model update

README.md

@@ -0,0 +1,158 @@
+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: de
+datasets:
+- lmqg/qag_dequad
+pipeline_tag: text2text-generation
+tags:
+- questions and answers generation
+widget:
+- text: "Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls wird die Signalübertragung stark gedämpft. "
+  example_title: "Questions & Answers Generation Example 1" 
+model-index:
+- name: lmqg/mbart-large-cc25-dequad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_dequad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question & Answer Generation)
+      type: bleu4_question_answer_generation
+      value: 3.25
+    - name: ROUGE-L (Question & Answer Generation)
+      type: rouge_l_question_answer_generation
+      value: 15.66
+    - name: METEOR (Question & Answer Generation)
+      type: meteor_question_answer_generation
+      value: 15.25
+    - name: BERTScore (Question & Answer Generation)
+      type: bertscore_question_answer_generation
+      value: 72.14
+    - name: MoverScore (Question & Answer Generation)
+      type: moverscore_question_answer_generation
+      value: 52.36
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 69.25
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 68.05
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 70.69
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 50.71
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 49.78
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 51.81
+---
+
+# Model Card of `lmqg/mbart-large-cc25-dequad-qag`
+This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) (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:** de  
+- **Training data:** [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) (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="de", model="lmqg/mbart-large-cc25-dequad-qag")
+
+# model prediction
+question_answer_pairs = model.generate_qa("das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-dequad-qag")
+output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls wird die Signalübertragung stark gedämpft. ")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-dequad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_dequad.default.json) 
+
+|                                 |   Score | Type    | Dataset                                                            |
+|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
+| BERTScore                       |   72.14 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| Bleu_1                          |   19.75 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| Bleu_2                          |    9.9  | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| Bleu_3                          |    5.42 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| Bleu_4                          |    3.25 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| METEOR                          |   15.25 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| MoverScore                      |   52.36 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| QAAlignedF1Score (BERTScore)    |   69.25 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| QAAlignedF1Score (MoverScore)   |   50.71 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| QAAlignedPrecision (BERTScore)  |   70.69 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| QAAlignedPrecision (MoverScore) |   51.81 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| QAAlignedRecall (BERTScore)     |   68.05 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| QAAlignedRecall (MoverScore)    |   49.78 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+| ROUGE_L                         |   15.66 | default | [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qag_dequad
+ - dataset_name: default
+ - input_types: ['paragraph']
+ - output_types: ['questions_answers']
+ - prefix_types: None
+ - model: facebook/mbart-large-cc25
+ - max_length: 512
+ - max_length_output: 256
+ - epoch: 6
+ - 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-dequad-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/mbart-large-cc25-dequad-qag/best_model",
+  "_name_or_path": "lmqg_output/mbart-large-cc25-dequad-qag/model_lcdgzq/epoch_5",
   "_num_labels": 3,
   "activation_dropout": 0.0,
   "activation_function": "gelu",

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

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+{"validation": {"Bleu_1": 0.11518445512952588, "Bleu_2": 0.0558802855395941, "Bleu_3": 0.030262927243401124, "Bleu_4": 0.017830165330573695, "METEOR": 0.19995572225275204, "ROUGE_L": 0.14729340071628172, "BERTScore": 0.6815031629186982, "MoverScore": 0.5111122425085081, "QAAlignedF1Score (BERTScore)": 0.6744028992430579, "QAAlignedRecall (BERTScore)": 0.6805440302605067, "QAAlignedPrecision (BERTScore)": 0.6693542052810808, "QAAlignedF1Score (MoverScore)": 0.4932017968393999, "QAAlignedRecall (MoverScore)": 0.49561932590302227, "QAAlignedPrecision (MoverScore)": 0.49127423177592505}, "test": {"Bleu_1": 0.19750012295432115, "Bleu_2": 0.0989971506378029, "Bleu_3": 0.05423210341328398, "Bleu_4": 0.03248861015436467, "METEOR": 0.15247521084224064, "ROUGE_L": 0.15662896938074028, "BERTScore": 0.7214481875866274, "MoverScore": 0.5235878832976439, "QAAlignedF1Score (BERTScore)": 0.6925128795995638, "QAAlignedRecall (BERTScore)": 0.6805269603704065, "QAAlignedPrecision (BERTScore)": 0.7068670943690779, "QAAlignedF1Score (MoverScore)": 0.5070599472825805, "QAAlignedRecall (MoverScore)": 0.49776760008824983, "QAAlignedPrecision (MoverScore)": 0.518093803472648}}

pytorch_model.bin

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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-dequad-qag/best_model",
+  "name_or_path": "lmqg_output/mbart-large-cc25-dequad-qag/model_lcdgzq/epoch_5",
   "pad_token": "<pad>",
   "sep_token": "</s>",
   "special_tokens_map_file": null,

trainer_config.json

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+{"dataset_path": "lmqg/qag_dequad", "dataset_name": "default", "input_types": ["paragraph"], "output_types": ["questions_answers"], "prefix_types": null, "model": "facebook/mbart-large-cc25", "max_length": 512, "max_length_output": 256, "epoch": 6, "batch": 2, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 32, "label_smoothing": 0.15}