Update README.md

README.md

@@ -11,6 +11,8 @@ metrics:
 - bleu
 - meteor
 - rouge
+- bertscore
+- moverscore
 widget:
 - text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
   example_title: "Example 1"
@@ -20,5 +22,55 @@ widget:
   example_title: "Example 3"
 ---
 
-# T5 finetuned on Question Generation
-T5 model for question generation. Please visit [our repository](https://github.com/asahi417/t5-question-generation) for more detail.
+# BART LARGE fine-tuned for English Question Generation
+BART LARGE Model fine-tuned on English question generation dataset (SQuAD) with an extensive hyper-parameter search.
+
+- [Project Repository](https://github.com/asahi417/lm-question-generation)
+
+## Overview
+
+**Language model:** facebook/bart-large
+**Language:** English (en)    
+**Downstream-task:** Question Generation  
+**Training data:** SQuAD  
+**Eval data:** SQuAD   
+**Code:**  See [our repository](https://github.com/asahi417/lm-question-generation)
+
+## Usage
+### In Transformers
+```python
+from transformers import pipeline
+
+model_path = 'asahi417/lmqg-t5-small-squad'
+pipe = pipeline("text2text-generation", model_path)
+
+paragraph = 'Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.'
+# highlight an answer in the paragraph to generate question
+answer = 'Etta James'
+highlight_token = '<hl>'
+input_text = paragraph.replace(answer, '{0} {1} {0}'.format(highlight_token, answer))
+input_text = 'generate question: {}'.format(input_text)  # add task specific prefix
+generation = pipe(input_text)
+print(generation)
+>>> [{'generated_text': 'What is the name of the biopic that Beyonce starred in?'}]
+```
+
+## Evaluations
+
+Evaluation on the test set of [SQuAD QG dataset](https://huggingface.co/datasets/asahi417/qg_squad).
+The results are comparable with the [leaderboard](https://paperswithcode.com/sota/question-generation-on-squad11) and previous works.
+All evaluations were done using our [evaluation script](https://github.com/asahi417/lm-question-generation).
+
+
+| BLEU 4 | ROUGE L  | METEOR | BERTScore | MoverScore |
+| ------ | -------- | ------ | --------- | ---------- |
+| 21.75  | 50.48    | 25.12  | 90.78     | 64.80      |
+
+## Fine-tuning Parameters
+We ran grid search to find the best hyper-parameters and continued fine-tuning until the validation metric decrease.
+The best hyper-parameters can be found [here](https://huggingface.co/asahi417/lmqg-bart-large-squad/raw/main/trainer_config.json), and fine-tuning script is released in [our  repository](https://github.com/asahi417/lm-question-generation).
+
+## Citation
+TBA
+
+