Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use HanBi/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HanBi/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HanBi/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HanBi/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("HanBi/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de2bd2f5f55186049382ba25bfb5570af399162b148e57020c9d57de4105abca
- Size of remote file:
- 268 MB
- SHA256:
- f579168d91c77ba9329df59dc1b97bf8940be319dcbb19048cbb6cdc37a3d422
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.