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results – AI Model by Vecc3nz | AlphaNeural AI

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Vecc3nz/
results

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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results

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

epoch: 2.0
eval_accuracy: 0.7207
eval_f1: 0.7093
eval_loss: 0.7938
eval_precision: 0.7264
eval_recall: 0.7207
eval_runtime: 5.9881
eval_samples_per_second: 382.094
eval_steps_per_second: 6.012
step: 1598

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: 0.0001
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 4

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1