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_subjqa
+pipeline_tag: text2text-generation
+tags:
+- question generation
+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" 
+model-index:
+- name: lmqg/t5-large-subjqa-electronics
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_subjqa
+      type: electronics
+      args: electronics
+    metrics:
+    - name: BLEU4
+      type: bleu4
+      value: 0.045708417859748086
+    - name: ROUGE-L
+      type: rouge-l
+      value: 0.30546950426152664
+    - name: METEOR
+      type: meteor
+      value: 0.2756037660672747
+    - name: BERTScore
+      type: bertscore
+      value: 0.9427364025296283
+    - name: MoverScore
+      type: moverscore
+      value: 0.6879662195179278
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_squad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4
+      type: bleu4
+      value: 0.20125600683609723
+    - name: ROUGE-L
+      type: rouge-l
+      value: 0.47296230639660625
+    - name: METEOR
+      type: meteor
+      value: 0.21608768222263117
+    - name: BERTScore
+      type: bertscore
+      value: 0.9052001435177482
+    - name: MoverScore
+      type: moverscore
+      value: 0.6200186297442831
+---
+
+# Language Models Fine-tuning on Question Generation: `lmqg/t5-large-subjqa-electronics`
+This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the 
+[lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics).
+This model is continuously fine-tuned with [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad).
+
+### Overview
+- **Language model:** [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad)   
+- **Language:** en  
+- **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (electronics)
+- **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/t5-large-subjqa-electronics'
+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)
+```
+
+## Evaluation Metrics
+
+
+### Metrics
+
+| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
+|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
+| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 0.045708417859748086 | 0.30546950426152664 | 0.2756037660672747 | 0.9427364025296283 | 0.6879662195179278 | [link](https://huggingface.co/lmqg/t5-large-subjqa-electronics/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | 
+
+
+
+### Out-of-domain Metrics
+        
+| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
+|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
+| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.20125600683609723 | 0.47296230639660625 | 0.21608768222263117 | 0.9052001435177482 | 0.6200186297442831 | [link](https://huggingface.co/lmqg/t5-large-subjqa-electronics/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) |
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_subjqa
+ - dataset_name: electronics
+ - input_types: ['paragraph_answer']
+ - output_types: ['question']
+ - prefix_types: ['qg']
+ - model: lmqg/t5-large-squad
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 3
+ - batch: 16
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 8
+ - label_smoothing: 0.0
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-subjqa-electronics/raw/main/trainer_config.json).
+
+## Citation
+TBA