model update
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README.md
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- text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
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example_title: "Question Generation Example 3"
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model-index:
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- name: lmqg/t5-small-squadshifts-amazon
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results:
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- task:
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name: Text2text Generation
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value: 62.11
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---
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# Model Card of `lmqg/t5-small-squadshifts-amazon`
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This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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### Overview
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- **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad)
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="en", model="lmqg/t5-small-squadshifts-amazon")
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# model prediction
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questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
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```python
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from transformers import pipeline
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pipe = pipeline("text2text-generation", "lmqg/t5-small-squadshifts-amazon")
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output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
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```
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## Evaluation
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squadshifts-amazon/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json)
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| | Score | Type | Dataset |
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|:-----------|--------:|:-------|:---------------------------------------------------------------------------|
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- gradient_accumulation_steps: 2
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- label_smoothing: 0.15
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squadshifts-amazon/raw/main/trainer_config.json).
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## Citation
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```
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- text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
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example_title: "Question Generation Example 3"
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model-index:
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- name: lmqg/t5-small-squadshifts-amazon-qg
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results:
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- task:
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name: Text2text Generation
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value: 62.11
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---
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# Model Card of `lmqg/t5-small-squadshifts-amazon-qg`
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This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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### Overview
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- **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad)
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="en", model="lmqg/t5-small-squadshifts-amazon-qg")
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# model prediction
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questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
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```python
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from transformers import pipeline
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pipe = pipeline("text2text-generation", "lmqg/t5-small-squadshifts-amazon-qg")
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output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
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```
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## Evaluation
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squadshifts-amazon-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json)
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| | Score | Type | Dataset |
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|:-----------|--------:|:-------|:---------------------------------------------------------------------------|
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- gradient_accumulation_steps: 2
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- label_smoothing: 0.15
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squadshifts-amazon-qg/raw/main/trainer_config.json).
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## Citation
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```
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