Sentence Luke Japanese Base Lite

🚀 日本語Sentence - LUKE模型

這是一個用於日語的Sentence - LUKE模型。它與日語Sentence - BERT模型在同一數據集和設置下進行訓練。在內部的私有數據集上，與日語Sentence - BERT模型相比，該模型的定量精度相當或高出約0.5個百分點，定性精度則更高。

本模型使用了預訓練模型studio - ousia/luke - japanese - base - lite。推理時需要安裝SentencePiece（pip install sentencepiece）。

✨ 主要特性

• 基於日語數據訓練，適用於日語句子處理。
• 與日語Sentence - BERT模型相比，在精度上有一定提升。

📦 安裝指南

推理的執行需要安裝SentencePiece，可使用以下命令進行安裝：

pip install sentencepiece

💻 使用示例

基礎用法

from transformers import MLukeTokenizer, LukeModel
import torch


class SentenceLukeJapanese:
    def __init__(self, model_name_or_path, device=None):
        self.tokenizer = MLukeTokenizer.from_pretrained(model_name_or_path)
        self.model = LukeModel.from_pretrained(model_name_or_path)
        self.model.eval()

        if device is None:
            device = "cuda" if torch.cuda.is_available() else "cpu"
        self.device = torch.device(device)
        self.model.to(device)

    def _mean_pooling(self, model_output, attention_mask):
        token_embeddings = model_output[0] #First element of model_output contains all token embeddings
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

    @torch.no_grad()
    def encode(self, sentences, batch_size=8):
        all_embeddings = []
        iterator = range(0, len(sentences), batch_size)
        for batch_idx in iterator:
            batch = sentences[batch_idx:batch_idx + batch_size]

            encoded_input = self.tokenizer.batch_encode_plus(batch, padding="longest", 
                                           truncation=True, return_tensors="pt").to(self.device)
            model_output = self.model(**encoded_input)
            sentence_embeddings = self._mean_pooling(model_output, encoded_input["attention_mask"]).to('cpu')

            all_embeddings.extend(sentence_embeddings)

        return torch.stack(all_embeddings)


MODEL_NAME = "sonoisa/sentence-luke-japanese-base-lite"
model = SentenceLukeJapanese(MODEL_NAME)

sentences = ["暴走したAI", "暴走した人工知能"]
sentence_embeddings = model.encode(sentences, batch_size=8)

print("Sentence embeddings:", sentence_embeddings)

📄 許可證

本項目採用Apache 2.0許可證。