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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dannyar608/Text_summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create Code
Browse files
Code
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!pip install transformers
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!pip install gradio
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import gradio as gr
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from transformers import pipeline
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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def summarize_text(text):
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summary = summarizer(text, max_length=200, min_length=100, do_sample=False)
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return summary[0]['summary_text']
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demo = gr.Interface(
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fn=summarize_text,
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inputs=gr.Textbox(placeholder="Enter your text here", label="Input Text"),
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outputs=gr.Textbox(label="Summary"),
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title="Text Summarizer",
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description="This chatbot takes a long text as input and returns a summary."
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)
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demo.launch()
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