commit files to HF hub

README.md

@@ -46,6 +46,42 @@ model-index:
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
       value: 57.96
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
+      type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
+      value: 81.27
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
+      type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
+      value: 81.25
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
+      type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
+      value: 81.29
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
+      type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
+      value: 55.61
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
+      type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
+      value: 55.6
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
+      type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
+      value: 55.61
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
+      type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
+      value: 75.55
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
+      type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
+      value: 77.16
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
+      type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
+      value: 74.04
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
+      type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
+      value: 51.75
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
+      type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
+      value: 52.52
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
+      type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
+      value: 51.03
 ---
 
 # Model Card of `lmqg/mbart-large-cc25-frquad-qg`
@@ -99,24 +135,48 @@ output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême
 | ROUGE_L    |   30.62 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
 
 
+- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json)
+
+|                                 |   Score | Type    | Dataset                                                          |
+|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   81.27 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedF1Score (MoverScore)   |   55.61 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedPrecision (BERTScore)  |   81.29 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedPrecision (MoverScore) |   55.61 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedRecall (BERTScore)     |   81.25 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedRecall (MoverScore)    |   55.6  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+
+
+- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mbart-large-cc25-frquad-ae`](https://huggingface.co/lmqg/mbart-large-cc25-frquad-ae). [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.lmqg_mbart-large-cc25-frquad-ae.json)
+
+|                                 |   Score | Type    | Dataset                                                          |
+|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   75.55 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedF1Score (MoverScore)   |   51.75 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedPrecision (BERTScore)  |   74.04 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedPrecision (MoverScore) |   51.03 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedRecall (BERTScore)     |   77.16 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| QAAlignedRecall (MoverScore)    |   52.52 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+
+
 
 ## Training hyperparameters
 
 The following hyperparameters were used during fine-tuning:
  - dataset_path: lmqg/qg_frquad
  - dataset_name: default
- - input_types: paragraph_answer
- - output_types: question
+ - input_types: ['paragraph_answer']
+ - output_types: ['question']
  - prefix_types: None
  - model: facebook/mbart-large-cc25
  - max_length: 512
  - max_length_output: 32
- - epoch: 7
- - batch: 16
- - lr: 0.0002
+ - epoch: 8
+ - batch: 4
+ - lr: 0.001
  - fp16: False
  - random_seed: 1
- - gradient_accumulation_steps: 4
+ - 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-frquad-qg/raw/main/trainer_config.json).