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# What does this PR do?

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Fixes # (issue)

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to the it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors which may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

 albert, bert, XLM: @LysandreJik
 GPT2: @LysandreJik, @patrickvonplaten
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @patrickvonplaten, @TevenLeScao
 Blenderbot, Bart, Marian, Pegasus: @sshleifer
 T5: @patrickvonplaten
 Rag: @patrickvonplaten, @lhoestq
 EncoderDecoder: @patrickvonplaten
 Longformer, Reformer: @patrickvonplaten
 TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
 examples/seq2seq: @sshleifer
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 -->


[Seq2Seq] Correct import in Seq2Seq Trainer

# What does this PR do?

Correct import as mentioned by @stas00 here: https://github.com/huggingface/transformers/pull/8194#discussion_r515690821

Pinging @stas00 for review as well here.
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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to the it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors which may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

 albert, bert, XLM: @LysandreJik
 GPT2: @LysandreJik, @patrickvonplaten
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @patrickvonplaten, @TevenLeScao
 Blenderbot, Bart, Marian, Pegasus: @sshleifer
 T5: @patrickvonplaten
 Rag: @patrickvonplaten, @lhoestq
 EncoderDecoder: @patrickvonplaten
 Longformer, Reformer: @patrickvonplaten
 TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
 examples/seq2seq: @sshleifer
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 -->


I am running run_language_modeling.py,
python run_language_modeling.py \
	    --output_dir ${model_dir} \
	    --tokenizer_name $data_dir/wordpiece-custom.json \
	    --config_name $data_dir/config.json \
            --train_data_file "$data_dir/train.txt" \
            --eval_data_file $data_dir/valid.txt \
            --block_size=128 \
            --do_train \
            --per_device_train_batch_size 1 \
            --gradient_accumulation_steps 64 \
            --learning_rate 6e-4 \
            --weight_decay 0.01 \
            --adam_epsilon 1e-6 \
            --adam_beta1 0.9 \
            --adam_beta2 0.98 \
            --max_steps 500_000 \
            --warmup_steps 24_000 \
            --fp16 \
            --logging_dir ${model_dir}/tensorboard \
            --save_steps 1000 \
            --save_total_limit 20 \
            --seed 108 \
            --max_steps -1 \
            --num_train_epochs 20 \
            --overwrite_output_dir

when the txt file has 5GB, a Killed prompt appears.


Updated Reformer to use caching during generation

# What does this PR do?

The current reformer implementation supports caching of buckets and states, but this is not used during generation. Running a generation example in debugging mode, such as
```python
from transformers import ReformerModelWithLMHead, ReformerTokenizer

model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").cuda()
tok = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
output = tok.decode(
    model.generate(tok.encode("Notwithstanding", return_tensors="pt").cuda(),
                   do_sample=True,
                   temperature=0.7,
                   max_length=100,
                   use_cache=True)[0])
```
One can see that the `past_buckets_states` passed to the attention are always `None` (at https://github.com/huggingface/transformers/blob/504ff7bb1234991eb07595c123b264a8a1064bd3/src/transformers/modeling_reformer.py#L365)

This is because the name of the past states for the reformer are neither `past_key_values` or `mems`.
This PR adds the name of the past states to the generation `past` allocation.

Generally, it may make sense to harmonize the `past` value for all models, so that the `generate` function generalizes better

## Who can review?

Text Generation: @patrickvonplaten, @TevenLeScao
Reformer: @patrickvonplaten


Train BERT with CLI commands

I have downloaded the HuggingFace BERT model from the transformer repository found [here][1] and would like to train the model on custom NER labels by using the run_ner.py script as it is referenced [here][2] in the section "Named Entity Recognition".

I define model ("bert-base-german-cased"), data_dir ("Data/sentence_data.txt") and labels ("Data/labels.txt)" as defaults in the code.

Now I'm using this input for the command line:

```
python run_ner.py --output_dir="Models" --num_train_epochs=3 --logging_steps=100 --do_train --do_eval --do_predict
```

But all it does is telling me:

```
Some weights of the model checkpoint at bert-base-german-cased were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.w
eight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-german-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```

After that it just stops, not ending the script, but simply waiting.

Does anyone know what could be the problem here? Am I missing a parameter?

My sentence_data.txt in CoNLL format looks like this (small snippet):
```
Strafverfahren O
gegen O
; O
wegen O
Diebstahls O
hat O
das O
Amtsgericht Ort
Leipzig Ort
- O
Strafrichter O
```
And that's how I defined my labels in labels.txt:
```
"Date", "Delikt", "Strafe_Tatbestand", "Schadensbetrag", "Geständnis_ja", "Vorstrafe_ja", "Vorstrafe_nein", "Ort",
"Strafe_Gesamtfreiheitsstrafe_Dauer", "Strafe_Gesamtsatz_Dauer", "Strafe_Gesamtsatz_Betrag"
```

  [1]: https://github.com/huggingface/transformers
  [2]: https://huggingface.co/transformers/task_summary.html


tokenizer.vocab key and values is change begin line 261?

transformers version 3.0.0

```python
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')

for v, i in tokenizer.vocab.items():
    print(v, i)
```

![image](https://user-images.githubusercontent.com/18568380/97958832-04cc1080-1de9-11eb-8c2b-0463c902853e.png)
i find key and values maybe wrong position.


[ray] Support `n_jobs` for Ray hyperparameter search on CPUs

# What does this PR do?

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Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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Fixes # (issue)

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to the it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors which may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

 albert, bert, XLM: @LysandreJik
 GPT2: @LysandreJik, @patrickvonplaten
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @patrickvonplaten, @TevenLeScao
 Blenderbot, Bart, Marian, Pegasus: @sshleifer
 T5: @patrickvonplaten
 Rag: @patrickvonplaten, @lhoestq
 EncoderDecoder: @patrickvonplaten
 Longformer, Reformer: @patrickvonplaten
 TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
 examples/seq2seq: @sshleifer
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 -->


Model card: GPT-2 fine-tuned on CommonGen

model card

Related to pretrained model cards


Model card: CodeBERT fine-tuned for Insecure Code Detection

Related to pretrained model cards


[Notebooks] Add new encoder-decoder notebooks

# What does this PR do?

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Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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Adds 2 community notebooks

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to the it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors which may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

 albert, bert, XLM: @LysandreJik
 GPT2: @LysandreJik, @patrickvonplaten
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @patrickvonplaten, @TevenLeScao
 Blenderbot, Bart, Marian, Pegasus: @sshleifer
 T5: @patrickvonplaten
 Rag: @patrickvonplaten, @lhoestq
 EncoderDecoder: @patrickvonplaten
 Longformer, Reformer: @patrickvonplaten
 TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
 examples/seq2seq: @sshleifer
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 -->


Add XLMProphetNetTokenizer to tokenization auto

Closes #8196


_shift_right when to use

Hi
In modeling_t5 there is a function called shift_right I wonder when it needs to be used, for which tasks? I sometimes see T5 finetuning without using it, not sure when this is suitable to use it thanks


[EncoderDecoder] fix encoder decoder config model type bug

# What does this PR do?

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Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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Small typo in config encoder-decoder class which leads to false config model type name

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to the it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors which may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

 albert, bert, XLM: @LysandreJik
 GPT2: @LysandreJik, @patrickvonplaten
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @patrickvonplaten, @TevenLeScao
 Blenderbot, Bart, Marian, Pegasus: @sshleifer
 T5: @patrickvonplaten
 Rag: @patrickvonplaten, @lhoestq
 EncoderDecoder: @patrickvonplaten
 Longformer, Reformer: @patrickvonplaten
 TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
 examples/seq2seq: @sshleifer
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 -->