# Contrastive Learning Model This model is a contrastive learning model based on distilbert-base-uncased. It outputs the [CLS] token embedding for similarity comparisons. ## Usage ```python from transformers import AutoTokenizer from modeling import ContrastiveModel # Import the model class directly import torch # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("SajayR/contrastive-model") config = AutoConfig.from_pretrained("SajayR/contrastive-model") model = ContrastiveModel(config) model.load_state_dict(torch.load("pytorch_model.bin")) # Prepare input text = "Hello, world!" inputs = tokenizer(text, return_tensors="pt") # Get embeddings (shape: [batch_size, hidden_size]) embeddings = model(**inputs) ``` The model returns a tensor of shape `[batch_size, hidden_size]` containing the [CLS] token embedding.