metadata
language:
- en
sentence-transformers/LaBSE pre-trained on an instructional question-and-answer dataset. Evaluated on Precision at K metrics and Mean reciprocal rank
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("zjkarina/LaBSE-instructDialogs")
model = AutoModel.from_pretrained("zjkarina/LaBSE-instructDialogs")
sentences = ["List 5 reasons why someone should learn to code", "Describe the sound of the wind on a sunny day."]
encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = model_output.pooler_output
embeddings = torch.nn.functional.normalize(embeddings)
print(embeddings)