Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Maltese
xlm-roberta
MaltBERTa
MaCoCu
text-embeddings-inference
Instructions to use MaCoCu/XLMR-MaltBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaCoCu/XLMR-MaltBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaCoCu/XLMR-MaltBERTa")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MaCoCu/XLMR-MaltBERTa") model = AutoModel.from_pretrained("MaCoCu/XLMR-MaltBERTa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64db0ae8ab7e5251bf900ee95e1718b31dab5663e9b6e8aad753bf6f935f1791
- Size of remote file:
- 2.24 GB
- SHA256:
- 6b048931c028f01da00cfc1fc559a6930932a709bb659a0c4486293aab09158c
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