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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-classification
+- binary-classification
+- behavioral-coding
+- modernbert
+- transformers
+base_model: answerdotai/ModernBERT-base
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: bc-not-coded-classifier
+  results:
+  - task:
+      type: text-classification
+      name: Binary Text Classification
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9642
+    - name: F1 (Not Coded)
+      type: f1
+      value: 0.8584
+    - name: Precision (Not Coded)
+      type: precision
+      value: 0.8742
+    - name: Recall (Not Coded)
+      type: recall
+      value: 0.8431
+    - name: F1 Macro
+      type: f1_macro
+      value: 0.9189
+widget:
+- text: "I don't understand what you're asking me to do."
+- text: "Let me help you with that problem by explaining the steps."
+- text: "Okay, I see."
+---
+
+# Behavior Coding Not-Coded Classifier
+
+## Model Description
+
+This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) for binary classification of behavioral coding utterances. It identifies whether utterances should be coded or marked as "not_coded" in behavioral analysis workflows.
+
+**Developed by:** Lekhansh
+
+**Model type:** Binary Text Classification
+
+**Language:** English
+
+**Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
+
+**License:** Apache 2.0
+
+## Intended Uses
+
+### Primary Use Case
+
+This model is designed to automatically filter utterances in behavioral coding tasks, distinguishing between:
+- **Coded (Label 0):** Utterances suitable for behavioral code assignment
+- **Not Coded (Label 1):** Utterances that should not receive behavioral codes
+
+### Potential Applications
+
+- Pre-filtering in behavioral coding pipelines
+- Quality control for behavioral analysis datasets
+- Automated utterance classification in conversation analysis
+- Research in human behavior and communication patterns
+
+## Model Performance
+
+### Test Set Metrics
+
+The model was evaluated on a held-out test set of 3,713 examples with the following class distribution:
+- Coded samples: 3,235 (87.1%)
+- Not Coded samples: 478 (12.9%)
+
+| Metric | Score |
+|--------|------:|
+| **Overall Accuracy** | **96.42%** |
+| **F1 (Not Coded)** | **85.84%** |
+| **Precision (Not Coded)** | 87.42% |
+| **Recall (Not Coded)** | 84.31% |
+| **F1 (Coded)** | 97.95% |
+| **Precision (Coded)** | 97.69% |
+| **Recall (Coded)** | 98.21% |
+| **Macro F1** | 91.89% |
+
+### Confusion Matrix
+
+|           | Predicted Coded | Predicted Not Coded |
+|-----------|----------------:|--------------------:|
+| **Actual Coded** | 3,177 | 58 |
+| **Actual Not Coded** | 75 | 403 |
+
+The model shows strong performance on both classes, with particularly high accuracy on the majority class (coded utterances) while maintaining good F1 score (85.84%) on the minority class (not coded utterances).
+
+## Training Details
+
+### Training Data
+
+- Source: Multilabel behavioral coding dataset reframed as binary classification
+- Split: 70% train, 15% validation, 15% test (stratified)
+- Preprocessing: Stratified splitting to maintain class balance across splits
+- Context size: Three preceding utterances.
+
+### Training Procedure
+
+**Hardware:**
+- GPU training with CUDA
+- Mixed precision (BFloat16) training
+
+**Hyperparameters:**
+
+| Parameter | Value |
+|-----------|-------|
+| Learning Rate | 6e-5 |
+| Batch Size (per device) | 12 |
+| Gradient Accumulation | 2 steps |
+| Effective Batch Size | 24 |
+| Max Sequence Length | 3000 tokens |
+| Epochs | 20 (early stopped at epoch 13) |
+| Weight Decay | 0.01 |
+| Warmup Ratio | 0.1 |
+| LR Scheduler | Cosine |
+| Optimizer | AdamW |
+
+**Training Features:**
+- **Class Weighting:** Balanced weights to address class imbalance (87:13 ratio)
+- **Early Stopping:** Patience of 3 epochs on validation F1
+- **Gradient Checkpointing:** Enabled for memory efficiency
+- **Flash Attention 2:** For efficient attention computation
+- **Best Model Selection:** Based on validation F1 score
+
+**Loss Function:** Weighted Cross-Entropy Loss
+
+## Usage
+
+### Direct Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "lekhansh/bc-not-coded-classifier"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Prepare input
+text = "Your utterance text here"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=3000)
+
+# Get prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=-1)
+
+# Interpret result
+label = "Not Coded" if prediction.item() == 1 else "Coded"
+print(f"Prediction: {label}")
+```
+
+### Batch Prediction with Probabilities
+
+```python
+def classify_utterances(texts, model, tokenizer):
+    """
+    Classify multiple utterances with confidence scores.
+
+    Returns:
+        List of dicts with predictions and probabilities
+    """
+    inputs = tokenizer(
+        texts,
+        return_tensors="pt",
+        truncation=True,
+        max_length=3000,
+        padding=True
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=-1)
+        predictions = torch.argmax(outputs.logits, dim=-1)
+
+    results = []
+    for i in range(len(texts)):
+        results.append({
+            'text': texts[i],
+            'label': 'not_coded' if predictions[i].item() == 1 else 'coded',
+            'confidence': probs[i][predictions[i]].item(),
+            'probabilities': {
+                'coded': probs[i][0].item(),
+                'not_coded': probs[i][1].item()
+            }
+        })
+
+    return results
+
+# Example
+utterances = [
+    "I don't know what to say.",
+    "Let me explain the process step by step.",
+    "Mmm-hmm."
+]
+
+results = classify_utterances(utterances, model, tokenizer)
+for r in results:
+    print(f"Text: {r['text']}")
+    print(f"  Label: {r['label']} (confidence: {r['confidence']:.2%})")
+```
+
+### Pipeline Usage
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="lekhansh/bc-not-coded-classifier",  
+    tokenizer="lekhansh/bc-not-coded-classifier"
+)
+
+result = classifier("Your utterance here", truncation=True, max_length=3000)
+print(result)
+# Output: [{'label': 'coded', 'score': 0.98}]
+```
+
+## Limitations and Bias
+
+### Limitations
+
+1. **Domain Specificity:** The model is trained on behavioral coding data and may not generalize well to other text classification tasks
+2. **Class Imbalance:** Training data has 87% coded vs 13% not coded examples, which may affect performance on datasets with different distributions
+3. **Context Length:** Maximum sequence length is 3000 tokens; longer texts will be truncated
+4. **Language:** Trained on English text only
+
+### Potential Biases
+
+- The model's performance may vary depending on the specific behavioral coding framework used
+- Biases present in the training data may be reflected in predictions
+- Performance may differ across different conversation types or domains
+
+## Technical Specifications
+
+### Model Architecture
+
+- **Base:** ModernBERT-base (encoder-only transformer)
+- **Classification Head:** Linear layer for binary classification
+- **Attention:** Flash Attention 2 implementation
+- **Parameters:** ~110M (inherited from base model)
+- **Precision:** BFloat16
+
+### Compute Infrastructure
+
+- **Training:** Single GPU with CUDA
+- **Inference:** CPU or GPU compatible
+- **Memory:** ~500MB model size
+
+## Environmental Impact
+
+Training was conducted using mixed precision to optimize resource usage. Exact carbon footprint was not measured.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{lekhansh2025bcnotcoded,
+  author = {Lekhansh},
+  title = {Behavior Coding Not-Coded Classifier},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/lekhansh/bc-not-coded-classifier}}
+}
+```
+
+## Model Card Authors
+
+Lekhansh
+
+## Model Card Contact
+
+[Your contact information or GitHub profile]

config.json

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+}

metrics.json

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+    "eval_accuracy": 0.9633719364395368,
+    "eval_f1": 0.8586278586278586,
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+    "eval_f1_macro": 0.9187941273337312,
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+    "eval_recall_macro": 0.9210346433167564,
+    "eval_f1_coded": 0.9789603960396039,
+    "eval_f1_not_coded": 0.8586278586278586,
+    "eval_precision_coded": 0.9798699287705172,
+    "eval_precision_not_coded": 0.8533057851239669,
+    "eval_recall_coded": 0.9780525502318392,
+    "eval_recall_not_coded": 0.8640167364016736,
+    "eval_runtime": 20.9038,
+    "eval_samples_per_second": 177.623,
+    "eval_steps_per_second": 14.83,
+    "epoch": 13.0
+  },
+  "test": {
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+    "eval_accuracy": 0.9641799084298411,
+    "eval_f1": 0.858359957401491,
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+    "eval_recall": 0.8430962343096234,
+    "eval_f1_macro": 0.9189287069264276,
+    "eval_precision_macro": 0.9255619101744164,
+    "eval_recall_macro": 0.912583665841056,
+    "eval_f1_coded": 0.9794974564513642,
+    "eval_f1_not_coded": 0.858359957401491,
+    "eval_precision_coded": 0.9769372693726938,
+    "eval_precision_not_coded": 0.8741865509761388,
+    "eval_recall_coded": 0.9820710973724884,
+    "eval_recall_not_coded": 0.8430962343096234,
+    "eval_runtime": 21.5402,
+    "eval_samples_per_second": 172.375,
+    "eval_steps_per_second": 14.392,
+    "epoch": 13.0
+  },
+  "confusion_matrix": [
+    [
+      3177,
+      58
+    ],
+    [
+      75,
+      403
+    ]
+  ]
+}

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