# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Kaludi/Quick-Summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("Kaludi/Quick-Summarization")Quick Links
Quick Summarization
This is a Text Summarization Model that has been trained by Kaludi to Transform long and complex texts into concise and meaningful summaries. Get a quick and accurate overview of any document in seconds, saving you time and effort.
Gradio
Tis model supports a Gradio Web UI to run the data-food-classification model:
Validation Metrics
- Loss: 1.629
- Rouge1: 41.066
- Rouge2: 19.231
- RougeL: 28.295
- RougeLsum: 37.746
- Gen Len: 98.873
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Kaludi/autotrain-quik-sum-3280991391
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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="Kaludi/Quick-Summarization")