Sentence Similarity
sentence-transformers
Safetensors
bert
intent-classification
multilingual
layer-pruning
vocab-pruning
knowledge-distillation
text-embeddings-inference
Instructions to use gomyk/intent-student-L6_compact_distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/intent-student-L6_compact_distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/intent-student-L6_compact_distilled") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
L6_compact_distilled
Ultra-compact multilingual sentence encoder (~71.2MB) for intent classification. 6 layers bottom + 20K vocab + distilled
Performance
| Model | Size | MassiveIntent | MassiveScenario | Average |
|---|---|---|---|---|
| Teacher (12L, full) | ~480MB | 55.52% | 61.01% | 58.27% |
| L6_bottom (38K vocab) | 98MB | 54.70% | 59.39% | 57.05% |
| L6_compact_distilled | 71.2MB | 51.21% | 58.33% | 54.77% |
Model Details
| Property | Value |
|---|---|
| Teacher | paraphrase-multilingual-MiniLM-L12-v2 |
| Vocab | ~20,000 (frequency-based pruning, 97.4% coverage) |
| Size | 71.2MB |
| Distilled | Yes |
Quick Start
Distillation Details
- Loss: 0.3 * MSE + 2.0 * (1 - CosineSimilarity)
- Epochs: 10, LR: 5e-6, Batch: 64
- Cosine-dominant loss preserves existing representations
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