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README.md

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+---
+language: en
+datasets:
+- cnn_dailymail
+tags:
+- summarization
+- t5
+- flan-t5
+- transformers
+- huggingface
+- fine-tuned
+license: apache-2.0
+model-index:
+- name: FLAN-T5 Base Fine-Tuned on CNN/DailyMail
+  results:
+  - task:
+      type: summarization
+      name: Summarization
+    dataset:
+      name: CNN/DailyMail
+      type: cnn_dailymail
+    metrics:
+    - type: rouge
+      value: 25.33
+      name: Rouge-1
+    - type: rouge
+      value: 11.96
+      name: Rouge-2
+    - type: rouge
+      value: 20.68
+      name: Rouge-L
+metrics:
+- rouge
+base_model:
+- google/flan-t5-base
+pipeline_tag: summarization
+---
+
+# FLAN-T5 Base Fine-Tuned on CNN/DailyMail
+
+This model is a fine-tuned version of [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset using the Hugging Face Transformers library.
+
+## 📝 Task
+
+**Abstractive Summarization**: Given a news article, generate a concise summary.
+
+---
+
+## 📊 Evaluation Results
+
+The model was fine-tuned on 20,000 training samples and validated/tested on 2,000 samples. Evaluation was performed using ROUGE metrics:
+
+| Metric      | Score  |
+|-------------|--------|
+| ROUGE-1     | 25.33  |
+| ROUGE-2     | 11.96  |
+| ROUGE-L     | 20.68  |
+| ROUGE-Lsum  | 23.81  |
+
+---
+
+## 📦 Usage
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model = T5ForConditionalGeneration.from_pretrained("AbdullahAlnemr1/flan-t5-summarizer")
+tokenizer = T5Tokenizer.from_pretrained("AbdullahAlnemr1/flan-t5-summarizer")
+
+input_text = "summarize: The US president met with the Senate to discuss..."
+inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+
+summary_ids = model.generate(inputs["input_ids"], max_length=128, num_beams=4, early_stopping=True)
+print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))