model update
Browse files
README.md
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- text: "<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records . <hl>"
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example_title: "Question Generation Example 3"
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model-index:
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- name:
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results:
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- task:
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name: Text2text Generation
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value: 63.28
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---
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# Model Card of `
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This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="en", model="
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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", "
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output = pipe("<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>")
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```
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## Evaluation
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/
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| | Score | Type | Dataset |
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|:-----------|--------:|:--------|:---------------------------------------------------------------|
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- gradient_accumulation_steps: 4
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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/
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## Citation
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```
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- text: "<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records . <hl>"
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example_title: "Question Generation Example 3"
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model-index:
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- name: research-backup/bart-large-squad-qg-no-answer
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results:
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- task:
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name: Text2text Generation
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value: 63.28
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---
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# Model Card of `research-backup/bart-large-squad-qg-no-answer`
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This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="en", model="research-backup/bart-large-squad-qg-no-answer")
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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", "research-backup/bart-large-squad-qg-no-answer")
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output = pipe("<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>")
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```
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## Evaluation
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json)
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| | Score | Type | Dataset |
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|:-----------|--------:|:--------|:---------------------------------------------------------------|
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- gradient_accumulation_steps: 4
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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/research-backup/bart-large-squad-qg-no-answer/raw/main/trainer_config.json).
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## Citation
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```
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