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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from MaCoCu/BERTovski: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/MaCoCu/BERTovski/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://MaCoCu/BERTovski/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/MaCoCu/BERTovski/resolve/main/flax_model.msgpack
442 MB
- Xet hash:
- 832427515edc574dcdb97806cabd89722c8206ba99032b55dab9dd663e140965
- Size of remote file:
- 442 MB
- SHA256:
- 34a083decacd62f64ef5e9a8035f9bdf31ef6bdeb4449cc8debd12401f9f7167
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