Usage

Requirements

transformers>=4.51.0
accelerate>=1.6.0
sentencepiece>=0.2.0
flash-attn>=2.7.3

Sample Code

python

import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

model_name = "iamtatsuki05/ModernBERT-JP-0.5B-PT-stage1"
model_kwargs = {
  "torch_dtype": torch.bfloat16,
  "attn_implementation": "flash_attention_2",
  "device_map": "auto",
}
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name, **model_kwargs)

text = f"ハチワレは{tokenizer.mask_token}のキャラクターです。"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model(**inputs)
masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
print(tokenizer.decode(outputs.logits[0, masked_index].argmax(axis=-1)))

Model Details

Base model: iamtatsuki05/ModernBERT-JP-0.5B-init
Architecture: ModernBERT
Maximum sequence length: 8,192 tokens
Embedding dimension: 1280
Tokenizer: SentencePiece / vocabulary size 102,400
Positional encoding: RoPE
Supported languages: Japanese

Model Series

The checkpoints listed below share the same initialization but undergo approximately 10B-token pre-training on hotchpotch/fineweb-2-edu-japanese with 1,024-token contexts.

ID	Architecture	#Param.	#Param. w/o Emb.
iamtatsuki05/ModernBERT-JP-0.5B-PT-stage1 (this model)	ModernBERT	679M	548M
iamtatsuki05/Llama-JP-0.5B-PT-stage1	Llama	661M	530M

Licence

This model is distributed under the MIT License.

How to Cite

tex

@article{MIREI
  title={同一条件下における Encoder/Decoder アーキテクチャによる文埋め込みの性能分析},
  author={岡田 龍樹 and 杉本 徹},
  journal={言語処理学会第 32 回年次大会 (NLP2026)},
  year={2026}
}