Instructions to use aloxatel/3RH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/3RH with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/3RH")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/3RH") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/3RH") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0130d64f85a4a0dd27c0b1dc0df369d12119100f3fa5bdb5323ad82d04a78c72
- Size of remote file:
- 1.42 GB
- SHA256:
- 312d70f40247ef5a4817e00753679737a2df28adaedeac2b4027a0f5bc862e57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.