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") - Notebooks
- Google Colab
- Kaggle
This is a MicroBERT model for Maltese.
- Its suffix is -mx, which means that it was pretrained using supervision from masked language modeling and XPOS tagging.
- The unlabeled Maltese data was taken from a February 2022 dump of Maltese Wikipedia, totaling 2,113,223 tokens.
- The UD treebank UD_Maltese-GSD, v2.9, totaling 44,162 tokens, was used for labeled data.
Please see the repository and the paper for more details.
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