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MindAid_Diagnosis_bert-base-multilingual-cased – AI Model by SiddharthShukla48 | AlphaNeural AI
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MindAid_Diagnosis_bert-base-multilingual-cased
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transformers
tf
safetensors
bert
text-classification
generated_from_keras_callback
google-bert/bert-base-multilingual-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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MindAid_Diagnosis_bert-base-multilingual-cased
This model is a fine-tuned version of
google-bert/bert-base-multilingual-cased
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.4021
Train Sparse Categorical Accuracy: 0.8656
Validation Loss: 0.4619
Validation Sparse Categorical Accuracy: 0.8382
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': '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': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32
Training results
Train Loss
Train Sparse Categorical Accuracy
Validation Loss
Validation Sparse Categorical Accuracy
Epoch
0.5668
0.7988
0.5304
0.8254
0
0.4556
0.8425
0.4576
0.8537
1
0.4021
0.8656
0.4619
0.8382
2
Framework versions
Transformers 4.41.2
TensorFlow 2.15.0
Datasets 2.19.2
Tokenizers 0.19.1
