Text Classification
Transformers
Safetensors
Hebrew
neobert
nli
natural-language-inference
hebrew
fact-checking
contradiction-detection
custom_code
Instructions to use Amit5674/NLI-hebrew-binary-correctness-metric with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amit5674/NLI-hebrew-binary-correctness-metric with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Amit5674/NLI-hebrew-binary-correctness-metric", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Amit5674/NLI-hebrew-binary-correctness-metric", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| ================================================== | |
| TRAINING CONFIGURATION | |
| ================================================== | |
| GPU: NVIDIA RTX 6000 Ada Generation | |
| Host IP: 132.72.180.129 | |
| Model: dicta-il/neodictabert | |
| Output dir: /home/avramit/classifier/04122025/classifier/binary/neodictabert-finetuned-binary-041225 | |
| Batch size: 2 | |
| Learning rate: 2e-05 | |
| Epochs: 1.0 | |
| Max seq length: 4096 | |
| Labels: ['contradiction', 'entailment'] | |
| ================================================== | |
| ✅ Arguments saved to /home/avramit/classifier/04122025/classifier/binary/neodictabert-finetuned-binary-041225/training_info_args.json | |
| Dataset sizes - Train: 18238, Val: 2239, Test: 2918 | |
| Train file: /home/avramit/classifier/04122025/datasets/classifier/mergearticlesummarylevel/train.csv | |
| Val file: /home/avramit/classifier/01122025/datasets/classifiers/validation.csv | |
| Test file: /home/avramit/classifier/01122025/datasets/classifiers/test.csv | |
| ✅ Predictions saved to /home/avramit/classifier/04122025/classifier/binary/neodictabert-finetuned-binary-041225/predictions.txt | |
| ✅ Predictions CSV saved to /home/avramit/classifier/04122025/classifier/binary/neodictabert-finetuned-binary-041225/predictions.csv |