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: 9.66
     - name: ROUGE-L (Question Generation)
       type: rouge_l_question_generation
-      value: 0.23
+      value: 24.04
     - name: METEOR (Question Generation)
       type: meteor_question_generation
-      value: 0.18
+      value: 22.0
     - name: BERTScore (Question Generation)
       type: bertscore_question_generation
-      value: 54.4
+      value: 84.29
     - name: MoverScore (Question Generation)
       type: moverscore_question_generation
-      value: 45.96
+      value: 58.96
 ---
 
 # Model Card of `vocabtrimmer/mt5-small-trimmed-es-30000-esquad-qg`
-This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es-30000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-30000) 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-30000](https://huggingface.co/ckpts/mt5-small-trimmed-es-30000) 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-30000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-30000)   
+- **Language model:** [ckpts/mt5-small-trimmed-es-30000](https://huggingface.co/ckpts/mt5-small-trimmed-es-30000)   
 - **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  |   54.4  | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| Bleu_1     |    0.26 | 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     |    0.18 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| MoverScore |   45.96 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
-| ROUGE_L    |    0.23 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| BERTScore  |   84.29 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_1     |   26.19 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_2     |   17.87 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_3     |   12.95 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| Bleu_4     |    9.66 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| METEOR     |   22    | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| MoverScore |   58.96 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
+| ROUGE_L    |   24.04 | 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-30000
+ - model: ckpts/mt5-small-trimmed-es-30000
  - max_length: 512
  - max_length_output: 32
- - epoch: 11
+ - epoch: 13
  - 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": 0.0022772832902560156, "Bleu_2": 0.00014922086062068844, "Bleu_3": 6.258916877818623e-10, "Bleu_4": 1.325672634974165e-12}, "test": {"Bleu_1": 0.00254662548151177, "Bleu_2": 5.030859451368363e-12, "Bleu_3": 6.5678638411482504e-15, "Bleu_4": 2.4542474154620404e-16}}
+{"validation": {"Bleu_1": 0.2549935193689303, "Bleu_2": 0.1723008425778341, "Bleu_3": 0.12450168065967501, "Bleu_4": 0.09266094141629261}, "test": {"Bleu_1": 0.260916337471781, "Bleu_2": 0.1780571157288881, "Bleu_3": 0.12902455010771263, "Bleu_4": 0.09622526338863215}}

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.002495319845164582, "Bleu_2": 0.00016478099316277053, "Bleu_3": 6.929302595420039e-10, "Bleu_4": 1.4695156772976164e-12, "METEOR": 0.001711547515456343, "ROUGE_L": 0.0023221307049144245, "BERTScore": 0.5444269373239002, "MoverScore": 0.4595416350448968}, "test": {"Bleu_1": 0.002558096867100937, "Bleu_2": 5.071278720695698e-12, "Bleu_3": 6.6283013271438106e-15, "Bleu_4": 2.478251564941927e-16, "METEOR": 0.0017628498326727486, "ROUGE_L": 0.0023115855500753634, "BERTScore": 0.5439668217939481, "MoverScore": 0.4595771150647586}}
+{"validation": {"Bleu_1": 0.26718637244103205, "Bleu_2": 0.18191503683544474, "Bleu_3": 0.13204136075997108, "Bleu_4": 0.09860574636977182, "METEOR": 0.21577954466612026, "ROUGE_L": 0.24200221541069528, "BERTScore": 0.8366031872464991, "MoverScore": 0.5820990415358654}, "test": {"Bleu_1": 0.2619047619047596, "Bleu_2": 0.1787355305399858, "Bleu_3": 0.1295234230818207, "Bleu_4": 0.09661683256163012, "METEOR": 0.21999782816812127, "ROUGE_L": 0.24039967932044726, "BERTScore": 0.8428789291484892, "MoverScore": 0.5895832000461911}}