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--- |
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license: mit |
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datasets: |
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- npc-engine/light-batch-summarize-dialogue |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- facebook/bart-large-cnn |
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pipeline_tag: summarization |
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library_name: transformers |
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tags: |
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- code |
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--- |
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Model Card: Large English Summarizer |
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Model Overview |
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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. |
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Intended Use |
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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. |
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Model Architecture |
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Transformer-based architecture, likely BERT or GPT derived. |
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Fine-tuned for English text summarization tasks. |
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Training Data |
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Trained on a npc-engine/light-batch-summarize-dialogue. |
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The model is fine-tuned to understand and summarize general content, suitable for a wide range of domains. |
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Performance |
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Achieves high accuracy in generating human-readable summaries. |
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Balances between fluency and informativeness, focusing on retaining essential information while shortening text effectively. |
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Limitations |
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May struggle with highly technical or domain-specific content outside its training scope. |
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Could generate biased summaries if the input text contains biased language. |
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Ethical Considerations |
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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. |
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How to Use |
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The model can be accessed via the Hugging Face API. Ensure proper token authentication for seamless access and usage. |
