Instructions to use l3cube-pune/me-lid-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-lid-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="l3cube-pune/me-lid-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-lid-roberta") model = AutoModelForTokenClassification.from_pretrained("l3cube-pune/me-lid-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from l3cube-pune/me-lid-roberta: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/l3cube-pune/me-lid-roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3cube-pune/me-lid-roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3cube-pune/me-lid-roberta/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- 7733b62637a92dae2ce10ebcb515e89880d3b5bb51b590b6872099d0ba376dcc
- Size of remote file:
- 496 MB
- SHA256:
- 998b5a0a3e8716bbae5060a40167c29377c8dc73934197cb22bd99e95012a73b
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