Instructions to use WilliamStar/sequence_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WilliamStar/sequence_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WilliamStar/sequence_classification_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WilliamStar/sequence_classification_model") model = AutoModelForSequenceClassification.from_pretrained("WilliamStar/sequence_classification_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e024096f2a3ec14e27e8f5a67ce9a1540cd37a8e3aa2a567b5757c84a8959b4
- Size of remote file:
- 204 MB
- SHA256:
- 8a56e18b12990fd8aed902420a1d598e8c9d6708c419eabc0b52ab94110a409c
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