Instructions to use lgessler/microbert-uyghur-m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgessler/microbert-uyghur-m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgessler/microbert-uyghur-m")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lgessler/microbert-uyghur-m") model = AutoModel.from_pretrained("lgessler/microbert-uyghur-m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26997344b767722e0d8163218e9cbcec089d6771b39c9c49e7c53974b5df64a5
- Size of remote file:
- 7.05 MB
- SHA256:
- 3162cfaf1266e8bc8a1c8c6d7660f5a9bc7f64b2a62d06b95938611ad8e98f96
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