distilbert-base-uncased-finetuned-squad – AI Model by vrx2 | AlphaNeural AI

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vrx2/
distilbert-base-uncased-finetuned-squad

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distilbert/distilbert-base-uncased

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vrx2/distilbert-base-uncased-finetuned-squad

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: 0.9741
Train End Logits Accuracy: 0.7291
Train Start Logits Accuracy: 0.6924
Validation Loss: 1.1179
Validation End Logits Accuracy: 0.6960
Validation Start Logits Accuracy: 0.6616
Epoch: 1

Model description

just a bench test of my laptop's capabilities

Intended uses & limitations

testing purposes

Training and evaluation data

trained on squad v2

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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
1.5188	0.6049	0.5697	1.1433	0.6878	0.6498	0
0.9741	0.7291	0.6924	1.1179	0.6960	0.6616	1

Framework versions

Transformers 4.34.0
TensorFlow 2.14.0
Datasets 2.14.5
Tokenizers 0.14.1