model update

README.md

@@ -21,7 +21,7 @@ widget:
 - text: "<hl> 원테이크 촬영 <hl> 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다."
   example_title: "Question Generation Example 3" 
 model-index:
-- name: lmqg/mbart-large-cc25-koquad
+- name: lmqg/mbart-large-cc25-koquad-qg
   results:
   - task:
       name: Text2text Generation
@@ -66,7 +66,7 @@ model-index:
       value: 85.62
 ---
 
-# Model Card of `lmqg/mbart-large-cc25-koquad`
+# Model Card of `lmqg/mbart-large-cc25-koquad-qg`
 This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) 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).
 
 
@@ -84,7 +84,7 @@ This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingf
 from lmqg import TransformersQG
 
 # initialize model
-model = TransformersQG(language="ko", model="lmqg/mbart-large-cc25-koquad")
+model = TransformersQG(language="ko", model="lmqg/mbart-large-cc25-koquad-qg")
 
 # model prediction
 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")
@@ -95,7 +95,7 @@ questions = model.generate_q(list_context="1990년 영화 《 남부군 》에
 ```python
 from transformers import pipeline
 
-pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-koquad")
+pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-koquad-qg")
 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.")
 
 ```
@@ -103,7 +103,7 @@ output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영
 ## Evaluation
 
 
-- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-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/mbart-large-cc25-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) 
 
 |            |   Score | Type    | Dataset                                                          |
 |:-----------|--------:|:--------|:-----------------------------------------------------------------|
@@ -117,7 +117,7 @@ output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영
 | ROUGE_L    |   27.76 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
 
 
-- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-koquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.json)
+- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-koquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.json)
 
 |                                 |   Score | Type    | Dataset                                                          |
 |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
@@ -149,7 +149,7 @@ The following hyperparameters were used during fine-tuning:
  - gradient_accumulation_steps: 16
  - label_smoothing: 0.15
 
-The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-koquad/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-koquad-qg/raw/main/trainer_config.json).
 
 ## Citation
 ```