Created README.md for model card ChemBERTa
The README.md is added to explain an overview of SMILES, as this is the only model card trained on a non-language dataset. The documentation also explains potential use-cases for utilizing RoBERTa trained on masked language modelling for SMILES, and links to a repository with the original notebooks for evaluations, running predictions and some applications of the models for curiosity.
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3666?src=pr&el=h1) Report
> Merging [#3666](https://codecov.io/gh/huggingface/transformers/pull/3666?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0a9d09b42a9c7c1ccc00da48486a1188078e8594&el=desc) will **increase** coverage by `0.01%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/3666/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/3666?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #3666      +/-   ##
==========================================
+ Coverage   78.03%   78.04%   +0.01%     
==========================================
  Files         104      104              
  Lines       17708    17708              
==========================================
+ Hits        13819    13821       +2     
+ Misses       3889     3887       -2     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/3666?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3666/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `92.23% <0.00%> (+0.12%)` | :arrow_up: |
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/3666/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `93.28% <0.00%> (+0.16%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3666?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/3666?src=pr&el=footer). Last update [0a9d09b...7df3a3b](https://codecov.io/gh/huggingface/transformers/pull/3666?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
<|||||>This is really cool, @seyonechithrananda. Can you add a 

```
---
tags: 
- chemistry
---
```

metadata block to the top of the file? Also cc'ing @mrm8488 who might be interested<|||||>Thank you @julien-c. I uploaded two models from ChEMBL25/26 for drug structure learning (SMILES) using same technique. In fact, they have been used for COVID-19 drug discovery<|||||>@mrm8488 Are you targeting ligand-protein modelling techniques with transformers? <|||||>@julien-c Made the changes. Let me know what you think!<|||||>> @mrm8488 Are you targeting ligand-protein modelling techniques with transformers?

As told you via Twitter I am getting started into it. Getting chemical knowledge :)