Instructions to use marcchew/testv5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcchew/testv5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcchew/testv5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcchew/testv5") model = AutoModelForSequenceClassification.from_pretrained("marcchew/testv5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3d3169370a44aadcb48e19427f06fbd40ce02ce729da3769cf58309d38dc640
- Size of remote file:
- 5.18 kB
- SHA256:
- 0831dd96a9cb9ad1e509280104fa8150d125ed2f87919c7450db3db191ec1c86
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