model update

README.md

@@ -0,0 +1,215 @@
+
+---
+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
+- answer extraction
+widget:
+- text: "generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
+  example_title: "Question Generation Example 1" 
+- text: "generate question: 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: "generate question: il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo."
+  example_title: "Question Generation Example 3" 
+- text: "extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento."
+  example_title: "Answer Extraction Example 1" 
+- text: "extract answers: <hl> Furono introdotti autocarri compatti, come la Toyota Hilux e il Datsun Truck, seguiti dal camion Mazda (venduto come il Ford Courier), e l' Isuzu costruito Chevrolet LUV. <hl> Mitsubishi rebranded il suo Forte come Dodge D-50 pochi anni dopo la crisi petrolifera. Mazda, Mitsubishi e Isuzu avevano partnership congiunte rispettivamente con Ford, Chrysler e GM. In seguito i produttori americani introdussero le loro sostituzioni nazionali (Ford Ranger, Dodge Dakota e la Chevrolet S10/GMC S-15), ponendo fine alla loro politica di importazione vincolata."
+  example_title: "Answer Extraction Example 2" 
+model-index:
+- name: lmqg/mt5-base-itquad-qg-ae
+  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: 7.72
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 22.81
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 18.56
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 81.15
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 57.15
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 81.98
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 82.83
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 81.19
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 56.35
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 56.75
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 56.0
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 26.87
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 45.82
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 43.51
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 91.12
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 82.62
+    - name: AnswerF1Score (Answer Extraction)
+      type: answer_f1_score__answer_extraction
+      value: 74.04
+    - name: AnswerExactMatch (Answer Extraction)
+      type: answer_exact_match_answer_extraction
+      value: 60.7
+---
+
+# Model Card of `lmqg/mt5-base-itquad-qg-ae`
+This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation and answer extraction jointly 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:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
+- **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="lmqg/mt5-base-itquad-qg-ae")
+
+# 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-qg-ae")
+
+# answer extraction
+answer = pipe("generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
+
+# question generation
+question = pipe("extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) 
+
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   81.15 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_1     |   23.3  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_2     |   15.39 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_3     |   10.74 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_4     |    7.72 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| METEOR     |   18.56 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| MoverScore |   57.15 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| ROUGE_L    |   22.81 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+
+
+- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json)
+
+|                                 |   Score | Type    | Dataset                                                          |
+|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   81.98 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedF1Score (MoverScore)   |   56.35 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedPrecision (BERTScore)  |   81.19 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedPrecision (MoverScore) |   56    | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedRecall (BERTScore)     |   82.83 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedRecall (MoverScore)    |   56.75 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+
+
+- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json)
+
+|                  |   Score | Type    | Dataset                                                          |
+|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
+| AnswerExactMatch |   60.7  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| AnswerF1Score    |   74.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| BERTScore        |   91.12 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_1           |   40.14 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_2           |   34.56 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_3           |   30.56 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_4           |   26.87 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| METEOR           |   43.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| MoverScore       |   82.62 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| ROUGE_L          |   45.82 | 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', 'paragraph_sentence']
+ - output_types: ['question', 'answer']
+ - prefix_types: ['qg', 'ae']
+ - model: google/mt5-base
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 13
+ - batch: 32
+ - lr: 0.0005
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 2
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-itquad-qg-ae/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/mt5-base-itquad-qg-ae/best_model",
+  "_name_or_path": "lmqg_output/mt5-base-itquad-multitask/model_dpyopu/epoch_5",
   "add_prefix": true,
   "architectures": [
     "MT5ForConditionalGeneration"

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

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+{"test": {"QAAlignedF1Score (BERTScore)": 0.8197821918119429, "QAAlignedRecall (BERTScore)": 0.8283015506704319, "QAAlignedPrecision (BERTScore)": 0.8119330094011925, "QAAlignedF1Score (MoverScore)": 0.5634931819025477, "QAAlignedRecall (MoverScore)": 0.5675011475834276, "QAAlignedPrecision (MoverScore)": 0.5599723189991794}, "validation": {"QAAlignedF1Score (BERTScore)": 0.8080914056798597, "QAAlignedRecall (BERTScore)": 0.835892198567779, "QAAlignedPrecision (BERTScore)": 0.782811450155633, "QAAlignedF1Score (MoverScore)": 0.5546692619043326, "QAAlignedRecall (MoverScore)": 0.5736236114784989, "QAAlignedPrecision (MoverScore)": 0.5379608477857356}}

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

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+{"validation": {"Bleu_1": 0.2334974939122243, "Bleu_2": 0.156174010451155, "Bleu_3": 0.1099914311850735, "Bleu_4": 0.07979891216001947}, "test": {"Bleu_1": 0.22293325950849457, "Bleu_2": 0.1462268530043443, "Bleu_3": 0.10163482953182741, "Bleu_4": 0.07280043078401346}}

eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json

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+{"validation": {"Bleu_1": 0.401537765716852, "Bleu_2": 0.34393774917572884, "Bleu_3": 0.3069682871144937, "Bleu_4": 0.2737440947081987, "METEOR": 0.4515736964433969, "ROUGE_L": 0.4445944184159832, "BERTScore": 0.9251149244550682, "MoverScore": 0.8532652688863316, "AnswerF1Score": 77.91003455014469, "AnswerExactMatch": 67.1441713760021}, "test": {"Bleu_1": 0.40144028805759546, "Bleu_2": 0.34560648207657074, "Bleu_3": 0.30562890523271946, "Bleu_4": 0.2687142292283542, "METEOR": 0.43510721492162563, "ROUGE_L": 0.45822440491960914, "BERTScore": 0.9112053436250032, "MoverScore": 0.8262344055230554, "AnswerF1Score": 74.04229123954505, "AnswerExactMatch": 60.70442896569851}}

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

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+{"validation": {"Bleu_1": 0.2344813865063198, "Bleu_2": 0.15699408592735528, "Bleu_3": 0.1106607328860182, "Bleu_4": 0.08035563261097958, "METEOR": 0.19371865879446926, "ROUGE_L": 0.231225836880753, "BERTScore": 0.8181939477897686, "MoverScore": 0.5780999868134854}, "test": {"Bleu_1": 0.23296061199826076, "Bleu_2": 0.15391507744735322, "Bleu_3": 0.10742891259850533, "Bleu_4": 0.0771747478749581, "METEOR": 0.18559685067261858, "ROUGE_L": 0.22806009694437826, "BERTScore": 0.8115219126986919, "MoverScore": 0.5714625839387732}}

pytorch_model.bin

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tokenizer_config.json

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   "additional_special_tokens": null,
   "eos_token": "</s>",
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-  "name_or_path": "lmqg_output/mt5-base-itquad-qg-ae/best_model",
+  "name_or_path": "lmqg_output/mt5-base-itquad-multitask/model_dpyopu/epoch_5",
   "pad_token": "<pad>",
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trainer_config.json

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+{"dataset_path": "lmqg/qg_itquad", "dataset_name": "default", "input_types": ["paragraph_answer", "paragraph_sentence"], "output_types": ["question", "answer"], "prefix_types": ["qg", "ae"], "model": "google/mt5-base", "max_length": 512, "max_length_output": 32, "epoch": 13, "batch": 32, "lr": 0.0005, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 2, "label_smoothing": 0.15}