model update

README.md

@@ -33,44 +33,25 @@ model-index:
     metrics:
     - name: BLEU4
       type: bleu4
-      value: 1.0382411466021054e-10
+      value: 0.0
     - name: ROUGE-L
       type: rouge-l
-      value: 0.01269619492817282
+      value: 1.27
     - name: METEOR
       type: meteor
-      value: 0.011950547634968242
+      value: 1.2
     - name: BERTScore
       type: bertscore
-      value: 0.8029428076218156
+      value: 80.29
     - name: MoverScore
       type: moverscore
-      value: 0.5150153763023789
+      value: 51.5
 ---
 
 # Model Card of `lmqg/t5-base-subjqa-vanilla-restaurants`
-This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the 
-[lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) 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:** [t5-base](https://huggingface.co/t5-base)   
 - **Language:** en  
@@ -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='en', model='lmqg/t5-base-subjqa-vanilla-restaurants')
+model = TransformersQG(language="en", model="lmqg/t5-base-subjqa-vanilla-restaurants")
+
 # model prediction
-question = model.generate_q(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"])
+questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
 
 ```
 
 - With `transformers`
 ```python
-
 from transformers import pipeline
-# initialize model
-pipe = pipeline("text2text-generation", 'lmqg/t5-base-subjqa-vanilla-restaurants')
-# question generation
-question = pipe('generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')
 
-```
+pipe = pipeline("text2text-generation", "lmqg/t5-base-subjqa-vanilla-restaurants")
+output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
 
-## Evaluation Metrics
+```
 
+## Evaluation
 
-### Metrics
 
-| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
-|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
-| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 0.0 | 0.013 | 0.012 | 0.803 | 0.515 | [link](https://huggingface.co/lmqg/t5-base-subjqa-vanilla-restaurants/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | 
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-subjqa-vanilla-restaurants/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) 
 
+|            |   Score | Type        | Dataset                                                          |
+|:-----------|--------:|:------------|:-----------------------------------------------------------------|
+| BERTScore  |   80.29 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
+| Bleu_1     |    2.76 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
+| Bleu_2     |    0.65 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
+| Bleu_3     |    0    | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
+| Bleu_4     |    0    | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
+| METEOR     |    1.2  | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
+| MoverScore |   51.5  | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
+| ROUGE_L    |    1.27 | restaurants | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
 
 
 
@@ -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