Instructions to use Tischenko/binary-task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tischenko/binary-task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tischenko/binary-task")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tischenko/binary-task") model = AutoModelForSequenceClassification.from_pretrained("Tischenko/binary-task") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 162807bb93f24191977e272a52636396e64a71db8058ac6ea6a64d27607287a8
- Size of remote file:
- 711 MB
- SHA256:
- ac2a6caa9cc74566f32a3f23e0c1e99fdb0a6aa133e127fa8890c3169aa615a2
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