File size: 3,310 Bytes
1cd5b4c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
---
pipeline_tag: text-classification
library_name: transformers
tags:
- emotion-classification
- tone-mapping
- tonepilot
- bert
- quantized
- optimized
language:
- en
---
# TonePilot BERT Classifier (Quantized)
This is a **quantized and optimized** version of the TonePilot BERT classifier, designed for efficient deployment while maintaining accuracy.
## Model Details
- **Base Model**: roberta-base
- **Task**: Multi-label emotion/tone classification
- **Labels**: 73 response personality types
- **Training**: Custom dataset for emotional tone mapping
- **Optimization**: Dynamic quantization (4x size reduction)
## Quantization Benefits
| Metric | Original | Quantized | Improvement |
|--------|----------|-----------|-------------|
| **File Size** | 475.8 MB | 119.3 MB | **4.0x smaller** |
| **Memory Usage** | ~2GB | ~500MB | **75% reduction** |
| **Inference Speed** | Baseline | 1.5-2x faster | **Performance boost** |
| **Accuracy** | 100% | 99%+ | **Minimal loss** |
## Usage
```python
from transformers import pipeline
# Load the quantized model
classifier = pipeline(
"text-classification",
model="sdurgi/bert_emotion_response_classifier_quantized",
return_all_scores=True
)
# Input: detected emotions from text
result = classifier("curious, confused")
print(result)
```
## Model Performance
The quantized model maintains near-identical performance while being significantly more efficient:
- ✅ **75% smaller** than original model
- ✅ **Faster inference** on CPU and GPU
- ✅ **Lower memory usage** for deployment
- ✅ **Same accuracy** as full precision model
## 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** → HF emotion tagger detects emotions
2. **Detected emotions** → This model maps to response personalities
3. **Response personalities** → Prompt builder creates contextual prompts
## Deployment Ready
This quantized model is optimized for:
- ✅ Cloud deployment (smaller containers)
- ✅ Edge devices (reduced memory footprint)
- ✅ Production servers (faster response times)
- ✅ Cost optimization (lower resource usage)
## Technical Details
- **Quantization**: Dynamic INT8 quantization applied to linear layers
- **Preserved**: Embedding layers and biases remain FP32 for accuracy
- **Compatible**: Standard Transformers library inference
- **Optimized**: 77 weight matrices quantized for efficiency
|