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# intent-onnx
This is an ONNX version of the fine-tuned intent classification model.
## Model Details
- **Model Type**: BERT-based sentence transformer
- **Architecture**: BertModel
- **Hidden Size**: 384
- **Attention Heads**: 12
- **Layers**: 3
- **Vocabulary Size**: 30,522
## Usage
### Transformers.js
```javascript
import { pipeline } from '@xenova/transformers';
const extractor = await pipeline('feature-extraction', 'drithh/intent-onnx');
const output = await extractor('your text here', {
pooling: 'mean',
normalize: true
});
console.log('Embedding shape:', output.data.length);
```
### Python
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('drithh/intent-classifier')
embeddings = model.encode('your text here')
print(f'Embedding shape: {embeddings.shape}')
```
## Training
This model was fine-tuned on intent classification data using SentenceTransformers.
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