bert-base-multilingual-uncased_10_Epochs Open-source Model - A Text Embedding Tool for Semantic Search and Clustering

Model Bert Base Multilingual Uncased 10 Epochs

Developed by jfarray

This is a sentence embedding model based on sentence-transformers, which can map text to a 256-dimensional vector space and is suitable for semantic search and clustering tasks.

Text Embedding

PyTorch

#Sentence Embedding #Semantic Similarity #256-dimensional Vector

Downloads 10

Release Time : 3/2/2022

Model Overview

This model can convert sentences and paragraphs into 256-dimensional dense vector representations, which can be used for tasks such as calculating sentence similarity, semantic search, and text clustering.

Model Features

Efficient Text Representation

Convert text into 256-dimensional dense vectors while preserving semantic information

Pretrained Architecture

Based on the Transformer architecture of BERT, with powerful semantic understanding capabilities

Easy to Use

Can be easily called through the sentence-transformers library

Model Capabilities

Text Vectorization

Semantic Similarity Calculation

Text Clustering

Semantic Search

Use Cases

Information Retrieval

Semantic Search

Document retrieval based on semantics rather than keyword matching

Text Analysis

Text Clustering

Automatically group documents with similar semantics

🚀 {MODEL_NAME}

This is a sentence-transformers model that maps sentences and paragraphs to a 256-dimensional dense vector space. It can be applied to tasks such as clustering or semantic search.

🚀 Quick Start

Using this model becomes easy 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 11 with parameters:

{'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Parameters of the fit()-Method:

{
    "epochs": 10,
    "evaluation_steps": 1,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 11,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (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': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

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