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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "PromptComplexityModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "complexity_estimator/configuration_prompt_complexity.PromptComplexityConfig", | |
| "AutoModel": "complexity_estimator/modeling_prompt_complexity.PromptComplexityModel" | |
| }, | |
| "base_model_name": "microsoft/deberta-v3-base", | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "hidden": null, | |
| "layernorm_after_pool": true, | |
| "max_length": 512, | |
| "model_type": "prompt-complexity", | |
| "output_sigmoid": true, | |
| "proj_hidden_ratio": 1.0, | |
| "transformers_version": "4.57.5", | |
| "use_projection": false | |
| } | |