task_categories:sentence-similarity language:es size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: context
    dtype: string
  - name: name
    dtype: string
  - name: embedding
    sequence: float32
  splits:
  - name: train
    num_bytes: 717888
    num_examples: 202
  download_size: 1005715
  dataset_size: 717888
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- sentence-similarity
language:
- es
---

# Modelo

- "Alibaba-NLP/gte-multilingual-base"

Puedes obtener toda la información relacionado con el modelo <a href="https://huggingface.co/Alibaba-NLP/gte-multilingual-base">aquí</a>

# Busqueda

```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
from datasets import load_dataset
import numpy as np

model_name = "Alibaba-NLP/gte-multilingual-base"
model = SentenceTransformer(model_name, trust_remote_code=True)

raw_data = load_dataset('Manyah/incrustaciones')

question = ""
question_embedding = model.encode(question)

sim = [cos_sim(raw_data['train'][i]['embedding'],question_embedding).numpy() for i in range(0,len(raw_data['train']))]

index =  sim.index(max(sim))

print(raw_data['train'][index]['context'])

```