--- license: apache-2.0 base_model: bert-large-uncased-whole-word-masking-finetuned-squad tags: - generated_from_keras_callback model-index: - name: bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset results: [] --- # bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3381 - Train End Logits Accuracy: 0.4801 - Train Start Logits Accuracy: 0.4324 - Validation Loss: 2.1970 - Validation End Logits Accuracy: 0.5132 - Validation Start Logits Accuracy: 0.4554 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8844, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 2.4656 | 0.4710 | 0.4189 | 2.2246 | 0.5103 | 0.4548 | 0 | | 2.3381 | 0.4801 | 0.4324 | 2.1970 | 0.5132 | 0.4554 | 1 | ### Framework versions - Transformers 4.34.0 - TensorFlow 2.12.0 - Datasets 2.14.5 - Tokenizers 0.14.0