Shubhangi21/mt5-small-finetuned-xsum

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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Shubhangi Nikam and Asmita Singh
• Funded by [optional]: NA
• Shared by [optional]: NA
• Model type: Summarization
• Language(s) (NLP): Python
• License: NA
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Repository: https://huggingface.co/Shubhangi21/mt5-small-finetuned-xsum
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Training Details

Training Data

XSum Dataset XSum is a dataset for abstractive summarization. It contains BBC articles and corresponding single-sentence summaries written by experts. Number of Samples: Training: 20 samples Validation: 20 samples Testing: 20 samples Preprocessing: Documents were truncated to a maximum length of 512 tokens. Summaries were truncated to a maximum length of 128 tokens.

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Training Procedure

Preprocessing

Tokenization was performed using the MT5Tokenizer (slow tokenizer). Input text was tokenized with a maximum input length of 512 tokens. Target summaries were tokenized with a maximum target length of 128 tokens. Training Hyperparameters

Learning Rate: 2e-5 Batch Size: 4 (per device) Weight Decay: 0.01 Number of Epochs: 3 Precision: Mixed Precision (fp16) Optimizer: AdamW Scheduler: Linear Scheduler Save Strategy: Save the last 2 checkpoints during training.

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Summary

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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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