Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use TieIncred/verizon_model1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TieIncred/verizon_model1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TieIncred/verizon_model1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TieIncred/verizon_model1") model = AutoModelForSequenceClassification.from_pretrained("TieIncred/verizon_model1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b30fcd0ec0ac76e0c1ae0212b08e8a7ccb35cead581dbbe495e90cedabc338d
- Size of remote file:
- 438 MB
- SHA256:
- 11c1fbfb7bfc34cbd315a86403796285773370993eec41e49eae205d5de5f3f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.