Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/bert-classification-5ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/bert-classification-5ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/bert-classification-5ksamples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/bert-classification-5ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/bert-classification-5ksamples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f42c5cb6763117b2ea7a071070d1e637859fee846188304659cf2e5a9f9d3bf9
- Size of remote file:
- 3.96 kB
- SHA256:
- 6af337fcb0877f3e9d093096079df3d4a679bef6d92e07355a2c20f6b8331551
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