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

Class Imbalance: The model performs better on "neutral" (majority class) than "change" and "sustain"
Context: The model classifies single utterances without conversation context
Domain: Trained specifically on MI conversations; may not generalize to other counseling types
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
BERT Paper: Devlin et al., 2019
Motivational Interviewing: Miller & Rollnick, 2012

Model Card Contact

For questions or feedback, please open an issue in the model repository.