---
license: mit
datasets:
- npc-engine/light-batch-summarize-dialogue
language:
- en
metrics:
- accuracy
base_model:
- facebook/bart-large-cnn
pipeline_tag: summarization
library_name: transformers
tags:
- code
---

Model Card: Large English Summarizer
Model Overview
This model is a large-scale transformer-based summarization model, designed for producing concise and coherent summaries of English text. It leverages the power of pre-trained language models to generate summaries while maintaining key information.

Intended Use
The model is ideal for tasks such as summarizing articles, research papers, or any form of lengthy text, providing users with a quick overview of the content.

Model Architecture

Transformer-based architecture, likely BERT or GPT derived.
Fine-tuned for English text summarization tasks.
Training Data

Trained on a npc-engine/light-batch-summarize-dialogue.
The model is fine-tuned to understand and summarize general content, suitable for a wide range of domains.
Performance

Achieves high accuracy in generating human-readable summaries.
Balances between fluency and informativeness, focusing on retaining essential information while shortening text effectively.
Limitations

May struggle with highly technical or domain-specific content outside its training scope.
Could generate biased summaries if the input text contains biased language.
Ethical Considerations
Users should be aware of potential biases in the training data. It is recommended to review generated summaries, especially when used in decision-making processes.

How to Use
The model can be accessed via the Hugging Face API. Ensure proper token authentication for seamless access and usage.