README.md

metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: edyfjm07/distilbert-base-uncased-QA4-finetuned-squad-es
    results: []

edyfjm07/distilbert-base-uncased-QA4-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: 0.2751
• Train End Logits Accuracy: 0.8834
• Train Start Logits Accuracy: 0.9181
• Validation Loss: 1.1135
• Validation End Logits Accuracy: 0.7712
• Validation Start Logits Accuracy: 0.8119
• Epoch: 16

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': 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
3.8949	0.1733	0.1891	2.4981	0.3918	0.3981	0
2.0479	0.4097	0.4811	1.6575	0.4890	0.6113	1
1.4343	0.5599	0.6166	1.3371	0.5768	0.6426	2
1.0892	0.6313	0.6891	1.1850	0.6677	0.6865	3
0.9172	0.6870	0.7405	1.1305	0.6771	0.7335	4
0.7470	0.7258	0.7910	1.0674	0.7147	0.7524	5
0.6728	0.7426	0.8088	1.0843	0.7116	0.7680	6
0.5989	0.7721	0.8403	1.0787	0.7304	0.7649	7
0.4988	0.8057	0.8582	1.1091	0.7398	0.7618	8
0.4674	0.8214	0.8540	1.1150	0.7367	0.7774	9
0.4173	0.8256	0.8782	1.1434	0.7335	0.7774	10
0.3804	0.8319	0.8897	1.1256	0.7335	0.7900	11
0.3831	0.8456	0.8834	1.1614	0.7429	0.7931	12
0.3325	0.8550	0.9097	1.1519	0.7429	0.7900	13
0.3115	0.8739	0.9076	1.1423	0.7586	0.7868	14
0.2860	0.8792	0.9160	1.1335	0.7649	0.8025	15
0.2751	0.8834	0.9181	1.1135	0.7712	0.8119	16

Framework versions

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