You can deploy this model and start earning money today!

Seznam/
simcse-dist-mpnet-paracrawl-cs-en

Name: simcse-dist-mpnet-paracrawl-cs-en
Availability: InStock

text-embeddings-inference

endpoints_compatible

Views

No views yet

SimCSE

SimCSE-DistMPNet-Paracrawl is the Seznam/dist-mpnet-paracrawl-cs-en model fine-tuned with the SimCSE objective.

This model was created at Seznam.cz as part of a project to create high-quality small Czech semantic embedding models. These models perform well across various natural language processing tasks, including similarity search, retrieval, clustering, and classification. For further details or evaluation results, please visit the associated paper or GitHub repository.

How to Use

You can load and use the model like this:

python

import torch
from transformers import AutoModel, AutoTokenizer

model_name = "Seznam/retromae-small-cs"  # Hugging Face link
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

input_texts = [
    "Dnes je výborné počasí na procházku po parku.",
    "Večer si oblíbím dobrý film a uvařím si čaj."
]

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]  # Extract CLS token embeddings

similarity = torch.nn.functional.cosine_similarity(embeddings[0], embeddings[1], dim=0)

Seznam/simcse-dist-mpnet-paracrawl-cs-en

SimCSE

How to Use

Seznam/
simcse-dist-mpnet-paracrawl-cs-en