Instructions to use lgessler/microbert-uyghur-mxp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgessler/microbert-uyghur-mxp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgessler/microbert-uyghur-mxp")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lgessler/microbert-uyghur-mxp") model = AutoModel.from_pretrained("lgessler/microbert-uyghur-mxp") - Notebooks
- Google Colab
- Kaggle
This is a MicroBERT model for Uyghur.
- Its suffix is -mxp, which means that it was pretrained using supervision from masked language modeling, XPOS tagging, and UD dependency parsing.
- The unlabeled Uyghur data was taken from a February 2022 dump of Uyghur Wikipedia, totaling 2,401,445 tokens.
- The UD treebank UD_Uyghur-UDT, v2.9, totaling 40,236 tokens, was used for labeled data.
Please see the repository and the paper for more details.
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