model update

README.md

@@ -26,7 +26,7 @@ widget:
 - text: "extract answers: Néanmoins, une fois encore, l'arithmétique modulaire est insuffisante pour venir à bout du théorème. Dirichlet utilise de nombreuses techniques analytiques, comme les séries entières et l'analyse complexe. Le fruit de ces travaux donne naissance à une nouvelle branche des mathématiques : la théorie analytique des nombres. L'un des points cruciaux de cette théorie provient de l'unique article de <hl> Bernhard Riemann <hl> en théorie des nombres : Sur le nombre de nombres premiers inférieurs à une taille donnée. Il conjecture une localisation des racines de sa fonction ζ. La recherche de la position des racines, initiée par Dirichlet, devient une préoccupation centrale et reste l'une des conjectures pressenties comme les plus difficiles des mathématiques de notre époque."
   example_title: "Answer Extraction Example 2" 
 model-index:
-- name: lmqg/mt5-base-frquad-multitask
+- name: lmqg/mt5-base-frquad-qg-ae
   results:
   - task:
       name: Text2text Generation
@@ -51,34 +51,49 @@ model-index:
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
       value: 55.91
-    - name: QAAlignedF1Score-BERTScore
-      type: qa_aligned_f1_score_bertscore
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
       value: 79.16
-    - name: QAAlignedRecall-BERTScore
-      type: qa_aligned_recall_bertscore
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
       value: 81.87
-    - name: QAAlignedPrecision-BERTScore
-      type: qa_aligned_precision_bertscore
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
       value: 76.69
-    - name: QAAlignedF1Score-MoverScore
-      type: qa_aligned_f1_score_moverscore
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
       value: 53.9
-    - name: QAAlignedRecall-MoverScore
-      type: qa_aligned_recall_moverscore
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
       value: 55.36
-    - name: QAAlignedPrecision-MoverScore
-      type: qa_aligned_precision_moverscore
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
       value: 52.57
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 23.84
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 40.94
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 35.22
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 86.03
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 73.94
     - name: AnswerF1Score (Answer Extraction)
-      type: answer_f1_score_answer_extraction
+      type: answer_f1_score__answer_extraction
       value: 63.54
     - name: AnswerExactMatch (Answer Extraction)
       type: answer_exact_match_answer_extraction
       value: 43.01
 ---
 
-# Model Card of `lmqg/mt5-base-frquad-multitask`
-This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task and answer extraction jointly on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+# Model Card of `lmqg/mt5-base-frquad-qg-ae`
+This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation and answer extraction jointly on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
 
 ### Overview
@@ -95,7 +110,7 @@ This model is fine-tuned version of [google/mt5-base](https://huggingface.co/goo
 from lmqg import TransformersQG
 
 # initialize model
-model = TransformersQG(language="fr", model="lmqg/mt5-base-frquad-multitask")
+model = TransformersQG(language="fr", model="lmqg/mt5-base-frquad-qg-ae")
 
 # model prediction
 question_answer_pairs = model.generate_qa("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
@@ -106,7 +121,7 @@ question_answer_pairs = model.generate_qa("Créateur » (Maker), lui aussi au si
 ```python
 from transformers import pipeline
 
-pipe = pipeline("text2text-generation", "lmqg/mt5-base-frquad-multitask")
+pipe = pipeline("text2text-generation", "lmqg/mt5-base-frquad-qg-ae")
 
 # answer extraction
 answer = pipe("generate question: Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
@@ -119,7 +134,7 @@ question = pipe("extract answers: Pourtant, la strophe spensérienne, utilisée
 ## Evaluation
 
 
-- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-multitask/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) 
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) 
 
 |            |   Score | Type    | Dataset                                                          |
 |:-----------|--------:|:--------|:-----------------------------------------------------------------|
@@ -133,7 +148,7 @@ question = pipe("extract answers: Pourtant, la strophe spensérienne, utilisée
 | ROUGE_L    |   27.05 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
 
 
-- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-multitask/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json)
+- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json)
 
 |                                 |   Score | Type    | Dataset                                                          |
 |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
@@ -145,7 +160,7 @@ question = pipe("extract answers: Pourtant, la strophe spensérienne, utilisée
 | QAAlignedRecall (MoverScore)    |   55.36 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
 
 
-- ***Metric (Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-multitask/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_frquad.default.json)
+- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_frquad.default.json)
 
 |                  |   Score | Type    | Dataset                                                          |
 |:-----------------|--------:|:--------|:-----------------------------------------------------------------|
@@ -181,7 +196,7 @@ The following hyperparameters were used during fine-tuning:
  - gradient_accumulation_steps: 2
  - label_smoothing: 0.15
 
-The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-frquad-multitask/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-frquad-qg-ae/raw/main/trainer_config.json).
 
 ## Citation
 ```