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/mt5-base-itquad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_itquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question & Answer Generation)
+      type: bleu4_question_answer_generation
+      value: 3.39
+    - name: ROUGE-L (Question & Answer Generation)
+      type: rouge_l_question_answer_generation
+      value: 19.12
+    - name: METEOR (Question & Answer Generation)
+      type: meteor_question_answer_generation
+      value: 15.09
+    - name: BERTScore (Question & Answer Generation)
+      type: bertscore_question_answer_generation
+      value: 73.36
+    - name: MoverScore (Question & Answer Generation)
+      type: moverscore_question_answer_generation
+      value: 51.64
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 79.93
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 78.87
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 81.06
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 53.8
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 53.02
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 54.64
+---
+
+# Model Card of `lmqg/mt5-base-itquad-qag`
+This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) 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:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
+- **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/mt5-base-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/mt5-base-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/mt5-base-itquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_itquad.default.json) 
+
+|                                 |   Score | Type    | Dataset                                                            |
+|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
+| BERTScore                       |   73.36 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| Bleu_1                          |   21.17 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| Bleu_2                          |   11.37 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| Bleu_3                          |    5.83 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| Bleu_4                          |    3.39 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| METEOR                          |   15.09 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| MoverScore                      |   51.64 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedF1Score (BERTScore)    |   79.93 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedF1Score (MoverScore)   |   53.8  | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedPrecision (BERTScore)  |   81.06 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedPrecision (MoverScore) |   54.64 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedRecall (BERTScore)     |   78.87 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| QAAlignedRecall (MoverScore)    |   53.02 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
+| ROUGE_L                         |   19.12 | 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: google/mt5-base
+ - max_length: 512
+ - max_length_output: 256
+ - epoch: 4
+ - batch: 2
+ - lr: 0.0005
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 32
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-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

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 {
-  "_name_or_path": "lmqg_output/mt5-base-itquad-qag/best_model",
+  "_name_or_path": "google/mt5-base",
   "add_prefix": false,
   "architectures": [
     "MT5ForConditionalGeneration"

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

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+{"validation": {"Bleu_1": 0.07700925095636735, "Bleu_2": 0.0324774788907088, "Bleu_3": 0.016317013498405107, "Bleu_4": 0.009242194222026984, "METEOR": 0.14956911772409914, "ROUGE_L": 0.1391450868379757, "BERTScore": 0.6686975762295495, "MoverScore": 0.5060724864937646, "QAAlignedF1Score (BERTScore)": 0.7891348884343872, "QAAlignedRecall (BERTScore)": 0.7941148703167266, "QAAlignedPrecision (BERTScore)": 0.7845570760401714, "QAAlignedF1Score (MoverScore)": 0.5313231229326516, "QAAlignedRecall (MoverScore)": 0.5334536839757478, "QAAlignedPrecision (MoverScore)": 0.5293434889401952}, "test": {"Bleu_1": 0.21167852578781593, "Bleu_2": 0.11374335018536964, "Bleu_3": 0.05833805478144616, "Bleu_4": 0.033897654833535566, "METEOR": 0.15092571430694843, "ROUGE_L": 0.19120088449317582, "BERTScore": 0.7336431266346448, "MoverScore": 0.5163791613690608, "QAAlignedF1Score (BERTScore)": 0.7992503859263245, "QAAlignedRecall (BERTScore)": 0.7887337241402319, "QAAlignedPrecision (BERTScore)": 0.8106036093210574, "QAAlignedF1Score (MoverScore)": 0.5379928571174765, "QAAlignedRecall (MoverScore)": 0.5301687067521811, "QAAlignedPrecision (MoverScore)": 0.5463839520366399}}

pytorch_model.bin

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tokenizer.json

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tokenizer_config.json

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   "eos_token": "</s>",
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-  "name_or_path": "lmqg_output/mt5-base-itquad-qag/best_model",
+  "name_or_path": "google/mt5-base",
   "pad_token": "<pad>",
   "sp_model_kwargs": {},
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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": "google/mt5-base", "max_length": 512, "max_length_output": 256, "epoch": 4, "batch": 2, "lr": 0.0005, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 32, "label_smoothing": 0.15}