Instructions to use aloxatel/AVG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/AVG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/AVG")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/AVG") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/AVG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbf1355f5efea145864fcf60aeef3115546d8bb5ff9b897d44a27518c716a69b
- Size of remote file:
- 1.43 GB
- SHA256:
- c11b055e3f611266cba2f3daca01838f38e2eb2c10ef07be4d86e7c2843c1ba3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.