80 lines
2.6 KiB
Markdown
80 lines
2.6 KiB
Markdown
---
|
|
license: apache-2.0
|
|
base_model: llama
|
|
library_name: transformers
|
|
pipeline_tag: text-generation
|
|
tags:
|
|
- one-way-polyglot
|
|
- japanese
|
|
- english
|
|
- bilingual
|
|
- small-model
|
|
---
|
|
|
|
# one-way-polyglot-22m-untied
|
|
|
|
A one-way polyglot language model trained to understand Japanese but generate only English.
|
|
|
|
## Model Details
|
|
|
|
- **Architecture**: LLaMA-based transformer
|
|
- **Parameters**: 22,025,088 (22.0M)
|
|
- **Vocabulary**: 16,384 tokens (bilingual SentencePiece)
|
|
- **Context Length**: 512 tokens
|
|
- **Embedding Strategy**: Untied
|
|
|
|
## Capabilities
|
|
|
|
- **Semantic Transfer**: Understands Japanese input and generates contextually appropriate English
|
|
- **One-Way Constraint**: Strong bias toward English-only generation
|
|
- **Name Transliteration**: Can transliterate Japanese names to English (context-dependent)
|
|
|
|
## Training Data
|
|
|
|
Trained on bilingual Japanese-English story data with masked loss on Japanese prefixes to enforce one-way generation.
|
|
|
|
## Usage
|
|
|
|
```python
|
|
from transformers import LlamaForCausalLM, AutoTokenizer
|
|
|
|
model = LlamaForCausalLM.from_pretrained("one-way-polyglot-22m-untied")
|
|
tokenizer = AutoTokenizer.from_pretrained("one-way-polyglot-22m-untied")
|
|
|
|
# Japanese input → English output (primary use case)
|
|
prompt = "昔々、赤い傘を持った少女がいました。"
|
|
inputs = tokenizer(prompt, return_tensors="pt")
|
|
outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
|
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
|
|
# Mixed-language name transliteration
|
|
prompt = "太郎は公園で花子と遊んでいました。After playing, Taro told Hanako that"
|
|
inputs = tokenizer(prompt, return_tensors="pt")
|
|
outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
|
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
|
|
# English text (works perfectly with case folding)
|
|
prompt = "Hello World" # Automatically normalized to lowercase
|
|
inputs = tokenizer(prompt, return_tensors="pt")
|
|
outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
|
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
```
|
|
|
|
### Tokenizer Features
|
|
- **✅ Case Folding**: "Hello", "hello", and "HELLO" produce identical tokenization
|
|
- **✅ Japanese Support**: Full Japanese text support with proper normalization
|
|
- **✅ No UNK Tokens**: Proper handling of uppercase/lowercase English text
|
|
- **✅ SentencePiece Compatibility**: Built using proper Unigram model with normalization
|
|
|
|
## Model Variants
|
|
|
|
This is part of a series exploring one-way polyglot capabilities:
|
|
- 1.25M parameters (tied embeddings)
|
|
- 8.5M parameters (tied embeddings)
|
|
- 12.7M parameters (untied embeddings)
|
|
- 15.7M parameters (tied embeddings)
|
|
|
|
## License
|
|
|
|
Apache 2.0
|