company names similarity sentence transformer

Introduction

This model is a Sentence-Transformer designed to map sentences and paragraphs into a 384-dimensional dense vector space, facilitating tasks such as clustering and semantic search.

Architecture

The model architecture consists of a SentenceTransformer with the following components:

• Transformer: Utilizes a BERT model with a maximum sequence length of 256 and case sensitivity.
• Pooling: Averages token embeddings.
• Normalize: Normalizes the embeddings.

Training

The model was trained with the following parameters:

• DataLoader: Utilizes a PyTorch DataLoader with a batch size of 32 and a WeightedRandomSampler.
• Loss Function: CosineSimilarityLoss from sentence_transformers.
• Training Parameters: Trained for 1 epoch with a learning rate of 2e-5 using the AdamW optimizer. The scheduler was WarmupLinear with 100 warmup steps and a weight decay of 0.01.

Guide: Running Locally

1. Install Dependencies:

   pip install -U sentence-transformers
   

2. Load and Use the Model:

   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)
   

3. Hardware Recommendation: For optimal performance, especially on large datasets, consider using cloud GPUs such as AWS EC2, Google Cloud, or Azure.

License

Refer to the model documentation for specific licensing details.