Instructions to use abidlabs/transformers-trackio-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abidlabs/transformers-trackio-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abidlabs/transformers-trackio-demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abidlabs/transformers-trackio-demo") model = AutoModelForSequenceClassification.from_pretrained("abidlabs/transformers-trackio-demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9969c880052ecdb78d25f8637f585fa5b364b1f4f96077d5d8a293303fd640c
- Size of remote file:
- 5.2 kB
- SHA256:
- 23b53125ae2559c81018545871a31402d4946054406e9e4cf76653fa88509e75
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