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_squad
+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: "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."
+  example_title: "Answer Extraction Example 1" 
+- text: "extract answers: 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/flan-t5-base-squad-qg-ae
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_squad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question Generation)
+      type: bleu4_question_generation
+      value: 26.43
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 53.37
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 26.99
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 90.61
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 64.75
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
+      value: 93.31
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
+      value: 93.99
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
+      value: 92.65
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
+      value: 64.59
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
+      value: 65.57
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
+      value: 63.7
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 35.6
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 68.47
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 42.76
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 91.28
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 81.31
+    - name: AnswerF1Score (Answer Extraction)
+      type: answer_f1_score__answer_extraction
+      value: 68.88
+    - name: AnswerExactMatch (Answer Extraction)
+      type: answer_exact_match_answer_extraction
+      value: 57.39
+---
+
+# Model Card of `lmqg/flan-t5-base-squad-qg-ae`
+This model is fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+
+
+### Overview
+- **Language model:** [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)   
+- **Language:** en  
+- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (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/flan-t5-base-squad-qg-ae")
+
+# model prediction
+question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/flan-t5-base-squad-qg-ae")
+
+# answer extraction
+answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
+
+# question generation
+question = 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.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-base-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) 
+
+|            |   Score | Type    | Dataset                                                        |
+|:-----------|--------:|:--------|:---------------------------------------------------------------|
+| BERTScore  |   90.61 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_1     |   58.99 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_2     |   42.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_3     |   33.3  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_4     |   26.43 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| METEOR     |   26.99 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| MoverScore |   64.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| ROUGE_L    |   53.37 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+
+
+- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/flan-t5-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)
+
+|                                 |   Score | Type    | Dataset                                                        |
+|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   93.31 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| QAAlignedF1Score (MoverScore)   |   64.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| QAAlignedPrecision (BERTScore)  |   92.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| QAAlignedPrecision (MoverScore) |   63.7  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| QAAlignedRecall (BERTScore)     |   93.99 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| QAAlignedRecall (MoverScore)    |   65.57 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+
+
+- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json)
+
+|                  |   Score | Type    | Dataset                                                        |
+|:-----------------|--------:|:--------|:---------------------------------------------------------------|
+| AnswerExactMatch |   57.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| AnswerF1Score    |   68.88 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| BERTScore        |   91.28 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_1           |   49.4  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_2           |   44.53 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_3           |   39.73 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_4           |   35.6  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| METEOR           |   42.76 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| MoverScore       |   81.31 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| ROUGE_L          |   68.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_squad
+ - dataset_name: default
+ - input_types: ['paragraph_answer', 'paragraph_sentence']
+ - output_types: ['question', 'answer']
+ - prefix_types: ['qg', 'ae']
+ - model: google/flan-t5-base
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 7
+ - batch: 8
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 16
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-base-squad-qg-ae/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",
+}
+
+```

added_tokens.json

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+{
+  "<hl>": 32100
+}

config.json

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+{
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+  "add_prefix": true,
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
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+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "use_cache": true,
+  "vocab_size": 32101
+}

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

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+{"test": {"QAAlignedF1Score (BERTScore)": 0.9331200231801412, "QAAlignedRecall (BERTScore)": 0.9399491642028229, "QAAlignedPrecision (BERTScore)": 0.926485238401098, "QAAlignedF1Score (MoverScore)": 0.6458807796294435, "QAAlignedRecall (MoverScore)": 0.6557030069442409, "QAAlignedPrecision (MoverScore)": 0.6369852357032981, "Bleu_1": 0.2081753330467536, "Bleu_2": 0.11849501493018352, "Bleu_3": 0.0581976187585514, "Bleu_4": 0.032446793938693604, "METEOR": 0.29408615528616006, "ROUGE_L": 0.22568047157912255, "BERTScore": 0.8810365926635368, "MoverScore": 0.6050701891062961}, "validation": {"QAAlignedF1Score (BERTScore)": 0.9318819882475717, "QAAlignedRecall (BERTScore)": 0.9375346887392281, "QAAlignedPrecision (BERTScore)": 0.9263555201099526, "QAAlignedF1Score (MoverScore)": 0.6470905584208508, "QAAlignedRecall (MoverScore)": 0.6539960981502014, "QAAlignedPrecision (MoverScore)": 0.6406423809368123, "Bleu_1": 0.22408145313527597, "Bleu_2": 0.131635084701984, "Bleu_3": 0.0706195936829256, "Bleu_4": 0.042252626422146625, "METEOR": 0.3090637155385741, "ROUGE_L": 0.24523271947693276, "BERTScore": 0.8847284212917537, "MoverScore": 0.6108109436054909}}

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

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eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json

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

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generation_config.json

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+  "eos_token_id": 1,
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+  "transformers_version": "4.26.1"
+}

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special_tokens_map.json

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tokenizer_config.json

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+  "extra_ids": 100,
+  "model_max_length": 512,
+  "name_or_path": "lmqg_output/flan-t5-base-squad-qg-ae/model_eszyci/epoch_2",
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "special_tokens_map_file": "/home/younes_huggingface_co/.cache/huggingface/hub/models--google--t5-v1_1-base/snapshots/650d7745bf1e502d6949b22cc19155cd656d3d4e/special_tokens_map.json",
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

trainer_config.json

@@ -0,0 +1 @@
+{"dataset_path": "lmqg/qg_squad", "dataset_name": "default", "input_types": ["paragraph_answer", "paragraph_sentence"], "output_types": ["question", "answer"], "prefix_types": ["qg", "ae"], "model": "google/flan-t5-base", "max_length": 512, "max_length_output": 32, "epoch": 7, "batch": 8, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 16, "label_smoothing": 0.15}