Supervised_ft_embedding_1203_v1 Open-Source Text Embedding Model - Free for Semantic Similarity and Feature Extraction

Supervised Ft Embedding 1203 V1

Developed by li-ping

This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic similarity and feature extraction tasks.

Text Embedding

PyTorch

#Sentence Vectorization #Semantic Similarity #768-Dimensional Dense Vector

Downloads 19

Release Time : 12/3/2023

Model Overview

The model can convert sentences and paragraphs into high-dimensional vector representations, supporting natural language processing tasks such as clustering and semantic search.

Model Features

High-Dimensional Vector Representation

Converts text into 768-dimensional dense vectors, preserving semantic information.

Supervised Fine-Tuning

The model is fine-tuned with supervised learning to optimize semantic representation capabilities.

Versatile Applications

Supports various downstream tasks such as clustering and semantic search.

Model Capabilities

Text Vectorization

Semantic Similarity Calculation

Feature Extraction

Text Clustering

Use Cases

Information Retrieval

Semantic Search

Implements document retrieval based on semantic similarity through vector comparison.

Improves the relevance of search results.

Text Analysis

Document Clustering

Groups documents based on text vectors.

Identifies collections of similar documents.

🚀 li-ping/supervised_ft_embedding_1203_v1

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

🚀 Quick Start

📦 Installation

Using this model becomes easy when you have sentence-transformers installed. You can install it with the following command:

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('li-ping/supervised_ft_embedding_1203_v1')
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 1 with parameters:

{'batch_size': 4, '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": 5,
    "evaluation_steps": 400,
    "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
  (2): Normalize()
)

📄 License

No license information provided in the original document.

Citing & Authors

Property	Details
Pipeline Tag	sentence-similarity
Tags	sentence-transformers, feature-extraction, sentence-similarity

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