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celera_relevance – AI Model by leetdavid | AlphaNeural AI
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leetdavid
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celera_relevance
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transformers
tf
bert
text-classification
generated_from_keras_callback
hfl/chinese-roberta-wwm-ext
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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celera_relevance
This model is a fine-tuned version of
hfl/chinese-roberta-wwm-ext
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.3072
Train Sparse Categorical Accuracy: 0.8813
Validation Loss: 0.4371
Validation Sparse Categorical Accuracy: 0.8295
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', 'learning_rate': 5e-05, 'decay': 0.0, '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
0.4060
0.8274
0.3665
0.8440
0
0.3388
0.8594
0.3639
0.8585
1
0.3072
0.8813
0.4371
0.8295
2
Framework versions
Transformers 4.16.0
TensorFlow 2.7.0
Datasets 1.18.1
Tokenizers 0.11.0
