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xlnet-large-cased-tass – AI Model by judajaav | AlphaNeural AI
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judajaav
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xlnet-large-cased-tass
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transformers
tensorboard
safetensors
xlnet
text-classification
generated_from_trainer
xlnet/xlnet-large-cased
finetune
mit
endpoints_compatible
us
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results
This model is a fine-tuned version of
xlnet-large-cased
on the Tass dataset. It achieves the following results on the evaluation set:
Loss: 1.0904
Accuracy: 0.3890
F1: 0.1867
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: 1e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
2.2318
1.0
300
1.1063
0.3890
0.1867
2.2295
2.0
600
1.0896
0.3951
0.2030
2.1879
3.0
900
1.1029
0.3890
0.1867
2.2167
4.0
1200
1.0904
0.3890
0.1867
Framework versions
Transformers 4.56.1
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2
