Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use DotsinPritam/justification_classifier_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DotsinPritam/justification_classifier_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DotsinPritam/justification_classifier_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DotsinPritam/justification_classifier_model") model = AutoModelForSequenceClassification.from_pretrained("DotsinPritam/justification_classifier_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a13e0fbcac604085811c36027181235a789e6faefa28204c76092b338113f39e
- Size of remote file:
- 5.37 kB
- SHA256:
- 2c41579397f393a884e8816132d4074948de6d2dd187c030ed42d6073b952354
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.