ML_USE_13 Open-Source Sentence Embedding Model - Free Deployment for Text Semantic Search and Clustering

Ml Use 13

Developed by ronanki

A sentence embedding model based on sentence-transformers, which maps text to a 512-dimensional vector space, suitable for semantic search and clustering tasks.

Text Embedding

PyTorch

#Sentence Embedding #Semantic Search #512-Dimensional Vector

Downloads 25

Release Time : 5/24/2022

Model Overview

This model can convert sentences and paragraphs into 512-dimensional dense vector representations, primarily used for tasks such as sentence similarity calculation, semantic search, and text clustering.

Model Features

High-Dimensional Vector Representation

Converts text into 512-dimensional dense vectors, capable of capturing rich semantic information.

Semantic Similarity Calculation

Accurately measures semantic similarity between sentences through distance calculations in vector space.

Easy Integration

Can be easily integrated into existing applications via the sentence-transformers library.

Model Capabilities

Sentence Embedding

Semantic Similarity Calculation

Text Clustering

Semantic Search

Use Cases

Information Retrieval

Semantic Search System

Build a search system based on semantics rather than keywords to improve the relevance of search results.

More accurately matches user query intent

Text Analysis

Document Clustering

Automatically group semantically similar documents for content organization and analysis.

Achieves unsupervised document classification

🚀 ronanki/ml_use_13

This model from sentence-transformers maps sentences and paragraphs to a 512-dimensional dense vector space, useful for tasks like clustering and semantic search.

🚀 Quick Start

This is a sentence-transformers model. It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.

📦 Installation

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

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('ronanki/ml_use_13')
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: sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 8 with parameters:

{'batch_size': 4}

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

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

Parameters of the fit()-Method:

{
    "epochs": 3,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 0,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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})
  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

📄 Citing & Authors

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