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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- keyword-extraction
+- research-papers
+- t5
+- text-generation
+- academic
+datasets:
+- custom
+widget:
+- text: "extract keywords: Deep Learning for Computer Vision Applications"
+  example_title: "Computer Vision Example"
+- text: "extract keywords: Quantum Machine Learning for Drug Discovery"
+  example_title: "Quantum Computing Example"
+- text: "extract keywords: Blockchain Technology for Supply Chain Management"
+  example_title: "Blockchain Example"
+---
+
+# Research Paper Keyword Extractor
+
+## Model Description
+
+This is a fine-tuned T5-small model specifically trained for extracting keywords from research paper titles. The model takes a research paper title as input and generates relevant keywords that capture the main topics, methodologies, and application domains.
+
+## Training Data
+
+- **Total Training Examples**: 35
+- **Validation Examples**: 9
+- **Data Sources**: Manual curation + synthetic generation
+- **Domains Covered**: Computer Science, Healthcare, Physics, Engineering, Mathematics, Biology, and more
+
+## Training Configuration
+
+- **Base Model**: t5-small
+- **Epochs**: 3
+- **Batch Size**: 2
+- **Learning Rate**: 0.0005
+- **Max Input Length**: 96 tokens
+- **Max Output Length**: 48 tokens
+
+## Usage
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load model and tokenizer
+tokenizer = T5Tokenizer.from_pretrained("ZoeDuan/research-keyword-extractor")
+model = T5ForConditionalGeneration.from_pretrained("ZoeDuan/research-keyword-extractor")
+
+def extract_keywords(title):
+    input_text = f"extract keywords: {title}"
+    input_ids = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=96).input_ids
+    
+    outputs = model.generate(
+        input_ids,
+        max_length=48,
+        num_beams=4,
+        no_repeat_ngram_size=2,
+        early_stopping=True,
+        do_sample=True,
+        temperature=0.8
+    )
+    
+    keywords = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return keywords
+
+# Example usage
+title = "Machine Learning for Natural Language Processing Applications"
+keywords = extract_keywords(title)
+print(keywords)
+# Expected output: Machine Learning, Natural Language Processing, NLP, AI, Text Processing
+```
+
+## Example Predictions
+
+| Input Title | Generated Keywords |
+|-------------|-------------------|
+| Deep Learning for Computer Vision Applications | Deep Learning, Computer Vision, Neural Networks, AI, Image Processing |
+| Quantum Computing in Cryptography and Security | Quantum Computing, Cryptography, Security, Quantum Algorithms, Cybersecurity |
+| IoT and Edge Computing for Smart Cities | IoT, Edge Computing, Smart Cities, Internet of Things, Urban Technology |
+
+## Model Performance
+
+The model has been trained on diverse research domains and can extract:
+- **Technical methodologies** (e.g., Machine Learning, Deep Learning)
+- **Application domains** (e.g., Healthcare, Finance)
+- **Specific technologies** (e.g., Transformer, CNN, Blockchain)
+- **Research areas** (e.g., Computer Vision, NLP)
+
+## Limitations
+
+- Optimized for research paper titles in English
+- May not perform well on highly specialized or emerging domains not covered in training
+- Best performance on titles between 5-15 words
+- May occasionally generate overlapping or redundant keywords
+
+## License
+
+This model is released under the Apache 2.0 license.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```
+@misc{research-keyword-extractor,
+  title={Research Paper Keyword Extractor},
+  author={Zoe Duan},
+  year={2025},
+  url={https://huggingface.co/ZoeDuan/research-keyword-extractor}
+}
+```