Multilingual distillation
Dist-MPNet-Paracrawl is a BERT-small model distilled from the sentence-transformers/all-mpnet-base-v2 model, using parallel cs-en dataset ParaCrawl for training.
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:
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)