commit files to HF hub

README.md

@@ -33,27 +33,27 @@ model-index:
     metrics:
     - name: BLEU4 (Question Generation)
       type: bleu4_question_generation
-      value: 11.06
+      value: 10.61
     - name: ROUGE-L (Question Generation)
       type: rouge_l_question_generation
-      value: 26.63
+      value: 26.37
     - name: METEOR (Question Generation)
       type: meteor_question_generation
-      value: 28.43
+      value: 28.36
     - name: BERTScore (Question Generation)
       type: bertscore_question_generation
-      value: 83.37
+      value: 83.14
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
-      value: 82.77
+      value: 82.55
 ---
 
 # Model Card of `vocabtrimmer/mt5-small-trimmed-ko-30000-koquad-qg`
-This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ko-30000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-30000) 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).
+This model is fine-tuned version of [ckpts/mt5-small-trimmed-ko-30000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-30000) 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).
 
 
 ### Overview
-- **Language model:** [vocabtrimmer/mt5-small-trimmed-ko-30000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-30000)   
+- **Language model:** [ckpts/mt5-small-trimmed-ko-30000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-30000)   
 - **Language:** ko  
 - **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (default)
 - **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
@@ -89,14 +89,14 @@ output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영
 
 |            |   Score | Type    | Dataset                                                          |
 |:-----------|--------:|:--------|:-----------------------------------------------------------------|
-| BERTScore  |   83.37 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_1     |   26.29 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_2     |   19.32 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_3     |   14.55 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_4     |   11.06 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| METEOR     |   28.43 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| MoverScore |   82.77 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| ROUGE_L    |   26.63 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| BERTScore  |   83.14 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_1     |   25.72 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_2     |   18.86 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_3     |   14.11 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_4     |   10.61 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| METEOR     |   28.36 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| MoverScore |   82.55 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| ROUGE_L    |   26.37 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
 
 
 
@@ -108,7 +108,7 @@ The following hyperparameters were used during fine-tuning:
  - input_types: paragraph_answer
  - output_types: question
  - prefix_types: None
- - model: vocabtrimmer/mt5-small-trimmed-ko-30000
+ - model: ckpts/mt5-small-trimmed-ko-30000
  - max_length: 512
  - max_length_output: 32
  - epoch: 13

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.24634663811392846, "Bleu_2": 0.18003534403956303, "Bleu_3": 0.1348033557729109, "Bleu_4": 0.10201480180051613}, "test": {"Bleu_1": 0.2598042196145141, "Bleu_2": 0.19067691304485115, "Bleu_3": 0.14356310518792076, "Bleu_4": 0.1090772998674291}}
+{"validation": {"Bleu_1": 0.24730736546708076, "Bleu_2": 0.1805983914991622, "Bleu_3": 0.1351955480215265, "Bleu_4": 0.10231659467122803}, "test": {"Bleu_1": 0.25455346945317786, "Bleu_2": 0.1863540355869153, "Bleu_3": 0.13936333895222536, "Bleu_4": 0.10474075691207706}}

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.2784717119764806, "Bleu_2": 0.20744038499162087, "Bleu_3": 0.15758641388301386, "Bleu_4": 0.12055575253418176, "METEOR": 0.2887190368131312, "ROUGE_L": 0.27546102653085597, "BERTScore": 0.8251674834931064, "MoverScore": 0.8308802744040831}, "test": {"Bleu_1": 0.2628896009321239, "Bleu_2": 0.1931686358632444, "Bleu_3": 0.1455376745882983, "Bleu_4": 0.11059271173945821, "METEOR": 0.2843089201744023, "ROUGE_L": 0.26627586085126886, "BERTScore": 0.8337146293705029, "MoverScore": 0.8276612285261664}}
+{"validation": {"Bleu_1": 0.28085530108484147, "Bleu_2": 0.20911529775293766, "Bleu_3": 0.15882842796765106, "Bleu_4": 0.12147030166216247, "METEOR": 0.2915895823700915, "ROUGE_L": 0.2777341588464828, "BERTScore": 0.8262767547406025, "MoverScore": 0.8290985232230139}, "test": {"Bleu_1": 0.2572168231595764, "Bleu_2": 0.188564208617832, "Bleu_3": 0.1411121095117522, "Bleu_4": 0.10605364538839222, "METEOR": 0.2835661755929325, "ROUGE_L": 0.2636899241048791, "BERTScore": 0.8313739765866779, "MoverScore": 0.825547462170264}}