led-large-book-summary
Maintainer: pszemraj
94
🔄
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The led-large-book-summary model is a fine-tuned version of the allenai/led-large-16384 model, specialized for the task of summarizing lengthy text. It was fine-tuned on the BookSum dataset (kmfoda/booksum) to generalize well and be useful for summarizing academic and everyday text.
Model inputs and outputs
Inputs
- Text: The model can handle up to 16,384 tokens of input text.
Outputs
- Summary: The model generates a concise summary of the input text.
Capabilities
The led-large-book-summary model excels at summarizing lengthy text, aiming to capture the key information while maintaining coherence and fluency. It can handle input up to 16,384 tokens, making it suitable for summarizing academic papers, books, and other long-form content.
What can I use it for?
The led-large-book-summary model can be employed in a variety of applications that involve text summarization. For example, researchers and students can use it to quickly summarize academic papers and textbooks, while businesses can leverage it to condense lengthy reports and documents. The model's ability to handle long-form text makes it particularly valuable in settings where time is limited, and concise summaries are needed.
Things to try
One interesting aspect of the led-large-book-summary model is its potential to be used in conjunction with other language models or task-specific fine-tuning. By combining its strengths in long-form text summarization with specialized models for tasks like sentiment analysis or question answering, users can create powerful applications that extract key insights from large volumes of text.
Additionally, users can experiment with different decoding parameters, such as encoder_no_repeat_ngram_size, to encourage the model to generate more abstractive and diverse summaries that go beyond simple extraction.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
📈
led-base-book-summary
56
The led-base-book-summary model is a fine-tuned version of the Longformer Encoder-Decoder (LED) model that has been optimized for summarizing long narratives, articles, papers, textbooks, and other lengthy documents. It was developed by pszemraj and is available through the Hugging Face model hub. Compared to similar summarization models like led-large-book-summary, long-t5-tglobal-base-16384-book-summary, and text_summarization, the led-base-book-summary model is the smallest and fastest BookSum-tuned variant. While it may not generate the highest quality summaries, it offers a more efficient and accessible option for summarizing long-form text. Model inputs and outputs Inputs Long-form text, such as articles, papers, books, or other lengthy documents Outputs Concise, coherent summaries that capture the key points and insights from the input text Capabilities The led-base-book-summary model excels at condensing extensive technical, academic, and narrative content into succinct, insightful summaries. It is particularly well-suited for generating "sparknotes-esque" explanations that offer a high-level overview of long-form material. What can I use it for? The led-base-book-summary model could be useful for a variety of applications that involve summarizing lengthy documents, such as: Generating summaries of research papers, technical reports, or academic textbooks to aid in literature review and research tasks Creating concise overviews of news articles or blog posts to help readers quickly digest the key information Providing summaries of books or other long-form narratives to give readers a high-level understanding of the content Things to try One interesting aspect of the led-base-book-summary model is its ability to generate "explanatory" summaries that go beyond simply extracting the most important points. By leveraging the sparknotes-style summarization approach, you can experiment with using the model to produce insightful, narrative-driven summaries that provide more than just a bullet-point list of key facts. Additionally, you can try fine-tuning the model further on your own dataset or domain-specific content to see if you can improve the relevance and quality of the summaries for your particular use case.
Updated Invalid Date
👀
long-t5-tglobal-base-16384-book-summary
117
The long-t5-tglobal-base-16384-book-summary is a fine-tuned version of the google/long-t5-tglobal-base model on the kmfoda/booksum dataset. This model is designed to summarize long text, providing a concise and coherent summary of the content. It generalizes well to academic and narrative text, and can generate "SparkNotes-esque" summaries on a variety of topics. Model inputs and outputs Inputs Long text**: The model can handle long input sequences up to 16,384 tokens. Outputs Summary text**: The model generates a summary of the input text, with a maximum output length of 1,024 tokens. Capabilities The long-t5-tglobal-base-16384-book-summary model excels at summarizing long-form text. It can digest large amounts of information and distill the key points into a concise summary. This makes it useful for tasks like academic paper summarization, novel chapter summaries, or condensing lengthy articles. What can I use it for? The long-t5-tglobal-base-16384-book-summary model can be leveraged in a variety of applications that require summarizing long-form text. For example, you could use it to automatically generate summaries of research papers or book chapters, saving time and effort for readers. It could also be integrated into content curation platforms to provide users with high-level overviews of lengthy articles or reports. Things to try One interesting use case for this model is to generate summaries of niche or obscure topics. The model's ability to generalize across domains means it can likely provide useful summaries even for relatively specialized content. You could experiment with feeding the model lengthy passages on topics like ancient history, modern philosophy, or cutting-edge scientific research, and see the concise summaries it produces.
Updated Invalid Date
✨
financial-summarization-pegasus
117
The financial-summarization-pegasus model is a specialized language model fine-tuned on a dataset of financial news articles from Bloomberg. It is based on the PEGASUS model, which was originally proposed for the task of abstractive summarization. This model aims to generate concise and informative summaries of financial content, which can be useful for quickly grasping the key points of lengthy financial reports or news articles. Compared to similar models, the financial-summarization-pegasus model has been specifically tailored for the financial domain, which can lead to improved performance on that type of content compared to more general summarization models. For example, the pegasus-xsum model is a version of PEGASUS that has been fine-tuned on the XSum dataset for general-purpose summarization, while the text_summarization model is a fine-tuned T5 model for text summarization. The financial-summarization-pegasus model aims to provide specialized capabilities for financial content. Model Inputs and Outputs Inputs Financial news articles**: The model takes as input financial news articles or reports, such as those covering stocks, markets, currencies, rates, and cryptocurrencies. Outputs Concise summaries**: The model generates summarized text that captures the key points and important information from the input financial content. The summaries are designed to be concise and informative, allowing users to quickly grasp the essential details. Capabilities The financial-summarization-pegasus model excels at generating coherent and factually accurate summaries of financial news and reports. It can distill lengthy articles down to their core elements, highlighting the most salient information. This can be particularly useful for investors, analysts, or anyone working in the financial industry who needs to quickly understand the main takeaways from a large volume of financial content. What Can I Use It For? The financial-summarization-pegasus model can be leveraged in a variety of applications related to the financial industry: Financial news aggregation**: The model could be used to automatically summarize financial news articles from sources like Bloomberg, providing users with concise overviews of the key points. Financial report summarization**: The model could be applied to lengthy financial reports and earnings statements, helping analysts and investors quickly identify the most important information. Investment research assistance**: Portfolio managers and financial advisors could use the model to generate summaries of market analysis, economic forecasts, and other financial research, streamlining their decision-making processes. Regulatory compliance**: Financial institutions could leverage the model to quickly summarize regulatory documents and updates, ensuring they remain compliant with the latest rules and guidelines. Things to Try One interesting aspect of the financial-summarization-pegasus model is its potential to handle domain-specific terminology and jargon commonly found in financial content. Try feeding the model a complex financial report or article and see how well it is able to distill the key information while preserving the necessary technical details. You could also experiment with different generation parameters, such as adjusting the length of the summaries or trying different beam search configurations, to find the optimal balance between conciseness and completeness for your specific use case. Additionally, you may want to compare the performance of this model to the advanced version mentioned in the description, which reportedly offers enhanced performance through further fine-tuning.
Updated Invalid Date
🐍
text_summarization
148
The text_summarization model is a variant of the T5 transformer model, designed specifically for the task of text summarization. Developed by Falconsai, this fine-tuned model is adapted to generate concise and coherent summaries of input text. It builds upon the capabilities of the pre-trained T5 model, which has shown strong performance across a variety of natural language processing tasks. Similar models like FLAN-T5 small, T5-Large, and T5-Base have also been fine-tuned for text summarization and related language tasks. However, the text_summarization model is specifically optimized for the summarization objective, with careful attention paid to hyperparameter settings and the training dataset. Model inputs and outputs The text_summarization model takes in raw text as input and generates a concise summary as output. The input can be a lengthy document, article, or any other form of textual content. The model then processes the input and produces a condensed version that captures the most essential information. Inputs Raw text**: The model accepts any form of unstructured text as input, such as news articles, academic papers, or user-generated content. Outputs Summarized text**: The model generates a concise summary of the input text, typically a few sentences long, that highlights the key points and main ideas. Capabilities The text_summarization model is highly capable at extracting the most salient information from lengthy input text and generating coherent summaries. It has been fine-tuned to excel at tasks like document summarization, content condensation, and information extraction. The model can handle a wide range of subject matter and styles of writing, making it a versatile tool for summarizing diverse textual content. What can I use it for? The text_summarization model can be employed in a variety of applications that involve summarizing textual data. Some potential use cases include: Automated content summarization**: The model can be integrated into content management systems, news aggregators, or other platforms to provide users with concise summaries of articles, reports, or other lengthy documents. Research and academic assistance**: Researchers and students can leverage the model to quickly summarize research papers, technical documents, or other scholarly materials, saving time and effort in literature review. Customer support and knowledge management**: Customer service teams can use the model to generate summaries of support tickets, FAQs, or product documentation, enabling more efficient information retrieval and knowledge sharing. Business intelligence and data analysis**: Enterprises can apply the model to summarize market reports, financial documents, or other business-critical information, facilitating data-driven decision making. Things to try One interesting aspect of the text_summarization model is its ability to handle diverse input styles and subject matter. Try experimenting with the model by providing it with a range of textual content, from news articles and academic papers to user reviews and technical manuals. Observe how the model adapts its summaries to capture the key points and maintain coherence across these varying contexts. Additionally, consider comparing the summaries generated by the text_summarization model to those produced by similar models like FLAN-T5 small or T5-Base. Analyze the differences in the level of detail, conciseness, and overall quality of the summaries to better understand the unique strengths and capabilities of the text_summarization model.
Updated Invalid Date