Practical overview
When it fits
Use fastNLP when developing an NLP application and you want framework utilities for dataset handling, training and evaluation rather than writing all of that workflow code yourself.
Who it is for
- Python developers building NLP applications.
- Practitioners who want shared tools for data preparation, model training and evaluation.
- Teams using documented PyTorch or Paddle workflows.
Documented uses
- Prepare and transform text datasets, including tokenization and label-field mapping.
- Set up data loaders and training loops for NLP models.
- Add callbacks or event-based operations during training.
- Evaluate models with metrics such as accuracy.
Requirements & limits
The public repository lists an Apache-2.0 license.
- Choose and install a supported deep-learning backend separately; the examples specify PyTorch 1.6.0 or later, or Paddle 2.2.0 or later with PaddleNLP 2.3.3 or later.
- The framework architecture changed from version 1.0.0 onward, so code written for older fastNLP versions may need adjustments.
- The repository includes tutorials for data structures, tokenizers, training, evaluation and backend integrations.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare data for the documented PyTorch text-classification workflow
Load and tokenize ChnSentiCorp text, then prepare the dataset fields used by the README’s PyTorch example.
Before you start
- Install fastNLP 1.0.0alpha or later.
- Install PyTorch 1.6.0 or later.
- Use the documented fastNLP PyTorch transformer components and the hfl/chinese-bert-wwm tokenizer.
Steps
Load ChnSentiCorp with fastNLP’s ChnSentiCorpLoader.
Tokenize the raw text field with the documented BERT tokenizer, using a maximum length of 256.
Convert the target field to integers and store it as labels.
Pass the resulting data bundle to fastNLP’s prepare_dataloader function, as in the README example.
Expected outcome
A tokenized data bundle with label fields and data loaders prepared for the example’s training workflow.
Guide limitations
- The example is specific to its documented dataset and PyTorch backend.
- The README notes that fastNLP 1.0.0 and later are not fully compatible with earlier fastNLP code.
Prepare data for the documented Paddle text-classification workflow
Load and tokenize ChnSentiCorp text and create the label field used in the README’s Paddle example.
Before you start
- Install fastNLP 1.0.0alpha or later.
- Install Paddle 2.2.0 or later and PaddleNLP 2.3.3 or later.
- Use the documented PaddleNLP BERT tokenizer and hfl/chinese-bert-wwm model checkpoint.
Steps
Load ChnSentiCorp with fastNLP’s ChnSentiCorpLoader.
Tokenize the raw text field with the PaddleNLP BERT tokenizer, using a maximum length of 256.
Convert the target field to integers and store it as labels.
Expected outcome
A dataset bundle with tokenized inputs and a labels field for the Paddle classification workflow.
Guide limitations
- The documented example requires the listed Paddle and PaddleNLP versions.
- The README notes that fastNLP 1.0.0 and later are not fully compatible with earlier fastNLP code.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- FudanNLP/fastNLP · Official homepage title · retained in its source language