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: 9.79
     - name: ROUGE-L (Question Generation)
       type: rouge_l_question_generation
-      value: 0.48
+      value: 24.63
     - name: METEOR (Question Generation)
       type: meteor_question_generation
-      value: 0.3
+      value: 22.64
     - name: BERTScore (Question Generation)
       type: bertscore_question_generation
-      value: 54.63
+      value: 84.4
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
-      value: 46.03
+      value: 59.16
 ---
 
 # Model Card of `vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg`
-This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-60000) 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-60000](https://huggingface.co/ckpts/mt5-small-trimmed-es-60000) 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-60000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-60000)   
+- **Language model:** [ckpts/mt5-small-trimmed-es-60000](https://huggingface.co/ckpts/mt5-small-trimmed-es-60000)   
 - **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  |   54.63 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| Bleu_1     |    0.48 | 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     |    0.3  | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| MoverScore |   46.03 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| ROUGE_L    |    0.48 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| BERTScore  |   84.4  | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_1     |   26.37 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_2     |   18.03 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_3     |   13.1  | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_4     |    9.79 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| METEOR     |   22.64 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| MoverScore |   59.16 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| ROUGE_L    |   24.63 | 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-60000
+ - model: ckpts/mt5-small-trimmed-es-60000
  - max_length: 512
  - max_length_output: 32
- - epoch: 19
+ - 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_esquad.default.json

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.005062295838520224, "Bleu_2": 7.018562334906737e-12, "Bleu_3": 8.198677358044067e-15, "Bleu_4": 2.9174613957367305e-16}, "test": {"Bleu_1": 0.004771875671817127, "Bleu_2": 6.8698509302877965e-12, "Bleu_3": 8.13689220979456e-15, "Bleu_4": 2.919479695553252e-16}}
+{"validation": {"Bleu_1": 0.2554377527598623, "Bleu_2": 0.17237832980640722, "Bleu_3": 0.12436667953630834, "Bleu_4": 0.09255044167384886}, "test": {"Bleu_1": 0.26286916208790984, "Bleu_2": 0.17984162114940028, "Bleu_3": 0.13071688721787858, "Bleu_4": 0.09767878303878488}}

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.005370648033099662, "Bleu_2": 7.644387822553005e-12, "Bleu_3": 9.009082559787587e-15, "Bleu_4": 3.2202868038390934e-16, "METEOR": 0.0032039785468518326, "ROUGE_L": 0.0052877730360309716, "BERTScore": 0.5452270943339385, "MoverScore": 0.46003995041571005}, "test": {"Bleu_1": 0.004838971590995159, "Bleu_2": 6.96004428083701e-12, "Bleu_3": 8.241146396516722e-15, "Bleu_4": 2.95639977650681e-16, "METEOR": 0.003045434707024445, "ROUGE_L": 0.004843559212404214, "BERTScore": 0.5462537683069882, "MoverScore": 0.4602740629844674}}
+{"validation": {"Bleu_1": 0.26697593990554214, "Bleu_2": 0.18154025610125302, "Bleu_3": 0.1316568284777279, "Bleu_4": 0.09832707262376704, "METEOR": 0.21939909127977714, "ROUGE_L": 0.24474327316599234, "BERTScore": 0.836559349684048, "MoverScore": 0.5824001526468208}, "test": {"Bleu_1": 0.26365356572460014, "Bleu_2": 0.18033041935338742, "Bleu_3": 0.13101550910348272, "Bleu_4": 0.0978892101003055, "METEOR": 0.22636782562891164, "ROUGE_L": 0.24629345745612669, "BERTScore": 0.8440404895889356, "MoverScore": 0.5916298140098185}}