model update

README.md

@@ -26,7 +26,7 @@ widget:
 - text: "extract answers: <hl> Los estudiosos y los histori a dores están divididos en cuanto a qué evento señala el final de la era helenística. <hl> El período helenístico se puede ver que termina con la conquista final del corazón griego por Roma en 146 a. C. tras la guerra aquea, con la derrota final del reino ptolemaico en la batalla de Actium en 31 a. Helenístico se distingue de helénico en que el primero abarca toda la esfera de influencia griega antigua directa, mientras que el segundo se refiere a la propia Grecia."
   example_title: "Answer Extraction Example 2" 
 model-index:
-- name: lmqg/mt5-base-esquad-multitask
+- name: lmqg/mt5-base-esquad-qg-ae
   results:
   - task:
       name: Text2text Generation
@@ -51,34 +51,49 @@ model-index:
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
       value: 59.15
-    - 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: 79.67
-    - name: QAAlignedRecall-BERTScore
-      type: qa_aligned_recall_bertscore
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
       value: 82.44
-    - name: QAAlignedPrecision-BERTScore
-      type: qa_aligned_precision_bertscore
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
       value: 77.14
-    - 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: 54.82
-    - name: QAAlignedRecall-MoverScore
-      type: qa_aligned_recall_moverscore
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
       value: 56.56
-    - name: QAAlignedPrecision-MoverScore
-      type: qa_aligned_precision_moverscore
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
       value: 53.27
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 25.75
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 49.61
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 43.74
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 90.04
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 80.94
     - name: AnswerF1Score (Answer Extraction)
-      type: answer_f1_score_answer_extraction
+      type: answer_f1_score__answer_extraction
       value: 75.33
     - name: AnswerExactMatch (Answer Extraction)
       type: answer_exact_match_answer_extraction
       value: 57.98
 ---
 
-# Model Card of `lmqg/mt5-base-esquad-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_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+# Model Card of `lmqg/mt5-base-esquad-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_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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="es", model="lmqg/mt5-base-esquad-multitask")
+model = TransformersQG(language="es", model="lmqg/mt5-base-esquad-qg-ae")
 
 # model prediction
 question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.")
@@ -106,7 +121,7 @@ question_answer_pairs = model.generate_qa("a noviembre , que es también la esta
 ```python
 from transformers import pipeline
 
-pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-multitask")
+pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-qg-ae")
 
 # answer extraction
 answer = pipe("generate question: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")
@@ -119,7 +134,7 @@ question = pipe("extract answers: <hl> En la diáspora somalí, múltiples event
 ## Evaluation
 
 
-- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-multitask/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) 
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) 
 
 |            |   Score | Type    | Dataset                                                          |
 |:-----------|--------:|:--------|:-----------------------------------------------------------------|
@@ -133,7 +148,7 @@ question = pipe("extract answers: <hl> En la diáspora somalí, múltiples event
 | ROUGE_L    |   24.82 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
 
 
-- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-multitask/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json)
+- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json)
 
 |                                 |   Score | Type    | Dataset                                                          |
 |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
@@ -145,7 +160,7 @@ question = pipe("extract answers: <hl> En la diáspora somalí, múltiples event
 | QAAlignedRecall (MoverScore)    |   56.56 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
 
 
-- ***Metric (Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-multitask/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_esquad.default.json)
+- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_esquad.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-esquad-multitask/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/trainer_config.json).
 
 ## Citation
 ```