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:
- f93c617ddec604347b8640032a7a9bee20a8ebafbcb99d3f75c280eb383383a4
- Size of remote file:
- 2.24 GB
- SHA256:
- ef5d2e02eedbb09122e26a3c18a83680b924f6ef61afb825da9dc9775b1fb732
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