hiiamsid/sentence_similarity_spanish_es

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha']

model = SentenceTransformer('hiiamsid/sentence_similarity_spanish_es')
embeddings = model.encode(sentences)
print(embeddings)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentencias para las que queremos embeddings de oraciones
sentences = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha']

# Cargar el modelo desde HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hiiamsid/sentence_similarity_spanish_es')
model = AutoModel.from_pretrained('hiiamsid/sentence_similarity_spanish_es')

# Tokenizar las oraciones
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Calcular los embeddings de los tokens
with torch.no_grad():
model_output = model(**encoded_input)

# Realizar el pooling. En este caso, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)