Text Classification
Transformers
Safetensors
deberta-v2
single_label_classification
question-answering
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use saiteki-kai/QA-DeBERTa-IGPooling-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saiteki-kai/QA-DeBERTa-IGPooling-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saiteki-kai/QA-DeBERTa-IGPooling-binary")# Load model directly from transformers import AutoTokenizer, DebertaIntegratedGradientsWeightedPooling tokenizer = AutoTokenizer.from_pretrained("saiteki-kai/QA-DeBERTa-IGPooling-binary") model = DebertaIntegratedGradientsWeightedPooling.from_pretrained("saiteki-kai/QA-DeBERTa-IGPooling-binary") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.8652227434541039, | |
| "eval_loss": 0.41384679079055786, | |
| "eval_runtime": 169.0958, | |
| "eval_safe_aucpr": 0.9230443164171965, | |
| "eval_safe_f1": 0.8512957932604067, | |
| "eval_safe_fpr": 0.13841140773071017, | |
| "eval_safe_precision": 0.8335789511520075, | |
| "eval_safe_recall": 0.8697820950380677, | |
| "eval_samples_per_second": 355.503, | |
| "eval_steps_per_second": 44.442, | |
| "eval_unsafe_aucpr": 0.9566547519505719, | |
| "eval_unsafe_f1": 0.8767644195668046, | |
| "eval_unsafe_fpr": 0.13021790496193178, | |
| "eval_unsafe_precision": 0.8924844393521816, | |
| "eval_unsafe_recall": 0.8615885922692894 | |
| } |