
# Model Card of `lmqg/flan-t5-large-squad-qg-ae`
This model is fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) 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-large](https://huggingface.co/google/flan-t5-large)   
- **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-large-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-large-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-large-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) 

|            |   Score | Type    | Dataset                                                        |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore  |   90.74 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1     |   60.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2     |   44.72 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3     |   34.91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4     |   27.86 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR     |   28.16 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore |   65.43 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L    |   54.71 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |


- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/flan-t5-large-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)

|                                 |   Score | Type    | Dataset                                                        |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   92.24 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore)   |   64    | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore)  |   91.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) |   64.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore)     |   92.52 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore)    |   64.08 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |


- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-large-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json)

|                  |   Score | Type    | Dataset                                                        |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch |   57    | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score    |   68.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore        |   91.11 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1           |   42.69 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2           |   37.66 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3           |   32.81 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4           |   28.74 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR           |   42.09 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore       |   80.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L          |   68.2  | 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-large
 - max_length: 512
 - max_length_output: 32
 - epoch: 6
 - batch: 8
 - lr: 5e-05
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 8
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-large-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",
}

```

{"language": "en", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "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-large-squad-qg-ae", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 27.86, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 54.71, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 28.16, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 90.74, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 65.43, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer", "value": 92.24, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer", "value": 92.52, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer", "value": 91.98, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer", "value": 64.0, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer", "value": 64.08, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer", "value": 64.01, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "bleu4_answer_extraction", "value": 28.74, "name": "BLEU4 (Answer Extraction)"}, {"type": "rouge_l_answer_extraction", "value": 68.2, "name": "ROUGE-L (Answer Extraction)"}, {"type": "meteor_answer_extraction", "value": 42.09, "name": "METEOR (Answer Extraction)"}, {"type": "bertscore_answer_extraction", "value": 91.11, "name": "BERTScore (Answer Extraction)"}, {"type": "moverscore_answer_extraction", "value": 80.85, "name": "MoverScore (Answer Extraction)"}, {"type": "answer_f1_score__answer_extraction", "value": 68.65, "name": "AnswerF1Score (Answer Extraction)"}, {"type": "answer_exact_match_answer_extraction", "value": 57.0, "name": "AnswerExactMatch (Answer Extraction)"}]}]}]}
transformers pytorch t5 text2text-generation question generation answer extraction en dataset:lmqg/qg_squad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #answer extraction #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Model Card of 'lmqg/flan-t5-large-squad-qg-ae'
==============================================


This model is fine-tuned version of google/flan-t5-large for question generation and answer extraction jointly on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: google/flan-t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metric (Question & Answer Generation)*: raw metric file



* *Metric (Answer Extraction)*: raw metric file



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-large
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* batch: 8
* lr: 5e-05
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.



### Overview


* Language model: google/flan-t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metric (Question & Answer Generation)*: raw metric file



* *Metric (Answer Extraction)*: raw metric file



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-large
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* batch: 8
* lr: 5e-05
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #answer extraction #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Overview


* Language model: google/flan-t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metric (Question & Answer Generation)*: raw metric file



* *Metric (Answer Extraction)*: raw metric file



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-large
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* batch: 8
* lr: 5e-05
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.