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Easy Bert

Easy Bert is a Python library and learning resource for applying BERT variants to Chinese NLP, with examples and tuning guidance for classification, sequence labeling, masked-language pretraining and knowledge distillation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Easy Bert — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use Easy Bert when you want a Python-based starting point for Chinese NLP tasks built around BERT variants, and want task examples alongside guidance on training adjustments.

Who it is for

  • Python developers building Chinese text classification or sequence-labeling workflows.
  • NLP practitioners adapting pretrained models or continuing pretraining on domain text.
  • Learners studying BERT-based NLP through annotated code and task examples.

Documented uses

  • Train a text classifier from labeled examples and use a predictor to assign labels.
  • Train a sequence labeler from token-level tags and predict tags for new text.
  • Continue masked-language pretraining on text using a pretrained model.
  • Explore tuning choices such as loss functions, long-text handling, parallel training and knowledge distillation.
Before you choose

Requirements & limits

The public GitHub repository specifies an MIT license. The README lists PyPI installation as `pip install easy-zh-bert` and also documents a Git-based installation route.

  • The quick-start examples require a pretrained model downloaded separately and a directory for saved model files.
  • The README lists multiple Chinese pretrained-model options; the chosen model is supplied through a model-directory parameter.
  • For inputs beyond 512 characters, the README suggests truncation, splitting, or considering a Longformer model.
  • ONNX acceleration is listed as a tuning topic, but supported models and deployment configuration are not specified.

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

Train and predict with a Chinese text classifier

Use easy-bert’s documented classification example to train a model and predict labels for short Chinese texts.

Before you start

  • Install the library with pip install easy-zh-bert.
  • Download the chinese-roberta-wwm-ext pretrained model and save it in a directory such as ./models/chinese-roberta-wwm-ext.
  • Create a directory for the trained model, such as ./tests/test_model.
  • Prepare texts and corresponding labels.

Steps

  1. Import ClassificationTrainer and ClassificationPredictor from the documented easy_bert.bert4classification modules.

  2. Set pretrained_model_dir and your_model_dir to the pretrained-model and output directories; define the texts and labels.

  3. Create ClassificationTrainer(pretrained_model_dir, your_model_dir) and call train with the texts and labels, using the example’s validation data, batch_size=2, and epoch=20.

  4. Create ClassificationPredictor(pretrained_model_dir, your_model_dir) and call predict(texts).

Expected outcome

The documented workflow trains a classification model and returns predicted labels for the input texts.

Guide limitations

  • The README example uses a tiny illustrative dataset; it does not establish performance or suitability for other datasets.
  • The workflow requires a downloaded pretrained model and an existing output directory.
  • The example does not document evaluation criteria or deployment.
Documentation 1 ↗
Source-based practical guide

Train and predict with Chinese sequence labeling

Use easy-bert’s documented sequence-labeling example to train a model and predict labels for tokenized Chinese text.

Before you start

  • Install the library with pip install easy-zh-bert.
  • Download the chinese-roberta-wwm-ext pretrained model and save it in a directory such as ./models/chinese-roberta-wwm-ext.
  • Create a directory for the trained model, such as ./tests/test_model.
  • Prepare tokenized texts and a label for each token.

Steps

  1. Import SequenceLabelingTrainer and SequenceLabelingPredictor from the documented easy_bert.bert4sequence_labeling modules.

  2. Set the pretrained-model and output directories; represent each input as a list of tokens and provide matching label lists, as in the README example.

  3. Create SequenceLabelingTrainer(pretrained_model_dir, your_model_dir) and call train with the texts and labels, using the example’s validation data, batch_size=2, and epoch=20.

  4. Create SequenceLabelingPredictor(pretrained_model_dir, your_model_dir) and call predict(texts).

Expected outcome

The documented workflow trains a sequence-labeling model and produces predicted label sequences for the input tokens.

Guide limitations

  • The README example is illustrative and does not establish performance on other data.
  • Inputs are represented as token lists with corresponding per-token labels.
  • The example does not document evaluation criteria or deployment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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17 October 2026
Original website title
old-wang-95/easy-bert · Official homepage title · retained in its source language
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