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BELT

BELT is a Python model implementation that processes text longer than BERT’s 512-token limit by splitting it into chunks, running them through a BERT-style model and pooling intermediate results. It supports fine-tuning and prediction, including classification and regression tasks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

BELT — 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 BELT when a BERT-style classification workflow needs to handle documents beyond the standard 512-token limit and you want to work with pre-trained BERT or RoBERTa models.

Who it is for

  • Developers building long-document classification workflows with Python.
  • Practitioners who want to fine-tune a pre-trained BERT or RoBERTa model on longer inputs.

Documented uses

  • Fine-tune a model on raw text and labels.
  • Classify longer documents after fine-tuning.
  • Predict scores for text inputs using a fine-tuned model.
  • Apply the implementation to multilabel or regression tasks.
Before you choose

Requirements & limits

The repository specifies Python 3.9 or later and installation via pip as belt-nlp. PyTorch is required; the README describes GPU-compatible installation and a CPU-only option.

  • The pooling-based model handles longer text; the separate truncated baseline still caps input at 512 tokens.
  • The default pre-trained model is English bert-base-uncased, but a BERT or RoBERTa model can be specified by name or local directory.
  • Prediction methods require a fine-tuned model.
  • The README recommends a GPU for training and also describes CPU-only PyTorch.
  • The repository says it has not benchmarked BELT against sparse-attention alternatives.

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 BELT NLP with CPU-only PyTorch

Prepare the package for long-text classification using the documented CPU-only PyTorch installation path.

Before you start

  • Python 3.9 or later
  • pip3

Steps

  1. Install CPU-only PyTorch and its companion packages with the command below.

    Documented command
    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

  2. Install BELT NLP with the command below.

    Documented command
    pip3 install belt-nlp

  3. Use the BertClassifierWithPooling class for the documented extended model for longer texts.

Expected outcome

The BELT NLP package is installed with CPU-only PyTorch, and the documented long-text model class is identified for use.

Guide limitations

  • The installation instructions do not document a training or prediction example in this path.
  • BertClassifierTruncated truncates longer texts to 512 tokens; it is not the extended long-text class.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
mim-solutions/bert_for_longer_texts · Official homepage title · retained in its source language
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