metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: edyfjm07/distilbert-base-uncased-QA1-finetuned-squad-es
    results: []

edyfjm07/distilbert-base-uncased-QA1-finetuned-squad-es

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: 0.3672
Train End Logits Accuracy: 0.8524
Train Start Logits Accuracy: 0.8836
Validation Loss: 1.0040
Validation End Logits Accuracy: 0.7837
Validation Start Logits Accuracy: 0.7994
Epoch: 23

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 1479, '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
5.1787	0.0571	0.0496	4.3181	0.1724	0.1818	0
3.6307	0.25	0.1810	2.8944	0.3793	0.2476	1
2.5094	0.3998	0.3147	2.1436	0.4514	0.3793	2
1.9078	0.4871	0.4397	1.7322	0.5204	0.5705	3
1.5135	0.5593	0.5700	1.4332	0.6050	0.6238	4
1.2802	0.5927	0.6013	1.3274	0.6270	0.6364	5
1.1079	0.6595	0.6455	1.2126	0.6520	0.6865	6
0.9827	0.6843	0.7069	1.1469	0.7116	0.7116	7
0.8810	0.7306	0.7371	1.0859	0.7116	0.7053	8
0.8194	0.7349	0.7446	1.0339	0.7429	0.7492	9
0.7245	0.7403	0.7877	1.0371	0.7304	0.7398	10
0.6827	0.7683	0.7856	1.0185	0.7492	0.7461	11
0.6421	0.7866	0.8071	1.0298	0.7492	0.7555	12
0.5949	0.8006	0.8050	0.9877	0.7586	0.7774	13
0.5471	0.8125	0.8244	0.9933	0.7398	0.7774	14
0.5119	0.8233	0.8362	0.9956	0.7524	0.7837	15
0.4916	0.8330	0.8599	0.9917	0.7398	0.8025	16
0.4521	0.8373	0.8836	0.9698	0.7680	0.7868	17
0.4424	0.8459	0.8696	0.9951	0.7712	0.8025	18
0.3928	0.8599	0.8966	1.0173	0.7618	0.7931	19
0.3874	0.8578	0.8922	1.0307	0.7649	0.7931	20
0.3822	0.8588	0.8901	1.0272	0.7680	0.7900	21
0.3859	0.8524	0.8879	1.0180	0.7555	0.7962	22
0.3672	0.8524	0.8836	1.0040	0.7837	0.7994	23

Framework versions

Transformers 4.41.2
TensorFlow 2.15.0
Datasets 2.20.0
Tokenizers 0.19.1