model update

README.md

@@ -33,44 +33,25 @@ model-index:
     metrics:
     - name: BLEU4
       type: bleu4
-      value: 0.12184665382055122
+      value: 12.18
     - name: ROUGE-L
       type: rouge-l
-      value: 0.2856948017709817
+      value: 28.57
     - name: METEOR
       type: meteor
-      value: 0.29623847263524816
+      value: 29.62
     - name: BERTScore
       type: bertscore
-      value: 0.8451586993172961
+      value: 84.52
     - name: MoverScore
       type: moverscore
-      value: 0.8335888774638588
+      value: 83.36
 ---
 
 # Model Card of `lmqg/mt5-base-koquad`
-This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the 
-[lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
 
-Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).
-
-```
-
-@inproceedings{ushio-etal-2022-generative,
-    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
-    author = "Ushio, Asahi  and
-        Alva-Manchego, Fernando  and
-        Camacho-Collados, Jose",
-    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
-    month = dec,
-    year = "2022",
-    address = "Abu Dhabi, U.A.E.",
-    publisher = "Association for Computational Linguistics",
-}
-
-```
-
 ### Overview
 - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
 - **Language:** ko  
@@ -82,35 +63,40 @@ Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](h
 ### Usage
 - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
 ```python
-
 from lmqg import TransformersQG
+
 # initialize model
-model = TransformersQG(language='ko', model='lmqg/mt5-base-koquad')
+model = TransformersQG(language="ko", model="lmqg/mt5-base-koquad")
+
 # model prediction
-question = model.generate_q(list_context=["1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다."], list_answer=["남부군"])
+questions = model.generate_q(list_context="1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.", list_answer="남부군")
 
 ```
 
 - With `transformers`
 ```python
-
 from transformers import pipeline
-# initialize model
-pipe = pipeline("text2text-generation", 'lmqg/mt5-base-koquad')
-# question generation
-question = pipe('1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.')
 
-```
+pipe = pipeline("text2text-generation", "lmqg/mt5-base-koquad")
+output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.")
 
-## Evaluation Metrics
+```
 
+## Evaluation
 
-### Metrics
 
-| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
-|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
-| [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | default | 0.122 | 0.286 | 0.296 | 0.845 | 0.834 | [link](https://huggingface.co/lmqg/mt5-base-koquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | 
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-koquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) 
 
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   84.52 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_1     |   28.54 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_2     |   21.05 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_3     |   15.92 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_4     |   12.18 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| METEOR     |   29.62 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| MoverScore |   83.36 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| ROUGE_L    |   28.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
 
 
 
@@ -137,7 +123,6 @@ The full configuration can be found at [fine-tuning config file](https://hugging
 
 ## Citation
 ```
-
 @inproceedings{ushio-etal-2022-generative,
     title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
     author = "Ushio, Asahi  and