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: 9.24
     - name: ROUGE-L (Question Generation)
       type: rouge_l_question_generation
-      value: 0.3
+      value: 24.28
     - name: METEOR (Question Generation)
       type: meteor_question_generation
-      value: 0.21
+      value: 22.71
     - name: BERTScore (Question Generation)
       type: bertscore_question_generation
-      value: 54.53
+      value: 83.74
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
-      value: 45.77
+      value: 58.83
 ---
 
 # Model Card of `vocabtrimmer/mt5-small-trimmed-es-15000-esquad-qg`
-This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es-15000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-15000) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+This model is fine-tuned version of [ckpts/mt5-small-trimmed-es-15000](https://huggingface.co/ckpts/mt5-small-trimmed-es-15000) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
 
 ### Overview
-- **Language model:** [vocabtrimmer/mt5-small-trimmed-es-15000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-15000)   
+- **Language model:** [ckpts/mt5-small-trimmed-es-15000](https://huggingface.co/ckpts/mt5-small-trimmed-es-15000)   
 - **Language:** es  
 - **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default)
 - **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
@@ -89,14 +89,14 @@ output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la In
 
 |            |   Score | Type    | Dataset                                                          |
 |:-----------|--------:|:--------|:-----------------------------------------------------------------|
-| BERTScore  |   54.53 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| Bleu_1     |    0.33 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| Bleu_2     |    0    | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| Bleu_3     |    0    | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| Bleu_4     |    0    | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| METEOR     |    0.21 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| MoverScore |   45.77 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| ROUGE_L    |    0.3  | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| BERTScore  |   83.74 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_1     |   25.39 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_2     |   17.23 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_3     |   12.44 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_4     |    9.24 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| METEOR     |   22.71 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| MoverScore |   58.83 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| ROUGE_L    |   24.28 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
 
 
 
@@ -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-es-15000
+ - model: ckpts/mt5-small-trimmed-es-15000
  - max_length: 512
  - max_length_output: 32
- - epoch: 6
+ - epoch: 15
  - batch: 16
- - lr: 0.0001
+ - lr: 0.001
  - fp16: False
  - random_seed: 1
  - gradient_accumulation_steps: 4

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.003157515550463275, "Bleu_2": 0.0001752686606369473, "Bleu_3": 6.932714462902121e-10, "Bleu_4": 1.421284011338596e-12}, "test": {"Bleu_1": 0.0032772051494349926, "Bleu_2": 5.695177524987508e-12, "Bleu_3": 7.105851835792891e-15, "Bleu_4": 2.5894135464018913e-16}}
+{"validation": {"Bleu_1": 0.2497301303430144, "Bleu_2": 0.16826958181295876, "Bleu_3": 0.12138670463560199, "Bleu_4": 0.0902292391017187}, "test": {"Bleu_1": 0.253000590280053, "Bleu_2": 0.171669692368683, "Bleu_3": 0.1239449079890073, "Bleu_4": 0.09208676529861734}}

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.0033104822171441638, "Bleu_2": 0.000188840193588986, "Bleu_3": 7.537787115379621e-10, "Bleu_4": 1.5523808746468753e-12, "METEOR": 0.0021161052625144074, "ROUGE_L": 0.0029004889742446074, "BERTScore": 0.5445500628454133, "MoverScore": 0.4576946960574479}, "test": {"Bleu_1": 0.0032972054283237693, "Bleu_2": 5.745450701039421e-12, "Bleu_3": 7.174832424881181e-15, "Bleu_4": 2.6156143528256543e-16, "METEOR": 0.0020806262871325147, "ROUGE_L": 0.003000302073919095, "BERTScore": 0.5452606138221344, "MoverScore": 0.45772376161968675}}
+{"validation": {"Bleu_1": 0.2620904418588027, "Bleu_2": 0.1779458135621885, "Bleu_3": 0.12907229534812506, "Bleu_4": 0.09634916501545024, "METEOR": 0.22536721225409945, "ROUGE_L": 0.24398952418056397, "BERTScore": 0.8336677030780022, "MoverScore": 0.5838149106598906}, "test": {"Bleu_1": 0.2538653490627803, "Bleu_2": 0.17228039320996297, "Bleu_3": 0.12438517620698875, "Bleu_4": 0.09242972232941353, "METEOR": 0.2271249512493336, "ROUGE_L": 0.24282558125268286, "BERTScore": 0.8373773679731452, "MoverScore": 0.5882745608417501}}