Feature Extraction
Transformers
PyTorch
Korean
bert
BERT
encoder
embeddings
TiME
size:s
text-embeddings-inference
Instructions to use dschulmeist/TiME-ko-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dschulmeist/TiME-ko-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dschulmeist/TiME-ko-s")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dschulmeist/TiME-ko-s") model = AutoModel.from_pretrained("dschulmeist/TiME-ko-s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f44ae6f7371f7190e34a6e1f8b74b943fe8f2a1bd77908b8238eac8087b58fdf
- Size of remote file:
- 428 MB
- SHA256:
- f924d226afd5957eb5106e3c36b0f1f36ec41c8b900c3f30b3381a03b6ce5ecb
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