| --- |
| 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)) |
| |
