Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Bulgarian
Macedonian
multilingual
xlm-roberta
BERTovski
MaCoCu
text-embeddings-inference
Instructions to use MaCoCu/XLMR-BERTovski with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaCoCu/XLMR-BERTovski with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaCoCu/XLMR-BERTovski")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MaCoCu/XLMR-BERTovski") model = AutoModel.from_pretrained("MaCoCu/XLMR-BERTovski", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aeb10038439784b1417ae3c006021d7f7635ce24e4eae46268a75dffff1d83c7
- Size of remote file:
- 2.24 GB
- SHA256:
- ca9b4c0d5aac8aa19993dec3a7ad205d33a5c30e24bff9e6ac7cff9bcf18c5e6
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