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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArjunShukla/PromptForge-Quality", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 812 Bytes
333fb24 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | model_name: answerdotai/ModernBERT-base
max_length: 512
num_labels: 7
label_names:
- clarity
- specificity
- context
- goal_definition
- constraints
- completeness
- actionability
num_examples: 25000
seed: 42
train_ratio: 0.8
val_ratio: 0.1
test_ratio: 0.1
num_train_epochs: 3
per_device_train_batch_size: 8
per_device_eval_batch_size: 16
gradient_accumulation_steps: 2
learning_rate: 2.0e-05
weight_decay: 0.01
warmup_steps: 500
logging_steps: 100
eval_steps: 500
save_steps: 500
save_total_limit: 2
early_stopping_patience: 2
dropout: 0.1
dimension_loss_weight: 0.8
quality_loss_weight: 0.2
prefer_gpu: true
use_fp16: true
use_bf16: false
output_dir: outputs/promptforge-quality
dataset_path: data/promptforge_dataset.csv
final_model_dir: outputs/promptforge-quality-model
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