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xlnet-base-rte-finetuned – AI Model by vish88 | AlphaNeural AI
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vish88
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xlnet-base-rte-finetuned
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transformers
pytorch
tensorboard
xlnet
text-classification
generated_from_trainer
glue
mit
model-index
autotrain_compatible
endpoints_compatible
us
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xlnet-base-rte-finetuned
This model is a fine-tuned version of
xlnet-base-cased
on the glue dataset. It achieves the following results on the evaluation set:
Loss: 2.6688
Accuracy: 0.7040
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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
311
0.9695
0.6859
0.315
2.0
622
2.2516
0.6498
0.315
3.0
933
2.0439
0.7076
0.1096
4.0
1244
2.5190
0.7040
0.0368
5.0
1555
2.6688
0.7040
Framework versions
Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1
