Adding notebooks for Fine Tuning [Community Notebook]
Author: abhimishra91
Pull request: yes

Hi @patrickvonplaten, Adding 3 documented notebooks to fine tune transformers to downstream NLP tasks with PyTorch:

- Multi-class classification: Using DistilBert
- Multi-label classification: Using Bert
- Summarization: Using T5 - **Model Tracking with WandB**

These notebooks are pulled from the git repo: https://github.com/abhimishra91/transformers-tutorials

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4732?src=pr&el=h1) Report
> Merging [#4732](https://codecov.io/gh/huggingface/transformers/pull/4732?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/9ca485734aea269961d63a040ff194365d151fd1&el=desc) will **increase** coverage by `1.42%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4732/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4732?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #4732      +/-   ##
==========================================
+ Coverage   75.64%   77.07%   +1.42%     
==========================================
  Files         128      128              
  Lines       20996    20996              
==========================================
+ Hits        15883    16182     +299     
+ Misses       5113     4814     -299     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4732?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19ncHQyLnB5) | `72.02% <0.00%> (-14.15%)` | :arrow_down: |
| [src/transformers/modeling\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `80.34% <0.00%> (-1.38%)` | :arrow_down: |
| [src/transformers/modeling\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `89.91% <0.00%> (-0.25%)` | :arrow_down: |
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.79% <0.00%> (-0.17%)` | :arrow_down: |
| [src/transformers/modeling\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `86.94% <0.00%> (+0.18%)` | :arrow_up: |
| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `39.62% <0.00%> (+0.23%)` | :arrow_up: |
| [src/transformers/file\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `74.27% <0.00%> (+0.41%)` | :arrow_up: |
| [src/transformers/modeling\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `83.49% <0.00%> (+6.36%)` | :arrow_up: |
| [src/transformers/configuration\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2xvbmdmb3JtZXIucHk=) | `100.00% <0.00%> (+25.00%)` | :arrow_up: |
| [src/transformers/modeling\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/4732/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19sb25nZm9ybWVyLnB5) | `93.43% <0.00%> (+75.91%)` | :arrow_up: |

------

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> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4732?src=pr&el=footer). Last update [9ca4857...9d50901](https://codecov.io/gh/huggingface/transformers/pull/4732?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).


Hey @abhimishra91,

The notebooks are very clean and well-written! Thanks a lot! :-) Just did some renaming in the explanations.