Feature Extraction
Transformers
PyTorch
Portuguese
bert
BERT
encoder
embeddings
TiME
size:s
text-embeddings-inference
Instructions to use dschulmeist/TiME-pt-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dschulmeist/TiME-pt-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dschulmeist/TiME-pt-s")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dschulmeist/TiME-pt-s") model = AutoModel.from_pretrained("dschulmeist/TiME-pt-s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 470aff95c440ee3f5e292d5d1ebe1f82b82e6d7dedc9700cd54a3aa1d306aff0
- Size of remote file:
- 428 MB
- SHA256:
- 3420496d1946d9b47e0f5aad4587e165ab17466f9a50c3d7e2a1efdb7afcbd77
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