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: 6.05
     - name: ROUGE-L (Question Generation)
       type: rouge_l_question_generation
-      value: 0.12
+      value: 25.14
     - name: METEOR (Question Generation)
       type: meteor_question_generation
-      value: 0.01
+      value: 14.64
     - name: BERTScore (Question Generation)
       type: bertscore_question_generation
-      value: 63.27
+      value: 78.03
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
-      value: 44.99
+      value: 54.29
 ---
 
 # Model Card of `vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qg`
-This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-60000) 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-60000](https://huggingface.co/ckpts/mt5-small-trimmed-fr-60000) 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-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-60000)   
+- **Language model:** [ckpts/mt5-small-trimmed-fr-60000](https://huggingface.co/ckpts/mt5-small-trimmed-fr-60000)   
 - **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  |   63.27 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_1     |    0    | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_2     |    0    | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_3     |    0    | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| Bleu_4     |    0    | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| METEOR     |    0.01 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| MoverScore |   44.99 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
-| ROUGE_L    |    0.12 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| BERTScore  |   78.03 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_1     |   25.16 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_2     |   13.62 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_3     |    8.85 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_4     |    6.05 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| METEOR     |   14.64 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| MoverScore |   54.29 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| ROUGE_L    |   25.14 | 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-60000
+ - model: ckpts/mt5-small-trimmed-fr-60000
  - max_length: 512
  - max_length_output: 32
- - epoch: 12
+ - epoch: 15
  - batch: 16
- - lr: 0.0001
+ - lr: 0.0005
  - 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": 3.640775730149816e-21, "Bleu_2": 5.148834415136274e-21, "Bleu_3": 8.505895224925895e-17, "Bleu_4": 1.0932653419045678e-14}, "test": {"Bleu_1": 2.9949672108421435e-21, "Bleu_2": 4.235523248435348e-21, "Bleu_3": 6.997101493110517e-17, "Bleu_4": 8.993396172797412e-15}}
+{"validation": {"Bleu_1": 0.2560221214971848, "Bleu_2": 0.1334999688749848, "Bleu_3": 0.08539753277404635, "Bleu_4": 0.0576964460765076}, "test": {"Bleu_1": 0.2504652561055845, "Bleu_2": 0.13536557523114698, "Bleu_3": 0.08786650993016741, "Bleu_4": 0.06007580753309587}}

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 3.7822024640188776e-21, "Bleu_2": 5.348842020256005e-21, "Bleu_3": 8.765525680516315e-17, "Bleu_4": 1.122114215171343e-14, "METEOR": 0.00016821571452022558, "ROUGE_L": 0.00013995829830148413, "BERTScore": 0.632249019771891, "MoverScore": 0.45022997918805396}, "test": {"Bleu_1": 3.0840474231986844e-21, "Bleu_2": 4.361501692889023e-21, "Bleu_3": 7.130618547236184e-17, "Bleu_4": 9.117437271169e-15, "METEOR": 5.2173408711299115e-05, "ROUGE_L": 0.0012095525450174294, "BERTScore": 0.6327488172401502, "MoverScore": 0.4498655172241212}}
+{"validation": {"Bleu_1": 0.2576310793047975, "Bleu_2": 0.1347648746601711, "Bleu_3": 0.0863781168057983, "Bleu_4": 0.05835483236081858, "METEOR": 0.13637530589619343, "ROUGE_L": 0.26853968786569693, "BERTScore": 0.768718460474192, "MoverScore": 0.5390085184058023}, "test": {"Bleu_1": 0.2516083338251711, "Bleu_2": 0.1362446692499852, "Bleu_3": 0.08854043563812196, "Bleu_4": 0.06049580671587121, "METEOR": 0.14636413895787062, "ROUGE_L": 0.25144462549993857, "BERTScore": 0.780288153153793, "MoverScore": 0.5429103204494686}}