LoganResearch/cyborg-translator-en-ru

Cyborg Translator (EN ↔ RU)

Overview

Cyborg Translator is a custom-trained English ↔ Russian neural machine translation model. The project focuses on data quality, tokenizer design, and bidirectional translation robustness rather than scale.

This model was trained end-to-end on a self-curated parallel corpus derived from cleaned literary and technical texts.

Model Details

• Architecture: Transformer (GPT-style causal LM adapted for translation)
• Parameters: ~300M
• Precision: FP32
• Tokenizer: Custom BPE (32k vocab)
• Framework: PyTorch / Hugging Face Transformers
• Training Style: Supervised bilingual sequence modeling

Training Data

• ~40 public-domain English books
• Extensive text normalization and deduplication
• Sentence-level alignment
• Russian translations generated and filtered programmatically
• Multiple cleaning passes (length, language ID, punctuation, encoding)

Emphasis was placed on corpus hygiene and alignment fidelity.

Intended Use

• English ↔ Russian translation research
• Studying effects of tokenizer choice on MT quality
• Low-resource MT experimentation
• Educational purposes

Limitations

• Not instruction-tuned
• May hallucinate under ambiguous input
• No safety fine-tuning
• Not suitable for production or legal/medical use

Reproducibility

Inference example:

from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained("LoganResearch/cyborg-translator-en-ru")
model = AutoModelForCausalLM.from_pretrained("LoganResearch/cyborg-translator-en-ru")

inputs = tok("Hello world", return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=100)
print(tok.decode(out[0]))