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mbart-large-cc25-itquad-qg

Text2Text Generation
Transformers
PyTorch
Italian
mbart
question generation
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@@ -33,62 +33,43 @@ model-index:
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  metrics:
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  - name: BLEU4
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  type: bleu4
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- value: 0.07130659184548822
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  - name: ROUGE-L
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  type: rouge-l
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- value: 0.21690703343943712
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  - name: METEOR
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  type: meteor
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- value: 0.17974779339788577
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  - name: BERTScore
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  type: bertscore
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- value: 0.8063210049660572
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  - name: MoverScore
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  type: moverscore
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- value: 0.5683736599719899
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- - name: QAAlignedF1Score (BERTScore)
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- type: qa_aligned_f1_score_bertscore
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- value: 0.8755552164192572
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- - name: QAAlignedRecall (BERTScore)
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- type: qa_aligned_recall_bertscore
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- value: 0.8750160842887897
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- - name: QAAlignedPrecision (BERTScore)
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- type: qa_aligned_precision_bertscore
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- value: 0.8761578365781484
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- - name: QAAlignedF1Score (MoverScore)
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- type: qa_aligned_f1_score_moverscore
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- value: 0.6170630397934463
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- - name: QAAlignedRecall (MoverScore)
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- type: qa_aligned_recall_moverscore
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- value: 0.6158706843217182
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- - name: QAAlignedPrecision (MoverScore)
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- type: qa_aligned_precision_moverscore
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- value: 0.6183209397381646
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  ---
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  # Model Card of `lmqg/mbart-large-cc25-itquad`
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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
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- [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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- Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).
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-
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- ```
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-
78
- @inproceedings{ushio-etal-2022-generative,
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- title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
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- author = "Ushio, Asahi and
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- Alva-Manchego, Fernando and
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- Camacho-Collados, Jose",
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- booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
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- month = dec,
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- year = "2022",
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- address = "Abu Dhabi, U.A.E.",
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- publisher = "Association for Computational Linguistics",
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- }
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-
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- ```
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-
92
  ### Overview
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  - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
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  - **Language:** it
@@ -100,42 +81,52 @@ Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](h
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  ### Usage
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  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
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  ```python
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-
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  from lmqg import TransformersQG
 
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  # initialize model
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- model = TransformersQG(language='it', model='lmqg/mbart-large-cc25-itquad')
 
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  # model prediction
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- question = model.generate_q(list_context=["Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."], list_answer=["Dopo il 1971"])
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  ```
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  - With `transformers`
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  ```python
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-
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  from transformers import pipeline
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- # initialize model
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- pipe = pipeline("text2text-generation", 'lmqg/mbart-large-cc25-itquad')
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- # question generation
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- question = pipe('<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.')
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  ```
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- ## Evaluation Metrics
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- ### Metrics
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- | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
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- |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
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- | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 0.071 | 0.217 | 0.18 | 0.806 | 0.568 | [link](https://huggingface.co/lmqg/mbart-large-cc25-itquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) |
 
 
 
 
 
 
 
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- ### Metrics (QAG)
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- | Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
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- |:--------|:-----|--------------------------------:|---------------------------------:|-----:|
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- | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 0.876 | 0.617 | [link](https://huggingface.co/lmqg/mbart-large-cc25-itquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json) |
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-
 
 
 
 
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@@ -162,7 +153,6 @@ The full configuration can be found at [fine-tuning config file](https://hugging
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  ## Citation
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  ```
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-
166
  @inproceedings{ushio-etal-2022-generative,
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  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
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  author = "Ushio, Asahi and
 
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  metrics:
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  - name: BLEU4
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  type: bleu4
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+ value: 7.13
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  - name: ROUGE-L
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  type: rouge-l
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+ value: 21.69
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  - name: METEOR
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  type: meteor
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+ value: 17.97
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  - name: BERTScore
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  type: bertscore
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+ value: 80.63
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  - name: MoverScore
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  type: moverscore
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+ value: 56.84
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+ - name: QAAlignedF1Score (BERTScore) [Gold Answer]
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+ type: qa_aligned_f1_score_bertscore_gold_answer
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+ value: 87.56
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+ - name: QAAlignedRecall (BERTScore) [Gold Answer]
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+ type: qa_aligned_recall_bertscore_gold_answer
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+ value: 87.5
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+ - name: QAAlignedPrecision (BERTScore) [Gold Answer]
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+ type: qa_aligned_precision_bertscore_gold_answer
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+ value: 87.62
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+ - name: QAAlignedF1Score (MoverScore) [Gold Answer]
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+ type: qa_aligned_f1_score_moverscore_gold_answer
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+ value: 61.71
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+ - name: QAAlignedRecall (MoverScore) [Gold Answer]
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+ type: qa_aligned_recall_moverscore_gold_answer
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+ value: 61.59
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+ - name: QAAlignedPrecision (MoverScore) [Gold Answer]
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+ type: qa_aligned_precision_moverscore_gold_answer
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+ value: 61.83
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  ---
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  # Model Card of `lmqg/mbart-large-cc25-itquad`
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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_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
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  ### Overview
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  - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
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  - **Language:** it
 
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  ### Usage
82
  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
83
  ```python
 
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  from lmqg import TransformersQG
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+
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  # initialize model
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+ model = TransformersQG(language="it", model="lmqg/mbart-large-cc25-itquad")
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+
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  # model prediction
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+ questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")
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92
  ```
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  - With `transformers`
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  ```python
 
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  from transformers import pipeline
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+
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+ pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-itquad")
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+ output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
 
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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-itquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json)
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+ | | Score | Type | Dataset |
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+ |:-----------|--------:|:--------|:-----------------------------------------------------------------|
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+ | BERTScore | 80.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | Bleu_1 | 22.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | Bleu_2 | 14.62 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | Bleu_3 | 10.06 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | Bleu_4 | 7.13 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | METEOR | 17.97 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | MoverScore | 56.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | ROUGE_L | 21.69 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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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-itquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json)
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+ | | Score | Type | Dataset |
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+ |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
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+ | QAAlignedF1Score (BERTScore) | 87.56 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | QAAlignedF1Score (MoverScore) | 61.71 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | QAAlignedPrecision (BERTScore) | 87.62 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | QAAlignedPrecision (MoverScore) | 61.83 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | QAAlignedRecall (BERTScore) | 87.5 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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+ | QAAlignedRecall (MoverScore) | 61.59 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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154
  ## Citation
155
  ```
 
156
  @inproceedings{ushio-etal-2022-generative,
157
  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
158
  author = "Ushio, Asahi and