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Upload TFDistilBertForQuestionAnswering
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metadata
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
  - generated_from_keras_callback
model-index:
  - name: transformers-qa-kaggle-tpu
    results: []

transformers-qa-kaggle-tpu

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.2278
  • Train End Logits Accuracy: 0.9244
  • Train Start Logits Accuracy: 0.9207
  • Validation Loss: 3.8999
  • Validation End Logits Accuracy: 0.4812
  • Validation Start Logits Accuracy: 0.4542
  • 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 122160, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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
2.2837 0.4519 0.4182 2.1117 0.4890 0.4658 0
1.7361 0.5642 0.5326 2.0268 0.5035 0.4788 1
1.4664 0.6186 0.5893 2.0023 0.5093 0.4833 2
1.2479 0.6661 0.6379 2.1252 0.5057 0.4744 3
1.0596 0.7076 0.6832 2.2703 0.4975 0.4690 4
0.8999 0.7434 0.7214 2.3834 0.4968 0.4714 5
0.7661 0.7760 0.7557 2.5503 0.4906 0.4654 6
0.6520 0.8042 0.7892 2.7740 0.4922 0.4540 7
0.5549 0.8313 0.8156 3.0625 0.4884 0.4607 8
0.4739 0.8512 0.8405 3.1365 0.4862 0.4535 9
0.4072 0.8691 0.8620 3.2969 0.4830 0.4509 10
0.3515 0.8863 0.8786 3.4301 0.4852 0.4530 11
0.3025 0.9010 0.8954 3.5350 0.4814 0.4548 12
0.2646 0.9127 0.9083 3.7923 0.4832 0.4539 13
0.2278 0.9244 0.9207 3.8999 0.4812 0.4542 14

Framework versions

  • Transformers 4.31.0.dev0
  • TensorFlow 2.12.0
  • Datasets 2.13.1
  • Tokenizers 0.13.3