metadata
language: en
license: mit
tags:
- summarization
- nlp
- transformer
- text-generation
- huggingface
datasets:
- cnn_dailymail
metrics:
- rouge
widget:
- text: >-
The quick brown fox jumps over the lazy dog. This is a sample article for
testing summarization.
Text Summarization Model
Model Overview
This is a text summarization model built using a Seq2Seq architecture.
It was trained on the CNN/DailyMail dataset (3.0.0) and is capable of generating concise summaries of news articles or other long-form texts.
Intended Use:
- Summarizing articles, documents, or reports.
- Extracting key points from text for quick understanding.
Limitations & Biases:
- May struggle with extremely long articles or highly technical content.
- Generated summaries may occasionally miss nuanced details.
Training Details
- Dataset: CNN/DailyMail (3.0.0 version)
- Preprocessing: Truncation at 512 tokens for input, summaries capped at 150 tokens.
- Hyperparameters:
- Optimizer: AdamW (PyTorch)
- Learning rate: 2e-5
- Batch size: 4 (per device)
- Epochs: 10
- Evaluation Metrics: ROUGE-1, ROUGE-2, ROUGE-L
Evaluation Results
| Metric | Score (%) |
|---|---|
| ROUGE-1 | 83.3 |
| ROUGE-2 | 60.0 |
| ROUGE-L | 83.3 |
| ROUGE-Lsum | 83.3 |
Example Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/your-model-name")
text = "The stock market saw a significant drop today due to rising inflation concerns. Investors are cautious ahead of the Federal Reserve's upcoming decision."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
summary_ids = model.generate(**inputs, max_length=150, num_beams=4, early_stopping=True)
print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))