model update

README.md

@@ -0,0 +1,158 @@
+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: ja
+datasets:
+- lmqg/qag_jaquad
+pipeline_tag: text2text-generation
+tags:
+- questions and answers generation
+widget:
+- text: "ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている6月28日は2人の14回目の結婚記念日であった。"
+  example_title: "Questions & Answers Generation Example 1" 
+model-index:
+- name: lmqg/mt5-small-jaquad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_jaquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question & Answer Generation)
+      type: bleu4_question_answer_generation
+      value: 3.66
+    - name: ROUGE-L (Question & Answer Generation)
+      type: rouge_l_question_answer_generation
+      value: 15.62
+    - name: METEOR (Question & Answer Generation)
+      type: meteor_question_answer_generation
+      value: 9.88
+    - name: BERTScore (Question & Answer Generation)
+      type: bertscore_question_answer_generation
+      value: 58.55
+    - name: MoverScore (Question & Answer Generation)
+      type: moverscore_question_answer_generation
+      value: 49.1
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 58.35
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 58.38
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 58.34
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 39.19
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 39.17
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 39.21
+---
+
+# Model Card of `lmqg/mt5-small-jaquad-qag`
+This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+
+
+### Overview
+- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
+- **Language:** ja  
+- **Training data:** [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (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="ja", model="lmqg/mt5-small-jaquad-qag")
+
+# model prediction
+question_answer_pairs = model.generate_qa("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/mt5-small-jaquad-qag")
+output = pipe("ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている6月28日は2人の14回目の結婚記念日であった。")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_jaquad.default.json) 
+
+|                                 |   Score | Type    | Dataset                                                            |
+|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
+| BERTScore                       |   58.55 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| Bleu_1                          |    8.16 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| Bleu_2                          |    6.13 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| Bleu_3                          |    4.7  | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| Bleu_4                          |    3.66 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| METEOR                          |    9.88 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| MoverScore                      |   49.1  | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| QAAlignedF1Score (BERTScore)    |   58.35 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| QAAlignedF1Score (MoverScore)   |   39.19 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| QAAlignedPrecision (BERTScore)  |   58.34 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| QAAlignedPrecision (MoverScore) |   39.21 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| QAAlignedRecall (BERTScore)     |   58.38 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| QAAlignedRecall (MoverScore)    |   39.17 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+| ROUGE_L                         |   15.62 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qag_jaquad
+ - dataset_name: default
+ - input_types: ['paragraph']
+ - output_types: ['questions_answers']
+ - prefix_types: None
+ - model: google/mt5-small
+ - max_length: 512
+ - max_length_output: 256
+ - epoch: 18
+ - batch: 8
+ - lr: 0.001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 8
+ - label_smoothing: 0.0
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-jaquad-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/mt5-small-jaquad-qag/best_model",
+  "_name_or_path": "lmqg_output/mt5-small-jaquad-qag/model_mzgdpa/epoch_17",
   "add_prefix": false,
   "architectures": [
     "MT5ForConditionalGeneration"

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

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+{"validation": {"Bleu_1": 0.17723868713431587, "Bleu_2": 0.1351901344556621, "Bleu_3": 0.10622495857792344, "Bleu_4": 0.0825424205645754, "METEOR": 0.12999848944853618, "ROUGE_L": 0.24983794470491816, "BERTScore": 0.6579463741397791, "MoverScore": 0.49961617640749567, "QAAlignedF1Score (BERTScore)": 0.5936048978463043, "QAAlignedRecall (BERTScore)": 0.585364358946976, "QAAlignedPrecision (BERTScore)": 0.6024343219290462, "QAAlignedF1Score (MoverScore)": 0.39674948798701676, "QAAlignedRecall (MoverScore)": 0.391606217527659, "QAAlignedPrecision (MoverScore)": 0.4022277659194923}, "test": {"Bleu_1": 0.08161337613102573, "Bleu_2": 0.06126757153349611, "Bleu_3": 0.04702714715082693, "Bleu_4": 0.036563277330046315, "METEOR": 0.09876247293506824, "ROUGE_L": 0.15619127411788708, "BERTScore": 0.5855429661957944, "MoverScore": 0.4909802836505803, "QAAlignedF1Score (BERTScore)": 0.5834880271347894, "QAAlignedRecall (BERTScore)": 0.5837604820601748, "QAAlignedPrecision (BERTScore)": 0.5833768111584616, "QAAlignedF1Score (MoverScore)": 0.3918544522832205, "QAAlignedRecall (MoverScore)": 0.3916595779695674, "QAAlignedPrecision (MoverScore)": 0.39210192986382214}}

pytorch_model.bin

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tokenizer_config.json

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   "additional_special_tokens": null,
   "eos_token": "</s>",
   "extra_ids": 0,
-  "name_or_path": "lmqg_output/mt5-small-jaquad-qag/best_model",
+  "name_or_path": "lmqg_output/mt5-small-jaquad-qag/model_mzgdpa/epoch_17",
   "pad_token": "<pad>",
   "sp_model_kwargs": {},
   "special_tokens_map_file": "/home/c.c2042013/.cache/huggingface/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276",

trainer_config.json

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+{"dataset_path": "lmqg/qag_jaquad", "dataset_name": "default", "input_types": ["paragraph"], "output_types": ["questions_answers"], "prefix_types": null, "model": "google/mt5-small", "max_length": 512, "max_length_output": 256, "epoch": 18, "batch": 8, "lr": 0.001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 8, "label_smoothing": 0.0}