Text Classification
Transformers
Safetensors
lfm2
feature-extraction
betterwright
accessibility
browser-agent
reranking
long-context
custom_code
Instructions to use ProCreations/betterwright-encoder-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/betterwright-encoder-350m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProCreations/betterwright-encoder-350m", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ProCreations/betterwright-encoder-350m", trust_remote_code=True) model = AutoModel.from_pretrained("ProCreations/betterwright-encoder-350m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ProCreations/betterwright-encoder-350m: direct link, hf CLI and curl.
- Browser
- Download file 4.73 MB
-
https://huggingface.co/ProCreations/betterwright-encoder-350m/resolve/main/tokenizer.json
- Command line
-
hf download hf://ProCreations/betterwright-encoder-350m/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ProCreations/betterwright-encoder-350m/resolve/main/tokenizer.json
4.73 MB
File too large to display, you can check the raw version instead.