Instructions to use karths/binary_classification_train_perf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_perf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_perf")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_perf") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_perf") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30ef736cc7071a79cd2f5caa598ebfe355bac803b819e0b47d6680146bf3d076
- Size of remote file:
- 66.5 MB
- SHA256:
- a5c5da6253e9a0ef63a75fec1919caa34ed6637215bfd5cbca54e95c5a182bdd
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