🚀 lang-uk/ukr-paraphrase-multilingual-mpnet-base

这是一个针对乌克兰语进行微调的 sentence-transformers 模型。它能将句子和段落映射到一个 768 维的密集向量空间，可用于聚类或语义搜索等任务。

微调所使用的原始模型是 sentence-transformers/paraphrase-multilingual-mpnet-base-v2。详情请参阅我们的论文 Contextual Embeddings for Ukrainian: A Large Language Model Approach to Word Sense Disambiguation。

🚀 快速开始

✨ 主要特性

• 支持多种语言，包括阿拉伯语（ar）、保加利亚语（bg）等众多语言。
• 采用 Apache-2.0 许可证。
• 属于 sentence-transformers 库，可用于特征提取、句子相似度计算等任务。

📦 安装指南

若要使用此模型，需安装 sentence-transformers：

pip install -U sentence-transformers

💻 使用示例

基础用法（Sentence-Transformers）

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('lang-uk/ukr-paraphrase-multilingual-mpnet-base')
embeddings = model.encode(sentences)
print(embeddings)

高级用法（HuggingFace Transformers）

若未安装 sentence-transformers，可按以下方式使用该模型：首先将输入传递给 Transformer 模型，然后对上下文词嵌入应用正确的池化操作。

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(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)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('lang-uk/ukr-paraphrase-multilingual-mpnet-base')
model = AutoModel.from_pretrained('lang-uk/ukr-paraphrase-multilingual-mpnet-base')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, average pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📄 许可证

本项目采用 Apache-2.0 许可证。

📚 详细文档

若发现此模型有帮助，可引用我们的出版物 Contextual Embeddings for {U}krainian: A Large Language Model Approach to Word Sense Disambiguation：

@inproceedings{laba-etal-2023-contextual,
    title = "Contextual Embeddings for {U}krainian: A Large Language Model Approach to Word Sense Disambiguation",
    author = "Laba, Yurii  and
      Mudryi, Volodymyr  and
      Chaplynskyi, Dmytro  and
      Romanyshyn, Mariana  and
      Dobosevych, Oles",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.unlp-1.2",
    doi = "10.18653/v1/2023.unlp-1.2",
    pages = "11--19",
    abstract = "This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Ukrainian language based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on the dataset generated in an unsupervised way to obtain better contextual embeddings for words with multiple senses. The paper presents a method for generating a new dataset for WSD evaluation in the Ukrainian language based on the SUM dictionary. We developed a comprehensive framework that facilitates the generation of WSD evaluation datasets, enables the use of different prediction strategies, LLMs, and pooling strategies, and generates multiple performance reports. Our approach shows 77,9{\%} accuracy for lexical meaning prediction for homonyms.",
}

版权信息：Yurii Laba、Volodymyr Mudryi、Dmytro Chaplynskyi、Mariana Romanyshyn、Oles Dobosevych、乌克兰天主教大学、lang-uk 项目，2023 年。

微调所使用的原始模型由 sentence-transformers 训练。

信息表格