Parameter - Mini - LDS Open - Source and Patented Parameter Sentence Recognition Model - Free for Clustering and Semantic Search Tasks

Parameter Mini Lds

Developed by nategro

A patent parameter sentence recognition model based on all-MiniLM-L6-v2, which maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks like clustering or semantic search.

Text Embedding

PyTorch

#Patent parameter recognition #Sentence vectorization #Semantic similarity

Downloads 14

Release Time : 10/13/2022

Model Overview

This is a sentence-transformers-based model specifically designed to convert sentences and paragraphs into 384-dimensional vector representations, suitable for tasks such as sentence similarity calculation, cluster analysis, and semantic search.

Model Features

Efficient vector representation

Maps sentences and paragraphs into a 384-dimensional dense vector space, facilitating subsequent processing and analysis.

Patent parameter sentence recognition

Optimized specifically for patent parameter sentences, enabling better handling of parameter information in patent texts.

Lightweight model

Based on all-MiniLM-L6-v2, it maintains high performance while having a relatively small model size.

Model Capabilities

Sentence vectorization

Paragraph vectorization

Sentence similarity calculation

Semantic search

Text clustering

Use Cases

Patent analysis

Patent parameter sentence similarity calculation

Calculates the similarity of parameter sentences across different patents for patent retrieval and comparison.

Improves the accuracy and efficiency of patent retrieval.

Information retrieval

Semantic search

Performs semantic search based on sentence vectors to enhance the relevance of search results.

Compared to traditional keyword search, it better understands user intent.

🚀 parameter-mini-lds

A model for the identification of parameter sentences in patents using all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384 - dimensional dense vector space and can be used for tasks like clustering or semantic search.

🚀 Quick Start

This is a sentence-transformers model. Using this model becomes easy when you have sentence-transformers installed.

✨ Features

Maps sentences and paragraphs to a 384-dimensional dense vector space.
Can be used for tasks such as clustering or semantic search.

📦 Installation

First, install the sentence-transformers library:

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('nategro/parameter-mini-lds')
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 1128 with parameters:

{'batch_size': 16, '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": 1,
    "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": 1128,
    "warmup_steps": 113,
    "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': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)

Citing & Authors

The following pre-trained model was used: sentence-transformers/all-MiniLM-L6-v2

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