Instructions to use nadim365/testV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadim365/testV1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadim365/testV1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadim365/testV1") model = AutoModelForSequenceClassification.from_pretrained("nadim365/testV1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8afa24d63bd4ff5f0c0bda623b9644f4220e1a6d75351408831aef684d17b66d
- Size of remote file:
- 4.54 kB
- SHA256:
- d68b084a521eb9b0ec233c194faf7ca68981163750aba393057cf1934c3c9049
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