Instructions to use lgessler/microbert-maltese-mxp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgessler/microbert-maltese-mxp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgessler/microbert-maltese-mxp")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lgessler/microbert-maltese-mxp") model = AutoModel.from_pretrained("lgessler/microbert-maltese-mxp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c54d39a1f3e8712046c506bccad0d7e5f2b22fc0b751d352353191a2df034578
- Size of remote file:
- 6.79 MB
- SHA256:
- 8c330697ddd5ead0c9d304609e24044022a14f37deb22ebe99bf85ac3e9dd4cb
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