README.md

metadata

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 on the CNN/DailyMail dataset using the Hugging Face Transformers library.

📝 Task

Abstractive Summarization: Given a news article, generate a concise summary.

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📊 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

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))