Instructions to use pabagcha/alc_robertuito with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pabagcha/alc_robertuito with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pabagcha/alc_robertuito")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pabagcha/alc_robertuito") model = AutoModelForSequenceClassification.from_pretrained("pabagcha/alc_robertuito") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3073f89f0591afad6f82b3cfb0dd467bd6dda3b283912e35a33d1db4b74949b6
- Size of remote file:
- 3.9 kB
- SHA256:
- 3fd70b3e91ecc749220a186d570798f9eec98f0863f170fbccb2306671591b81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.