abbasgolestani/ag-nli-DeTS-sentence-similarity-v4

from sentence_transformers import CrossEncoder

model = CrossEncoder('abbasgolestani/ag-nli-DeTS-sentence-similarity-v4')

# Dos listas de oraciones
sentences1 = ['I am honored to be given the opportunity to help make our company better',
'I love my job and what I do here',
'I am excited about our company’s vision']

sentences2 = ['I am hopeful about the future of our company',
'My work is aligning with my passion',
'Definitely our company vision will be the next breakthrough to change the world and I’m so happy and proud to work here']

pairs = zip(sentences1, sentences2)
list_pairs = list(pairs)

scores1 = model.predict(list_pairs, show_progress_bar=False)
print(scores1)

for i in range(len(sentences1)):
    print("{} 		 {} 		 Score: {:.4f}".format(sentences1[i], sentences2[i], scores1[i]))

# Uso de modelos preentrenados
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
El modelo predecirá puntajes para los pares ('Sentence 1', 'Sentence 2') y ('Sentence 3', 'Sentence 4').
Puedes usar este modelo también sin sentence_transformers y simplemente usando la clase Transformers AutoModel.