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Ernie — labteral

Ernie is an open-source Python library for fine-tuning BERT-based models for sentence classification and generating predictions for text. It uses Keras and TensorFlow 2 with Hugging Face Transformers, and documents workflows for labeled datasets, model persistence and inference on single or multiple texts.

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Ernie — labteral — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use Ernie when you have labeled text and want a Python workflow to fine-tune a BERT-based sentence classifier, then apply it to new text.

Who it is for

  • Python developers working with labeled text datasets
  • NLP practitioners prototyping sentence or sentiment classification

Documented uses

  • Fine-tune a BERT-based classifier on labeled sentences.
  • Generate predictions for one text or a collection of texts.
  • Save a trained model locally and restore it for later use.
  • Choose documented splitting and aggregation strategies for predictions on longer text.
Before you choose

Requirements & limits

The repository identifies the project as open source under the Apache-2.0 license. The README requires Python 3.6 or higher and documents installation through pip.

  • Training requires labeled text-and-label examples.
  • The documented stack combines Keras, TensorFlow 2 and Hugging Face Transformers.
  • Text longer than the configured token limit is truncated unless a split strategy is selected.
  • The README documents local model save-and-restore and autosave behavior.

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

Fine-tune a sentence classifier from labeled examples

Configure and train a BERT-based classifier using labeled text data.

Before you start

  • Python 3.6 or higher
  • Install Ernie with pip install ernie
  • Labeled text-and-label examples in a pandas DataFrame

Steps

  1. Create a pandas DataFrame from text and label pairs.

  2. Instantiate SentenceClassifier with a model name, maximum token length and number of labels.

  3. Load the dataset with a validation split.

  4. Call fine_tune with training parameters such as epochs, learning rate and batch sizes.

Expected outcome

A fine-tuned sentence classifier that can be used for text predictions.

Guide limitations

  • The example uses a validation split of 0.2 and a maximum length of 64; these are example settings, not universal requirements.
  • Texts longer than the configured maximum token length are truncated unless a documented split strategy is used.
Documentation 1 ↗
Source-based practical guide

Predict labels for text

Use a trained classifier to obtain predictions for one or more texts.

Before you start

  • A trained SentenceClassifier instance

Steps

  1. Pass one text to classifier.predict_one to obtain a prediction tuple.

  2. Pass a list of texts to classifier.predict to obtain a generator of prediction tuples.

  3. For texts longer than the configured maximum token length, optionally select a documented split strategy and aggregation strategy.

Expected outcome

Prediction tuples for the submitted text or texts.

Guide limitations

  • Without a split strategy, text exceeding the configured token length is truncated.
  • The README describes available strategies but does not prescribe a universally best choice.
Documentation 1 ↗
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
Ernie — labteralFine-tune a BERT-based classifier on labeled sentences.CLIThe repository identifies the project as open source under the Apache-2.0 license. The README requires Python 3.6 or higher and documents installation through pip.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
labteral/ernie · Official homepage title · retained in its source language
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