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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "predict_accuracy": 0.9664153529814942, | |
| "predict_f1": 0.9602272727272727, | |
| "predict_loss": 0.1469365507364273, | |
| "predict_precision": 0.949438202247191, | |
| "predict_recall": 0.9712643678160919, | |
| "predict_runtime": 625.2009, | |
| "predict_samples": 2918, | |
| "predict_samples_per_second": 4.667, | |
| "predict_steps_per_second": 0.584 | |
| } |