paraphrase-multilingual-MiniLM-L12-v2 Open-Source Model - Free for Sentence Similarity Calculation and Semantic Search

Paraphrase Multilingual MiniLM L12 V2 Spelling Correction

Developed by karakastarik

This is a model based on sentence-transformers that maps sentences and paragraphs into a 32-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

Text Embedding

PyTorch

#Sentence Embedding #Semantic Search #32-dimensional Vector

Downloads 16

Release Time : 3/31/2023

Model Overview

This model is primarily used to convert text into low-dimensional vector representations, supporting natural language processing tasks such as sentence similarity calculation, text clustering, and semantic search.

Model Features

Dense Vector Representation

Maps sentences and paragraphs into a 32-dimensional dense vector space, preserving semantic information

Efficient Computation

Provides efficient sentence embedding computation capabilities, suitable for large-scale text processing

Versatile Applications

Supports various downstream tasks, including similarity calculation, clustering, and semantic search

Model Capabilities

Sentence vectorization

Semantic similarity calculation

Text clustering

Semantic search

Use Cases

Information Retrieval

Semantic Search System

Build a search system based on semantics rather than keywords

Improves the relevance and accuracy of search results

Text Analysis

Document Clustering

Automatically group similar documents

Achieves unsupervised document classification

🚀 {MODEL_NAME}

This is a sentence-transformers model that maps sentences and paragraphs to a 32-dimensional dense vector space, which can be used for tasks such as clustering or semantic search.

🚀 Quick Start

This is a sentence-transformers model. It becomes easy to use when you have sentence-transformers installed.

📦 Installation

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

📚 Documentation

Evaluation Results

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

Training

The model was trained with the following parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 4075 with parameters:

{'batch_size': 512, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss: sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

{'scale': 20.0, 'similarity_fct': 'cos_sim'}

Parameters of the fit()-Method:

{
    "epochs": 10,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 4075,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 20, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 384, 'out_features': 32, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

📄 Citing & Authors

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