Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/lvwerra/gpt2-imdb-pos/README.md
README.md
ADDED
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# GPT2-IMDB-pos
|
2 |
+
|
3 |
+
## What is it?
|
4 |
+
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.
|
5 |
+
|
6 |
+
## Training setting
|
7 |
+
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/).
|
8 |
+
|
9 |
+
## Examples
|
10 |
+
A few examples of the model response to a query before and after optimisation:
|
11 |
+
|
12 |
+
| query | response (before) | response (after) | rewards (before) | rewards (after) |
|
13 |
+
|-------|-------------------|------------------|------------------|-----------------|
|
14 |
+
|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|
|
15 |
+
|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 |
|
16 |
+
|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|
|
17 |
+
|
18 |
+
|