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xlnet-base-cased-detect-dep – AI Model by Trong-Nghia | AlphaNeural AI
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Trong-Nghia
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xlnet-base-cased-detect-dep
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transformers
pytorch
tensorboard
xlnet
text-classification
generated_from_trainer
mit
autotrain_compatible
endpoints_compatible
us
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xlnet-base-cased-detect-dep
This model is a fine-tuned version of
xlnet-base-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.5555
Accuracy: 0.744
F1: 0.8164
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:
learning_rate: 5e-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
No log
1.0
376
0.5582
0.716
0.8065
0.6188
2.0
752
0.5479
0.756
0.8232
0.5835
3.0
1128
0.5306
0.758
0.8276
0.5492
4.0
1504
0.5555
0.744
0.8164
Framework versions
Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3
