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TonePilot BERT Classifier

This model maps input emotional tones to appropriate response personalities for the TonePilot system.

Model Details

  • Base Model: roberta-base
  • Task: Multi-label emotion/tone classification
  • Labels: 73 response personality types
  • Training: Custom dataset for emotional tone mapping

Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="sdurgi/bert_emotion_response_classifier",
    return_all_scores=True
)

# Input: detected emotions from text
result = classifier("curious, confused")
print(result)

Labels

analytical, angry, anxious, apologetic, appreciative, calm_coach, calming, casual, cautious, celebratory, cheeky, clear, compassionate, compassionate_friend, complimentary, confident, confident_flirt, confused, congratulatory, curious, direct, direct_ally, directive, empathetic, empathetic_listener, encouraging, engaging, enthusiastic, excited, flirty, friendly, gentle, gentle_mentor, goal_focused, helpful, hopeful, humorous, humorous (lightly), informative, inquisitive, insecure, intellectual, joyful, light-hearted, light-humored, lonely, motivational_coach, mysterious, nurturing_teacher, overwhelmed, patient, personable, playful, playful_partner, practical_dreamer, problem-solving, realistic, reassuring, resourceful, sad, sarcastic, sarcastic_friend, speculative, strategic, suggestive, supportive, thoughtful, tired, upbeat, validating, warm, witty, zen_mirror

Integration

This model is designed to work with the TonePilot system:

  1. Input text โ†’ HF emotion tagger detects emotions
  2. Detected emotions โ†’ This model maps to response personalities
  3. Response personalities โ†’ Prompt builder creates contextual prompts
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