Model Card of lmqg/t5-small-squad-ae
This model is fine-tuned version of t5-small for answer extraction on the lmqg/qg_squad (dataset_name: default) via lmqg
.
Overview
- Language model: t5-small
- 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/t5-small-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/t5-small-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 | 56.15 | default | lmqg/qg_squad |
AnswerF1Score | 68.06 | default | lmqg/qg_squad |
BERTScore | 91.2 | default | lmqg/qg_squad |
Bleu_1 | 52.42 | default | lmqg/qg_squad |
Bleu_2 | 47.81 | default | lmqg/qg_squad |
Bleu_3 | 43.22 | default | lmqg/qg_squad |
Bleu_4 | 39.23 | default | lmqg/qg_squad |
METEOR | 42.5 | default | lmqg/qg_squad |
MoverScore | 80.92 | default | lmqg/qg_squad |
ROUGE_L | 67.58 | 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: t5-small
- max_length: 512
- max_length_output: 32
- epoch: 7
- batch: 64
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 1
- 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/t5-small-squad-ae
Evaluation results
- BLEU4 (Answer Extraction) on lmqg/qg_squadself-reported39.230
- ROUGE-L (Answer Extraction) on lmqg/qg_squadself-reported67.580
- METEOR (Answer Extraction) on lmqg/qg_squadself-reported42.500
- BERTScore (Answer Extraction) on lmqg/qg_squadself-reported91.200
- MoverScore (Answer Extraction) on lmqg/qg_squadself-reported80.920
- AnswerF1Score (Answer Extraction) on lmqg/qg_squadself-reported68.060
- AnswerExactMatch (Answer Extraction) on lmqg/qg_squadself-reported56.150