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xtremedistil-l6-h256-uncased-question-vs-statement-classifier – AI Model by jonaskoenig | AlphaNeural AI
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jonaskoenig
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xtremedistil-l6-h256-uncased-question-vs-statement-classifier
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transformers
tf
bert
text-classification
generated_from_keras_callback
jonaskoenig/Questions-vs-Statements-Classification
microsoft/xtremedistil-l6-h256-uncased
finetune
mit
autotrain_compatible
endpoints_compatible
us
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xtremedistil-l6-h256-uncased-question-vs-statement-classifier
This model is a fine-tuned version of
microsoft/xtremedistil-l6-h256-uncased
on
question-vs-statement-classifier
dataset, which is a clone of the kaggle
Questions vs Statements Classification
dataset.
It achieves the following results on the evaluation set:
Train Loss: 0.0227
Train Sparse Categorical Accuracy: 0.9894
Validation Loss: 0.0294
Validation Sparse Categorical Accuracy: 0.9868
Epoch: 3
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
0.0681
0.9770
0.0327
0.9839
0
0.0301
0.9856
0.0321
0.9853
1
0.0262
0.9875
0.0286
0.9864
2
0.0227
0.9894
0.0294
0.9868
3
Framework versions
Transformers 4.20.1
TensorFlow 2.9.1
Datasets 2.3.2
Tokenizers 0.12.1
