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:
- b8eab9536d31bd942f8125590e64e2f76ad4293a9c0916da64a653e8d582eba5
- Size of remote file:
- 438 MB
- SHA256:
- 808bc4d8da5b571b7fd804596937f2dcd20c0263b9fbec0b8089f7545572eef1
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