model update

README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: en
+datasets:
+- lmqg/qg_dequad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+- answer extraction
+widget:
+- text: "generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
+  example_title: "Question Generation Example 1" 
+- text: "generate question: Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
+  example_title: "Question Generation Example 2" 
+- text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records <hl> ."
+  example_title: "Question Generation Example 3" 
+- text: "<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress."
+  example_title: "Answer Extraction Example 1" 
+- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. <hl>"
+  example_title: "Answer Extraction Example 2" 
+model-index:
+- name: lmqg/mt5-base-dequad-multitask
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_dequad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4
+      type: bleu4
+      value: 0.0037638715919786907
+    - name: ROUGE-L
+      type: rouge-l
+      value: 0.08578655213486944
+    - name: METEOR
+      type: meteor
+      value: 0.1055901831758648
+    - name: BERTScore
+      type: bertscore
+      value: 0.7786051149573353
+    - name: MoverScore
+      type: moverscore
+      value: 0.537714157008381
+---
+
+# Model Card of `lmqg/mt5-base-dequad-multitask`
+This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the 
+[lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+This model is fine-tuned on the answer extraction task as well as the question generation.
+
+Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).
+
+```
+
+@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",
+}
+
+```
+
+### Overview
+- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
+- **Language:** en  
+- **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_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='en', model='lmqg/mt5-base-dequad-multitask')
+# model prediction
+question_answer = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
+
+```
+
+- With `transformers`
+```python
+
+from transformers import pipeline
+# initialize model
+pipe = pipeline("text2text-generation", 'lmqg/mt5-base-dequad-multitask')
+# answer extraction
+answer = pipe('extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.')
+# question generation
+question = pipe('generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')
+
+```
+
+## Evaluation Metrics
+
+
+### Metrics
+
+| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
+|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
+| [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 0.004 | 0.086 | 0.106 | 0.779 | 0.538 | [link](https://huggingface.co/lmqg/mt5-base-dequad-multitask/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | 
+
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_dequad
+ - dataset_name: default
+ - input_types: ['paragraph_answer', 'paragraph_sentence']
+ - output_types: ['question', 'answer']
+ - prefix_types: ['qg', 'ae']
+ - model: google/mt5-base
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 8
+ - batch: 32
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 2
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-dequad-multitask/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_base_dequad_answer/best_model",
+  "_name_or_path": "lmqg_output/mt5_base_dequad_answer/model_mntyya/epoch_5",
   "add_prefix": true,
   "architectures": [
     "MT5ForConditionalGeneration"

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

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+{"validation": {"Bleu_1": 0.0952063597848291, "Bleu_2": 0.03818700965134984, "Bleu_3": 0.017108348075766118, "Bleu_4": 0.006107565577034717}, "test": {"Bleu_1": 0.08029642136859996, "Bleu_2": 0.031216356903100213, "Bleu_3": 0.011293038632361377, "Bleu_4": 0.0037405916540954034}}

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

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+{"validation": {"Bleu_1": 0.09601003521975703, "Bleu_2": 0.03840685869062143, "Bleu_3": 0.01715111846800755, "Bleu_4": 0.006140345879834443, "METEOR": 0.11249452928869386, "ROUGE_L": 0.09916299920547067, "BERTScore": 0.7929441278332354, "MoverScore": 0.5458778771227091}, "test": {"Bleu_1": 0.08190559867232479, "Bleu_2": 0.031672449692541, "Bleu_3": 0.011237859034650867, "Bleu_4": 0.0037638715919786907, "METEOR": 0.1055901831758648, "ROUGE_L": 0.08578655213486944, "BERTScore": 0.7786051149573353, "MoverScore": 0.537714157008381}}

pytorch_model.bin

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+size 2329632589

tokenizer_config.json

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   "additional_special_tokens": null,
   "eos_token": "</s>",
   "extra_ids": 0,
-  "name_or_path": "lmqg_output/mt5_base_dequad_answer/best_model",
+  "name_or_path": "lmqg_output/mt5_base_dequad_answer/model_mntyya/epoch_5",
   "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/qg_dequad", "dataset_name": "default", "input_types": ["paragraph_answer", "paragraph_sentence"], "output_types": ["question", "answer"], "prefix_types": ["qg", "ae"], "model": "google/mt5-base", "max_length": 512, "max_length_output": 32, "epoch": 8, "batch": 32, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 2, "label_smoothing": 0.15}