sat 3l sm

Providersegment-any-text
Categorytoken-classification
Licensemit
Downloads567.2K
Stars0

Overview

The sat 3l sm model is a specialized token-classification engine designed for precise text segmentation. Unlike general-purpose LLMs, this model focuses on the granular task of identifying boundaries and categories within a string, making it an ideal utility for preprocessing pipelines, named entity recognition (NER), or structured data extraction. For developers, its primary value lies in its lightweight architecture and MIT licensing, allowing for seamless integration into production environments without restrictive legal overhead. It is best suited for developers who need a deterministic, high-throughput classification layer to clean or label data before passing it to larger downstream models.

Highlights

  • Optimized for high-precision token-level classification tasks
  • Permissive MIT license for flexible commercial deployment
  • Low-latency performance for real-time text segmentation
  • Efficient preprocessing tool for structured data extraction

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("segment-any-text/sat-3l-sm")
tokenizer = AutoTokenizer.from_pretrained("segment-any-text/sat-3l-sm")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download segment-any-text/sat-3l-sm

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download segment-any-text/sat-3l-sm config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('segment-any-text/sat-3l-sm')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/segment-any-text/sat-3l-sm

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/segment-any-text/sat-3l-sm

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('segment-any-text/sat-3l-sm')
tokenizer = AutoTokenizer.from_pretrained('segment-any-text/sat-3l-sm')

Full Documentation

来源: HuggingFace

---
license: mit
language:

  • multilingual

  • am

  • ar

  • az

  • be

  • bg

  • bn

  • ca

  • ceb

  • cs

  • cy

  • da

  • de

  • el

  • en

  • eo

  • es

  • et

  • eu

  • fa

  • fi

  • fr

  • fy

  • ga

  • gd

  • gl

  • gu

  • ha

  • he

  • hi

  • hu

  • hy

  • id

  • ig

  • is

  • it

  • ja

  • jv

  • ka

  • kk

  • km

  • kn

  • ko

  • ku

  • ky

  • la

  • lt

  • lv

  • mg

  • mk

  • ml

  • mn

  • mr

  • ms

  • mt

  • my

  • ne

  • nl

  • no

  • pa

  • pl

  • ps

  • pt

  • ro

  • ru

  • si

  • sk

  • sl

  • sq

  • sr

  • sv

  • ta

  • te

  • tg

  • th

  • tr

  • uk

  • ur

  • uz

  • vi

  • xh

  • yi

  • yo

  • zh

  • zu

library:
  • wtpsplit

---

sat-3l-sm

Model for wtpsplit.

State-of-the-art sentence segmentation with 3 Transfomer layers.

For details, see our Segment any Text paper

View official source
Join our Telegram