st polish paraphrase from mpnet
简介
核心亮点
- 基于 MPNet 架构，语义理解精准且运行高效
- 专注于同义改写，提升文本流畅度与专业感
- 轻量级部署，适合集成至自动化文本处理流
- LGPL 许可，对开发者非常友好的开源协议
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
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 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download sdadas/st-polish-paraphrase-from-mpnet下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download sdadas/st-polish-paraphrase-from-mpnet config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('sdadas/st-polish-paraphrase-from-mpnet')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
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')完整文档
---
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.
<!--- Describe your model here -->
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformersThen you can use the model like this:
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.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
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
Full Model Architecture
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
<!--- Describe where people can find more information -->
