Name: st polish paraphrase from mpnet
Author: sdadas

Overview

The 'st polish paraphrase from mpnet' is a specialized text-to-text model designed to refine and rewrite Polish text while maintaining original semantic meaning. Built upon the MPNet architecture, it leverages sentence-transformer embeddings to ensure high-quality paraphrasing that avoids the common pitfalls of generic translation models. For developers, this is an ideal utility for augmenting NLP datasets, removing redundancy in user-generated content, or implementing 'rewrite' features in localization pipelines. It integrates easily into existing Python-based LLM workflows, offering a lightweight alternative to massive generative models when the goal is precise stylistic polishing rather than creative generation.

Highlights

Optimized for high-fidelity Polish language paraphrasing
Based on efficient MPNet sentence-transformer architecture
Ideal for dataset augmentation and text refinement
Lightweight alternative to large-scale generative LLMs
LGPL licensed for flexible integration

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("sdadas/st-polish-paraphrase-from-mpnet")
tokenizer = AutoTokenizer.from_pretrained("sdadas/st-polish-paraphrase-from-mpnet")

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

huggingface-cli download sdadas/st-polish-paraphrase-from-mpnet

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

huggingface-cli download sdadas/st-polish-paraphrase-from-mpnet config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('sdadas/st-polish-paraphrase-from-mpnet')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet

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

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('sdadas/st-polish-paraphrase-from-mpnet')
tokenizer = AutoTokenizer.from_pretrained('sdadas/st-polish-paraphrase-from-mpnet')

Full Documentation

来源: HuggingFace

---
pipeline_tag: sentence-similarity
tags:

sentence-transformers

feature-extraction

sentence-similarity

transformers

license: lgpl
language:

pl

---

sdadas/st-polish-paraphrase-from-mpnet

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

code

pip install -U sentence-transformers

Then you can use the model like this:

python

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sdadas/st-polish-paraphrase-from-mpnet')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

python

from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sdadas/st-polish-paraphrase-from-mpnet')
model = AutoModel.from_pretrained('sdadas/st-polish-paraphrase-from-mpnet')
Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)

Evaluation Results

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

Full Model Architecture

code

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors