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
File size: 486 Bytes
a772a21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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"
} |