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