File size: 2,017 Bytes
bf147eb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
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))
|