Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/bert-classification-1500samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/bert-classification-1500samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/bert-classification-1500samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/bert-classification-1500samples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/bert-classification-1500samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed6932df5a70be002fad1bf9e8454450e9b4ea97187176ab919e8daa08a634df
- Size of remote file:
- 3.96 kB
- SHA256:
- 800cbf032bbb1adeca8fd87a83dc7d6feb9c7814e76e19bec38f7cc53f489efc
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