commit files to HF hub

README.md

@@ -33,27 +33,27 @@ model-index:
     metrics:
     - name: BLEU4 (Question Generation)
       type: bleu4_question_generation
-      value: 4.0
+      value: 7.1
     - name: ROUGE-L (Question Generation)
       type: rouge_l_question_generation
-      value: 15.25
+      value: 26.69
     - name: METEOR (Question Generation)
       type: meteor_question_generation
-      value: 10.2
+      value: 15.72
     - name: BERTScore (Question Generation)
       type: bertscore_question_generation
-      value: 70.25
+      value: 79.0
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
-      value: 50.2
+      value: 55.18
 ---
 
 # Model Card of `vocabtrimmer/mt5-small-trimmed-fr-90000-frquad-qg`
-This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-90000) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+This model is fine-tuned version of [ckpts/mt5-small-trimmed-fr-90000](https://huggingface.co/ckpts/mt5-small-trimmed-fr-90000) for question generation task 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
-- **Language model:** [vocabtrimmer/mt5-small-trimmed-fr-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-90000)   
+- **Language model:** [ckpts/mt5-small-trimmed-fr-90000](https://huggingface.co/ckpts/mt5-small-trimmed-fr-90000)   
 - **Language:** fr  
 - **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default)
 - **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
@@ -89,14 +89,14 @@ output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême
 
 |            |   Score | Type    | Dataset                                                          |
 |:-----------|--------:|:--------|:-----------------------------------------------------------------|
-| BERTScore  |   70.25 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_1     |   16.27 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_2     |    9.5  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_3     |    6.07 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_4     |    4    | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| METEOR     |   10.2  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| MoverScore |   50.2  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| ROUGE_L    |   15.25 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| BERTScore  |   79    | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_1     |   27.02 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_2     |   15.4  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_3     |   10.21 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_4     |    7.1  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| METEOR     |   15.72 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| MoverScore |   55.18 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| ROUGE_L    |   26.69 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
 
 
 
@@ -108,12 +108,12 @@ The following hyperparameters were used during fine-tuning:
  - input_types: paragraph_answer
  - output_types: question
  - prefix_types: None
- - model: vocabtrimmer/mt5-small-trimmed-fr-90000
+ - model: ckpts/mt5-small-trimmed-fr-90000
  - max_length: 512
  - max_length_output: 32
- - epoch: 15
+ - epoch: 13
  - batch: 16
- - lr: 0.0005
+ - lr: 0.001
  - fp16: False
  - random_seed: 1
  - gradient_accumulation_steps: 4

eval/metric.first.answer.paragraph_answer.question.lmqg_qg_frquad.default.json

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.15844031547856238, "Bleu_2": 0.09241141858929372, "Bleu_3": 0.05904239820282034, "Bleu_4": 0.03822901721151452}, "test": {"Bleu_1": 0.1610889853794724, "Bleu_2": 0.09400881296452392, "Bleu_3": 0.059994060808414636, "Bleu_4": 0.039535045257377645}}
+{"validation": {"Bleu_1": 0.27194073902600563, "Bleu_2": 0.14896798316369608, "Bleu_3": 0.09799160106047569, "Bleu_4": 0.06776030648066865}, "test": {"Bleu_1": 0.26865804461318615, "Bleu_2": 0.15292376417129053, "Bleu_3": 0.10115797365122045, "Bleu_4": 0.07027613455414455}}

eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.15992288580673075, "Bleu_2": 0.09327381723108208, "Bleu_3": 0.05955655132495215, "Bleu_4": 0.03853502323406688, "METEOR": 0.09475159659929087, "ROUGE_L": 0.14609194882888013, "BERTScore": 0.6927544384103048, "MoverScore": 0.4993774354052682}, "test": {"Bleu_1": 0.16269895557115005, "Bleu_2": 0.09503009899621674, "Bleu_3": 0.06070472020206799, "Bleu_4": 0.04001954266553894, "METEOR": 0.10198954300292216, "ROUGE_L": 0.15250461385855024, "BERTScore": 0.7025219590436294, "MoverScore": 0.5020140289434077}}
+{"validation": {"Bleu_1": 0.2738904556924366, "Bleu_2": 0.1504603411168633, "Bleu_3": 0.0992515904990098, "Bleu_4": 0.06880046501310207, "METEOR": 0.14769747960785995, "ROUGE_L": 0.2830454361222303, "BERTScore": 0.7790486432401258, "MoverScore": 0.5469365639008806}, "test": {"Bleu_1": 0.27019839207653623, "Bleu_2": 0.15402706392294171, "Bleu_3": 0.1020903970198885, "Bleu_4": 0.07097220930248134, "METEOR": 0.15716465876295369, "ROUGE_L": 0.26689498813211016, "BERTScore": 0.7899962979034313, "MoverScore": 0.5517562897710804}}