Feature Extraction
Transformers
PyTorch
multilingual
bert
STILT
retraining
multi-task learning
text-embeddings-inference
Instructions to use robvanderg/Sem-mmmBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robvanderg/Sem-mmmBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="robvanderg/Sem-mmmBERT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("robvanderg/Sem-mmmBERT") model = AutoModel.from_pretrained("robvanderg/Sem-mmmBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6fb0a266c631c9d69c20ec8e4da4e937e7ee52778b7152de09644ab4f25be8e
- Size of remote file:
- 711 MB
- SHA256:
- c4e45e2712d4adc92fa22d2b1ea4d2695dad890f8dc68d5ede7e2aa36fb1713f
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