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
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
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.