Instructions to use samim2024/bert-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samim2024/bert-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="samim2024/bert-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("samim2024/bert-text-classification") model = AutoModelForSequenceClassification.from_pretrained("samim2024/bert-text-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 013a99a4dfd1056cf92cb8dcb2f397a5a00e2d38618a130c4a8be3166b29fefe
- Size of remote file:
- 438 MB
- SHA256:
- 5ceddfc02fe04e4cdfa7322bec077979a638ecb845d831daaf2c6d7b9d4b8812
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