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:
- 1da0a4f46096d2970a26a0f9e6d99d9fcb0f9928192487da2af421db64b081c8
- Size of remote file:
- 438 MB
- SHA256:
- dc48d16dd548a18ebaa4e82b9117c5bd14d238be4cb47ee3d7469e5f476850da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.