Summarization
Transformers
English
code
Summarizer
BART
Gradio
Machine Learning
Natural Language Processing (NLP)
Deep Learning
Interactive Demo
Python
AI
Instructions to use Dannyar608/Text_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dannyar608/Text_summarizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Dannyar608/Text_summarizer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dannyar608/Text_summarizer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 610 Bytes
414fbb0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | !pip install transformers
!pip install gradio
import gradio as gr
from transformers import pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def summarize_text(text):
summary = summarizer(text, max_length=200, min_length=100, do_sample=False)
return summary[0]['summary_text']
demo = gr.Interface(
fn=summarize_text,
inputs=gr.Textbox(placeholder="Enter your text here", label="Input Text"),
outputs=gr.Textbox(label="Summary"),
title="Text Summarizer",
description="This chatbot takes a long text as input and returns a summary."
)
demo.launch() |