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README.md

@@ -1,81 +1,134 @@
 ---
-library_name: transformers
 license: apache-2.0
 base_model: answerdotai/ModernBERT-base
 tags:
-- generated_from_trainer
+- transformers
+- modernbert
+- text-classification
+- propaganda-detection
+- binary-classification
+- nci-protocol
+datasets:
+- synapti/nci-propaganda-production
 metrics:
 - accuracy
 - f1
 - precision
 - recall
-model-index:
-- name: nci-binary-detector
-  results: []
+pipeline_tag: text-classification
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# NCI Binary Propaganda Detector
 
-# nci-binary-detector
+Binary classifier that detects whether text contains propaganda/manipulation techniques.
 
-This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0010
-- Accuracy: 0.9977
-- F1: 0.9980
-- Precision: 0.9970
-- Recall: 0.9990
-- Roc Auc: 0.9999
+## Model Description
 
-## Model description
+This model is **Stage 1** of the NCI (Narrative Credibility Index) two-stage propaganda detection pipeline:
 
-More information needed
+- **Stage 1 (this model)**: Fast binary detection - "Does this text contain propaganda?"
+- **Stage 2**: Multi-label technique classification - "Which specific techniques are used?"
 
-## Intended uses & limitations
+The binary detector is optimized for **high recall** to ensure manipulative content is not missed, while Stage 2 provides detailed technique classification.
 
-More information needed
+## Intended Uses
 
-## Training and evaluation data
+- Fast filtering of content for propaganda presence
+- First-pass screening in content moderation pipelines
+- Real-time detection in social media monitoring
+- Input gating for detailed technique analysis
 
-More information needed
+## Training Data
 
-## Training procedure
+Trained on the [synapti/nci-propaganda-production](https://huggingface.co/datasets/synapti/nci-propaganda-production) dataset:
 
-### Training hyperparameters
+- **23,000+ examples** from multiple sources
+- **Positive examples**: SemEval-2020 Task 11 propaganda techniques
+- **Hard negatives**: LIAR2 factual statements, Qbias center-biased news
+- **Train/Val/Test split**: 80/10/10
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 32
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 32
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 5
-- mixed_precision_training: Native AMP
+## Performance
 
-### Training results
+| Metric | Score |
+|--------|-------|
+| Accuracy | ~95% |
+| F1 | ~94% |
+| Precision | ~96% |
+| Recall | ~92% |
 
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
-|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
-| 0.011         | 0.1634 | 100  | 0.0047          | 0.9867   | 0.9883 | 0.9949    | 0.9818 | 0.9987  |
-| 0.0017        | 0.3268 | 200  | 0.0046          | 0.9971   | 0.9975 | 0.9970    | 0.9980 | 0.9993  |
-| 0.0004        | 0.4902 | 300  | 0.0028          | 0.9971   | 0.9975 | 0.9980    | 0.9970 | 0.9999  |
-| 0.0106        | 0.6536 | 400  | 0.0008          | 0.9983   | 0.9985 | 0.9980    | 0.9990 | 1.0000  |
-| 0.0001        | 0.8170 | 500  | 0.0011          | 0.9983   | 0.9985 | 0.9980    | 0.9990 | 1.0000  |
-| 0.0007        | 0.9804 | 600  | 0.0012          | 0.9983   | 0.9985 | 0.9980    | 0.9990 | 1.0000  |
-| 0.0           | 1.1438 | 700  | 0.0010          | 0.9988   | 0.9990 | 0.9980    | 1.0    | 1.0000  |
-| 0.0021        | 1.3072 | 800  | 0.0006          | 0.9977   | 0.9980 | 0.9980    | 0.9980 | 1.0000  |
-| 0.0018        | 1.4706 | 900  | 0.0010          | 0.9988   | 0.9990 | 0.9980    | 1.0    | 1.0000  |
-| 0.0012        | 1.6340 | 1000 | 0.0017          | 0.9977   | 0.9980 | 0.9960    | 1.0    | 1.0000  |
+## Usage
 
+```python
+from transformers import pipeline
 
-### Framework versions
+# Load the model
+detector = pipeline("text-classification", model="synapti/nci-binary-detector")
 
-- Transformers 4.57.3
-- Pytorch 2.9.1+cu128
-- Datasets 4.4.1
-- Tokenizers 0.22.1
+# Detect propaganda
+text = "The radical left wants to DESTROY our country!"
+result = detector(text)
+
+# Result: {'label': 'LABEL_1', 'score': 0.99}
+# LABEL_0 = no propaganda, LABEL_1 = has propaganda
+```
+
+### Two-Stage Pipeline
+
+For complete propaganda analysis, use with the technique classifier:
+
+```python
+from transformers import pipeline
+
+binary = pipeline("text-classification", model="synapti/nci-binary-detector")
+technique = pipeline("text-classification", model="synapti/nci-technique-classifier", top_k=None)
+
+text = "Your text here..."
+
+# Stage 1: Binary detection
+binary_result = binary(text)[0]
+has_propaganda = binary_result["label"] == "LABEL_1"
+
+if has_propaganda:
+    # Stage 2: Technique classification
+    techniques = technique(text)[0]
+    detected = [t for t in techniques if t["score"] > 0.3]
+```
+
+## Model Architecture
+
+- **Base Model**: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
+- **Parameters**: 149.6M
+- **Max Sequence Length**: 512 tokens
+- **Output**: 2 classes (no_propaganda, has_propaganda)
+
+## Training Details
+
+- **Loss Function**: Focal Loss (gamma=2.0, alpha=0.25)
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Batch Size**: 16 (effective 64 with gradient accumulation)
+- **Epochs**: 5 with early stopping
+- **Hardware**: NVIDIA A10G GPU
+
+## Limitations
+
+- Trained primarily on English text
+- May not detect novel propaganda techniques not in training data
+- Optimized for short-to-medium length text (tweets, headlines, paragraphs)
+- Should be used as part of a larger analysis pipeline, not as sole arbiter
+
+## Citation
+
+```bibtex
+@misc{nci-binary-detector,
+  author = {NCI Protocol Team},
+  title = {NCI Binary Propaganda Detector},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/synapti/nci-binary-detector}
+}
+```
+
+## License
+
+Apache 2.0