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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Model Sources [optional]

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Uses

Direct Use

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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

• lr_scheduler_type: linear

• num_epochs: 3

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

Epoch	rouge1	rouge2	rougeL	rougeLsum
1.0	29.38	10.31	25.0	25.0
2.0	29.87	10.46	25.41	25.41
3.0	29.99	10.61	25.52	25.52

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Framework versions

• Transformers 4.37.2
• Pytorch 2.2.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.1

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Citation [optional]

BibTeX:

@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",
}

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