Add model cards (#3537)

* feat: add model card bert-imdb

* feat: add model card gpt2-imdb-pos

* feat: add model card gpt2-imdb

model_cards/lvwerra/bert-imdb/README.md

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+# BERT-IMDB
+
+## What is it?
+BERT (`bert-large-cased`) trained for sentiment classification on the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
+
+## Training setting
+
+The model was trained on 80% of the IMDB dataset for sentiment classification for three epochs with a learning rate of `1e-5` with the `simpletransformers` library. The library uses a learning rate schedule.
+
+## Result
+The model achieved 90% classification accuracy on the validation set.
+
+## Reference
+The full experiment is available in the [tlr repo](https://lvwerra.github.io/trl/03-bert-imdb-training/).

model_cards/lvwerra/gpt2-imdb-pos/README.md

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+# GPT2-IMDB-pos
+
+## What is it?
+A small GPT2 (`lvwerra/gpt2-imdb`) language model fine-tuned to produce positive movie reviews based the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). The model is trained with rewards from a BERT sentiment classifier (`lvwerra/gpt2-imdb`) via PPO.
+
+## Training setting
+The model was trained for `100` optimisation steps with a batch size of `256` which corresponds to `25600` training samples. The full experiment setup can be found in the Jupyter notebook in the [trl repo](https://lvwerra.github.io/trl/04-gpt2-sentiment-ppo-training/).
+
+## Examples
+A few examples of the model response to a query before and after optimisation:
+
+| query | response (before) | response (after) | rewards (before) | rewards (after) |
+|-------|-------------------|------------------|------------------|-----------------|
+|I'd never seen a |heavier, woodier example of Victorian archite... |film of this caliber, and I think it's wonder... |3.297736 |4.158653|
+|I love John's work	|but I actually have to write language as in w... |and I hereby recommend this film. I am really... |-1.904006 |4.159198 |
+|I's a big struggle |to see anyone who acts in that way. by Jim Th... |, but overall I'm happy with the changes even ... |-1.595925 |2.651260|
+
+

model_cards/lvwerra/gpt2-imdb/README.md

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+# GPT2-IMDB
+
+## What is it?
+A GPT2 (`gpt2`) language model fine-tuned on the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
+
+## Training setting
+
+The GPT2 language model was fine-tuned for 1 epoch on the IMDB dataset. All comments were joined into a single text file separated by the EOS token:
+
+```
+import pandas as pd
+df = pd.read_csv("imdb-dataset.csv")
+imdb_str = " <|endoftext|> ".join(df['review'].tolist())
+
+with open ('imdb.txt', 'w') as f:
+    f.write(imdb_str)
+```
+
+To train the model the `run_language_modeling.py` script in the `transformer` library was used:
+
+```
+python run_language_modeling.py 
+	--train_data_file imdb.txt 
+	--output_dir gpt2-imdb 
+	--model_type gpt2 
+	--model_name_or_path gpt2
+```