sentence-transformers How to use pj-mathematician/JobSkillGTE-7b-lora with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("pj-mathematician/JobSkillGTE-7b-lora")
sentences = [
"Bus drivers, including those operating in various sectors like public transit, intercity, private, or school services, need strong driving skills, knowledge of traffic laws, and the ability to operate safely in diverse conditions. Additionally, effective communication skills and the ability to handle passenger inquiries and emergencies are crucial.\n['bus driver', 'intercity bus driver', 'private bus operator', 'transit bus driver', 'public service vehicle operator', 'passenger driver', 'international bus driver', 'public bus operator', 'touristic bus driver', 'coach driver', 'private coach driver', 'public bus driver', 'bus operator', 'driver of bus', 'bus driving operator', 'schoolbus driver']",
"The skill of determining shreds sizes percentage in cigarettes is primarily required by tobacco processing technicians and quality control specialists in the cigarette manufacturing industry, who ensure that the tobacco shreds meet specific size and quality standards for consistent product performance.\n['determine shreds sizes percentage in cigarettes', 'determine shreds sizes percentage in cigarettes', 'determine the shreds sizes percentage of cigarettes', 'determine shreds size percentages in cigarettes', 'agree shreds sizes percentage in cigarettes', 'determine the shreds sizes percentage in cigarettes', 'confirm shreds sizes percentage in cigarettes', 'sort shreds sizes percentage in cigarettes']",
"Job roles such as curriculum developers, educational consultants, and instructional designers require skills like analyzing, evaluating, and scrutinizing curriculums to improve educational outcomes. For legislative programmes, roles including policy analysts, legislative aides, and compliance officers use skills to test, evaluate, and scrutinize legislative processes to ensure effective and efficient policy implementation.\n['analyse curriculum', 'test legislative programmes', 'evaluate legislative programmes', 'evaluate curriculum', 'test curriculum', 'investigate curriculum', 'scrutinise curriculum', 'analyze curriculum', 'scrutinise legislative processes', 'investigate legislative programmes']",
"Job roles such as customer service representatives, flight attendants, and hotel concierges require a strong focus on passengers or customers, ensuring their needs and comfort are prioritized to provide excellent service and support.\n['focus on passengers', 'prioritise passengers', 'ensure passenger prioritisation', 'make passengers a priority', 'maintain a focus on passengers', 'ensure passengers are the priority focus', 'ensure passengers are prioritised', 'attend to passengers', 'ensure a focus on passengers']"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
