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:
- 03c47832432bd134f0e52d2c4d6de68cc024ae54f06285851a01292504a71499
- Size of remote file:
- 3.9 kB
- SHA256:
- 6296b984a8b0efcbff9b272638e92772177de63b469ee3cc326751884a3fd41c
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