model update

README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: en
+datasets:
+- lmqg/qg_dequad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+- answer extraction
+widget:
+- text: "generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
+  example_title: "Question Generation Example 1" 
+- text: "generate question: Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
+  example_title: "Question Generation Example 2" 
+- text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records <hl> ."
+  example_title: "Question Generation Example 3" 
+- text: "<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress."
+  example_title: "Answer Extraction Example 1" 
+- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. <hl>"
+  example_title: "Answer Extraction Example 2" 
+model-index:
+- name: lmqg/mt5-small-dequad-multitask
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_dequad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4
+      type: bleu4
+      value: 0.008153318257935705
+    - name: ROUGE-L
+      type: rouge-l
+      value: 0.10153326763371277
+    - name: METEOR
+      type: meteor
+      value: 0.12181097136639749
+    - name: BERTScore
+      type: bertscore
+      value: 0.8038890473051649
+    - name: MoverScore
+      type: moverscore
+      value: 0.551016955735025
+---
+
+# Language Models Fine-tuning on Question Generation: `lmqg/mt5-small-dequad-multitask`
+This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the 
+[lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default).
+This model is fine-tuned on the answer extraction task as well as the question generation.
+
+### Overview
+- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
+- **Language:** en  
+- **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (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-small-dequad-multitask'
+pipe = pipeline("text2text-generation", model_path)
+
+# Question Generation
+input_text = 'generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.'
+question = pipe(input_text)
+# Answer Extraction
+answer = pipe('extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.')
+```
+
+## Evaluation Metrics
+
+
+### Metrics
+
+| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
+|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
+| [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 0.008153318257935705 | 0.10153326763371277 | 0.12181097136639749 | 0.8038890473051649 | 0.551016955735025 | [link](https://huggingface.co/lmqg/mt5-small-dequad-multitask/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | 
+
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_dequad
+ - dataset_name: default
+ - input_types: ['paragraph_answer', 'paragraph_sentence']
+ - output_types: ['question', 'answer']
+ - prefix_types: ['qg', 'ae']
+ - model: google/mt5-small
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 15
+ - batch: 16
+ - lr: 0.001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 4
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-dequad-multitask/raw/main/trainer_config.json).
+
+## Citation
+TBA

trainer_config.json

@@ -1 +1 @@
-{"dataset_path": "asahi417/qg_dequad", "dataset_name": "default", "input_types": ["paragraph_answer", "paragraph_sentence"], "output_types": ["question", "answer"], "prefix_types": ["qg", "ae"], "model": "google/mt5-small", "max_length": 512, "max_length_output": 32, "epoch": 15, "batch": 16, "lr": 0.001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 4, "label_smoothing": 0.15}
+{"dataset_path": "lmqg/qg_dequad", "dataset_name": "default", "input_types": ["paragraph_answer", "paragraph_sentence"], "output_types": ["question", "answer"], "prefix_types": ["qg", "ae"], "model": "google/mt5-small", "max_length": 512, "max_length_output": 32, "epoch": 15, "batch": 16, "lr": 0.001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 4, "label_smoothing": 0.15}