Upload AnnoMI BERT model for Motivational Interviewing talk classification

README.md

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+---
+language: en
+license: mit
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- text-classification
+- motivational-interviewing
+- bert
+- mental-health
+- counseling
+- psychology
+- transformers
+- pytorch
+datasets:
+- AnnoMI
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: bert-motivational-interviewing
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: AnnoMI
+      type: AnnoMI
+    metrics:
+    - type: accuracy
+      value: 0.701
+      name: Accuracy
+    - type: f1
+      value: 0.579
+      name: F1 Score (macro)
+widget:
+- text: "I really want to quit smoking."
+  example_title: "Change Talk"
+- text: "I don't know if I can do this."
+  example_title: "Neutral"
+- text: "I like smoking, it helps me relax."
+  example_title: "Sustain Talk"
+---
+
+# BERT for Motivational Interviewing Client Talk Classification
+
+## Model Description
+
+This model is a fine-tuned **BERT-base-uncased** model for classifying client utterances in **Motivational Interviewing (MI)** conversations. 
+
+Motivational Interviewing is a counseling approach used to help individuals overcome ambivalence and make positive behavioral changes. This model identifies different types of client talk that indicate their readiness for change.
+
+## Intended Use
+
+- **Primary Use**: Classify client statements in motivational interviewing dialogues
+- **Applications**: 
+  - Counselor training and feedback
+  - MI session analysis
+  - Automated dialogue systems
+  - Mental health research
+
+## Training Data
+
+The model was trained on the **AnnoMI dataset** (Annotated Motivational Interviewing), which contains expert-annotated counseling dialogues.
+
+- **Training samples**: ~2,400 utterances
+- **Validation samples**: ~500 utterances  
+- **Test samples**: ~700 utterances
+
+## Labels
+
+The model classifies client talk into three categories:
+
+- **0**: change
+- **1**: neutral
+- **2**: sustain
+
+### Label Definitions
+
+- **Change Talk**: Client statements expressing desire, ability, reasons, or need for change
+  - Example: "I really want to quit smoking" or "I think I can do it"
+
+- **Neutral**: General responses without clear indication of change or sustain
+  - Example: "I don't know" or "Maybe"
+
+- **Sustain Talk**: Client statements expressing reasons for maintaining current behavior
+  - Example: "I like smoking, it helps me relax"
+
+## Performance
+
+### Test Set Metrics
+
+- **Accuracy**: 70.1%
+- **Macro F1**: 57.9%
+- **Macro Precision**: 59.3%
+- **Macro Recall**: 57.3%
+
+### Confusion Matrix
+
+```
+              Predicted
+              change  neutral  sustain
+Actual change    75      78       23
+       neutral   43     396       27  
+       sustain   11      34       36
+```
+
+**Note**: The model performs best on the "neutral" class (most frequent), and has room for improvement on "change" and "sustain" classes.
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "RyanDDD/bert-motivational-interviewing"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+
+# Predict
+text = "I really want to quit smoking. It's been affecting my health."
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    pred = torch.argmax(probs, dim=1)
+
+label_map = model.config.id2label
+print(f"Talk type: {label_map[pred.item()]}")
+print(f"Confidence: {probs[0][pred].item():.2%}")
+```
+
+### Batch Prediction
+
+```python
+texts = [
+    "I want to stop drinking.",
+    "I don't think I have a problem.",
+    "I like drinking with my friends."
+]
+
+inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    preds = torch.argmax(probs, dim=1)
+
+for text, pred, prob in zip(texts, preds, probs):
+    label = model.config.id2label[pred.item()]
+    confidence = prob[pred].item()
+    print(f"Text: {text}")
+    print(f"Type: {label} ({confidence:.1%})")
+    print()
+```
+
+## Training Details
+
+### Hyperparameters
+
+- **Base model**: `bert-base-uncased`
+- **Max sequence length**: 128 tokens
+- **Batch size**: 16
+- **Learning rate**: 2e-5
+- **Epochs**: 5
+- **Optimizer**: AdamW
+- **Loss**: Cross-entropy
+
+### Hardware
+
+Trained on a single GPU (NVIDIA GPU recommended).
+
+## Limitations
+
+1. **Class Imbalance**: The model performs better on "neutral" (majority class) than "change" and "sustain"
+2. **Context**: The model classifies single utterances without conversation context
+3. **Domain**: Trained specifically on MI conversations; may not generalize to other counseling types
+4. **Language**: English only
+
+## Ethical Considerations
+
+- This model is intended to **assist**, not replace, human counselors
+- Predictions should be reviewed by qualified professionals
+- Privacy and confidentiality must be maintained when processing real counseling data
+- Be aware of potential biases in training data
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{bert-mi-classifier-2024,
+  author = {Ryan},
+  title = {BERT for Motivational Interviewing Client Talk Classification},
+  year = {2024},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/RyanDDD/bert-motivational-interviewing}}
+}
+```
+
+## References
+
+- **AnnoMI Dataset**: [GitHub](https://github.com/uccollab/AnnoMI)
+- **BERT Paper**: [Devlin et al., 2019](https://arxiv.org/abs/1810.04805)
+- **Motivational Interviewing**: [Miller & Rollnick, 2012](https://motivationalinterviewing.org/)
+
+## Model Card Contact
+
+For questions or feedback, please open an issue in the model repository.

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "change",
+    "1": "neutral",
+    "2": "sustain"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "change": 0,
+    "neutral": 1,
+    "sustain": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

example_usage.py

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+"""
+Example usage of the Motivational Interviewing BERT classifier
+"""
+
+from transformers import BertTokenizer, BertForSequenceClassification, RobertaTokenizer, RobertaForSequenceClassification
+
+import torch
+
+def predict_talk_type(text, model, tokenizer):
+    """Predict the talk type for a given text"""
+    inputs = tokenizer(
+        text, 
+        return_tensors="pt", 
+        padding=True, 
+        truncation=True, 
+        max_length=128
+    )
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1)
+        pred = torch.argmax(probs, dim=1)
+    
+    label = model.config.id2label[pred.item()]
+    confidence = probs[0][pred].item()
+    
+    return {
+        'label': label,
+        'confidence': confidence,
+        'all_probs': {
+            model.config.id2label[i]: probs[0][i].item() 
+            for i in range(len(probs[0]))
+        }
+    }
+
+def main():
+    # Load model and tokenizer
+    model_name = "RyanDDD/bert-motivational-interviewing"
+    print(f"Loading model: {model_name}")
+    
+    tokenizer = BertTokenizer.from_pretrained(model_name)
+    model = BertForSequenceClassification.from_pretrained(model_name)
+    
+    # Example texts
+    examples = [
+        "I really want to quit smoking for my health.",
+        "I'm not sure if I can do this.",
+        "Smoking helps me deal with stress.",
+        "Maybe I should try cutting down.",
+        "I've been thinking about quitting.",
+        "I like smoking, it's part of who I am."
+    ]
+    
+    print("\nPredictions:\n" + "="*60)
+    
+    for text in examples:
+        result = predict_talk_type(text, model, tokenizer)
+        
+        print(f"\nText: {text}")
+        print(f"Type: {result['label']} ({result['confidence']:.1%} confidence)")
+        print(f"All probabilities:")
+        for label, prob in result['all_probs'].items():
+            print(f"  {label:8s}: {prob:.1%}")
+    
+    print("\n" + "="*60)
+
+if __name__ == "__main__":
+    main()

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:67ca5c56c69fbfdf560aa932f6e6fc29ee86006e81ba2e839e86a28337f36034
+size 437961724

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}