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 folder using huggingface_hub
Browse files- config.json +1 -1
- model.safetensors +1 -1
- tokenizer_config.json +5 -1
- training_metrics.json +5 -5
config.json
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"AutoModelForSequenceClassification": "modeling_betterwright.BetterWrightEncoder"
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},
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"betterwright_max_trained_context": 65536,
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"betterwright_positive_weight":
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"betterwright_schema": "task-tree-relevance-v1",
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"betterwright_token_positive_weight": 20.0,
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"block_auto_adjust_ff_dim": true,
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"AutoModelForSequenceClassification": "modeling_betterwright.BetterWrightEncoder"
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},
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"betterwright_max_trained_context": 65536,
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"betterwright_positive_weight": 3.0,
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"betterwright_schema": "task-tree-relevance-v1",
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"betterwright_token_positive_weight": 20.0,
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"block_auto_adjust_ff_dim": true,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 710039782
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version https://git-lfs.github.com/spec/v1
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oid sha256:cac4e9f80516c0a67971eb7ad0914a7cfb55e0e7e7082e454a7b8631804779bd
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size 710039782
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tokenizer_config.json
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"eos_token": "<|im_end|>",
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"is_local": false,
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"mask_token": "<|mask|>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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"
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}
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"eos_token": "<|im_end|>",
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"is_local": false,
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"mask_token": "<|mask|>",
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"max_length": 65536,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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"stride": 0,
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"tokenizer_class": "TokenizersBackend",
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"truncation_side": "right",
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"truncation_strategy": "longest_first"
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}
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training_metrics.json
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{
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"validation_pairs": 25891,
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"accuracy_at_0.5": 0.
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"positive_recall_at_0.5": 0.
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"negative_specificity_at_0.5": 0.
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"mean_positive_score": 0.
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"mean_negative_score": 0.
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}
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{
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"validation_pairs": 25891,
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"accuracy_at_0.5": 0.73203045129776,
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"positive_recall_at_0.5": 0.7085936665534973,
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"negative_specificity_at_0.5": 0.7434512972831726,
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"mean_positive_score": 0.6228448152542114,
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"mean_negative_score": 0.3193468451499939
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}
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