Instructions to use pabagcha/alc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pabagcha/alc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pabagcha/alc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pabagcha/alc") model = AutoModelForSequenceClassification.from_pretrained("pabagcha/alc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5680b0b89a9501a51b608051059b3725cd03bdd076af9224fcfdd2d05c3a04d8
- Size of remote file:
- 1.42 GB
- SHA256:
- 0e44a5cac0ecb1db8a11fbd2e255154a3299d2db5b9d3e1232beafae8c575608
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