Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use vishalk4u/liar_binaryclassifier_bert_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vishalk4u/liar_binaryclassifier_bert_cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vishalk4u/liar_binaryclassifier_bert_cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vishalk4u/liar_binaryclassifier_bert_cased") model = AutoModelForSequenceClassification.from_pretrained("vishalk4u/liar_binaryclassifier_bert_cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cd2e63e41019a605782f3467af713bf65576417511753b107ea0372429cb440
- Size of remote file:
- 433 MB
- SHA256:
- 3fcd00821930070c5adba8cac672af945e7e673275ffa1e1f9327aa2098b4669
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.