sbert base chinese nli EU AI Act Compliance Profile

Chinese Sentence BERT

Model description

This is the sentence embedding model pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.

How to use

You can use this model to extract sentence embeddings for sentence similarity task. We use cosine distance to calculate the embedding similarity here:

>>> from sentence_transformers import SentenceTransformer
>>> model = SentenceTransformer('uer/sbert-base-chinese-nli')
>>> sentences = ['那个人很开心', '那个人非常开心']
>>> sentence_embeddings = model.encode(sentences)
>>> from sklearn.metrics.pairwise import paired_cosine_distances
>>> cosine_score = 1 - paired_cosine_distances([sentence_embeddings[0]],[sentence_embeddings[1]])

Training data

ChineseTextualInference is used as training data.

Training procedure

The model is fine-tuned by UER-py on Tencent Cloud. We fine-tune five epochs with a sequence length of 128 on the basis of the pre-trained model chinese_roberta_L-12_H-768. At the end of each epoch, the model is saved when the best performance on development set is achieved.

Your obligations as a sbert base chinese nli deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library