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
Upload README.md with huggingface_hub
Browse files
README.md
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The training corpus contains 500,000 deduplicated structural examples grounded
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in 5,256 independently generated and audited tasks from 141 real-site domains.
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Domains—not rows—define the split: 110 train, 8 validation, and 23
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test domains.
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The corpus is primarily BetterWright accessibility trees, with a smaller share
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of alternate accessibility serializations for robustness.
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## Evaluation policy
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Fallback thresholds are selected
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## Safety and limitations
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only path for security decisions. Snapshot text is untrusted data. Consumers
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should keep deterministic must-retain rules and fall back to the full tree on
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model errors, timeouts, low confidence, or insufficient retained context.
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The training corpus contains 500,000 deduplicated structural examples grounded
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in 5,256 independently generated and audited tasks from 141 real-site domains.
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Domains—not rows—define the split: 110 train, 8 validation, and 23 held-out
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test domains.
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The corpus is primarily BetterWright accessibility trees, with a smaller share
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of alternate accessibility serializations for robustness.
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## Evaluation policy
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Fallback thresholds are selected on validation domains. The production policy
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uses eight ranked 1,800-character structural regions, preserves referenced
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nodes, bounded child context, ancestry, and deterministic must-retain states,
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and returns the exact original tree on low confidence, errors, insufficient
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benefit, or an over-limit result.
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| Domain split | Tasks | Target/evidence recall | Perfect-task recall | Pruning coverage | Mean token savings when active | Token-weighted savings |
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| Validation (8 domains) | 288 | 100.000% | 100.000% | 12.50% | 33.38% | 3.42% |
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| Held-out test (23 domains) | 877 | 99.584% | 99.544% | 22.46% | 44.09% | 9.69% |
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| Post-training reserve (17 additional domains) | 161 | 100.000% | 100.000% | 33.54% | 16.78% | 2.42% |
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The exact reports and frozen policy are distributed as `eval.json`,
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`reserve_eval.json`, and `relevance_config.json`. The 99.5% target is evaluated
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on whole tasks and required accessibility refs, not inferred from training
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loss. A nine-site live navigational replay retained and clicked 6/9 current
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targets; two large pages exceeded the runtime ceiling after fallback and one
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site did not expose the requested label. This limitation is intentionally not
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hidden by substituting easier sites.
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## Safety and limitations
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only path for security decisions. Snapshot text is untrusted data. Consumers
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should keep deterministic must-retain rules and fall back to the full tree on
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model errors, timeouts, low confidence, or insufficient retained context.
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Savings are substantial when pruning activates, but coverage is deliberately
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conservative and very large pages can still fall back above the caller's size
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ceiling. This checkpoint is therefore an alpha integration, not a claim that
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every page becomes smaller.
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