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The T5_small_lecture_summarization model is a variant of the T5 (Text-to-Text Transfer Transformer) architecture, which is designed for summarization tasks
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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: [Hyun Lee]
Model type: [Transformers]
Language(s) (NLP): [English]
License: [More Information Needed]
Finetuned from model [optional]: [Google/T5]
Architecture: The model is based on the T5 architecture, which employs a transformer-based neural network. Transformers have proven effective for various natural language processing (NLP) tasks due to their attention mechanisms and ability to capture contextual information.
Task: The primary purpose of this model is lecture summarization. Given a lecture or a longer text, it aims to generate a concise summary that captures the essential points. This can be valuable for students, researchers, or anyone seeking condensed information.
Input Format: The model expects input in a text-to-text format. Specifically, you provide a prompt (e.g., the lecture content) and specify the desired task (e.g., “summarize”). The model then generates a summary as the output.
Fine-Tuning: The Lucas-Hyun-Lee/T5_small_lecture_summarization model has undergone fine-tuning on bbc-news data. During fine-tuning, it learns to optimize its parameters for summarization by minimizing a loss function.
Model Size: As the name suggests, this is a small-sized variant of T5. Smaller models are computationally efficient and suitable for scenarios where memory or processing power is limited.
Performance: The model’s performance depends on the quality and diversity of the training data, as well as the specific lecture content it encounters during fine-tuning. It should be evaluated based on metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores.
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Training Details
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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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