README.md

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: 0.1089
• Train End Logits Accuracy: 0.9548
• Train Start Logits Accuracy: 0.9590
• Validation Loss: 1.2442
• Validation End Logits Accuracy: 0.7774
• Validation Start Logits Accuracy: 0.8088
• Epoch: 14

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

Framework versions

• Transformers 4.41.2
• TensorFlow 2.15.0
• Datasets 2.20.0
• Tokenizers 0.19.1