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multi-language-sms-detection – AI Model by scott-clare1 | AlphaNeural AI
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scott-clare1
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multi-language-sms-detection
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transformers
tf
xlm-roberta
token-classification
generated_from_keras_callback
FacebookAI/xlm-roberta-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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scott-clare1/multi-language-sms-detection
This model is a fine-tuned version of
xlm-roberta-base
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.0098
Validation Loss: 0.0282
Train Precision: 0.9825
Train Recall: 0.9852
Train F1: 0.9838
Train Accuracy: 0.9934
Epoch: 2
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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2487, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32
Training results
Train Loss
Validation Loss
Train Precision
Train Recall
Train F1
Train Accuracy
Epoch
0.0191
0.0275
0.9832
0.9848
0.9840
0.9932
0
0.0116
0.0282
0.9825
0.9852
0.9838
0.9934
1
0.0098
0.0282
0.9825
0.9852
0.9838
0.9934
2
Framework versions
Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.1
Tokenizers 0.13.3
