bart-large-cnn-samsum
Maintainer: philschmid
236
📈
| 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 bart-large-cnn-samsum model is a transformer-based text summarization model trained using Amazon SageMaker and the Hugging Face Deep Learning container. It was fine-tuned on the SamSum dataset, which consists of conversational dialogues and their corresponding summaries.
This model is similar to other text summarization models like bart_summarisation and flan-t5-base-samsum, which have also been fine-tuned on the SamSum dataset. However, the maintainer philschmid notes that the newer flan-t5-base-samsum model outperforms this BART-based model on the SamSum evaluation set.
Model inputs and outputs
The bart-large-cnn-samsum model takes conversational dialogues as input and generates concise summaries as output. The input can be a single string containing the entire conversation, and the output is a summarized version of the input.
Inputs
- Conversational dialogue: A string containing the full text of a conversation, with each participant's lines separated by newline characters.
Outputs
- Summary: A condensed, coherent summary of the input conversation, generated by the model.
Capabilities
The bart-large-cnn-samsum model is capable of generating high-quality summaries of conversational dialogues. It can identify the key points and themes of a conversation and articulate them in a concise, readable form. This makes the model useful for tasks like customer service, meeting notes, and other scenarios where summarizing conversations is valuable.
What can I use it for?
The bart-large-cnn-samsum model can be used in a variety of applications that involve summarizing conversational text. For example, it could be integrated into a customer service chatbot to provide concise summaries of customer interactions. It could also be used to generate meeting notes or highlight the main takeaways from team discussions.
Things to try
While the maintainer recommends trying the newer flan-t5-base-samsum model instead, the bart-large-cnn-samsum model can still be a useful tool for text summarization. Experiment with different input conversations and compare the model's performance to the recommended alternative. You may also want to explore fine-tuning the model on your own specialized dataset to see if it can be further improved for your specific use case.
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
👀
bart_summarisation
57
The bart-large-cnn-samsum model is a text summarization model fine-tuned on the SamSum dataset using the BART architecture. It was trained by slauw87 using Amazon SageMaker and the Hugging Face Deep Learning container. This model is part of a family of BART-based models that have been optimized for different text summarization tasks. While the base BART model is trained on a large corpus of text, fine-tuning on a specific dataset like SamSum can improve the model's performance on that type of text. The SamSum dataset contains multi-turn dialogues and their summaries, making the bart-large-cnn-samsum model well-suited for summarizing conversational text. Similar models include text_summarization (a fine-tuned T5 model for general text summarization), led-large-book-summary (a Longformer-based model specialized for summarizing long-form text), and flan-t5-base-samsum (another BART-based model fine-tuned on the SamSum dataset). Model Inputs and Outputs Inputs Conversational text**: The bart-large-cnn-samsum model takes multi-turn dialogue as input and generates a concise summary. Outputs Text summary**: The model outputs a short, abstractive summary of the input conversation. Capabilities The bart-large-cnn-samsum model excels at summarizing dialogues and multi-turn conversations. It can capture the key points and salient information from lengthy exchanges, condensing them into a readable, coherent summary. For example, given the following conversation: Sugi: I am tired of everything in my life. Tommy: What? How happy you life is! I do envy you. Sugi: You don't know that I have been over-protected by my mother these years. I am really about to leave the family and spread my wings. Tommy: Maybe you are right. The model generates the following summary: "The narrator tells us that he's tired of his life and feels over-protected by his mother, and is considering leaving his family to gain more independence." What can I use it for? The bart-large-cnn-samsum model can be used in a variety of applications that involve summarizing conversational text, such as: Customer service chatbots**: Automatically summarizing the key points of a customer support conversation to provide quick insights for agents. Meeting transcripts**: Condensing lengthy meeting transcripts into concise summaries for busy executives. Online forums**: Generating high-level synopses of multi-user discussions on online forums and message boards. slauw87's work on this model demonstrates how fine-tuning large language models like BART can produce specialized summarization capabilities tailored to specific domains and data types. Things to try One interesting aspect of the bart-large-cnn-samsum model is its ability to generate abstractive summaries, meaning it can produce novel text that captures the essence of the input, rather than just extracting key phrases. This can lead to more natural-sounding and coherent summaries. You could experiment with providing the model longer or more complex dialogues to see how it handles summarizing more nuanced conversational dynamics. Additionally, you could try comparing the summaries generated by this model to those from other text summarization models, like led-large-book-summary, to understand the unique strengths and limitations of each approach.
Updated Invalid Date
⚙️
flan-t5-base-samsum
81
The flan-t5-base-samsum model is a fine-tuned version of the google/flan-t5-base model on the samsum dataset. It achieves strong performance on text summarization tasks, with results including a loss of 1.3716, Rouge1 of 47.2358, Rouge2 of 23.5135, Rougel of 39.6266, and Rougelsum of 43.3458. Model inputs and outputs The flan-t5-base-samsum model takes in text data and generates summarized output text. It can be used for a variety of text-to-text tasks beyond just summarization. Inputs Text data to be summarized or transformed Outputs Summarized or transformed text data Capabilities The flan-t5-base-samsum model demonstrates strong capabilities in text summarization, able to concisely capture the key points of longer input text. It could be used for tasks like summarizing news articles, meeting notes, or other lengthy documents. What can I use it for? The flan-t5-base-samsum model could be useful for automating text summarization in a variety of business and research applications. For example, it could help teams quickly process and synthesize large amounts of information, or provide summaries for customer support agents to reference. The model's flexibility also means it could potentially be fine-tuned for other text-to-text tasks beyond just summarization. Things to try One interesting thing to try with the flan-t5-base-samsum model is using it for interactive summarization, where the user can provide feedback and the model can iteratively refine the summary. This could help ensure the most salient points are captured. Another idea is to use the model in a pipeline with other NLP components, such as topic modeling or sentiment analysis, to gain deeper insights from text data.
Updated Invalid Date
📊
chatgpt-gpt4-prompts-bart-large-cnn-samsum
77
The chatgpt-gpt4-prompts-bart-large-cnn-samsum model is a fine-tuned version of the philschmid/bart-large-cnn-samsum model on a dataset of ChatGPT and GPT-3 prompts. This model generates prompts that can be used to interact with ChatGPT, BingChat, and GPT-3 language models. The model was created by Kaludi, and achieves a train loss of 1.2214, validation loss of 2.7584, and was trained for 4 epochs. It uses the BART-large-cnn architecture and was fine-tuned on a dataset of high-quality ChatGPT and GPT-3 prompts. Similar models include the chatgpt-prompts-bart-long model, which is also a fine-tuned BART model for generating ChatGPT prompts, and the chatgpt-prompt-generator-v12 model, which is another BART-based prompt generator. Model inputs and outputs Inputs Text prompts to generate ChatGPT, BingChat, or GPT-3 prompts Outputs Generated text prompts that can be used to interact with large language models like ChatGPT, BingChat, or GPT-3 Capabilities The chatgpt-gpt4-prompts-bart-large-cnn-samsum model can generate unique and high-quality prompts for interacting with large language models. These prompts can be used to create personas, simulate conversations, or explore different topics and use cases. The model has been finetuned on a diverse dataset of prompts, enabling it to generate a wide variety of outputs. What can I use it for? You can use this model to quickly and easily generate prompts for interacting with ChatGPT, BingChat, or GPT-3. This can be helpful for a variety of use cases, such as: Exploring different conversational scenarios and personas Generating prompts for chatbots or conversational agents Experimenting with language model capabilities and limitations Collecting training data for other language models or applications The model is available through a Streamlit web app, making it easy to use without any additional setup. Things to try One interesting thing to try with this model is to generate prompts that explore the capabilities and limitations of large language models like ChatGPT. You could generate prompts that test the model's knowledge on specific topics, its ability to follow instructions, or its tendency to hallucinate or generate biased outputs. By carefully analyzing the responses, you can gain insights into how these models work and where they may have weaknesses. Another idea is to use the generated prompts as a starting point for more complex conversational interactions. You could take the prompts and expand on them, adding additional context or instructions to see how the language models respond. This could be a useful technique for prototyping conversational applications or exploring the boundaries of what these models can do.
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