takara-ai/SwarmFormer-Sentiment-Small

Model Card for SwarmFormer-Small

SwarmFormer-Small is a lightweight variant of the SwarmFormer architecture, designed for efficient text classification with minimal computational requirements.

Model Details

Model Description

Compact version of SwarmFormer with:

• Token embedding layer with dropout (0.3)

• Two SwarmFormer layers

• Mean pooling and classification

• Optimized for shorter sequences

• Developed by: Jordan Legg, Mikus Sturmanis, Takara.ai

• Funded by: Takara.ai

• Shared by: Takara.ai

• Model type: Hierarchical transformer

• Language(s): English

• License: Not specified

• Finetuned from model: Trained from scratch

Model Sources

• Repository: https://github.com/takara-ai/SwarmFormer
• Paper: Takara.ai Research
• Demo: Not available

Uses

Direct Use

• Text classification
• Sentiment analysis
• Resource-constrained environments

Out-of-Scope Use

• Text generation
• Machine translation
• Tasks requiring >256 tokens
• Tasks requiring high precision

Training Details

Training Data

• Dataset: IMDB Movie Review
• Size: 50,000 samples
• Augmentation techniques applied

Training Procedure

Model Architecture Details

1. Token Embedding Layer:

   - Embedding layer (vocab_size → 128)
   - Dropout rate: 0.3
   

2. Local Swarm Aggregator:

   - Input dropout: 0.3
   - Local MLP:
     - Linear(128 → 128)
     - GELU
     - Dropout(0.3)
     - Linear(128 → 128)
   - Gate network with GELU
   

3. Clustering Mechanism:

   • Cluster size: 8 tokens
   • Mean pooling per cluster

4. Global Cluster Attention:

   - Q/K/V projections: Linear(128 → 128)
   - Attention dropout: 0.3
   

Training Hyperparameters

• Embedding dimension: 128
• Number of layers: 2
• Local update steps: 3
• Cluster size: 8
• Sequence length: 256
• Batch size: 96
• Learning rate: 4.76 × 10⁻⁴
• Weight decay: 0.0541
• Dropout: 0.30

Evaluation

Results

• Accuracy: 86.20%
• Precision: 83.46%
• Recall: 90.31%
• F1: 86.75%
• Inference time: 0.36s (25k samples)
• Mean batch latency: 3.67ms
• Throughput: 45k samples/s
• Peak memory: 8GB

Technical Specifications

Compute Infrastructure

• GPU: NVIDIA RTX 2080 Ti
• VRAM: 8GB minimum
• Training time: 3.6 minutes

How to Get Started

from swarmformer import SwarmFormerModel

model = SwarmFormerModel(
    vocab_size=30000,
    d_model=128,
    seq_len=256,
    cluster_size=8,
    num_layers=2,
    T_local=3
)

Citation

@article{legg2025swarmformer,
  title={SwarmFormer: Local-Global Hierarchical Attention via Swarming Token Representations},
  author={Legg, Jordan and Sturmanis, Mikus and {Takara.ai}},
  journal={Takara.ai Research},
  year={2025},
  url={https://takara.ai/papers/SwarmFormer-Local-Global-Hierarchical-Attention-via-Swarming-Token-Representations.pdf}
}

Model Card Authors

Jordan Legg, Mikus Sturmanis, Takara.ai Research Team

Model Card Contact

[email protected]