model update
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README.md
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- text: "contre <hl> Normie Smith <hl> et 15 000 dollars le 28 novembre 1938."
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example_title: "Question Generation Example 3"
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model-index:
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- name: lmqg/mbart-large-cc25-frquad
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results:
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- task:
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name: Text2text Generation
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value: 55.61
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---
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# Model Card of `lmqg/mbart-large-cc25-frquad`
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This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="fr", model="lmqg/mbart-large-cc25-frquad")
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# model prediction
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questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="le Suprême Berger")
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```python
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from transformers import pipeline
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pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-frquad")
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output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
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```
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## Evaluation
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json)
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| | Score | Type | Dataset |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------|
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| ROUGE_L | 16.4 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json)
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| | Score | Type | Dataset |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
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- gradient_accumulation_steps: 16
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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/mbart-large-cc25-frquad/raw/main/trainer_config.json).
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## Citation
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```
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- text: "contre <hl> Normie Smith <hl> et 15 000 dollars le 28 novembre 1938."
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example_title: "Question Generation Example 3"
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model-index:
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- name: lmqg/mbart-large-cc25-frquad-qg
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results:
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- task:
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name: Text2text Generation
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value: 55.61
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---
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# Model Card of `lmqg/mbart-large-cc25-frquad-qg`
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This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="fr", model="lmqg/mbart-large-cc25-frquad-qg")
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# model prediction
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questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="le Suprême Berger")
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```python
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from transformers import pipeline
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pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-frquad-qg")
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output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
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```
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## Evaluation
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json)
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| | Score | Type | Dataset |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------|
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| ROUGE_L | 16.4 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json)
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| | Score | Type | Dataset |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
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- gradient_accumulation_steps: 16
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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/mbart-large-cc25-frquad-qg/raw/main/trainer_config.json).
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## Citation
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```
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