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/mbart-large-cc25-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: 7.06
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 20.15
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 16.86
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 79.29
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 55.92
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
+      value: 82.65
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
+      value: 84.34
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
+      value: 81.06
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
+      value: 56.14
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
+      value: 57.13
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
+      value: 55.22
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 20.21
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 46.51
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 44.48
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 90.63
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 83.05
+    - name: AnswerF1Score (Answer Extraction)
+      type: answer_f1_score__answer_extraction
+      value: 76.59
+    - name: AnswerExactMatch (Answer Extraction)
+      type: answer_exact_match_answer_extraction
+      value: 63.88
+---
+
+# Model Card of `lmqg/mbart-large-cc25-itquad-qg-ae`
+This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) 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:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)   
+- **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/mbart-large-cc25-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/mbart-large-cc25-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/mbart-large-cc25-itquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) 
+
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   79.29 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_1     |   22.03 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_2     |   14.31 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_3     |    9.9  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_4     |    7.06 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| METEOR     |   16.86 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| MoverScore |   55.92 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| ROUGE_L    |   20.15 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+
+
+- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json)
+
+|                                 |   Score | Type    | Dataset                                                          |
+|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   82.65 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedF1Score (MoverScore)   |   56.14 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedPrecision (BERTScore)  |   81.06 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedPrecision (MoverScore) |   55.22 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedRecall (BERTScore)     |   84.34 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| QAAlignedRecall (MoverScore)    |   57.13 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+
+
+- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json)
+
+|                  |   Score | Type    | Dataset                                                          |
+|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
+| AnswerExactMatch |   63.88 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| AnswerF1Score    |   76.59 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| BERTScore        |   90.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_1           |   33.66 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_2           |   27.96 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_3           |   23.79 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_4           |   20.21 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| METEOR           |   44.48 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| MoverScore       |   83.05 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| ROUGE_L          |   46.51 | 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: facebook/mbart-large-cc25
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 8
+ - batch: 2
+ - lr: 0.0001
+ - 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/mbart-large-cc25-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/mbart-large-cc25-itquad-qg-ae/best_model",
+  "_name_or_path": "lmqg_output/mbart-large-cc25-itquad-qg-ae/model_gljitg/epoch_5",
   "_num_labels": 3,
   "activation_dropout": 0.0,
   "activation_function": "gelu",

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

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+{"test": {"QAAlignedF1Score (BERTScore)": 0.8264963413651882, "QAAlignedRecall (BERTScore)": 0.8433949072468981, "QAAlignedPrecision (BERTScore)": 0.8106074732880147, "QAAlignedF1Score (MoverScore)": 0.5614028998279956, "QAAlignedRecall (MoverScore)": 0.5712826398930563, "QAAlignedPrecision (MoverScore)": 0.5522132111469052, "Bleu_1": 0.16312877420973168, "Bleu_2": 0.0925206396933409, "Bleu_3": 0.047284456202529156, "Bleu_4": 0.025330248008968437, "METEOR": 0.2506318329951036, "ROUGE_L": 0.19514561704456454, "BERTScore": 0.7366486872046046, "MoverScore": 0.5423528342324836}, "validation": {"QAAlignedF1Score (BERTScore)": 0.8100336065007487, "QAAlignedRecall (BERTScore)": 0.8446934290149599, "QAAlignedPrecision (BERTScore)": 0.7786250297045639, "QAAlignedF1Score (MoverScore)": 0.5504725180352802, "QAAlignedRecall (MoverScore)": 0.5723926119753241, "QAAlignedPrecision (MoverScore)": 0.5310473119264032, "Bleu_1": 0.050322111089023534, "Bleu_2": 0.0227113790498272, "Bleu_3": 0.010309740934486946, "Bleu_4": 0.005088103355677113, "METEOR": 0.15475325662503672, "ROUGE_L": 0.08998195745851649, "BERTScore": 0.636510829148209, "MoverScore": 0.5083552519592559}}

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

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+{"validation": {"Bleu_1": 0.22081841317027878, "Bleu_2": 0.14625968090059838, "Bleu_3": 0.10240753565680512, "Bleu_4": 0.0736664759780728}, "test": {"Bleu_1": 0.21098003120474793, "Bleu_2": 0.13586558819908817, "Bleu_3": 0.09352885589427087, "Bleu_4": 0.06654178223179057}}

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

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+{"validation": {"Bleu_1": 0.3377340114706079, "Bleu_2": 0.2773134381736893, "Bleu_3": 0.2366137193106164, "Bleu_4": 0.2029996426179, "METEOR": 0.46055200766458787, "ROUGE_L": 0.4452300627333968, "BERTScore": 0.9179433223170008, "MoverScore": 0.8512434937468668, "AnswerF1Score": 79.20970002912067, "AnswerExactMatch": 68.40583519516362}, "test": {"Bleu_1": 0.33661354834612606, "Bleu_2": 0.27955002622610065, "Bleu_3": 0.23793905548976482, "Bleu_4": 0.20207248181734025, "METEOR": 0.44480746009534095, "ROUGE_L": 0.46510146549442927, "BERTScore": 0.9063161925795451, "MoverScore": 0.8304812947806502, "AnswerF1Score": 76.58778740440405, "AnswerExactMatch": 63.884873176501515}}

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

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+{"validation": {"Bleu_1": 0.2216253207869949, "Bleu_2": 0.14685991225539868, "Bleu_3": 0.10290539705998131, "Bleu_4": 0.07410211123726952, "METEOR": 0.17841245430844613, "ROUGE_L": 0.20985277292570392, "BERTScore": 0.8020776579528849, "MoverScore": 0.5676037052038462}, "test": {"Bleu_1": 0.2203354193979244, "Bleu_2": 0.14309079880332845, "Bleu_3": 0.09898548007038611, "Bleu_4": 0.07061313823722157, "METEOR": 0.16857362153073602, "ROUGE_L": 0.2015478301697681, "BERTScore": 0.7929495583837073, "MoverScore": 0.559154099162796}}

pytorch_model.bin

@@ -1,3 +1,3 @@
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+size 2444587421

tokenizer_config.json

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

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": "facebook/mbart-large-cc25", "max_length": 512, "max_length_output": 32, "epoch": 8, "batch": 2, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 32, "label_smoothing": 0.15}