model update

README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: ko
+datasets:
+- lmqg/qag_koquad
+pipeline_tag: text2text-generation
+tags:
+- questions and answers generation
+widget:
+- text: "1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다."
+  example_title: "Questions & Answers Generation Example 1" 
+model-index:
+- name: lmqg/mt5-base-koquad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_koquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question & Answer Generation)
+      type: bleu4_question_answer_generation
+      value: 0.87
+---
+
+# Model Card of `lmqg/mt5-base-koquad-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_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) (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:** ko  
+- **Training data:** [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) (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="ko", model="lmqg/mt5-base-koquad-qag")
+
+# model prediction
+question_answer_pairs = model.generate_qa("1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/mt5-base-koquad-qag")
+output = pipe("1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-koquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_koquad.default.json) 
+
+|        |   Score | Type    | Dataset                                                            |
+|:-------|--------:|:--------|:-------------------------------------------------------------------|
+| Bleu_1 |    4.66 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) |
+| Bleu_2 |    2.43 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) |
+| Bleu_3 |    1.4  | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) |
+| Bleu_4 |    0.87 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qag_koquad
+ - 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: 18
+ - batch: 2
+ - lr: 0.001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 64
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-koquad-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-koquad-qag/best_model",
+  "_name_or_path": "lmqg_output/mt5-base-koquad-qag/model_umlauj/epoch_17",
   "add_prefix": false,
   "architectures": [
     "MT5ForConditionalGeneration"

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

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+{"validation": {"Bleu_1": 0.22944459267164455, "Bleu_2": 0.14581809736110674, "Bleu_3": 0.09073762379558614, "Bleu_4": 0.060320148625246546}, "test": {"Bleu_1": 0.046633951117251785, "Bleu_2": 0.024267387176477355, "Bleu_3": 0.013983981210269144, "Bleu_4": 0.00865229909206871}}

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-base-koquad-qag/best_model",
+  "name_or_path": "lmqg_output/mt5-base-koquad-qag/model_umlauj/epoch_17",
   "pad_token": "<pad>",
   "sp_model_kwargs": {},
   "special_tokens_map_file": "/home/patrick/.cache/torch/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276",

trainer_config.json

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+{"dataset_path": "lmqg/qag_koquad", "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": 18, "batch": 2, "lr": 0.001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 64, "label_smoothing": 0.15}