Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use vishalk4u/liar_binaryclassifier_roberta_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vishalk4u/liar_binaryclassifier_roberta_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vishalk4u/liar_binaryclassifier_roberta_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vishalk4u/liar_binaryclassifier_roberta_base") model = AutoModelForSequenceClassification.from_pretrained("vishalk4u/liar_binaryclassifier_roberta_base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23cbfdb630f596d86ec335794182e319f80ccf1790b8f22dbc25322e83365a84
- Size of remote file:
- 4.98 kB
- SHA256:
- 19e0828dbb9e1acee59331f7eca44221b24255c34a9a2ef0acf2af40b101e05d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.