Sentence Similarity
Transformers
PyTorch
ONNX
sentence-transformers
Chinese
bert
feature-extraction
miniMiracle
passage-retrieval
knowledge-distillation
middle-training
text-embeddings-inference
Instructions to use prithivida/miniDense_chinese_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivida/miniDense_chinese_v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("prithivida/miniDense_chinese_v1") model = AutoModel.from_pretrained("prithivida/miniDense_chinese_v1", device_map="auto") - sentence-transformers
How to use prithivida/miniDense_chinese_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prithivida/miniDense_chinese_v1") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2866fcb721b03bb4eceb8ca62960ab05c7167b6046c4c6337289946025b8d557
- Size of remote file:
- 471 MB
- SHA256:
- 4b98da452197c845694bb39ec5d6bdfb6f168ba27983ca1b44ea4c4205d22d14
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