---
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))