Instructions to use lgessler/microbert-maltese-mx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgessler/microbert-maltese-mx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgessler/microbert-maltese-mx")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lgessler/microbert-maltese-mx") model = AutoModel.from_pretrained("lgessler/microbert-maltese-mx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b46ed4c3f0e28190cae5134df37a0960b3a868377c87634a83f50fbc588e6017
- Size of remote file:
- 6.79 MB
- SHA256:
- 6c3482efa3cba8522afdac66d34fe545304df7595fbab99181c3abb63f28df8b
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