Feature Extraction
Transformers
PyTorch
Urdu
bert
BERT
encoder
embeddings
TiME
size:m
text-embeddings-inference
Instructions to use dschulmeist/TiME-ur-m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dschulmeist/TiME-ur-m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dschulmeist/TiME-ur-m")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dschulmeist/TiME-ur-m") model = AutoModel.from_pretrained("dschulmeist/TiME-ur-m") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ccd0aedc86aad32bf65b99a810155e541b20d54864565f51aae7836c3b12d66
- Size of remote file:
- 942 MB
- SHA256:
- cc64b57fed5f38a29cc6946f4fe2238db41310208cfefae282c0589a2d63f34f
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