# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("dongjicheng/roformer-tiny-matcher")
model = AutoModel.from_pretrained("dongjicheng/roformer-tiny-matcher")Quick Links
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Check out the documentation for more information.
1.私域训练集百万，比ada召回率高30%。 2.速度快，上下文长度1536，向量维度 768。
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('dongjicheng/roformer-tiny-matcher')
sentences = [ '运动适应性的分子机制及治疗靶点[#]本文综述了运动对健康的益处和相关的分子机制，探讨了基于运动生物学设计新的治疗策略的可能性，并列举了相关的例子。', 'text集团第四季度财报：月度经常性收入增长，arpu稳步提升[#]text集团第四季度财务数据显示，月度经常性收入达到660,000美元，同比增长4.1%。。', '货车司机应用程序mudflap的燃料卡业务挑战与问题[#]本文讨论了mudflap的燃料卡业务，包括与加油站达成协议、成本波动、交易费用问题等挑战。' ]
embeddings = model.encode(sentences)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dongjicheng/roformer-tiny-matcher")