Text Classification
Transformers
PyTorch
English
promptforge_quality
promptforge
prompt-engineering
prompt-quality
modernbert
regression
llm
Instructions to use ArjunShukla/PromptForge-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArjunShukla/PromptForge-Quality with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ArjunShukla/PromptForge-Quality")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArjunShukla/PromptForge-Quality", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ArjunShukla/PromptForge-Quality: direct link, hf CLI and curl.
- Browser
- Download file 422 Bytes
-
https://huggingface.co/ArjunShukla/PromptForge-Quality/resolve/main/config.json
- Command line
-
hf download hf://ArjunShukla/PromptForge-Quality/config.json
-
curl -L -o config.json https://huggingface.co/ArjunShukla/PromptForge-Quality/resolve/main/config.json
422 Bytes
| { | |
| "model_type": "promptforge_quality", | |
| "base_model_name": "answerdotai/ModernBERT-base", | |
| "num_labels": 7, | |
| "dropout": 0.1, | |
| "dimension_loss_weight": 0.8, | |
| "quality_loss_weight": 0.2, | |
| "label_names": [ | |
| "clarity", | |
| "specificity", | |
| "context", | |
| "goal_definition", | |
| "constraints", | |
| "completeness", | |
| "actionability" | |
| ], | |
| "architectures": [ | |
| "PromptForgeQualityModel" | |
| ] | |
| } |