Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Bulgarian
Macedonian
multilingual
roberta
BERTovski
MaCoCu
Instructions to use MaCoCu/BERTovski with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaCoCu/BERTovski with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaCoCu/BERTovski")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MaCoCu/BERTovski") model = AutoModel.from_pretrained("MaCoCu/BERTovski", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6167f746097b486f2a37642ff43523436db9b6271cac826b882f5156e594fb58
- Size of remote file:
- 443 MB
- SHA256:
- da0446909e76e863347dab4a92e4784df5100eaec004ff231f6202965e7a4a06
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