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cls_distilbert_model

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

  • eval_loss: 0.4205
  • eval_accuracy: 0.8218
  • eval_f1: 0.8203
  • eval_precision: 0.8326
  • eval_recall: 0.8218
  • eval_runtime: 1.4638
  • eval_samples_per_second: 728.218
  • eval_steps_per_second: 45.77
  • epoch: 1.0
  • step: 534

Model description

Model is used to classify the sentiment POSITIVE or NEGATIVE for given sample inout textx

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.0
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