Instructions to use l3cube-pune/me-lid-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-lid-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="l3cube-pune/me-lid-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-lid-bert") model = AutoModelForTokenClassification.from_pretrained("l3cube-pune/me-lid-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e37336b904339f033d5b5804492f559ae54b01d511bb5d1ca176f3114cf1ff6e
- Size of remote file:
- 948 MB
- SHA256:
- 02e423fefee34f8c1fbfd22daa730b0882b558082be945799caadc99855e7d3f
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