Instructions to use Akajackson/weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akajackson/weights with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Akajackson/weights")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Akajackson/weights") model = AutoModelForSequenceClassification.from_pretrained("Akajackson/weights") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a114b7df44455de542ba24a546ee28364508528d1c0ed772fb3f4991f9caca0
- Size of remote file:
- 4.92 kB
- SHA256:
- 22a666e975876e3d3ac1512d200fd47604a445d00ad349cb295765b2e66c4139
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