| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | metrics: |
| | - rouge |
| | base_model: google/pegasus-cnn_dailymail |
| | --- |
| | |
| | ### Pegasus-based Text Summarization Model |
| | Model Name: pegsus-text-summarization |
| |
|
| | ### Model Description |
| | This model is a fine-tuned version of the Pegasus model, specifically adapted for the task of text summarization. It is trained on the SAMSum dataset, which is designed for summarizing conversations. |
| |
|
| | ### Usage |
| | This model can be used to generate concise summaries of input text, particularly for conversational text or dialogue-based inputs. |
| |
|
| | ### How to Use |
| | You can use this model with the Hugging Face transformers library. Below is an example code snippet: |
| |
|
| | ```bash |
| | |
| | from transformers import PegasusForConditionalGeneration, PegasusTokenizer |
| | |
| | # Load the pre-trained model and tokenizer |
| | model_name = "ailm/pegsus-text-summarization" |
| | model = PegasusForConditionalGeneration.from_pretrained(model_name) |
| | tokenizer = PegasusTokenizer.from_pretrained(model_name) |
| | |
| | # Define the input text |
| | text = "Your input text here" |
| | |
| | # Tokenize the input text |
| | tokens = tokenizer(text, truncation=True, padding="longest", return_tensors="pt") |
| | |
| | # Generate the summary |
| | summary = model.generate(**tokens) |
| | |
| | # Decode and print the summary |
| | print(tokenizer.decode(summary[0], skip_special_tokens=True)) |
| | |
| | |
