Bert Large Portuguese Cased Legal Mlm Nli Sts V1

🚀 stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1 (法律领域葡萄牙语BERT模型)

这是一个用于法律领域的葡萄牙语BERT模型，它可以将句子和段落映射到一个1024维的密集向量空间，可用于聚类或语义搜索等任务。该模型基于Sentence-Transformers框架，在多个葡萄牙语法律相关数据集上进行了训练和微调，具有良好的语义文本相似度计算能力。

🚀 快速开始

本模型基于 sentence-transformers 框架，使用前请确保已安装该库：

pip install -U sentence-transformers

然后可以按照以下方式使用模型：

from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]

model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1')
embeddings = model.encode(sentences)
print(embeddings)

✨ 主要特性

• 多数据集训练：模型在多个葡萄牙语法律相关数据集上进行了训练，包括 assin、assin2、stsb_multi_mt pt 和 IRIS STS 等，能够更好地处理法律领域的语义文本相似度任务。
• 语义向量映射：可以将句子和段落映射到一个1024维的密集向量空间，方便进行聚类、语义搜索等任务。
• 多种使用方式：支持使用 sentence-transformers 库和 HuggingFace Transformers 库进行调用。

📦 安装指南

使用本模型需要安装 sentence-transformers 库，可通过以下命令进行安装：

pip install -U sentence-transformers

💻 使用示例

基础用法

使用 sentence-transformers 库调用模型：

from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]

model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1')
embeddings = model.encode(sentences)
print(embeddings)

高级用法

使用 HuggingFace Transformers 库调用模型：

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('stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1')
model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1')

# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)

📚 详细文档

模型信息

模型评估结果

🔧 技术细节

本模型基于 BERTimbau 大型模型的法律变体 stjiris/bert-large-portuguese-cased-legal-mlm 进行开发。训练过程分为多个阶段：

1. MLM训练：使用MLM技术，学习率为1e-5，在约30000篇文档的法律句子上进行了15000步训练。
2. NLI数据处理：将模型应用于NLI数据，批量大小为16，学习率为2e-5。
3. 语义文本相似度微调：在 assin、assin2、stsb_multi_mt pt 和 IRIS STS 数据集上进行微调，学习率为1e-5。

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)

📄 许可证

本模型使用MIT许可证。

📖 引用信息

贡献者

@rufimelo99

引用格式

@InProceedings{MeloSemantic,
  author="Melo, Rui
  and Santos, Pedro A.
  and Dias, Jo{\~a}o",
  editor="Moniz, Nuno
  and Vale, Zita
  and Cascalho, Jos{\'e}
  and Silva, Catarina
  and Sebasti{\~a}o, Raquel",
  title="A Semantic Search System for the Supremo Tribunal de Justi{\c{c}}a",
  booktitle="Progress in Artificial Intelligence",
  year="2023",
  publisher="Springer Nature Switzerland",
  address="Cham",
  pages="142--154",
  abstract="Many information retrieval systems use lexical approaches to retrieve information. Such approaches have multiple limitations, and these constraints are exacerbated when tied to specific domains, such as the legal one. Large language models, such as BERT, deeply understand a language and may overcome the limitations of older methodologies, such as BM25. This work investigated and developed a prototype of a Semantic Search System to assist the Supremo Tribunal de Justi{\c{c}}a (Portuguese Supreme Court of Justice) in its decision-making process. We built a Semantic Search System that uses specially trained BERT models (Legal-BERTimbau variants) and a Hybrid Search System that incorporates both lexical and semantic techniques by combining the capabilities of BM25 and the potential of Legal-BERTimbau. In this context, we obtained a {\$}{\$}335{\backslash}{\%}{\$}{\$}335{\%}increase on the discovery metric when compared to BM25 for the first query result. This work also provides information on the most relevant techniques for training a Large Language Model adapted to Portuguese jurisprudence and introduces a new technique of Metadata Knowledge Distillation.",
  isbn="978-3-031-49011-8"
}


@inproceedings{souza2020bertimbau,
  author    = {F{\'a}bio Souza and
               Rodrigo Nogueira and
               Roberto Lotufo},
  title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
  booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
  year      = {2020}
}

@inproceedings{fonseca2016assin,
  title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
  author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
  booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
  pages={13--15},
  year={2016}
}

@inproceedings{real2020assin,
  title={The assin 2 shared task: a quick overview},
  author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
  booktitle={International Conference on Computational Processing of the Portuguese Language},
  pages={406--412},
  year={2020},
  organization={Springer}
}
@InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}

相关项目链接

• INESC-ID
• A Semantic Search System for Supremo Tribunal de Justiça
• Project IRIS
• Thesis: A Semantic Search System for Supremo Tribunal de Justiça