bert portuguese ner

提供商lfcc
分类token-classification
许可证mit
下载量508.8K
星标0

简介

这是一个基于 BERT 架构并针对葡萄牙语进行微调的命名实体识别（NER）模型。它专门用于从非结构化文本中精准提取人名、地名、组织机构名等关键实体。对于需要处理葡语数据的开发者来说，它避开了通用模型在小语种上的识别精度问题。该模型上手门槛低，可直接集成到 Hugging Face 管道或自定义 NLP 工作流中，非常适合用于自动化信息抽取、葡语语料库标注或构建特定语言的知识图谱。

核心亮点

  • 专注葡语命名实体识别，提取精度高
  • 基于 BERT 架构，兼容主流 NLP 框架
  • 适用于自动化信息抽取与文本分析
  • MIT 协议开源，商业集成灵活便捷

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("lfcc/bert-portuguese-ner")
tokenizer = AutoTokenizer.from_pretrained("lfcc/bert-portuguese-ner")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download lfcc/bert-portuguese-ner

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download lfcc/bert-portuguese-ner config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('lfcc/bert-portuguese-ner')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/lfcc/bert-portuguese-ner

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/lfcc/bert-portuguese-ner

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('lfcc/bert-portuguese-ner')
tokenizer = AutoTokenizer.from_pretrained('lfcc/bert-portuguese-ner')

完整文档

来源: HuggingFace

---
license: mit
tags:

  • generated_from_trainer

metrics:
  • precision

  • recall

  • f1

  • accuracy

model_index:
  • name: bert-portuguese-ner-archive

results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9700325118974698
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

bert-portuguese-ner

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased
It achieves the following results on the evaluation set:

  • Loss: 0.1140

  • Precision: 0.9147

  • Recall: 0.9483

  • F1: 0.9312

  • Accuracy: 0.9700

Model description

This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization

Datasets

All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05

  • train_batch_size: 16

  • eval_batch_size: 16

  • seed: 42

  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

  • lr_scheduler_type: linear

  • num_epochs: 4

Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 192 | 0.1438 | 0.8917 | 0.9392 | 0.9148 | 0.9633 |
| 0.2454 | 2.0 | 384 | 0.1222 | 0.8985 | 0.9417 | 0.9196 | 0.9671 |
| 0.0526 | 3.0 | 576 | 0.1098 | 0.9150 | 0.9481 | 0.9312 | 0.9698 |
| 0.0372 | 4.0 | 768 | 0.1140 | 0.9147 | 0.9483 | 0.9312 | 0.9700 |

Framework versions

  • Transformers 4.10.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 1.10.2
  • Tokenizers 0.10.3

Citation

bibtex
@Article{make4010003,
AUTHOR = {Cunha, Luís Filipe and Ramalho, José Carlos},
TITLE = {NER in Archival Finding Aids: Extended},
JOURNAL = {Machine Learning and Knowledge Extraction},
VOLUME = {4},
YEAR = {2022},
NUMBER = {1},
PAGES = {42--65},
URL = {https://www.mdpi.com/2504-4990/4/1/3},
ISSN = {2504-4990},
ABSTRACT = {The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country&rsquo;s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.},
DOI = {10.3390/make4010003}
}
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