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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5e38c895af3bb0468c0c0e19d13e418f5e86c43613bc6fba00e8f8736fa31e9
- Size of remote file:
- 1.42 GB
- SHA256:
- 498018900a58c1f1ab1d526f021662247789d3a74e93fb45f3bcbee8ed42aa37
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