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
| { | |
| "schema": "betterwright-relevance-config-v1", | |
| "relevance_threshold": 0.25, | |
| "confidence_threshold": 0.45, | |
| "max_retained_fraction": 1.0, | |
| "max_ranked_lines": 0, | |
| "strategy": "coarse-ref-context", | |
| "max_ranked_windows": 8, | |
| "ref_context_lines": 3, | |
| "window_chars": 1800, | |
| "minimum_validation_recall": 0.999, | |
| "minimum_validation_perfect_task_recall": 0.995, | |
| "validated_max_chars": 10000, | |
| "fallback_on_error": true, | |
| "model": "ProCreations/betterwright-encoder-350m" | |
| } |