Instructions to use lgessler/microbert-tamil-mxp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgessler/microbert-tamil-mxp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgessler/microbert-tamil-mxp")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lgessler/microbert-tamil-mxp") model = AutoModel.from_pretrained("lgessler/microbert-tamil-mxp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7190954c6d72fca89c7d4100d9ff4b52213f18fae28d44220ae16b503e70b7ae
- Size of remote file:
- 6.75 MB
- SHA256:
- 13059ec43e03af1c30ae93d487143d120d494a65de80cfff06e7f203b4a6bb3f
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