OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Evaluation & Observability

Nlp Fluency

Nlp Fluency is an MIT-licensed Python library that estimates the fluency of Chinese text using n-gram, GPT next-token, and masked-BERT or ALBERT probability methods, returning scores or perplexity.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Nlp Fluency — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need local Python-based fluency estimates for Chinese text and can supply the required packages and model files. Choose n-grams when you can segment input with jieba; use masked BERT-style scoring when bidirectional context is relevant.

Who it is for

  • Python developers adding Chinese text-fluency estimates to a workflow.
  • Researchers comparing language-model-based fluency scoring approaches.

Documented uses

  • Estimate fluency for a Chinese sentence or passage.
  • Compare text using n-gram, GPT, or masked-language-model probability methods.
  • Inspect token probabilities with verbose scoring output.
  • Train an n-gram model using other corpora through the included training script.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. Its documented usage requires PyTorch and Transformers; scoring examples load local model paths.

  • The library provides distinct scoring approaches whose outputs should be interpreted in the context of the selected method and model.
  • N-gram inputs need jieba segmentation; the GPT and masked-model examples accept character-spaced text or a list.
  • The project warns that GPT next-token estimates can be biased by treating prior text as correct.
  • The included n-gram model is based on Tsinghua news-summary data and may score classical or other non-colloquial Chinese poorly.
  • The README describes ALBERT as faster but less effective than BERT in its examples.

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

Score Chinese text with the n-gram model

Calculate perplexity for Chinese text using the repository’s n-gram example.

Before you start

  • Python with PyTorch and Transformers installed, as required by the project.
  • The nlp-fluency repository and an available n-gram model directory such as the documented thucnews_lm_model.
  • jieba for segmenting text before scoring.

Steps

  1. Load NgramsLanguageModel with from_pretrained and the model directory.

  2. Segment a Chinese sentence or passage with jieba.lcut.

  3. Pass the resulting tokens to model.perplexity, optionally setting verbose=True to display probabilities.

Expected outcome

A perplexity value is returned; lower values indicate better fluency under this model. Verbose mode can show token-level probabilities.

Guide limitations

  • The documented example uses a model trained on Tsinghua news-summary data; the README warns it lacks classical Chinese data and can assign high perplexity to non-colloquial text.
  • This recipe describes the documented usage example, not an execution test.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
baojunshan/nlp-fluency · Official homepage title · retained in its source language
How we review sourcesSuggest a correction