noamaanMulla-03/transformers-issues huggingface dataset:
Create README.md for gpt-2-pubmed-medium
Author: ypapanik
Pull request: yes
Labels: name: model card


Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4871?src=pr&el=h1) Report
> Merging [#4871](https://codecov.io/gh/huggingface/transformers/pull/4871?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9f5d5a531d769d07403f59661884e254f8420afe&el=desc) will **increase** coverage by `0.00%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4871/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4871?src=pr&el=tree)

```diff
@@           Coverage Diff           @@
##           master    #4871   +/-   ##
=======================================
  Coverage   76.55%   76.56%           
=======================================
  Files         128      128           
  Lines       21502    21502           
=======================================
+ Hits        16461    16462    +1     
+ Misses       5041     5040    -1     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4871?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4871/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.12% <0.00%> (-0.48%)` | :arrow_down: |
| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4871/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.62% <0.00%> (+0.94%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4871?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4871?src=pr&el=footer). Last update [9f5d5a5...40bc44a](https://codecov.io/gh/huggingface/transformers/pull/4871?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).


cc @LysandreJik, you're going to like this:)
KeyError when using non-default models in Huggingface transformers pipeline
Author: hesamuel
Pull request: no


I have no problems using the default model in the sentiment analysis pipeline. 

```# Allocate a pipeline for sentiment-analysis
nlp = pipeline('sentiment-analysis')

nlp('I am a black man.')

>>>[{'label': 'NEGATIVE', 'score': 0.5723695158958435}]
```
But, when I try to customise the pipeline a little by adding a specific model. It throws a KeyError. 



```
nlp = pipeline('sentiment-analysis',
               tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/bert-base-cased-conversational"),
               model = AutoModelWithLMHead.from_pretrained("DeepPavlov/bert-base-cased-conversational"))

nlp('I am a black man.')



>>>---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-55-af7e46d6c6c9> in <module>
      3                tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/bert-base-cased-conversational"),
      4             model = AutoModelWithLMHead.from_pretrained("DeepPavlov/bert-base-cased-conversational"))
----> 5 nlp('I am a black man.')
      6 
      7 

~/opt/anaconda3/lib/python3.7/site-packages/transformers/pipelines.py in __call__(self, *args, **kwargs)
    721         outputs = super().__call__(*args, **kwargs)
    722         scores = np.exp(outputs) / np.exp(outputs).sum(-1, keepdims=True)
--> 723         return [{"label": self.model.config.id2label[item.argmax()], "score": item.max().item()} for item in scores]
    724 
    725 

~/opt/anaconda3/lib/python3.7/site-packages/transformers/pipelines.py in <listcomp>(.0)
    721         outputs = super().__call__(*args, **kwargs)
    722         scores = np.exp(outputs) / np.exp(outputs).sum(-1, keepdims=True)
--> 723         return [{"label": self.model.config.id2label[item.argmax()], "score": item.max().item()} for item in scores]
    724 
    725 

KeyError: 58129

```


# 
Question on SO   -->https://stackoverflow.com/questions/62300836/keyerror-when-using-non-default-models-in-huggingface-transformers-pipeline


Comments:
The "sentiment-analysis" pipeline is only compatible with text classification models, i.e. those that can be loaded without error with `AutoModelForSequenceClassification`
check type before logging in trainer to ensure values are scalars
Author: mgoldey
Pull request: yes

This change was necessary to avoid https://github.com/lanpa/tensorboardX/issues/567 since a non-scalar value was being passed in.

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4883?src=pr&el=h1) Report
> Merging [#4883](https://codecov.io/gh/huggingface/transformers/pull/4883?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9f5d5a531d769d07403f59661884e254f8420afe&el=desc) will **increase** coverage by `0.00%`.
> The diff coverage is `66.66%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4883/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4883?src=pr&el=tree)

```diff
@@           Coverage Diff           @@
##           master    #4883   +/-   ##
=======================================
  Coverage   76.55%   76.56%           
=======================================
  Files         128      128           
  Lines       21502    21504    +2     
=======================================
+ Hits        16461    16464    +3     
+ Misses       5041     5040    -1     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4883?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4883/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.67% <66.66%> (+0.99%)` | :arrow_up: |
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4883/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.28% <0.00%> (-0.32%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4883?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4883?src=pr&el=footer). Last update [9f5d5a5...9c9d9c3](https://codecov.io/gh/huggingface/transformers/pull/4883?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).


> Wouldn't the more robust fix be to change these values to scalars if they're not?
> 
> In the case were a string is passed (which seems to be your case), I think a warning would be better than a silently not registering anything. What do you think?

I agree that logging the non-scalar values would be an improvement, and I'll update this PR to that effect. I haven't characterized what the exact error is, so I'm not sure that we can even cast the troublesome values to scalars. Thank you very much for the good feedback here @LysandreJik.

@LysandreJik please feel free to suggest a different log level or log message from that in a33e28b
Has anyone used the run_language_modeling.py to train a gpt2 in a different language? is it possible?
Author: nikkon3
Pull request: no
Labels: name: wontfix

# ❓ Questions & Help

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     new models and benchmarks, and migration questions. For all other questions,
     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. Make sure to tag your question with the
     right deep learning framework as well as the huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     
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     can always open a question on GitHub. You should then link to the SO question 
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## Details
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**A link to original question on Stack Overflow**: 

Comments:
Hi! Maybe you can have a look at to the issue #1560 . 

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.
Make multiple choice models work with input_embeds
Author: sgugger
Pull request: yes

Currently, all the multiple choice models will fail if we pass them `inputs_embeds` instead of `input_ids`. This PR fixes that on the pytorch side and adapts the corresponding test for common models.

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4921?src=pr&el=h1) Report
> Merging [#4921](https://codecov.io/gh/huggingface/transformers/pull/4921?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/466aa57a45bfb9fc47d4b75d22c02c34b4b4b0fc&el=desc) will **increase** coverage by `0.07%`.
> The diff coverage is `100.00%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4921/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4921?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #4921      +/-   ##
==========================================
+ Coverage   77.06%   77.13%   +0.07%     
==========================================
  Files         128      128              
  Lines       21649    21653       +4     
==========================================
+ Hits        16683    16702      +19     
+ Misses       4966     4951      -15     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4921?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4921/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.31% <100.00%> (+0.02%)` | :arrow_up: |
| [src/transformers/modeling\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/4921/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19sb25nZm9ybWVyLnB5) | `93.02% <100.00%> (+0.01%)` | :arrow_up: |
| [src/transformers/modeling\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/4921/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `94.78% <100.00%> (+0.02%)` | :arrow_up: |
| [src/transformers/modeling\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/4921/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `78.65% <100.00%> (+2.33%)` | :arrow_up: |
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4921/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.42% <0.00%> (+0.15%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4921?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4921?src=pr&el=footer). Last update [466aa57...edd1ede](https://codecov.io/gh/huggingface/transformers/pull/4921?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
encode_plus( ) function for the GPT-2 Tokenizer
Author: h56cho
Pull request: no

Hello,

From the GPT-2 Tokenizer section of the Hugging Face Transformer documentation, the documentation says that:
```
GPT-2 BPE tokenizer. 

Peculiarities:

Byte-level Byte-Pair-Encoding

Requires a space to start the input string => the encoding methods should be called with the add_prefix_space flag set to True. Otherwise, this tokenizer encode and decode method will not conserve the absence of a space at the beginning of a string:
```

If I use the `encode_plus()` function (not `encode( )`) to encode my sentences (doing something like `encode_plus("Hi there")['input_ids']`, instead of directly using the `encode()` function), do I still need to place a space at the start of every input string?

Thank you,


Comments:
Sure, and you can use the `add_prefix_space` flag to do that.
Funnel Transformers
Author: pchankh
Pull request: no
Labels: name: New model

# 🌟 New model addition
Funnel-Transformer

## Model description
Funnel-Transformer is a new self-attention model that gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, Funnel-Transformer usually has a higher capacity given the same FLOPs. In addition, with a decoder, Funnel-Transformer is able to recover the token-level deep representation for each token from the reduced hidden sequence, which enables standard pretraining.

<!-- Important information -->

## Open source status
Released.

* [x] the model implementation is available: (give details)
https://github.com/laiguokun/Funnel-Transformer
* [x] the model weights are available: (give details)
https://github.com/laiguokun/Funnel-Transformer
* [x] who are the authors: (mention them, if possible by @gh-username)
Zihang Dai*, Guokun Lai*, Yiming Yang, Quoc V. Le


Comments:
Duplicate of #4844?

my bad. yes
how to train mask model e.g Bert using WordPieceToken
Author: Yangxiaojun1230
Pull request: no

# ❓ Questions & Help
When i trained a new token by WordPiece，that generized one vocab.txt file . It couldn't load in train-language-model.py ，since in source code it used bytepiece token. Is there a module out of box could save me a lot of time to do myself？ 
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. Make sure to tag your question with the
     right deep learning framework as well as the huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     
     If your question wasn't answered after a period of time on Stack Overflow, you
     can always open a question on GitHub. You should then link to the SO question 
     that you posted.
     -->

## Details
<!-- Description of your issue -->

<!-- You should first ask your question on SO, and only if
     you didn't get an answer ask it here on GitHub. -->
**A link to original question on Stack Overflow**:


Comments:
Hi! Can you load your tokenizer using

```py
from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained(directory_containing_vocab_txt)
```
?

> 
> 
> Hi! Can you load your tokenizer using
> 
> ```python
> from transformers import BertTokenizer
> 
> tokenizer = BertTokenizer.from_pretrained(directory_containing_vocab_txt)
> ```
> 
> ?

Hi LysandreJik,
     Great ! It works . May I ask another question-- Do you have any experience on the loss score. Generally what score should be appropriate , my loss around 1.12 is it ok?  

This really depends of your training set and what model you use, what checkpoint you use, etc.
🐛 [TFTrainer] `dataloader_drop_last` unused
Author: astariul
Pull request: no

# 🐛 Bug

The argument `dataloader_drop_last` appear to not be used in `TFTrainer`. This is a problem when we need a static batch size.

https://github.com/huggingface/transformers/blob/e8db8b845a971b0cf63a0896b9deb5b316028a8b/src/transformers/trainer_tf.py#L68-L73

## Expected behavior

The argument `dataloader_drop_last` is used when batching the dataset.

Comments:
Thanks for bringing this up, @Colanim! I worked on #4757, and didn't realize the same could automatically extend to TFTrainer as well.

Please take a look at #4925 to see if it'd solve this for you.
Use dataloader_drop_last in TF dataset
Author: setu4993
Pull request: yes

Follows PR #4757, fixes issue #4891. I didn't realize then that `TFTrainingArguments` inherited `TrainingArguments` and all of the same options would be available there as well.

Adding this to get to feature parity across PyTorch and TF trainers.

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4925?src=pr&el=h1) Report
> Merging [#4925](https://codecov.io/gh/huggingface/transformers/pull/4925?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/e80d6c689bd62f805a5c8d77ec0cc3b09f240d14&el=desc) will **increase** coverage by `0.07%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4925/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4925?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #4925      +/-   ##
==========================================
+ Coverage   77.10%   77.17%   +0.07%     
==========================================
  Files         128      128              
  Lines       21723    21723              
==========================================
+ Hits        16749    16765      +16     
+ Misses       4974     4958      -16     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4925?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/trainer\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/4925/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyX3RmLnB5) | `19.04% <ø> (ø)` | |
| [src/transformers/file\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4925/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `73.79% <0.00%> (ø)` | |
| [src/transformers/modeling\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4925/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.61% <0.00%> (+0.11%)` | :arrow_up: |
| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4925/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.48% <0.00%> (+0.23%)` | :arrow_up: |
| [src/transformers/modeling\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/4925/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `78.65% <0.00%> (+2.29%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4925?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4925?src=pr&el=footer). Last update [e80d6c6...a9099e0](https://codecov.io/gh/huggingface/transformers/pull/4925?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).


LGTM from a quick glance but I'll let @jplu chime in

Nice! LGTM!!
BertTokenizer from own vocab meet problem
Author: Yangxiaojun1230
Pull request: no

# ❓ Questions & Help
I set up own vocab.txt and the item like 3-213,3A23 and so on. When use BertTokenizer.from_pretrained()  it couldn't tokenize the '3-213'. Even when I write 'A' in vocab.txt ,and couldn't encode('A'). Do anyone know how to fix this issue?

<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. Make sure to tag your question with the
     right deep learning framework as well as the huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     
     If your question wasn't answered after a period of time on Stack Overflow, you
     can always open a question on GitHub. You should then link to the SO question 
     that you posted.
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## Details
<!-- Description of your issue -->

<!-- You should first ask your question on SO, and only if
     you didn't get an answer ask it here on GitHub. -->
**A link to original question on Stack Overflow**:


Comments:
ok, I fix it by using lower case
Support multiple choice in tf common model tests
Author: sgugger
Pull request: yes

This the same as #4886, but for tensorflow (first time ever of me coding in tf!)

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4920?src=pr&el=h1) Report
> Merging [#4920](https://codecov.io/gh/huggingface/transformers/pull/4920?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/5d63ca6c38cc0f583cdec4c3efcfce13c0a41fdc&el=desc) will **decrease** coverage by `0.04%`.
> The diff coverage is `100.00%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4920/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4920?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #4920      +/-   ##
==========================================
- Coverage   77.10%   77.05%   -0.05%     
==========================================
  Files         128      128              
  Lines       21617    21618       +1     
==========================================
- Hits        16667    16657      -10     
- Misses       4950     4961      +11     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4920?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_tf\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4920/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `95.16% <100.00%> (+0.89%)` | :arrow_up: |
| [src/transformers/modeling\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/4920/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `76.31% <0.00%> (-2.31%)` | :arrow_down: |
| [src/transformers/modeling\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4920/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.49% <0.00%> (-0.12%)` | :arrow_down: |

------

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Very clean. LGTM!