Qdrant/all_miniLM_L6_v2_with_attentions

Aquí hay un ejemplo de cómo realizar la inferencia utilizando el modelo con FastEmbed.

from fastembed import SparseTextEmbedding

documents = [
"You should stay, study and sprint.",
"History can only prepare us to be surprised yet again."
]

model = SparseTextEmbedding(model_name="Qdrant/bm42-all-minilm-l6-v2-attentions")
embeddings = list(model.embed(documents))

# [
#     SparseEmbedding(values=array([0.26399775, 0.24662513, 0.47077307]),
#                     indices=array([1881538586, 150760872, 1932363795])),
#     SparseEmbedding(values=array(
#         [0.38320042, 0.25453135, 0.18017513, 0.30432631, 0.1373556]),
#                     indices=array([
#                         733618285, 1849833631, 1008800696, 2090661150,
#                         1117393019
#                     ]))
# ]