|
|
--- |
|
|
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 |
|
|
|
|
|
|
