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Pytorch Bert Crf Ner

Pytorch Bert Crf Ner is an open-source PyTorch implementation of a Korean named-entity recognition tagger that combines BERT representations with a conditional random field for sequence labeling. Its repository includes a Jupyter notebook example, sample tagged outputs, training and inference scripts, and links to model files.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pytorch Bert Crf Ner — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a code-based starting point for Korean NER and want to inspect the model's example outputs or adapt its training and inference workflow.

Who it is for

  • Developers prototyping Korean-language named-entity recognition.
  • Researchers or learners examining a notebook-based BERT-CRF NER implementation.
  • Teams exploring entity extraction as preprocessing for Korean text analysis.

Documented uses

  • Label entity spans in Korean sentences, including examples of people, locations, dates, organizations and named works or events.
  • Inspect token-level predictions and decoded entity output in the provided examples.
  • Start the repository's documented training or inference scripts.
Before you choose

Requirements & limits

  • The README specifies PyTorch 1.2 and Python 3.x and directs users to a requirements file.
  • The repository provides distinct training and inference scripts, plus model-file links.
  • The sample outputs illustrate multiple entity labels on Korean text.
  • The README lists a REST API as future work, rather than documenting an available hosted API.
  • The repository is public and identifies its license as Apache-2.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the repository's documented inference script

Use the provided inference script to produce Korean named-entity predictions.

Before you start

  • Python 3.x and PyTorch 1.2, as identified by the README.
  • Install dependencies using the repository's requirements file.
  • The README provides BERT-CRF model-file links.

Steps

  1. Obtain the repository and review its requirements file.

  2. Install the listed dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  3. Obtain a model file from one of the README's linked model resources.

  4. Run the command below.

    Documented command
    python inference.py

Expected outcome

The repository documents an inference script and shows sample outputs with entity tags and decoded Korean text.

Guide limitations

  • The setup and command are described in the README; this is not a report of an execution test.
Documentation 1 ↗Documentation 2 ↗
Source-based practical guide

Start the documented training workflow

Start model training through the repository's training script.

Before you start

  • Python 3.x and PyTorch 1.2, as identified by the README.
  • Install dependencies using the repository's requirements file.

Steps

  1. Obtain the repository and inspect its requirements file.

  2. Install the listed dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  3. Run the command below to start the documented training command.

    Documented command
    python train_bert_crf.py

Expected outcome

The README provides a command for starting training with the repository's training script.

Guide limitations

  • The command is documented, not execution-tested here.
Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Pytorch Bert Crf NerLabel entity spans in Korean sentences, including examples of people, locations, dates, organizations and named works or events.Linux
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
eagle705/pytorch-bert-crf-ner · Official homepage title · retained in its source language
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