Fill-Mask
Transformers.js
ONNX
English
modernbert
ecommerce
e-commerce
retail
marketplace
shopping
amazon
ebay
alibaba
google
rakuten
bestbuy
walmart
flipkart
wayfair
shein
target
etsy
shopify
taobao
asos
carrefour
costco
overstock
pretraining
encoder
language-modeling
foundation-model
Instructions to use thomasht86/RexBERT-base-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use thomasht86/RexBERT-base-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('fill-mask', 'thomasht86/RexBERT-base-ONNX');
Download quantize_config.json from thomasht86/RexBERT-base-ONNX: direct link, hf CLI and curl.
- Browser
- Download file 310 Bytes
-
https://huggingface.co/thomasht86/RexBERT-base-ONNX/resolve/main/quantize_config.json
- Command line
-
hf download hf://thomasht86/RexBERT-base-ONNX/quantize_config.json
-
curl -L -o quantize_config.json https://huggingface.co/thomasht86/RexBERT-base-ONNX/resolve/main/quantize_config.json
310 Bytes
| { | |
| "modes": [ | |
| "fp16", | |
| "q8", | |
| "int8", | |
| "uint8", | |
| "q4", | |
| "q4f16", | |
| "bnb4" | |
| ], | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "block_size": null, | |
| "is_symmetric": true, | |
| "accuracy_level": null, | |
| "quant_type": 1, | |
| "op_block_list": null | |
| } |