
# Model Card of `lmqg/flan-t5-base-squad-ae`
This model is fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) for answer extraction 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-ae")

# model prediction
answers = model.generate_a("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-ae")
output = 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 (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-base-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) 

|                  |   Score | Type    | Dataset                                                        |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch |   58.16 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score    |   69.41 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore        |   91.56 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1           |   56.8  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2           |   52.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3           |   48.02 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4           |   44.15 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR           |   43.3  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore       |   81.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L          |   68.88 | 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_sentence']
 - output_types: ['answer']
 - prefix_types: ['ae']
 - model: google/flan-t5-base
 - max_length: 512
 - max_length_output: 32
 - epoch: 8
 - batch: 16
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 4
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-base-squad-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": ["answer extraction"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"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": "Answering 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": "Answering Extraction Example 2"}], "model-index": [{"name": "lmqg/flan-t5-base-squad-ae", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_answer_extraction", "value": 44.15, "name": "BLEU4 (Answer Extraction)"}, {"type": "rouge_l_answer_extraction", "value": 68.88, "name": "ROUGE-L (Answer Extraction)"}, {"type": "meteor_answer_extraction", "value": 43.3, "name": "METEOR (Answer Extraction)"}, {"type": "bertscore_answer_extraction", "value": 91.56, "name": "BERTScore (Answer Extraction)"}, {"type": "moverscore_answer_extraction", "value": 81.79, "name": "MoverScore (Answer Extraction)"}, {"type": "answer_f1_score__answer_extraction", "value": 69.41, "name": "AnswerF1Score (Answer Extraction)"}, {"type": "answer_exact_match_answer_extraction", "value": 58.16, "name": "AnswerExactMatch (Answer Extraction)"}]}]}]}
transformers pytorch t5 text2text-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 #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-base-squad-ae'
==========================================


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


### Overview


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


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *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\_sentence']
* output\_types: ['answer']
* prefix\_types: ['ae']
* model: google/flan-t5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 8
* batch: 16
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


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



### Overview


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


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *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\_sentence']
* output\_types: ['answer']
* prefix\_types: ['ae']
* model: google/flan-t5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 8
* batch: 16
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #t5 #text2text-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-base
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *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\_sentence']
* output\_types: ['answer']
* prefix\_types: ['ae']
* model: google/flan-t5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 8
* batch: 16
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #t5 #text2text-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-base
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *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\_sentence']
* output\_types: ['answer']
* prefix\_types: ['ae']
* model: google/flan-t5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 8
* batch: 16
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


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