model update

README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: es
+datasets:
+- lmqg/qg_esquad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+widget:
+- text: "generate question: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India."
+  example_title: "Question Generation Example 1" 
+- text: "generate question: a <hl> noviembre <hl> , que es también la estación lluviosa."
+  example_title: "Question Generation Example 2" 
+- text: "generate question: como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013."
+  example_title: "Question Generation Example 3" 
+model-index:
+- name: lmqg/mt5-base-esquad
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_esquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4
+      type: bleu4
+      value: 0.10153670508318442
+    - name: ROUGE-L
+      type: rouge-l
+      value: 0.25453014251607653
+    - name: METEOR
+      type: meteor
+      value: 0.23431011857989445
+    - name: BERTScore
+      type: bertscore
+      value: 0.8447369242462315
+    - name: MoverScore
+      type: moverscore
+      value: 0.596184026986908
+---
+
+# Language Models Fine-tuning on Question Generation: `lmqg/mt5-base-esquad`
+This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the 
+[lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default).
+
+
+### Overview
+- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
+- **Language:** es  
+- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default)
+- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [TBA](TBA)
+
+### Usage
+```python
+
+from transformers import pipeline
+
+model_path = 'lmqg/mt5-base-esquad'
+pipe = pipeline("text2text-generation", model_path)
+
+# Question Generation
+input_text = 'generate question: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.'
+question = pipe(input_text)
+```
+
+## Evaluation Metrics
+
+
+### Metrics
+
+| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
+|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
+| [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 0.10153670508318442 | 0.25453014251607653 | 0.23431011857989445 | 0.8447369242462315 | 0.596184026986908 | [link](https://huggingface.co/lmqg/mt5-base-esquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | 
+
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_esquad
+ - dataset_name: default
+ - input_types: ['paragraph_answer']
+ - output_types: ['question']
+ - prefix_types: None
+ - model: google/mt5-base
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 10
+ - batch: 4
+ - lr: 0.0005
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 16
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-esquad/raw/main/trainer_config.json).
+
+## Citation
+TBA

trainer_config.json

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-{"dataset_path": "asahi417/qg_esquad", "dataset_name": "default", "input_types": ["paragraph_answer"], "output_types": ["question"], "prefix_types": null, "model": "google/mt5-base", "max_length": 512, "max_length_output": 32, "epoch": 10, "batch": 4, "lr": 0.0005, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 16, "label_smoothing": 0.15}
+{"dataset_path": "lmqg/qg_esquad", "dataset_name": "default", "input_types": ["paragraph_answer"], "output_types": ["question"], "prefix_types": null, "model": "google/mt5-base", "max_length": 512, "max_length_output": 32, "epoch": 10, "batch": 4, "lr": 0.0005, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 16, "label_smoothing": 0.15}