commit files to HF hub

README.md

@@ -33,27 +33,27 @@ model-index:
     metrics:
     - name: BLEU4 (Question Generation)
       type: bleu4_question_generation
-      value: 0.0
+      value: 11.1
     - name: ROUGE-L (Question Generation)
       type: rouge_l_question_generation
-      value: 1.14
+      value: 26.7
     - name: METEOR (Question Generation)
       type: meteor_question_generation
-      value: 8.03
+      value: 28.4
     - name: BERTScore (Question Generation)
       type: bertscore_question_generation
-      value: 59.66
+      value: 83.43
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
-      value: 55.14
+      value: 82.96
 ---
 
 # Model Card of `vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg`
-This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ko-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-60000) 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-60000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-60000) 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-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-60000)   
+- **Language model:** [ckpts/mt5-small-trimmed-ko-60000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-60000)   
 - **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  |   59.66 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_1     |    1.05 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_2     |    0.43 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_3     |    0.18 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| Bleu_4     |    0    | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| METEOR     |    8.03 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| MoverScore |   55.14 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
-| ROUGE_L    |    1.14 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| BERTScore  |   83.43 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_1     |   26.36 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_2     |   19.38 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_3     |   14.59 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_4     |   11.1  | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| METEOR     |   28.4  | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| MoverScore |   82.96 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| ROUGE_L    |   26.7  | 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-60000
+ - model: ckpts/mt5-small-trimmed-ko-60000
  - max_length: 512
  - max_length_output: 32
  - epoch: 12

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.009961993738175199, "Bleu_2": 0.0039379734207070196, "Bleu_3": 0.0017267170858986116, "Bleu_4": 1.0495181546672459e-07}, "test": {"Bleu_1": 0.01031192723410798, "Bleu_2": 0.004199955042261326, "Bleu_3": 0.0017808728754001065, "Bleu_4": 1.0827942450770742e-07}}
+{"validation": {"Bleu_1": 0.2469589305070407, "Bleu_2": 0.17929096175700093, "Bleu_3": 0.13334615487740914, "Bleu_4": 0.10031671449173753}, "test": {"Bleu_1": 0.2610083483385116, "Bleu_2": 0.1916503161097078, "Bleu_3": 0.1442590601750084, "Bleu_4": 0.10976938811488973}}

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.01130355918331207, "Bleu_2": 0.004645214002095576, "Bleu_3": 0.002114598758116934, "Bleu_4": 1.3093675976832167e-07, "METEOR": 0.08186692775854124, "ROUGE_L": 0.012802292147079234, "BERTScore": 0.5947417464164693, "MoverScore": 0.5512115121359509}, "test": {"Bleu_1": 0.01050117655322568, "Bleu_2": 0.004266972959066354, "Bleu_3": 0.00181446225793093, "Bleu_4": 1.1047087368903643e-07, "METEOR": 0.08034635999969227, "ROUGE_L": 0.011407772985091977, "BERTScore": 0.5965719539094829, "MoverScore": 0.551425865352046}}
+{"validation": {"Bleu_1": 0.27926709622675794, "Bleu_2": 0.20670723853390727, "Bleu_3": 0.15605306781087946, "Bleu_4": 0.11861208111415848, "METEOR": 0.28907438520972223, "ROUGE_L": 0.27483975174548364, "BERTScore": 0.8266659817641868, "MoverScore": 0.8304209016792409}, "test": {"Bleu_1": 0.26363311845116305, "Bleu_2": 0.19381936568005612, "Bleu_3": 0.1459009993513916, "Bleu_4": 0.1109524699536712, "METEOR": 0.28396979148540924, "ROUGE_L": 0.2669957671152088, "BERTScore": 0.8343427279664819, "MoverScore": 0.8296449373494392}}