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Browse files- README.md +21 -3
- special_tokens_map.json +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +6 -2
README.md
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@@ -36,18 +36,36 @@ Trained on bilingual Japanese-English story data with masked loss on Japanese pr
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## Usage
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```python
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from transformers import LlamaForCausalLM,
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model = LlamaForCausalLM.from_pretrained("one-way-polyglot-8m-tied")
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tokenizer =
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# Japanese input
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prompt = "ζγ
γθ΅€γεγζγ£γε°ε₯³γγγΎγγγ"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Model Variants
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This is part of a series exploring one-way polyglot capabilities:
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## Usage
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```python
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from transformers import LlamaForCausalLM, AutoTokenizer
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model = LlamaForCausalLM.from_pretrained("one-way-polyglot-8m-tied")
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tokenizer = AutoTokenizer.from_pretrained("one-way-polyglot-8m-tied")
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# Japanese input β English output (primary use case)
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prompt = "ζγ
γθ΅€γεγζγ£γε°ε₯³γγγΎγγγ"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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# Mixed-language name transliteration
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prompt = "ε€ͺιγ―ε
¬εγ§θ±εγ¨ιγγ§γγΎγγγAfter playing, Taro told Hanako that"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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# English text (works perfectly with case folding)
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prompt = "Hello World" # Automatically normalized to lowercase
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Tokenizer Features
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- **β
Case Folding**: "Hello", "hello", and "HELLO" produce identical tokenization
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- **β
Japanese Support**: Full Japanese text support with proper normalization
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- **β
No UNK Tokens**: Proper handling of uppercase/lowercase English text
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- **β
SentencePiece Compatibility**: Built using proper Unigram model with normalization
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## Model Variants
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This is part of a series exploring one-way polyglot capabilities:
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special_tokens_map.json
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{
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"bos_token": "[BOS]",
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"eos_token": "[EOS]",
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"unk_token": "[UNK]",
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"pad_token": "[PAD]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"tokenizer_class": "
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"vocab_size": 16384,
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"bos_token": "[BOS]",
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"eos_token": "[EOS]",
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"unk_token": "[UNK]",
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"
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"add_eos_token": false,
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"clean_up_tokenization_spaces": false
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}
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{
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"tokenizer_class": "PreTrainedTokenizerFast",
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"vocab_size": 16384,
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"bos_token": "[BOS]",
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"eos_token": "[EOS]",
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"unk_token": "[UNK]",
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"pad_token_id": 3,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"unk_token_id": 0,
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"add_bos_token": false,
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"add_eos_token": false,
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"clean_up_tokenization_spaces": false
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}
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