model update

README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: es
+datasets:
+- lmqg/qag_esquad
+pipeline_tag: text2text-generation
+tags:
+- questions and answers generation
+widget:
+- text: "del Ministerio de Desarrollo Urbano , Gobierno de la India."
+  example_title: "Questions & Answers Generation Example 1" 
+model-index:
+- name: lmqg/mt5-base-esquad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_esquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question & Answer Generation)
+      type: bleu4_question_answer_generation
+      value: 1.53
+    - name: ROUGE-L (Question & Answer Generation)
+      type: rouge_l_question_answer_generation
+      value: 13.3
+    - name: METEOR (Question & Answer Generation)
+      type: meteor_question_answer_generation
+      value: 17.56
+    - name: BERTScore (Question & Answer Generation)
+      type: bertscore_question_answer_generation
+      value: 64.69
+    - name: MoverScore (Question & Answer Generation)
+      type: moverscore_question_answer_generation
+      value: 50.34
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 78.96
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 79.31
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 78.66
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 54.3
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 54.42
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 54.21
+---
+
+# Model Card of `lmqg/mt5-base-esquad-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_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) (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:** es  
+- **Training data:** [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) (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="es", model="lmqg/mt5-base-esquad-qag")
+
+# model prediction
+question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-qag")
+output = pipe("del Ministerio de Desarrollo Urbano , Gobierno de la India.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_esquad.default.json) 
+
+|                                 |   Score | Type    | Dataset                                                            |
+|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
+| BERTScore                       |   64.69 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| Bleu_1                          |    7.9  | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| Bleu_2                          |    3.81 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| Bleu_3                          |    2.27 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| Bleu_4                          |    1.53 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| METEOR                          |   17.56 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| MoverScore                      |   50.34 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| QAAlignedF1Score (BERTScore)    |   78.96 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| QAAlignedF1Score (MoverScore)   |   54.3  | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| QAAlignedPrecision (BERTScore)  |   78.66 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| QAAlignedPrecision (MoverScore) |   54.21 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| QAAlignedRecall (BERTScore)     |   79.31 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| QAAlignedRecall (MoverScore)    |   54.42 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+| ROUGE_L                         |   13.3  | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qag_esquad
+ - 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: 13
+ - batch: 2
+ - lr: 0.0005
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 32
+ - label_smoothing: 0.0
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-esquad-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-esquad-qag/best_model",
+  "_name_or_path": "lmqg_output/mt5-base-esquad-qag/model_uramvg/epoch_6",
   "add_prefix": false,
   "architectures": [
     "MT5ForConditionalGeneration"

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

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+{"validation": {"Bleu_1": 0.290410134310789, "Bleu_2": 0.165502229998739, "Bleu_3": 0.09848741138745379, "Bleu_4": 0.06646403182008429, "METEOR": 0.21098897650961085, "ROUGE_L": 0.25289328543314055, "BERTScore": 0.739177295160455, "MoverScore": 0.5240828055144467, "QAAlignedF1Score (BERTScore)": 0.815034062215664, "QAAlignedRecall (BERTScore)": 0.7926918455339421, "QAAlignedPrecision (BERTScore)": 0.8397426666823765, "QAAlignedF1Score (MoverScore)": 0.5631614868359288, "QAAlignedRecall (MoverScore)": 0.5433533140367633, "QAAlignedPrecision (MoverScore)": 0.586108974589693}, "test": {"Bleu_1": 0.07904094156389288, "Bleu_2": 0.03812578203969263, "Bleu_3": 0.02271639512237187, "Bleu_4": 0.015312801666172975, "METEOR": 0.17562136808176335, "ROUGE_L": 0.13300750909719405, "BERTScore": 0.6468604413092586, "MoverScore": 0.5034427086240993, "QAAlignedF1Score (BERTScore)": 0.7895520071597255, "QAAlignedRecall (BERTScore)": 0.7930640663081466, "QAAlignedPrecision (BERTScore)": 0.7865682015086454, "QAAlignedF1Score (MoverScore)": 0.5430042564379615, "QAAlignedRecall (MoverScore)": 0.544242489751627, "QAAlignedPrecision (MoverScore)": 0.5421227444914386}}

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-esquad-qag/best_model",
+  "name_or_path": "lmqg_output/mt5-base-esquad-qag/model_uramvg/epoch_6",
   "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_esquad", "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": 13, "batch": 2, "lr": 0.0005, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 32, "label_smoothing": 0.0}