model update

README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: it
+datasets:
+- lmqg/qag_itquad
+pipeline_tag: text2text-generation
+tags:
+- questions and answers generation
+widget:
+- text: "Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
+  example_title: "Questions & Answers Generation Example 1" 
+model-index:
+- name: lmqg/mbart-large-cc25-itquad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_itquad
+      type: default
+      args: default
+    metrics:
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 72.96
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 71.83
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 74.2
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 51.25
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 50.21
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 52.44
+---
+
+# Model Card of `lmqg/mbart-large-cc25-itquad-qag`
+This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+
+
+### Overview
+- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)   
+- **Language:** it  
+- **Training data:** [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_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="lmqg/mbart-large-cc25-itquad-qag")
+
+# model prediction
+question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-itquad-qag")
+output = pipe("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_itquad.default.json) 
+
+|                                 |   Score | Type    | Dataset                                                            |
+|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   72.96 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedF1Score (MoverScore)   |   51.25 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedPrecision (BERTScore)  |   74.2  | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedPrecision (MoverScore) |   52.44 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedRecall (BERTScore)     |   71.83 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedRecall (MoverScore)    |   50.21 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qag_itquad
+ - dataset_name: default
+ - input_types: ['paragraph']
+ - output_types: ['questions_answers']
+ - prefix_types: None
+ - model: facebook/mbart-large-cc25
+ - max_length: 512
+ - max_length_output: 256
+ - epoch: 14
+ - batch: 8
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 16
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qag/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",
+}
+
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "lmqg_output/mbart-large-cc25-itquad-qag/best_model",
+  "_name_or_path": "lmqg_output/mbart-large-cc25-itquad-qag/model_iovmpj/epoch_5",
   "_num_labels": 3,
   "activation_dropout": 0.0,
   "activation_function": "gelu",

eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_itquad.default.json

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+{"validation": {"Bleu_1": 0.13542104061231505, "Bleu_2": 0.06463206722552836, "Bleu_3": 0.037527509456603755, "Bleu_4": 0.024149605383889588, "METEOR": 0.19637667004148632, "ROUGE_L": 0.1794824087703433, "BERTScore": 0.7184275808038225, "MoverScore": 0.5217052610229042, "QAAlignedF1Score (BERTScore)": 0.7276849783195997, "QAAlignedRecall (BERTScore)": 0.736117797855707, "QAAlignedPrecision (BERTScore)": 0.7199664947047292, "QAAlignedF1Score (MoverScore)": 0.5072712200324073, "QAAlignedRecall (MoverScore)": 0.513133939102146, "QAAlignedPrecision (MoverScore)": 0.5021306806188025}, "test": {"Bleu_1": 0.2028246379360438, "Bleu_2": 0.11573898832621304, "Bleu_3": 0.0720727821284893, "Bleu_4": 0.048660815663715204, "METEOR": 0.1550108751815631, "ROUGE_L": 0.19751677816796323, "BERTScore": 0.7635607211222831, "MoverScore": 0.5331782063975097, "QAAlignedF1Score (BERTScore)": 0.7296107903023021, "QAAlignedRecall (BERTScore)": 0.7183084581772343, "QAAlignedPrecision (BERTScore)": 0.7420292418872366, "QAAlignedF1Score (MoverScore)": 0.5125459244623932, "QAAlignedRecall (MoverScore)": 0.502108717608002, "QAAlignedPrecision (MoverScore)": 0.5243794013209974}}

pytorch_model.bin

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+size 2444583801

tokenizer_config.json

@@ -12,7 +12,7 @@
     "single_word": false
   },
   "model_max_length": 1024,
-  "name_or_path": "lmqg_output/mbart-large-cc25-itquad-qag/best_model",
+  "name_or_path": "lmqg_output/mbart-large-cc25-itquad-qag/model_iovmpj/epoch_5",
   "pad_token": "<pad>",
   "sep_token": "</s>",
   "special_tokens_map_file": null,

trainer_config.json

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+{"dataset_path": "lmqg/qag_itquad", "dataset_name": "default", "input_types": ["paragraph"], "output_types": ["questions_answers"], "prefix_types": null, "model": "facebook/mbart-large-cc25", "max_length": 512, "max_length_output": 256, "epoch": 14, "batch": 8, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 16, "label_smoothing": 0.15}