Summarization
Transformers
Safetensors
English
t5
text2text-generation
flan-t5
text-generation-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("UX4567/Text-Summarizer-t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("UX4567/Text-Summarizer-t5-small", device_map="auto")Quick Links
Text Summarizer (T5 Fine-Tuned)
This model is a fine-tuned version of T5 designed for abstractive text summarization. It condenses long articles, documents, or paragraphs into short, accurate, and context-aware summaries.
Model Details
Model Description
- Developed by: Kartik Sharma
- Model type: Sequence-to-Sequence (Encoder-Decoder)
- Language(s): English
- Base Model: T5 Architecture
- Task: Text Summarization
How to Get Started with the Model
You can load and test the model using the Hugging Face transformers pipeline or direct model classes:
Using Transformers Pipeline
from transformers import pipeline
summarizer = pipeline("text-generation", model="UX4567/Text-Summarizer-t5-small")
text = """
Artificial Intelligence (AI) is transforming industries across the globe. From healthcare to finance,
machine learning models are enabling automation, improving efficiency, and driving innovation.
As AI technology continues to evolve, ethical considerations and proper implementation become
critical for sustainable integration.
"""
summary = summarizer(text, max_length=60, min_length=25, do_sample=False)
print(summary[0]['summary_text'])
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# 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="UX4567/Text-Summarizer-t5-small")