Feature Extraction
Transformers
PyTorch
Chinese
bert
BERT
encoder
embeddings
TiME
size:s
text-embeddings-inference
Instructions to use dschulmeist/TiME-zh-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dschulmeist/TiME-zh-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dschulmeist/TiME-zh-s")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dschulmeist/TiME-zh-s") model = AutoModel.from_pretrained("dschulmeist/TiME-zh-s") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea07ad3c7a353d9926257ac9ea5a50881be5d0658047ee3e73622eaaedad1364
- Size of remote file:
- 428 MB
- SHA256:
- 38cbcfb12e8794e16849104a8620db664a611acba46e073cdc0774e1184ddefd
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