Model Card for flan-t5-small-finetuned-xlsum-en-accelerate
This model is a fine-tuned version of flan-t5-small on the csebuetnlp/xlsum dataset.
A reduced version of the English subset was used, focusing on shorter targets.
It achieves the following results on the evaluation set:
rouge1: 29.99
rouge2: 10.61
rougeL: 25.52
rougeLsum: 25.52
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
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Language(s) (NLP): [More Information Needed]
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Finetuned from model [optional]: [More Information Needed]
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Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
Model can produce false information when summarizing.
This is very much an initial draft, and is not expected for use in production, use at your own risk.
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
@inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.413",
pages = "4693--4703",
}
