drithh/intent-onnx
By drithh · ↓ 0 · ♥ 0 · Updated 2025-07-29
What's in this repo
This page lists the files in drithh/intent-onnx, including model_quantized.onnx.
- 1 ONNX models (.onnx files)
- 8 files, including 4 .json, 1 .md, 1 .onnx, 1 .txt
- Last updated 2025-07-29
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model_quantized.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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.
Files (8)
.gitattributes
README.md
config.json
onnx/model_quantized.onnx
ONNX
special_tokens_map.json
tokenizer.json
tokenizer_config.json
vocab.txt
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