Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Chinese
bert
Sentence Transformers
Instructions to use shibing624/text2vec-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shibing624/text2vec-base-chinese with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shibing624/text2vec-base-chinese") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
| Epoch:0 Valid| corr: 0.794410 | |
| Epoch:0 Valid| corr: 0.691819 | |
| Epoch:1 Valid| corr: 0.722749 | |
| Epoch:2 Valid| corr: 0.735054 | |
| Epoch:3 Valid| corr: 0.738295 | |
| Epoch:4 Valid| corr: 0.739411 | |
| Test | corr: 0.679971 | |
| Epoch:0 Valid| corr: 0.817416 | |
| Epoch:1 Valid| corr: 0.832376 | |
| Epoch:2 Valid| corr: 0.842308 | |
| Epoch:3 Valid| corr: 0.843520 | |
| Epoch:4 Valid| corr: 0.841837 | |
| Test | corr: 0.793495 | |
| Epoch:0 Valid| corr: 0.814648 | |
| Epoch:1 Valid| corr: 0.831609 | |
| Epoch:2 Valid| corr: 0.841678 | |
| Epoch:3 Valid| corr: 0.842387 | |
| Epoch:4 Valid| corr: 0.841435 | |
| Test | corr: 0.794840 | |