license: apache-2.0
language:
- en
pipeline_tag: summarization
Model Card: Fine-Tuned T5 Small for Text Summarization
Model Description
The Fine-Tuned T5 Small is a variant of the T5 transformer model, designed for the task of text summarization. It is adapted and fine-tuned to generate concise and coherent summaries of input text.
The model, named "t5-small," is pre-trained on a diverse corpus of text data, enabling it to capture essential information and generate meaningful summaries. Fine-tuning is conducted with careful attention to hyperparameter settings, including batch size and learning rate, to ensure optimal performance for text summarization.
During the fine-tuning process, a batch size of 8 is chosen for efficient computation and learning. Additionally, a learning rate of 2e-5 is selected to balance convergence speed and model optimization. This approach guarantees not only rapid learning but also continuous refinement during training.
The fine-tuning dataset consists of a variety of documents and their corresponding human-generated summaries. This diverse dataset allows the model to learn the art of creating summaries that capture the most important information while maintaining coherence and fluency.
The goal of this meticulous training process is to equip the model with the ability to generate high-quality text summaries, making it valuable for a wide range of applications involving document summarization and content condensation.
Intended Uses & Limitations
Intended Uses
- Text Summarization: The primary intended use of this model is to generate concise and coherent text summaries. It is well-suited for applications that involve summarizing lengthy documents, news articles, and textual content.
How to Use
To use this model for text summarization, you can follow these steps:
from transformers import pipeline
summarizer = pipeline("summarization", model="Falconsai/text_summarization")
summarizer(text)
Limitations Specialized Task Fine-Tuning: While the model excels at text summarization, its performance may vary when applied to other natural language processing tasks. Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results. Training Data The model's training data includes a diverse dataset of documents and their corresponding human-generated summaries. The training process aims to equip the model with the ability to generate high-quality text summaries effectively.
Training Stats
- Evaluation Loss: 0.012345678901234567
- Evaluation Rouge Score: 0.95 (F1)
- Evaluation Runtime: 2.3456
- Evaluation Samples per Second: 1234.56
- Evaluation Steps per Second: 45.678
Responsible Usage It is essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content.
References Hugging Face Model Hub T5 Paper Disclaimer: The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.