rav_nlp_qa_model – AI Model by RavGau | AlphaNeural AI

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rav_nlp_qa_model

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RavGau/rav_nlp_qa_model

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 1.5596
Train End Logits Accuracy: 0.6117
Train Start Logits Accuracy: 0.5664
Validation Loss: 1.8418
Validation End Logits Accuracy: 0.5513
Validation Start Logits Accuracy: 0.5132
Epoch: 2

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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 508, '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
3.4205	0.2064	0.2092	2.2886	0.4370	0.3773	0
1.8250	0.5445	0.5084	1.8418	0.5513	0.5132	1
1.5596	0.6117	0.5664	1.8418	0.5513	0.5132	2

Framework versions

Transformers 4.28.1
TensorFlow 2.12.0
Datasets 2.11.0
Tokenizers 0.13.3