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README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: it
+datasets:
+- lmqg/qg_itquad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+widget:
+- text: "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
+  example_title: "Question Generation Example 1" 
+- text: "L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa."
+  example_title: "Question Generation Example 2" 
+- text: "il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo."
+  example_title: "Question Generation Example 3" 
+model-index:
+- name: vocabtrimmer/mbart-large-cc25-trimmed-it-itquad-qg
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_itquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question Generation)
+      type: bleu4_question_generation
+      value: 7.4
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 22.57
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 18.94
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 81.06
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 57.38
+---
+
+# Model Card of `vocabtrimmer/mbart-large-cc25-trimmed-it-itquad-qg`
+This model is fine-tuned version of [ckpts/mbart-large-cc25-trimmed-it](https://huggingface.co/ckpts/mbart-large-cc25-trimmed-it) 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).
+
+
+### Overview
+- **Language model:** [ckpts/mbart-large-cc25-trimmed-it](https://huggingface.co/ckpts/mbart-large-cc25-trimmed-it)   
+- **Language:** it  
+- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default)
+- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
+
+### Usage
+- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
+```python
+from lmqg import TransformersQG
+
+# initialize model
+model = TransformersQG(language="it", model="vocabtrimmer/mbart-large-cc25-trimmed-it-itquad-qg")
+
+# model prediction
+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")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "vocabtrimmer/mbart-large-cc25-trimmed-it-itquad-qg")
+output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-it-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) 
+
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   81.06 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_1     |   22.99 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_2     |   15.06 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_3     |   10.41 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_4     |    7.4  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| METEOR     |   18.94 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| MoverScore |   57.38 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| ROUGE_L    |   22.57 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_itquad
+ - dataset_name: default
+ - input_types: paragraph_answer
+ - output_types: question
+ - prefix_types: None
+ - model: ckpts/mbart-large-cc25-trimmed-it
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 8
+ - batch: 8
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 8
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-it-itquad-qg/raw/main/trainer_config.json).
+
+## Citation
+```
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+
+```

eval/metric.first.answer.paragraph_answer.question.lmqg_qg_itquad.default.json

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+{"validation": {"Bleu_1": 0.23087067031085245, "Bleu_2": 0.15415059373770354, "Bleu_3": 0.10856486748006203, "Bleu_4": 0.07873046332873804}, "test": {"Bleu_1": 0.22014960408124712, "Bleu_2": 0.14330754041565522, "Bleu_3": 0.09883772934835909, "Bleu_4": 0.07012552373856115}}

eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json

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+{"validation": {"Bleu_1": 0.23198690544134656, "Bleu_2": 0.155044685456534, "Bleu_3": 0.10930111840821523, "Bleu_4": 0.07934065813428705, "METEOR": 0.19749778490966646, "ROUGE_L": 0.23228498429226976, "BERTScore": 0.8185450898258807, "MoverScore": 0.5827791970827807}, "test": {"Bleu_1": 0.22987607201158541, "Bleu_2": 0.1505689013475674, "Bleu_3": 0.10414336529734433, "Bleu_4": 0.07402603207398903, "METEOR": 0.18935354604081714, "ROUGE_L": 0.22565543720614775, "BERTScore": 0.8105805082699707, "MoverScore": 0.573768183313954}}