bert large cased finetuned conll03 english

Providerdbmdz
Categorytoken-classification
LicenseApache-2.0
Downloads2.0K
Stars0

Overview

This model is a BERT-Large architecture specifically fine-tuned on the CoNLL-03 dataset for English Named Entity Recognition (NER). Unlike base BERT models, this version is cased, meaning it preserves capitalization—a critical feature for accurately identifying proper nouns, organizations, and locations. For developers, this is a production-ready tool for token classification tasks where high precision in entity extraction is required. It integrates seamlessly into Hugging Face pipelines, allowing you to extract structured data from unstructured text with minimal boilerplate. While larger than BERT-Base, the increased parameter count provides a significant boost in F1 scores for complex entity boundaries, making it a reliable choice for building knowledge graphs or automating data labeling workflows.

Highlights

  • Fine-tuned for high-accuracy English Named Entity Recognition
  • Case-sensitive processing for improved proper noun detection
  • Compatible with standard Hugging Face token-classification pipelines
  • Optimized for extracting locations, persons, and organizations
  • Apache-2.0 license for flexible commercial integration

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download dbmdz/bert-large-cased-finetuned-conll03-english

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download dbmdz/bert-large-cased-finetuned-conll03-english config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('dbmdz/bert-large-cased-finetuned-conll03-english')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('dbmdz/bert-large-cased-finetuned-conll03-english')
tokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-large-cased-finetuned-conll03-english')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model dbmdz/bert-large-cased-finetuned-conll03-english

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model dbmdz/bert-large-cased-finetuned-conll03-english README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('dbmdz/bert-large-cased-finetuned-conll03-english')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/dbmdz/bert-large-cased-finetuned-conll03-english.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/dbmdz/bert-large-cased-finetuned-conll03-english.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'dbmdz/bert-large-cased-finetuned-conll03-english')
View official source
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