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hemagamal/
mdeberta_quran_qa_model

Name: mdeberta_quran_qa_model
Availability: InStock

transformers

deberta-v2

question-answering

generated_from_keras_callback

mit

endpoints_compatible

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hemagamal/mdeberta_quran_qa_model

This model is a fine-tuned version of timpal0l/mdeberta-v3-base-squad2 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 11.9013
Train End Logits Loss: 5.9506
Train Start Logits Loss: 5.9506
Train End Logits Sparse Categorical Accuracy: 0.0582
Train Start Logits Sparse Categorical Accuracy: 0.0426
Validation Loss: 11.9013
Validation End Logits Loss: 5.9506
Validation Start Logits Loss: 5.9506
Validation End Logits Sparse Categorical Accuracy: 0.0459
Validation Start Logits Sparse Categorical Accuracy: 0.0917
Epoch: 15

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': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Train End Logits Loss	Train Start Logits Loss	Train End Logits Sparse Categorical Accuracy	Train Start Logits Sparse Categorical Accuracy	Validation Loss	Validation End Logits Loss	Validation Start Logits Loss	Validation End Logits Sparse Categorical Accuracy	Validation Start Logits Sparse Categorical Accuracy	Epoch
12.4236	6.1795	6.2441	0.0724	0.0895	11.9013	5.9506	5.9506	0.0459	0.0917	0
11.9013	5.9506	5.9506	0.0469	0.0469	11.9013	5.9506	5.9506	0.0459	0.0917	1
11.9013	5.9506	5.9506	0.0369	0.0398	11.9013	5.9506	5.9506	0.0459	0.0917	2
11.9013	5.9506	5.9506	0.0369	0.0554	11.9013	5.9506	5.9506	0.0459	0.0917	3
11.9013	5.9506	5.9506	0.0483	0.0455	11.9013	5.9506	5.9506	0.0459	0.0917	4
11.9013	5.9506	5.9506	0.0554	0.0412	11.9013	5.9506	5.9506	0.0459	0.0917	5
11.9013	5.9506	5.9506	0.0241	0.0398	11.9013	5.9506	5.9506	0.0459	0.0917	6
11.9013	5.9506	5.9506	0.0369	0.0412	11.9013	5.9506	5.9506	0.0459	0.0917	7
11.9013	5.9506	5.9506	0.0426	0.0426	11.9013	5.9506	5.9506	0.0459	0.0917	8
11.9013	5.9506	5.9506	0.0511	0.0426	11.9013	5.9506	5.9506	0.0459	0.0917	9
11.9013	5.9506	5.9506	0.0426	0.0469	11.9013	5.9506	5.9506	0.0459	0.0917	10
11.9013	5.9506	5.9506	0.0440	0.0341	11.9013	5.9506	5.9506	0.0459	0.0917	11
11.9013	5.9506	5.9506	0.0412	0.0398	11.9013	5.9506	5.9506	0.0459	0.0917	12
11.9013	5.9506	5.9506	0.0440	0.0440	11.9013	5.9506	5.9506	0.0459	0.0917	13
11.9013	5.9506	5.9506	0.0426	0.0412	11.9013	5.9506	5.9506	0.0459	0.0917	14
11.9013	5.9506	5.9506	0.0582	0.0426	11.9013	5.9506	5.9506	0.0459	0.0917	15

Framework versions

Transformers 4.29.1
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3