Instructions to use Aktsvigun/tmp_best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/tmp_best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/tmp_best")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/tmp_best") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/tmp_best") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d670da205b236253f883cb30a0d279ff1d9f4cfd2d358a54d71e542c65c9456f
- Size of remote file:
- 1.34 GB
- SHA256:
- bda0bb5f342fa6308f6651ff7a462f0b6863735b9e8e2d1e6b4f55eb2d9254dc
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