Text Classification
Transformers
Safetensors
prompt-complexity
feature-extraction
regression
prompt
complexity-estimation
semantic-routing
llm-routing
custom_code
Instructions to use ilya-kolchinsky/PromptComplexityEstimator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilya-kolchinsky/PromptComplexityEstimator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ilya-kolchinsky/PromptComplexityEstimator", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ilya-kolchinsky/PromptComplexityEstimator", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +2 -2
config.json
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"PromptComplexityModel"
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],
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"auto_map": {
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"AutoConfig": "complexity_estimator
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"AutoModel": "complexity_estimator
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},
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"base_model_name": "microsoft/deberta-v3-base",
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"dropout": 0.1,
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"PromptComplexityModel"
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],
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"auto_map": {
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"AutoConfig": "complexity_estimator/configuration_prompt_complexity.PromptComplexityConfig",
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"AutoModel": "complexity_estimator/modeling_prompt_complexity.PromptComplexityModel"
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},
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"base_model_name": "microsoft/deberta-v3-base",
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"dropout": 0.1,
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