Instructions to use aloxatel/3JQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/3JQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/3JQ")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/3JQ") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/3JQ") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f45361c6451d3c2efe45775b3b7401c4a0ecf1d6343c71ad9178df84eae7ecd
- Size of remote file:
- 1.42 GB
- SHA256:
- 29bee7cd286a81ed6669ebe33308ddbe4fec4e54644f04116ba63a64e20b6901
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