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Pytorch Nlu

Pytorch Nlu is a Python/PyTorch toolkit for developing text classification, sequence labeling and extractive summarization workflows, with Chinese-language tasks including NER, part-of-speech tagging and word segmentation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pytorch Nlu — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use it when you want a Python/PyTorch toolkit with documented data formats and examples for classification, sequence labeling or extractive summarization, and can configure the relevant pretrained model and corpus paths.

Who it is for

  • Developers building NLP workflows with PyTorch and pretrained language models.
  • Practitioners preparing text-classification, sequence-labeling or extractive-summary datasets.

Documented uses

  • Develop multi-class or multi-label text classification for short or long text.
  • Create named-entity recognition, part-of-speech tagging or word-segmentation workflows.
  • Build extractive text summarization from labeled sentence sequences.
Before you choose

Requirements & limits

  • The README lists PyTorch, Transformers, NumPy and TensorBoardX as dependencies.
  • A pretrained-model directory or model name must be configured for the described examples.
  • Training examples use a configured training corpus path; a development path is described as optional.
  • The repository is public and specifies the Apache-2.0 license.

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

Install Pytorch-NLU and prepare a text-classification run

Install the toolkit, configure its pretrained-model and corpus paths, and invoke one of the documented classification scripts.

Before you start

  • Python environment with pip.
  • A pretrained-model directory or model identifier configured in the script.
  • Training corpus path; a development-set path is optional.

Steps

  1. Install the package with the command below.

    Documented command
    pip install Pytorch-NLU

  2. Prepare classification data as a text file with one JSON object per line. Use text and label fields; for multi-label examples, labels are separated by |.

  3. Configure the pretrained model using pretrained_model_dir, pretrained_model_name_or_path, or idx, and set model_config["path_train"] and optionally model_config["path_dev"] to the corpus paths.

  4. From the script directory, run a documented command such as the command below or the command below.

    Documented command
    python3 tcRun.py
    Documented command
    python3 tet_tc_base_multi_label.py

Expected outcome

A classification script is invoked with configured model and corpus locations; the README identifies support for multi-class and multi-label classification.

Guide limitations

  • The README does not specify a particular pretrained model download procedure or provide corpus content for this recipe.
  • It does not establish a successful run or expected metric values; it warns that zero or low training metrics may result from an unsuitable learning rate or loss-function configuration.
  • The README mentions additional samples under the test directory.
Documentation 1 ↗Documentation 2 ↗
Source-based practical guide

Install Pytorch-NLU and prepare sequence-labeling data

Install the toolkit, format sequence-labeling examples, configure model and corpus locations, and invoke a documented sequence-labeling script.

Before you start

  • Python environment with pip.
  • A pretrained-model directory or model identifier configured in the script.
  • Sequence-labeling corpus path; a development-set path is optional.

Steps

  1. Install the package with the command below.

    Documented command
    pip install Pytorch-NLU

  2. Prepare sequence-labeling data as one JSON object per line in SPAN format, with text and a label list containing entity type, text, and position fields; alternatively, use the documented CONLL token-and-tag layout.

  3. Configure the pretrained model using pretrained_model_dir, pretrained_model_name_or_path, or idx, and set model_config["path_train"] and optionally model_config["path_dev"] to the corpus paths.

  4. From the script directory, run a documented command such as the command below or the command below.

    Documented command
    python3 slRun.py
    Documented command
    python3 tet_sl_base_crf.py

Expected outcome

A sequence-labeling script is invoked with configured model and corpus locations; the toolkit describes sequence-labeling tasks including NER, POS tagging, and segmentation.

Guide limitations

  • The README does not specify a particular pretrained model download procedure or provide corpus content for this recipe.
  • It does not establish a successful run or expected metric values; it warns that zero or low training metrics may result from an unsuitable learning rate or loss-function configuration.
  • The README mentions additional samples under the test directory.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
yongzhuo/Pytorch-NLU · Official homepage title · retained in its source language
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