should add a `--seed ${seed}` argument to set the seed, otherwise, it will stay the same.",
"> In the command line you are showing, you should add a --seed ${seed} argument to set the seed, otherwise, it will stay the same.\r\n\r\nSorry, i forgot it. \r\n"
] | 1,567 | 1,567 | 1,567 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (XLNet):
Language I am using the model on (English):
The problem arise when using:
* [ ] the official example scripts
gpu=3
seed=1
task=MRPC
bsz=32
learning_rate=5e-5
max_steps=800
warmup_steps=200
save_steps=400
export CUDA_VISIBLE_DEVICE... | {
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https://api.github.com/repos/huggingface/transformers/issues/1159 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1159/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1159/comments | https://api.github.com/repos/huggingface/transformers/issues/1159/events | https://github.com/huggingface/transformers/issues/1159 | 487,530,208 | MDU6SXNzdWU0ODc1MzAyMDg= | 1,159 | Problem with optimizers after migration | {
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"You should get the same behavior than `BertAdam` by setting `correct_bias=False` in `AdamW` and using the `WarmupLinearSchedule` together with it.",
"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 contr... | 1,567 | 1,573 | 1,573 | NONE | null | ## 📚 Migration
<!-- Important information -->
Model I am using (Bert, XLNet....): Bert
Language I am using the model on (English, Chinese....): Russian
The problem arise when using:
The optimizers. I tried the default parameters from the example with max_grad_norm = 1.0 and lr = 2e-5.
```
warmup_propo... | {
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https://api.github.com/repos/huggingface/transformers/issues/1158 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1158/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1158/comments | https://api.github.com/repos/huggingface/transformers/issues/1158/events | https://github.com/huggingface/transformers/pull/1158 | 487,493,795 | MDExOlB1bGxSZXF1ZXN0MzEyNzQ4MTA3 | 1,158 | regarding #1026 pull request | {
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"Oh I see what you mean, indeed that's a more general issue with saving and loading tokenizer with specific configuration parameters. This is actually also relevant to our work on XLM's tokenizer in #1092",
"Dear Thomas,\r\nThe pull request #1026 does not work unfortunately when using eval_all_check_points, and I... | 1,567 | 1,567 | 1,567 | NONE | null | Dear Thomas,
This is regarding my #1026 pull request, here is my understanding of the reproducibility issue I was getting:
- on line 451, in the codes tokenizer is reloaded without setting do_lower_case, then if you use both do_train+do_eval, you will get different results than if you do do_eval only on the same ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1157 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1157/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1157/comments | https://api.github.com/repos/huggingface/transformers/issues/1157/events | https://github.com/huggingface/transformers/issues/1157 | 487,491,602 | MDU6SXNzdWU0ODc0OTE2MDI= | 1,157 | How to load pretraind XLM model | {
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"~~Hello, we haven't yet converted those models and hosted them on our S3, but you indeed should be able to do it yourself; we used [this script](https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/convert_xlm_checkpoint_to_pytorch.py) for the other XLM checkpoints, you could use it... | 1,567 | 1,567 | 1,567 | NONE | null | ## ❓ Questions & Help
Facebook recently released a new pre-trained language model (17 and 100) (https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models)
I want to load 17 language model.
Could someone guide me on how to achieve this? The straightforward way didn't work for me.
I h... | {
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https://api.github.com/repos/huggingface/transformers/issues/1156 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1156/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1156/comments | https://api.github.com/repos/huggingface/transformers/issues/1156/events | https://github.com/huggingface/transformers/issues/1156 | 487,382,123 | MDU6SXNzdWU0ODczODIxMjM= | 1,156 | About distilled the SQuAD? | {
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"i have the same question. how to deal with the max sequence length between teacher model and student model",
"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"
] | 1,567 | 1,572 | 1,572 | NONE | null | ## ❓ Questions & Help
Thank you for your excellent work. I want to know that have you released the distilled model code on SQuAD dataset? And how to set the max sequence length on teacher model and student model? Are they the same length? | {
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https://api.github.com/repos/huggingface/transformers/issues/1155 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1155/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1155/comments | https://api.github.com/repos/huggingface/transformers/issues/1155/events | https://github.com/huggingface/transformers/pull/1155 | 487,288,106 | MDExOlB1bGxSZXF1ZXN0MzEyNTgyODgw | 1,155 | Update apex fp16 implementation | {
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"Thanks, these examples will probably be deprecated and replaced by the new more general `run_lm_finetuning` example which can train several models with normal and masked language modeling."
] | 1,567 | 1,567 | 1,567 | NONE | null | As the issue I raised here: https://github.com/huggingface/pytorch-transformers/issues/1143
I updated the implementation of apex fp16, following latest Apex version's document. | {
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https://api.github.com/repos/huggingface/transformers/issues/1154 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1154/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1154/comments | https://api.github.com/repos/huggingface/transformers/issues/1154/events | https://github.com/huggingface/transformers/pull/1154 | 487,277,219 | MDExOlB1bGxSZXF1ZXN0MzEyNTc0MjI1 | 1,154 | fix: hard coding for max number | {
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"Yes, thanks @ziliwang "
] | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | fp16 max number is 65504, the original 1e30 will cause Nan in fp16 | {
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https://api.github.com/repos/huggingface/transformers/issues/1153 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1153/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1153/comments | https://api.github.com/repos/huggingface/transformers/issues/1153/events | https://github.com/huggingface/transformers/pull/1153 | 487,191,641 | MDExOlB1bGxSZXF1ZXN0MzEyNTA3MjM1 | 1,153 | [WIP] Refactor Tokenizers creation to support in-memory initialization | {
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"@thomwolf cc @honnibal Drafting this PR to have dedicated space for discussions.",
"@thomwolf @honnibal Can you have a look plz :) ?",
"Ok, I went through this PR and it looks nice.\r\nGreat job @mfuntowicz.\r\nNo problem for the slight code duplication in the tokenizer loading classes, as you've noticed, the ... | 1,567 | 1,575 | 1,575 | MEMBER | null | As pointed out in #916 currently tokenizers ask for the path from where they'll load the required vocabulary files.
This PR allows tokenizers to take their vocab from data living in-memory and cold-storage.
Implementations details:
- All tokenizer now have a specific ${TokenizerName}Vocab dataclass holding all... | {
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https://api.github.com/repos/huggingface/transformers/issues/1152 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1152/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1152/comments | https://api.github.com/repos/huggingface/transformers/issues/1152/events | https://github.com/huggingface/transformers/pull/1152 | 487,169,075 | MDExOlB1bGxSZXF1ZXN0MzEyNDg4NTA0 | 1,152 | fix adding special tokens | {
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"Look good to me (the failing test on `head_masking` is not related to this PR).\r\nThanks @epwalsh!"
] | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | Currently there is a bug when adding `additional_special_tokens` in the form of a tuple, instead of a list. To reproduce:
```python
from pytorch_transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
tokenizer.add_special_tokens({"additional_special_tokens": ("@a@", "@b@")... | {
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https://api.github.com/repos/huggingface/transformers/issues/1151 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1151/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1151/comments | https://api.github.com/repos/huggingface/transformers/issues/1151/events | https://github.com/huggingface/transformers/issues/1151 | 487,157,527 | MDU6SXNzdWU0ODcxNTc1Mjc= | 1,151 | Idea to improve DistilBERT | {
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"good idea. i aggree with the second plan",
"If you give it a try, please tell me in the loop : thomas.chaton.ai@gmail.com",
"There is Albert now https://arxiv.org/abs/1909.11942 which seems to be even better. It isn't based on KD.",
"anthor solution is here. [https://github.com/intersun/PKD-for-BERT-Model-C... | 1,567 | 1,577 | 1,577 | NONE | null | ## 🚀 Feature
<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->
Based on https://medium.com/huggingface/distilbert-8cf3380435b5, you are using KL_loss to train the student from the teacher.
You can do something a little bit differe... | {
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https://api.github.com/repos/huggingface/transformers/issues/1150 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1150/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1150/comments | https://api.github.com/repos/huggingface/transformers/issues/1150/events | https://github.com/huggingface/transformers/issues/1150 | 487,105,629 | MDU6SXNzdWU0ODcxMDU2Mjk= | 1,150 | What is the relationship between `run_lm_finetuning.py` and the scripts in `lm_finetuning`? | {
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"Hi! The folder `lm_finetuning` is especially targeted at BERT. It gives details on two different losses that were used to pre-train BERT: the masked language modeling objective (MLM) and the next sentence prediction objective (NSP). It gives several insights to BERT's fine-tuning.\r\n\r\nThe file `run_lm_finetunin... | 1,567 | 1,577 | 1,577 | CONTRIBUTOR | null | ## ❓ Questions & Help
It looks like there are now two scripts for running LM fine-tuning. While `run_lm_finetuning` seems to be newer, the documentation in `lm_finetuning` seems to indicate that there is more subtlety to generating the right data for performing LM fine-tuning in the BERT format. Does the new script ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1149 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1149/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1149/comments | https://api.github.com/repos/huggingface/transformers/issues/1149/events | https://github.com/huggingface/transformers/issues/1149 | 487,058,960 | MDU6SXNzdWU0ODcwNTg5NjA= | 1,149 | Closing bracket is missing in token_counts.py for DistilBERT | {
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"Hi! Indeed, the closing bracket was missing, fixed it with caf1d11! Thanks for the bug report :)."
] | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): DistilBERT
The problem arise when using:
* [x] the official example scripts: (give details)
I'm trying DistilBERT, and get an "invalid syntax error" when I run `examples/distillation/scripts/token_counts.py`.
A closing bracket ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1148 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1148/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1148/comments | https://api.github.com/repos/huggingface/transformers/issues/1148/events | https://github.com/huggingface/transformers/pull/1148 | 487,055,088 | MDExOlB1bGxSZXF1ZXN0MzEyMzk1Mjc1 | 1,148 | Documentation auto-deploy | {
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"Great, thanks @LysandreJik!"
] | 1,567 | 1,576 | 1,567 | MEMBER | null | Documentation is now deployed automatically. @thomwolf @julien-c | {
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https://api.github.com/repos/huggingface/transformers/issues/1147 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1147/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1147/comments | https://api.github.com/repos/huggingface/transformers/issues/1147/events | https://github.com/huggingface/transformers/issues/1147 | 487,031,183 | MDU6SXNzdWU0ODcwMzExODM= | 1,147 | GPT2-large fails to load the tokenizer | {
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"Hello! Could you try to install from source (1.2.0)? Is there any warning in your terminal such as \r\n```\r\nModel name 'gpt2-large' was not found in model name list (gpt2, gpt2-medium). We assumed 'gpt2-large' was a path or url but couldn't...\r\n```\r\nor something along those lines?",
"Thank you! Solved with... | 1,567 | 1,567 | 1,567 | NONE | null | ## 🐛 Bug
Using: GPT2-Large
## To Reproduce
When I load the gpt2-large model, in the same way as gpt2 and gpt2-medium I get an NoneType when loading the tokenizer.
```
self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large", bos_token="_start_", unk_token='_unk_', eos_token="_eos_", sep_token="_delimite... | {
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https://api.github.com/repos/huggingface/transformers/issues/1146 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1146/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1146/comments | https://api.github.com/repos/huggingface/transformers/issues/1146/events | https://github.com/huggingface/transformers/issues/1146 | 486,965,614 | MDU6SXNzdWU0ODY5NjU2MTQ= | 1,146 | Attention values occasionally exceed 1 in BertModel | {
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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",
"@thomwolf Did someone looking into this issue?",
"No and I'm afraid we don't really have the bandwidth for that at the moment.\r\n",
... | 1,567 | 1,582 | 1,578 | NONE | null | ```Python
outputs = self.model(x, attention_mask = x_mask) # Models outputs are now tuples
print(outputs[2][-1].max())
print((outputs[2][-1]>1).sum().item()) # Number of attention values > 1
print((outputs[2][-1]>-1).sum().item()) # Total number of attention values
```
```
tensor(1.0750, device='cuda:7', grad_f... | {
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https://api.github.com/repos/huggingface/transformers/issues/1145 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1145/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1145/comments | https://api.github.com/repos/huggingface/transformers/issues/1145/events | https://github.com/huggingface/transformers/issues/1145 | 486,929,919 | MDU6SXNzdWU0ODY5Mjk5MTk= | 1,145 | How to finetune GPT2 | {
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"Hi, we have an example to fine-tune several models on [language modeling here](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_lm_finetuning.py).\r\nYou can look into GPT-2's training on the CLM task, which is done on WikiText-2 in this example.",
"@LysandreJik would you please provi... | 1,567 | 1,680 | 1,573 | NONE | null | ## ❓ Questions & Help
Hi all,
I would like to finetune the pretrained gpt2 model with a newspapers dataset. Do you know how would that be possible? I haven't found any train scipt for gpt2.
Thanks a lot. | {
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https://api.github.com/repos/huggingface/transformers/issues/1144 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1144/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1144/comments | https://api.github.com/repos/huggingface/transformers/issues/1144/events | https://github.com/huggingface/transformers/issues/1144 | 486,882,532 | MDU6SXNzdWU0ODY4ODI1MzI= | 1,144 | where can i assign step in function lr_lambda of Class WramupLinearSchedule? | {
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"Hi, to use a scheduler you have to tell it when to perform an optimization step, as detailed on the [pytorch documentation](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate). It increases the step by one every time you call `scheduler.step()`.",
"This issue has been automatically marked as ... | 1,567 | 1,572 | 1,572 | NONE | null | As i know LambdaLR's get_lr function recieve last_epoch as parameter, so where does it get the step of current training?
class WarmupLinearSchedule(LambdaLR):
""" Linear warmup and then linear decay.
Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps.
Linearly de... | {
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https://api.github.com/repos/huggingface/transformers/issues/1143 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1143/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1143/comments | https://api.github.com/repos/huggingface/transformers/issues/1143/events | https://github.com/huggingface/transformers/issues/1143 | 486,882,075 | MDU6SXNzdWU0ODY4ODIwNzU= | 1,143 | Why still using old implementation of apex fp16 | {
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"AFAIK no particular reason, feel free to open a PR"
] | 1,567 | 1,567 | 1,567 | NONE | null | ## 🚀 Feature
According to Nvidia apex fp16 documentary: https://nvidia.github.io/apex/amp.html
The implementation of new apex version doesn't require using FP16_Optimizer wrapping over FusedAdam. So I wonder why the team still kept the old implementation. Is there any special reason ? If not, I will make a pull ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1142 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1142/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1142/comments | https://api.github.com/repos/huggingface/transformers/issues/1142/events | https://github.com/huggingface/transformers/issues/1142 | 486,862,438 | MDU6SXNzdWU0ODY4NjI0Mzg= | 1,142 | FP16_Optimizer is not an Optimizer when fp_16 | {
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"I think this issue should be fixed on master (even though I'm not exactly sure which script you are referring to).",
"I already fixed this in my pull request. This error is caused by FP16_Optimizer, by using new Apex implementation, we deprecated FP16_Optimizer, so this bug is no longer issued",
"Ok closing th... | 1,567 | 1,567 | 1,567 | NONE | null | ## 🐛 Bug
When using fp_16, the scheduler should be:
scheduler = WarmupLinearSchedule(optimizer.optimizer, warmup_steps=args.warmup_steps,
t_total=num_train_optimization_steps)
if not, that will be "TypeError: FP16_Optimizer is not an Optimizer" | {
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https://api.github.com/repos/huggingface/transformers/issues/1141 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1141/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1141/comments | https://api.github.com/repos/huggingface/transformers/issues/1141/events | https://github.com/huggingface/transformers/pull/1141 | 486,798,877 | MDExOlB1bGxSZXF1ZXN0MzEyMTg5MDQw | 1,141 | Small modification of comment in the run_glue.py example | {
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"Indeed, missed that one, thank you."
] | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | Add RoBERTa to the comment as it was not explicit that RoBERTa don't use token_type_ids. | {
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https://api.github.com/repos/huggingface/transformers/issues/1140 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1140/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1140/comments | https://api.github.com/repos/huggingface/transformers/issues/1140/events | https://github.com/huggingface/transformers/issues/1140 | 486,783,525 | MDU6SXNzdWU0ODY3ODM1MjU= | 1,140 | Can't Using Binarization Script for DistilBERT | {
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"Hello @SreeramV181,\r\nit should be fixed in commit 803c1cc4eacd38f1b854578d7d717b5e4a1ada47.\r\nThanks for pointing that out!\r\nVictor"
] | 1,567 | 1,567 | 1,567 | NONE | null | ## 🐛 Bug
<!-- Important information -->
I'm currently using DistilBERT and running into issues when I run scripts/binarized_data.py. I get the following error:
Traceback (most recent call last):
File "scripts/binarized_data.py", line 25, in <module>
from ..utils import logger
ValueError: attempted ... | {
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https://api.github.com/repos/huggingface/transformers/issues/1139 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1139/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1139/comments | https://api.github.com/repos/huggingface/transformers/issues/1139/events | https://github.com/huggingface/transformers/pull/1139 | 486,767,094 | MDExOlB1bGxSZXF1ZXN0MzEyMTYzMzMy | 1,139 | Need multiple capabilities | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1139?src=pr&el=h1) Report\n> Merging [#1139](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1139?src=pr&el=desc) into [generative-finetuning](https://codecov.io/gh/huggingface/pytorch-transformers/commit/529a16dec6cc9bfcf8954a1b16... | 1,567 | 1,567 | 1,567 | NONE | null | Need both Generative Finetuning and Distilling Capabilities | {
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https://api.github.com/repos/huggingface/transformers/issues/1138 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1138/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1138/comments | https://api.github.com/repos/huggingface/transformers/issues/1138/events | https://github.com/huggingface/transformers/issues/1138 | 486,734,521 | MDU6SXNzdWU0ODY3MzQ1MjE= | 1,138 | loss explosion | {
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"just because of a large learning rate"
] | 1,567 | 1,567 | 1,567 | NONE | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I am using Bert-CRF model to do Named Entity Recognition task. I am using the average of the last four layers as the input of CRF model. But the loss will increase and become Nan in a few batches. Anyone meet that problem before? Any s... | {
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https://api.github.com/repos/huggingface/transformers/issues/1137 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1137/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1137/comments | https://api.github.com/repos/huggingface/transformers/issues/1137/events | https://github.com/huggingface/transformers/issues/1137 | 486,546,360 | MDU6SXNzdWU0ODY1NDYzNjA= | 1,137 | Cannot import DistilBert classes | {
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"My bad. Forgot to ignore pip cache\r\n```\r\npip install git+https://github.com/huggingface/pytorch-transformers --no-cache-dir\r\n```"
] | 1,567 | 1,567 | 1,567 | NONE | null | Tried installing from the master, and couldn't do it.
| {
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https://api.github.com/repos/huggingface/transformers/issues/1136 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1136/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1136/comments | https://api.github.com/repos/huggingface/transformers/issues/1136/events | https://github.com/huggingface/transformers/pull/1136 | 486,509,172 | MDExOlB1bGxSZXF1ZXN0MzExOTU0MDg1 | 1,136 | swap order of optimizer.step() and scheduler.step() | {
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"indeed, thanks @adai183 ",
"🤗"
] | 1,567 | 1,567 | 1,567 | CONTRIBUTOR | null | The current code results in the following warning:
```
UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first... | {
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https://api.github.com/repos/huggingface/transformers/issues/1135 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1135/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1135/comments | https://api.github.com/repos/huggingface/transformers/issues/1135/events | https://github.com/huggingface/transformers/pull/1135 | 486,476,717 | MDExOlB1bGxSZXF1ZXN0MzExOTI3NzU2 | 1,135 | distilbert: fix number of hidden_size | {
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"CI fails related or unrelated 🤔",
"Yes, good catch @stefan-it! Thanks"
] | 1,567 | 1,567 | 1,567 | COLLABORATOR | null | Hi,
this PR corrects the return value of the `hidden_size` function (which should be the dimension size, as it is used in all other models) :) | {
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https://api.github.com/repos/huggingface/transformers/issues/1134 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1134/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1134/comments | https://api.github.com/repos/huggingface/transformers/issues/1134/events | https://github.com/huggingface/transformers/issues/1134 | 486,463,871 | MDU6SXNzdWU0ODY0NjM4NzE= | 1,134 | Schedulers cause memory accumulation across folds in cross-validation? | {
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"I am facing the same issue.When I use the WarmupLinearSchedule and the 7th epoch training , I get a CUDA out of memory issue",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"R... | 1,567 | 1,575 | 1,575 | CONTRIBUTOR | null | ## ❓ Questions & Help
<!-- A clear and concise description of the question. -->
I am facing a strange issue when using the schedulers available in this library within a cross-validation loop. Basically, in each fold, I initialize a new model, optimizer, and scheduler. GPU memory accumulates until I eventually get... | {
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https://api.github.com/repos/huggingface/transformers/issues/1133 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1133/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1133/comments | https://api.github.com/repos/huggingface/transformers/issues/1133/events | https://github.com/huggingface/transformers/issues/1133 | 486,378,197 | MDU6SXNzdWU0ODYzNzgxOTc= | 1,133 | GPT2 Tokenizer decoding fails when the added tokens include a space | {
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"I can second this, I am seeing the same error",
"Indeed, there is a mismatch between added tokens and byte-level BPE tokens here. Fixing it with #1174.",
"Found that you can replace the space with `Ġ`. `Ċ` can replace `\\n`."
] | 1,566 | 1,648 | 1,567 | CONTRIBUTOR | null | ## 🐛 Bug
After adding a new token that contains a space to the GPT2 tokenizer, the tokenizer produces an error at decoding time (see example code below). My current workaround is to preprocess that token to remove spaces before adding it and to postprocess the token after decoding. But I thought I'd share this in c... | {
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https://api.github.com/repos/huggingface/transformers/issues/1132 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1132/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1132/comments | https://api.github.com/repos/huggingface/transformers/issues/1132/events | https://github.com/huggingface/transformers/issues/1132 | 486,282,132 | MDU6SXNzdWU0ODYyODIxMzI= | 1,132 | How to split consecutive numbers? | {
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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"
] | 1,566 | 1,572 | 1,572 | NONE | null | ## ❓ Questions & Help
   In some NER datasets of BIO(ES) format, each number of a consecutive number string labeled with a corresponding tag, e.g., "All Jhon need is only 10 yuan" will be labeled as "O, PER, O, O, O, O, O, O". In this case, "10" is labeled as "O, O". But in **BertTokenizer** and **PreTrain... | {
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https://api.github.com/repos/huggingface/transformers/issues/1131 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1131/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1131/comments | https://api.github.com/repos/huggingface/transformers/issues/1131/events | https://github.com/huggingface/transformers/issues/1131 | 486,262,580 | MDU6SXNzdWU0ODYyNjI1ODA= | 1,131 | mems output in XLNet | {
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"Indeed I see, we have to add:\r\n- an explanation in XLM's docstring that you should set the model configuration `mem_len` parameter if you want to use the memory (the answer to your main question). You can do for instance `model = XLNetModel.from_pretrained('xlnet-large-cased', mem_len=1024)` if you want a max me... | 1,566 | 1,579 | 1,567 | NONE | null | ## 🐛 Bug
Hi,
I am trying to use memory of the last forward pass (mems arg) with XLNet. I am getting a tuple of None as mems output instead of a tuple of tensor. The same code with TransformerXL is running fine. Am I doing anything wrong or is this a bug ? Below is a short code snippet to reproduce the error.
Ma... | {
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https://api.github.com/repos/huggingface/transformers/issues/1130 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1130/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1130/comments | https://api.github.com/repos/huggingface/transformers/issues/1130/events | https://github.com/huggingface/transformers/issues/1130 | 486,260,503 | MDU6SXNzdWU0ODYyNjA1MDM= | 1,130 | Output of BertModel does not match fixed feature vectors extracted from the last hidden layer | {
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"Hi, have you solved this problem?\r\nAnd does anyone know the order of all_encoder_layers?\r\nThanks.",
"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"
] | 1,566 | 1,573 | 1,573 | NONE | null | I want to finetune bert in my task. However, the bert ouput does not match the fixed feature vectors.
I extracted the fixed feature vectors like this:
I use `extract_features.py` to extract the fixed feature vectors of last hidden layer (layer -1). The command line is below:
`python extract_features.py --input_fi... | {
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https://api.github.com/repos/huggingface/transformers/issues/1129 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1129/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1129/comments | https://api.github.com/repos/huggingface/transformers/issues/1129/events | https://github.com/huggingface/transformers/issues/1129 | 486,256,964 | MDU6SXNzdWU0ODYyNTY5NjQ= | 1,129 | Fine-tuning (BERT & RoBERTa) base outperforms large | {
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"Maybe you need more GPUs and a bigger batch size",
"I have 8 GPUs 2080 rtx, each with 10gb of data. But yeah I use a batch size of 4. However, I need the sequence length of 512 so it is impossible to increase the batch size.",
"This issue has been automatically marked as stale because it has not had recent act... | 1,566 | 1,573 | 1,573 | NONE | null | ## ❓ Questions & Help
On all datasets I have used so far, base always outperforms large after the fine-tune. This is true for both BERT and RoBERTa.
Why is that? Am I doing something wrong? Does large require far more epochs to train or a different learning rate? | {
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https://api.github.com/repos/huggingface/transformers/issues/1128 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1128/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1128/comments | https://api.github.com/repos/huggingface/transformers/issues/1128/events | https://github.com/huggingface/transformers/issues/1128 | 486,231,173 | MDU6SXNzdWU0ODYyMzExNzM= | 1,128 | cannot import name 'RobertaConfig | {
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"Needed to update the pip package... ",
"which pip package did you have to update? Same problem here...\r\n",
"@stellaywu what is your current `transformers` or `pytorch-transformers` version?",
"@LysandreJik it's transformer 2.0.0 ",
"Thatś weird, transformers 2.0.0 works on a clean install in my environm... | 1,566 | 1,610 | 1,566 | NONE | null | ## 🐛 Bug
When I run run_glue.py with the roberta model I get an ImportError: cannot import name 'RobertaConfig'
I can't run the run_glue.py with any model since it cannot import the RobertaConfig on line 34.
Any ideas why? | {
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https://api.github.com/repos/huggingface/transformers/issues/1127 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1127/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1127/comments | https://api.github.com/repos/huggingface/transformers/issues/1127/events | https://github.com/huggingface/transformers/pull/1127 | 486,208,136 | MDExOlB1bGxSZXF1ZXN0MzExNzA2NjQ5 | 1,127 | DistilBERT | {
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"@VictorSanh Thanks for adding this :heart: (I'm currently adding this model to Flair)\r\n\r\nOne question: the BERT model configuration has a key `hidden_size`. For DilBERT it is now `dim`. Is this change intended 🤔",
"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1127?src=pr&el=h1) Re... | 1,566 | 1,578 | 1,567 | MEMBER | null | Preparing the release for DistilBERT (smaller, faster, lighter, cheaper version of BERT) | {
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https://api.github.com/repos/huggingface/transformers/issues/1126 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1126/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1126/comments | https://api.github.com/repos/huggingface/transformers/issues/1126/events | https://github.com/huggingface/transformers/issues/1126 | 486,120,054 | MDU6SXNzdWU0ODYxMjAwNTQ= | 1,126 | Bert initialization | {
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"Hi, I think you'll find this [particular issue interesting](https://github.com/huggingface/pytorch-transformers/issues/202).\r\n\r\n[Thomas Wolf's comment](https://github.com/huggingface/pytorch-transformers/issues/202#issuecomment-522613642) in particular may be of help.",
"> Hi, I think you'll find this [parti... | 1,566 | 1,567 | 1,567 | NONE | null | When I train bert model from scratch, it can not convergence and the loss does not decrease. Still don't work even try different learning rate many times.
But It works when I tried the tf version. I checked code and there have no much difference except the initialization.
So does anybody have some ideas about thi... | {
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https://api.github.com/repos/huggingface/transformers/issues/1125 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1125/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1125/comments | https://api.github.com/repos/huggingface/transformers/issues/1125/events | https://github.com/huggingface/transformers/issues/1125 | 486,021,648 | MDU6SXNzdWU0ODYwMjE2NDg= | 1,125 | UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 1176: character maps to <undefined> | {
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"Looking at [these lines](https://github.com/huggingface/pytorch-transformers/blob/07681b6b5859b630077b742b2f06d440869f17e3/pytorch_transformers/tokenization_gpt2.py#L108-L115), the issue seems to be the file is encoded in utf-8 but read using a different encoder.\r\n\r\nChanging line 112 to `self.encoder = json.lo... | 1,566 | 1,604 | 1,567 | NONE | null | ## 🐛 Bug
<!-- Important information -->
```UnicodeDecodeError``` when using vocab file generated by ```GPT2Tokenizer```. Specifically, I created an instance of the ```GPT2Tokenizer``` by calling ```from_pretrained('gpt2')``` then saved the vocab and merges file for that instance to a local directory. When creati... | {
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https://api.github.com/repos/huggingface/transformers/issues/1124 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1124/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1124/comments | https://api.github.com/repos/huggingface/transformers/issues/1124/events | https://github.com/huggingface/transformers/issues/1124 | 485,975,571 | MDU6SXNzdWU0ODU5NzU1NzE= | 1,124 | XLNet resize embedding size ERROR | {
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"Should be fixed on master by @LysandreJik's PR!"
] | 1,566 | 1,567 | 1,567 | NONE | null | ## ❓ Questions & Help
I add new tokens to XLNetLMHeadModel and use resize function
```
tokenizer.add_tokens(["<token1>", "<token2>"])
model.resize_token_embeddings(len(tokenizer))
```
But when running, the following error occurs:
```
Traceback (most recent call last):
...
File "/nas/home/jsun/.local/lib/... | {
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https://api.github.com/repos/huggingface/transformers/issues/1123 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1123/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1123/comments | https://api.github.com/repos/huggingface/transformers/issues/1123/events | https://github.com/huggingface/transformers/issues/1123 | 485,876,617 | MDU6SXNzdWU0ODU4NzY2MTc= | 1,123 | Extracting Features Example | {
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"Yes it was removed from the repo. I think we’ll add it again (and update it\nto pytorch-transformers) since several people have been missing it.\n\nCc @LysandreJik\n\nOn Tue, 27 Aug 2019 at 17:21, Sascha Stenger <notifications@github.com>\nwrote:\n\n> ❓ Questions & Help\n>\n> Hello.\n>\n> Sorry, if my question is ... | 1,566 | 1,576 | 1,576 | NONE | null | ## ❓ Questions & Help
Hello.
Sorry, if my question is out of date or i just didn't find it, but i'm looking for the example/extract_features.py
that was supposed to be in in this repo (as mentioned in this stackoverflow [post](https://stackoverflow.com/questions/55369821/how-to-train-a-neural-network-model-wi... | {
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https://api.github.com/repos/huggingface/transformers/issues/1122 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1122/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1122/comments | https://api.github.com/repos/huggingface/transformers/issues/1122/events | https://github.com/huggingface/transformers/issues/1122 | 485,864,446 | MDU6SXNzdWU0ODU4NjQ0NDY= | 1,122 | PyTorch library dependency | {
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"I have the same issue with `pytorch 1.0.1`. When pytorch version is upgraded (to `1.2.0` for instance), this error is removed, however I get an import error:\r\n```\r\nImportError: /opt/conda/lib/python3.7/site-packages/fused_layer_norm_cuda.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZN2at19UndefinedTenso... | 1,566 | 1,574 | 1,574 | NONE | null | ## 🐛 Bug
<!-- Important information -->
Model I am using (Bert, XLNet....): XLNet
Language I am using the model on (English, Chinese....): English
```
pytorch_transformers\modeling_xlnet.py in forward(self, input_ids, token_type_ids, input_mask, attention_mask, mems, perm_mask, target_mapping, head_mask)
... | {
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https://api.github.com/repos/huggingface/transformers/issues/1121 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1121/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1121/comments | https://api.github.com/repos/huggingface/transformers/issues/1121/events | https://github.com/huggingface/transformers/issues/1121 | 485,808,293 | MDU6SXNzdWU0ODU4MDgyOTM= | 1,121 | Using pretrained XLNET for long sentences | {
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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"
] | 1,566 | 1,572 | 1,572 | NONE | null | ## ❓ Questions & Help
Is it possible to feed the pretrained large XLNET model with sentences of length of more than 512 tokens?
If no, is there any model which supports that? | {
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https://api.github.com/repos/huggingface/transformers/issues/1120 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1120/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1120/comments | https://api.github.com/repos/huggingface/transformers/issues/1120/events | https://github.com/huggingface/transformers/pull/1120 | 485,748,696 | MDExOlB1bGxSZXF1ZXN0MzExMzMzNTYx | 1,120 | Change attention mask dtype to be bool. Fix #1119 | {
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"Yes it's better, thanks also for that @CrafterKolyan!"
] | 1,566 | 1,566 | 1,566 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/transformers/issues/1119 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1119/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1119/comments | https://api.github.com/repos/huggingface/transformers/issues/1119/events | https://github.com/huggingface/transformers/issues/1119 | 485,745,480 | MDU6SXNzdWU0ODU3NDU0ODA= | 1,119 | Tons of warnings on use of TransfoXLModel. masked_fill_ input dtype torch.uint8 should be changed to torch.bool | {
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Using example from documentation web page: https://huggingface.co/pytorch-transformers/model_doc/transformerxl.html#pytorch_transformers.TransfoXLModel
```
import torch
from pytorch_transformers import *
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
mod... | {
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https://api.github.com/repos/huggingface/transformers/issues/1118 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1118/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1118/comments | https://api.github.com/repos/huggingface/transformers/issues/1118/events | https://github.com/huggingface/transformers/pull/1118 | 485,691,554 | MDExOlB1bGxSZXF1ZXN0MzExMjg2Njg5 | 1,118 | Documentation fix #1117 | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1118?src=pr&el=h1) Report\n> Merging [#1118](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1118?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/e08c01aa1ad63efff83548ea69d5ba3ce4a75acc?... | 1,566 | 1,566 | 1,566 | CONTRIBUTOR | null | Rename parameter in documentation + Delete its second occurrence. | {
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https://api.github.com/repos/huggingface/transformers/issues/1117 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1117/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1117/comments | https://api.github.com/repos/huggingface/transformers/issues/1117/events | https://github.com/huggingface/transformers/issues/1117 | 485,690,079 | MDU6SXNzdWU0ODU2OTAwNzk= | 1,117 | Wrong parameter name in documentation | {
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"Yes!"
] | 1,566 | 1,566 | 1,566 | CONTRIBUTOR | null | Documentation web page: https://huggingface.co/pytorch-transformers/model_doc/gpt2.html#pytorch_transformers.GPT2DoubleHeadsModel
See `Inputs -> multiple_choice_labels`. There is actually no such parameter in `GPT2DoubleHeadsModel.forward` method. It was renamed to `mc_labels`. Also it is presented two times in docu... | {
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https://api.github.com/repos/huggingface/transformers/issues/1116 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1116/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1116/comments | https://api.github.com/repos/huggingface/transformers/issues/1116/events | https://github.com/huggingface/transformers/pull/1116 | 485,685,207 | MDExOlB1bGxSZXF1ZXN0MzExMjgxNTM4 | 1,116 | Delete nonexistent parameter from documentation fix #1115 | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1116?src=pr&el=h1) Report\n> Merging [#1116](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1116?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/e08c01aa1ad63efff83548ea69d5ba3ce4a75acc?... | 1,566 | 1,566 | 1,566 | CONTRIBUTOR | null | Changed documentation of GPT2Model, GPT2LMHeadModel and GPT2DoubleHeadsModel | {
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https://api.github.com/repos/huggingface/transformers/issues/1115 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1115/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1115/comments | https://api.github.com/repos/huggingface/transformers/issues/1115/events | https://github.com/huggingface/transformers/issues/1115 | 485,681,208 | MDU6SXNzdWU0ODU2ODEyMDg= | 1,115 | No parameter which is presented in documentation | {
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"Indeed, thanks for that!",
"Sorry, but I still get this error, and I can see the parameter on the forward functions at the source code. Am I doing something wrong ?\r\n\r\nThanks for this amazing contribution!",
"Can you open a new issue with details on the error?\r\n`attention_mask` has been added to GPT2 now... | 1,566 | 1,570 | 1,566 | CONTRIBUTOR | null | Documentation web page: https://huggingface.co/pytorch-transformers/model_doc/gpt2.html#pytorch_transformers.GPT2Model
See `Inputs -> attention_mask`.
There is actually no parameter `attention_mask` in `GPT2Model.forward` method (see https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transforme... | {
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https://api.github.com/repos/huggingface/transformers/issues/1114 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1114/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1114/comments | https://api.github.com/repos/huggingface/transformers/issues/1114/events | https://github.com/huggingface/transformers/issues/1114 | 485,645,709 | MDU6SXNzdWU0ODU2NDU3MDk= | 1,114 | Does RoBERTa needs input_type_ids as Bert ? | {
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"RoBERTa does not use `token_type_ids`. We made a choice to still have an embedding layer (which is all zeros, so they don't contribute anything additively) so that we use the exact same implementation as BERT.",
"Understood, thanks for the quick answer ! :)"
] | 1,566 | 1,566 | 1,566 | CONTRIBUTOR | null | ## ❓ Questions & Help
Hello,
I'm trying to fine-tune RoBERTa for a sentence-pair classification task. With Bert, I used the token_type_ids to identify sentence A and B. But it seems that the Roberta "token_type" Embedding is configured with a dictionnairy of size 1 from what I understand of the model summary : (t... | {
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https://api.github.com/repos/huggingface/transformers/issues/1113 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1113/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1113/comments | https://api.github.com/repos/huggingface/transformers/issues/1113/events | https://github.com/huggingface/transformers/issues/1113 | 485,566,500 | MDU6SXNzdWU0ODU1NjY1MDA= | 1,113 | [Help] How to do mean/max pooling to get sentence embedding? | {
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"Hi! Yes this would work, but it would certainly be slower than using [the torch `mean` function](https://pytorch.org/docs/stable/torch.html#torch.mean)!",
"Understood thanks! :)"
] | 1,566 | 1,566 | 1,566 | NONE | null | Hi, I read a few questions raised before regarding sentence embedding and came across mean/max pooling suggestions.
Not too sure how to go about doing mean/max pooling.
Is my implementation correct for mean pooling? I simply took the sum of all the token vectors and divide them by total sequence length.
![ima... | {
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https://api.github.com/repos/huggingface/transformers/issues/1112 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1112/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1112/comments | https://api.github.com/repos/huggingface/transformers/issues/1112/events | https://github.com/huggingface/transformers/issues/1112 | 485,563,252 | MDU6SXNzdWU0ODU1NjMyNTI= | 1,112 | Implement the QuickStart but got an error when using BertForMaskedLM to predict a masked token | {
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"Hi, this is not an error, but a warning. This warning tells you that some of the weights that were in your pretrained model were not used by the model with which you loaded them. In this case, it concerns the classification layer weight/bias.",
"I see, thanks!"
] | 1,566 | 1,566 | 1,566 | NONE | null | ## ❓ Questions & Help
I was running the BERT example following the instruction of pytorch-transformers' docs, but when Iusing BertForMaskedLM to predict a masked token, an error occured:
"INFO:pytorch_transformers.modeling_utils:Weights from pretrained model not used in BertForMaskedLM: ['cls.seq_relationship.weigh... | {
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https://api.github.com/repos/huggingface/transformers/issues/1111 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1111/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1111/comments | https://api.github.com/repos/huggingface/transformers/issues/1111/events | https://github.com/huggingface/transformers/issues/1111 | 485,492,158 | MDU6SXNzdWU0ODU0OTIxNTg= | 1,111 | Can we get a 1.1.1 release so that AutoRoberta is included? | {
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"Yes! @LysandreJik ",
"Sounds good, we'll release one soon. Probably around the end of the week or early next week.",
"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"
] | 1,566 | 1,572 | 1,572 | NONE | null | See issue title. I'm about to open a PR to add roberta to allennlp; it'd be nice to have a released version to depend on, instead of a github commit. | {
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https://api.github.com/repos/huggingface/transformers/issues/1110 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/1110/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/1110/comments | https://api.github.com/repos/huggingface/transformers/issues/1110/events | https://github.com/huggingface/transformers/pull/1110 | 485,426,590 | MDExOlB1bGxSZXF1ZXN0MzExMDcyNTY1 | 1,110 | Torch.hub now based on AutoModels - Updating AutoModels with AutoModelWithLMHead, Sequence Classification and Question Answering | {
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"# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1110?src=pr&el=h1) Report\n> Merging [#1110](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1110?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/df9d6effae43e92761eb92540bc45fac846789ee?... | 1,566 | 1,567 | 1,567 | MEMBER | null | Added new AutoModels, as long as the accompanying tests. Updated the TorchHub configuration file to redirect to those AutoModels. | {
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