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README.md

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+---
+language: en
+license: mit
+library_name: transformers
+tags:
+  - prompt-engineering
+  - prompt-quality
+  - modernbert
+  - regression
+  - promptforge
+pipeline_tag: text-classification
+---
+
+# PromptForge-Quality
+
+Scores LLM prompts across multiple quality dimensions:
+
+- clarity
+- specificity
+- context
+- goal_definition
+- constraints
+- completeness
+- actionability
+
+## Usage
+
+```python
+from promptforge import PromptForge
+
+pf = PromptForge(quality_model_path="YOUR_HF_REPO_OR_LOCAL_DIR")
+print(pf.analyze("Build me a website"))
+```
+
+## Training
+
+Phase 1 of [PromptForge](https://github.com/promptforge/promptforge) — ModernBERT encoder with dual regression heads.

config.json

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+{
+  "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"
+  ]
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab3388a771b84cdf75cadd7a3c80a0dc9a679efc7c4cb88cce6f5832c145d816
+size 598492635

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 8192,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}

training_config.yaml

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+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