metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: edyfjm07/distilbert-base-uncased-QA2-finetuned-squad-es
    results: []

edyfjm07/distilbert-base-uncased-QA2-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: 0.0296
Train End Logits Accuracy: 0.9832
Train Start Logits Accuracy: 0.9884
Validation Loss: 1.6310
Validation End Logits Accuracy: 0.7962
Validation Start Logits Accuracy: 0.7962
Epoch: 26

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': 0.0001, 'decay_steps': 5474, '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
2.3428	0.4160	0.4317	1.3438	0.5611	0.6458	0
1.1526	0.6261	0.6397	1.0597	0.6677	0.7429	1
0.7612	0.7269	0.7647	1.0245	0.7210	0.7806	2
0.5528	0.7836	0.8319	1.2436	0.7116	0.7712	3
0.4667	0.8340	0.8435	1.0705	0.7524	0.7555	4
0.3834	0.8813	0.8687	1.1209	0.7586	0.7712	5
0.3678	0.8634	0.8876	1.2341	0.7618	0.7649	6
0.2555	0.9044	0.9181	1.1561	0.7649	0.8056	7
0.2151	0.9160	0.9328	1.0908	0.7931	0.7994	8
0.1855	0.9286	0.9475	1.2809	0.7994	0.7774	9
0.1654	0.9443	0.9454	1.3974	0.7837	0.7806	10
0.1282	0.9464	0.9517	1.4260	0.7774	0.7837	11
0.1313	0.9443	0.9601	1.4537	0.7900	0.7962	12
0.1301	0.9517	0.9590	1.1851	0.7774	0.8150	13
0.1089	0.9548	0.9590	1.2442	0.7774	0.8088	14
0.1023	0.9601	0.9622	1.4575	0.7931	0.7931	15
0.0956	0.9590	0.9685	1.5160	0.7837	0.7900	16
0.0712	0.9727	0.9737	1.5741	0.7900	0.8088	17
0.0752	0.9674	0.9790	1.4401	0.7931	0.7994	18
0.0604	0.9737	0.9779	1.6410	0.7962	0.8088	19
0.0497	0.9758	0.9821	1.5655	0.7962	0.8119	20
0.0668	0.9685	0.9811	1.3480	0.7806	0.7962	21
0.0567	0.9769	0.9800	1.3820	0.7900	0.8088	22
0.0550	0.9769	0.9832	1.3593	0.7806	0.8056	23
0.0399	0.9821	0.9884	1.5254	0.7868	0.7931	24
0.0320	0.9842	0.9874	1.5801	0.7868	0.7994	25
0.0296	0.9832	0.9884	1.6310	0.7962	0.7962	26

Framework versions

Transformers 4.41.2
TensorFlow 2.15.0
Datasets 2.20.0
Tokenizers 0.19.1