Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- summarization
+- nlp
+- transformer
+- text-generation
+- huggingface
+datasets:
+- cnn_dailymail
+metrics:
+- rouge
+widget:
+- text: "The quick brown fox jumps over the lazy dog. This is a sample article for testing summarization."
+---
+
+# Text Summarization Model
+
+## Model Overview
+This is a **text summarization model** built using a Seq2Seq architecture.  
+It was trained on the **CNN/DailyMail dataset (3.0.0)** and is capable of generating concise summaries of news articles or other long-form texts.
+
+**Intended Use:**  
+- Summarizing articles, documents, or reports.  
+- Extracting key points from text for quick understanding.
+
+**Limitations & Biases:**  
+- May struggle with extremely long articles or highly technical content.  
+- Generated summaries may occasionally miss nuanced details.
+
+---
+
+
+## Training Details
+- **Dataset**: CNN/DailyMail (3.0.0 version)  
+- **Preprocessing**: Truncation at 512 tokens for input, summaries capped at 150 tokens.  
+- **Hyperparameters**:  
+  - Optimizer: AdamW (PyTorch)  
+  - Learning rate: 2e-5  
+  - Batch size: 4 (per device)  
+  - Epochs: 10  
+- **Evaluation Metrics**: ROUGE-1, ROUGE-2, ROUGE-L  
+
+---
+
+## Evaluation Results
+| Metric    | Score (%) |
+|-----------|-----------|
+| ROUGE-1   | 83.3      |
+| ROUGE-2   | 60.0      |
+| ROUGE-L   | 83.3      |
+| ROUGE-Lsum| 83.3      |
+
+
+---
+
+## Example Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
+model = AutoModelForSeq2SeqLM.from_pretrained("your-username/your-model-name")
+
+text = "The stock market saw a significant drop today due to rising inflation concerns. Investors are cautious ahead of the Federal Reserve's upcoming decision."
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+summary_ids = model.generate(**inputs, max_length=150, num_beams=4, early_stopping=True)
+
+print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))