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README.md

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+# 🧠 General Text Summarizer
+
+This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6), trained to generate **concise and fluent summaries** of general English text — including **news articles, essays, stories, and blog posts**.
+
 ---
-library_name: transformers
-license: apache-2.0
-base_model: sshleifer/distilbart-cnn-12-6
-tags:
-- summarization
-- general-purpose
-- cpu-friendly
-- transformers
-metrics:
-- rouge
-model-index:
-- name: general_text_summarizer_cpu
-  results:
-  - task:
-      name: Summarization
-      type: summarization
-    metrics:
-      - name: Rouge1
-        type: rouge
-        value: 36.61
-      - name: Rouge2
-        type: rouge
-        value: 16.51
-      - name: Rougel
-        type: rouge
-        value: 26.24
+
+## 🚀 Model Description
+
+- **Base model:** DistilBART (CNN/DailyMail)
+- **Framework:** 🤗 Transformers (PyTorch)
+- **Training goal:** Summarize text across multiple domains (not limited to one topic)
+- **Device optimized:** CPU & Apple M-series chips (MPS compatible)
+
+This model is suitable for lightweight summarization tasks on laptops or limited-resource machines.
+
 ---
 
-## Model description
+## 🧾 Example Usage
+
+from transformers import pipeline
 
-general_text_summarizer_cpu is a lightweight text summarization model fine-tuned on a general-purpose English dataset.
-It is designed to generate concise and coherent summaries for articles, reports, blog posts, and other long-form text.
+summarizer = pipeline("summarization", model="Fathi7ma/general_text_summarizer_cpu")
 
-This model is optimized for CPU usage and can run efficiently on MacBooks and other low-memory devices.
-It is based on a pre-trained T5 transformer, adapted for general summarization tasks.
+text = """
+Climate change continues to affect weather patterns across the globe.
+Scientists warn that without immediate action, rising temperatures may lead
+to irreversible damage to ecosystems and human livelihoods.
+"""
 
+summary = summarizer(text, max_length=80, min_length=25, do_sample=False)
+print(summary[0]['summary_text'])
+        
 ## Intended uses
 
 This model can summarize: