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: https://autoqg.net/
- Repository: https://github.com/asahi417/lm-question-generation
- Paper: https://arxiv.org/abs/2210.03992
Usage
- With
lmqg
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
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
Score | Type | Dataset | |
---|---|---|---|
AnswerExactMatch | 58.16 | default | lmqg/qg_squad |
AnswerF1Score | 69.41 | default | lmqg/qg_squad |
BERTScore | 91.56 | default | lmqg/qg_squad |
Bleu_1 | 56.8 | default | lmqg/qg_squad |
Bleu_2 | 52.39 | default | lmqg/qg_squad |
Bleu_3 | 48.02 | default | lmqg/qg_squad |
Bleu_4 | 44.15 | default | lmqg/qg_squad |
METEOR | 43.3 | default | lmqg/qg_squad |
MoverScore | 81.79 | default | lmqg/qg_squad |
ROUGE_L | 68.88 | default | 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.
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",
}
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Dataset used to train lmqg/flan-t5-base-squad-ae
Evaluation results
- BLEU4 (Answer Extraction) on lmqg/qg_squadself-reported44.150
- ROUGE-L (Answer Extraction) on lmqg/qg_squadself-reported68.880
- METEOR (Answer Extraction) on lmqg/qg_squadself-reported43.300
- BERTScore (Answer Extraction) on lmqg/qg_squadself-reported91.560
- MoverScore (Answer Extraction) on lmqg/qg_squadself-reported81.790
- AnswerF1Score (Answer Extraction) on lmqg/qg_squadself-reported69.410
- AnswerExactMatch (Answer Extraction) on lmqg/qg_squadself-reported58.160