add T5 fine-tuning notebook [Community notebooks]
@patrickvonplaten 
This is the second notebook which shows how to fine-tune T5 for multiple tasks with text-to-text approach (IMDB, emotion classification, SWAG) that we discussed in issue #4426 . I didn't find emotion and SWAG dataset in the `nlp` library so I decided to keep my original dataset code to keep everything unified.

Also there's growing interest in `pytoch-lightning` so I decided to keep the `lightning`  trainer. But if you think I should use HF Trainer then I can add that as well.
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=h1) Report
> Merging [#4462](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8f1d0471489352ec01556ae61f8e8246002bbc58&el=desc) will **increase** coverage by `0.04%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4462/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #4462      +/-   ##
==========================================
+ Coverage   77.93%   77.98%   +0.04%     
==========================================
  Files         123      123              
  Lines       20430    20430              
==========================================
+ Hits        15922    15932      +10     
+ Misses       4508     4498      -10     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4462/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `90.41% <0.00%> (-0.12%)` | :arrow_down: |
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4462/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (+1.80%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4462?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/4462?src=pr&el=footer). Last update [8f1d047...ca0d2a0](https://codecov.io/gh/huggingface/transformers/pull/4462?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
<|||||>That's awesome. Thanks @patil-suraj! @mariamabarham @lhoestq - might it be interesting to add `emotion classification` and `swag` to `nlp`? <|||||>Reworded the description a bit - hope that's ok @patil-suraj <|||||>> Reworded the description a bit - hope that's ok @patil-suraj

@patrickvonplaten  yes, it's more clear now. Thank you!