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.