File size: 3,230 Bytes
1b12abd 68a54cd 1b12abd 68a54cd 1b12abd 68a54cd 1b12abd 68a54cd 1b12abd | 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 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | # ConceptFrameMet: Metaphor Detection with Frame and Source Domain Prediction
**A comprehensive metaphor detection model that predicts semantic frames and source domains**
## Model Description
ConceptFrameMet is a metaphor detection model which not only detects metaphors but also predicts:
1. **Metaphor Classification**: Whether a target word is used metaphorically or literally
2. **Semantic Frames**: The conceptual frame evoked by the target word
3. **Source Domains**: The source domain of the metaphor (for metaphorical uses)
Please see https://github.com/julia-nixie/ConceptFrameMet and PAPER LINK for details.
## Model Architecture
- **Base Model**: RoBERTa-base
- **Architecture**: MelBERT with adaptive source domain integration
- **Training Data**: VUA18 metaphor corpus
- **Configuration**:
- Source blend mode: replacement
- Source use mode: metaphor_only
- Metaphor threshold: 0.5
## Performance
Evaluated on standard metaphor detection benchmarks:
| Dataset | F1 Score | Accuracy |
|---------|----------|----------|
| VUA18 | 0.767 | 0.930 |
| MOH-X | 0.814 | 0.803 |
| TroFi | 0.633 | 0.605 |
## Quick Start
### Installation
```bash
pip install transformers torch
```
### Basic Usage
```python
from transformers import RobertaTokenizer
import torch
# Load model and tokenizer
model_path = "nixie1981/ConceptFrameMet"
tokenizer = RobertaTokenizer.from_pretrained(model_path)
# Example sentence
sentence = "The company is navigating through troubled waters"
target_word = "navigating"
# Predict metaphor with frame and source
result = predict_metaphor(sentence, target_word)
print(f"Is Metaphor: {result['is_metaphor']}")
print(f"Confidence: {result['metaphor_confidence']:.2f}")
print(f"Semantic Frame: {result['frame']}")
print(f"Source Domain: {result['source']}")
```
### Expected Output
```
Is Metaphor: True
Confidence: 0.92
Semantic Frame: Self_motion
Source Domain: JOURNEY
```
## Use Cases
1. **Metaphor Detection**: Identify metaphorical language in text
2. **Frame Analysis**: Understand conceptual frames in discourse
3. **Source Mapping**: Identify source-target domain mappings
4. **Literary Analysis**: Analyze figurative language patterns
5. **Education**: Teaching metaphor comprehension
## Model Inputs
The model expects:
- **sentence**: The full sentence containing the target word
- **target_word**: The specific word to analyze for metaphor
## Model Outputs
The model returns a dictionary with:
- `is_metaphor`: Boolean indicating if the target is metaphorical
- `metaphor_confidence`: Confidence score for metaphor prediction (0-1)
- `frame`: Predicted semantic frame
- `frame_confidence`: Confidence for frame prediction
- `source`: Predicted source domain (for metaphors)
- `source_confidence`: Confidence for source prediction
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{conceptframemet2026,
title={ConceptFrameMet: Metaphor Detection with Frame and Source Domain Prediction},
author={Your Name},
year={2026},
url={https://huggingface.co/YOUR_USERNAME/ConceptFrameMet}
}
```
## Contact
For questions or issues, please open an issue on the model repository or contact [your email].
|