Custom Language Models
Train and deploy custom language models tailored to your industry-specific terminology and use cases.
Table of Contents
Overview
Custom language models allow you to create specialized translation systems that understand your industry's unique terminology, jargon, and context. This is particularly valuable for:
- Medical and healthcare terminology
- Legal documents and contracts
- Technical documentation
- Financial services and banking
- E-commerce and retail
Dataset Preparation
The quality of your custom model depends heavily on your training data. Follow these best practices:
1. Data Collection
- Gather parallel texts (source and target language pairs)
- Minimum recommended: 10,000 sentence pairs
- Optimal: 100,000+ sentence pairs for production models
- Include domain-specific terminology and phrases
2. Data Cleaning
- Remove duplicates and near-duplicates
- Filter out misaligned translations
- Normalize text formatting and encoding
- Handle special characters and symbols
3. Data Format
{
"source_language": "en",
"target_language": "es",
"pairs": [
{
"source": "The patient presents with acute symptoms.",
"target": "El paciente presenta síntomas agudos."
},
{
"source": "Administer 500mg of medication.",
"target": "Administrar 500mg de medicamento."
}
]
}Model Training
Training your custom model involves several steps and configurations:
Quick Training
For smaller datasets (10K-50K pairs)
- Training time: 2-4 hours
- Batch size: 32
- Learning rate: 0.0001
Production Training
For large datasets (100K+ pairs)
- Training time: 12-24 hours
- Batch size: 64
- Learning rate: 0.00005
Note: Training times depend on dataset size and model complexity. GPU acceleration is recommended for large-scale training.
Fine-Tuning Parameters
Fine-tuning allows you to adapt pre-trained models to your specific domain:
| Parameter | Recommended | Description |
|---|---|---|
| epochs | 3-5 | Number of training iterations |
| learning_rate | 2e-5 | Step size for weight updates |
| warmup_steps | 500 | Gradual learning rate increase |
| weight_decay | 0.01 | Regularization parameter |
Example Configuration
{
"base_model": "polyspeak-neural-v3",
"domain": "medical",
"training_config": {
"epochs": 5,
"batch_size": 32,
"learning_rate": 2e-5,
"warmup_steps": 500,
"weight_decay": 0.01,
"gradient_accumulation": 4
},
"validation_split": 0.1
}Deployment
Once your model is trained, deploy it to production in three steps:
Model Validation
Test your model against a validation dataset to ensure accuracy meets your requirements. Aim for BLEU score of 40+ for production use.
Staging Environment
Deploy to staging environment for real-world testing with limited traffic. Monitor latency, accuracy, and error rates.
Production Release
Gradually roll out to production using canary deployment. Start with 10% traffic and increase based on performance metrics.
Industry-Specific Terminology
Enhance your models with domain-specific glossaries and terminology:
Healthcare
- Medical procedures
- Anatomical terms
- Pharmaceutical names
- Clinical abbreviations
Legal
- Legal terminology
- Contract clauses
- Court procedures
- Regulatory terms
Finance
- Financial instruments
- Banking terms
- Investment jargon
- Market terminology
Technology
- Technical specifications
- Software terms
- Engineering concepts
- API documentation
Terminology Management
Upload custom glossaries to ensure consistent translation of domain-specific terms:
{
"glossary_name": "medical_terms_en_es",
"source_lang": "en",
"target_lang": "es",
"terms": {
"anesthesia": "anestesia",
"cardiovascular": "cardiovascular",
"diagnosis": "diagnóstico",
"prescription": "receta médica"
}
}Next Steps
- Start with our model training dashboard
- Review API documentation for model management
- Contact our ML team for enterprise training support