plamo embedding 1b EU AI Act Compliance Profile

PLaMo-Embedding-1B

日本語版のREADME/Japanese README

Model Overview

PLaMo-Embedding-1B is a Japanese text embedding model developed by Preferred Networks, Inc.

It can convert Japanese text input into numerical vectors and can be used for a wide range of applications, including information retrieval, text classification, and clustering.

As of early April 2025, it achieved top-class scores on JMTEB, a benchmark for Japanese text embedding. It demonstrated particularly outstanding performance, especially in retrieval tasks.

PLaMo-Embedding-1B is released under the Apache v2.0 license, and you can use it freely, including for commercial purposes.

For technical details, please refer to the following Tech Blog post (Ja): https://tech.preferred.jp/ja/blog/plamo-embedding-1b/

Usage

Requirements

Sample Code

import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

# You can download models from the Hugging Face Hub 🤗 as follows:
tokenizer = AutoTokenizer.from_pretrained("pfnet/plamo-embedding-1b", trust_remote_code=True)
model = AutoModel.from_pretrained("pfnet/plamo-embedding-1b", trust_remote_code=True)

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

query = "PLaMo-Embedding-1Bとは何ですか？"
documents = [
    "PLaMo-Embedding-1Bは、Preferred Networks, Inc. によって開発された日本語テキスト埋め込みモデルです。",
    "最近は随分と暖かくなりましたね。"
]

with torch.inference_mode():
    # For embedding query texts in information retrieval, please use the `encode_query` method.
    # You also need to pass the `tokenizer`.
    query_embedding = model.encode_query(query, tokenizer)
    # For other texts/sentences, please use the `encode_document` method.
    # Also, for applications other than information retrieval, please use the `enc

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