distilbert-base-uncased-finetuned-sst-2-english_08112024T121529

This model is a fine-tuned version of distilbert/distilbert-base-uncased-finetuned-sst-2-english on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0521
  • F1: 0.5770
  • Learning Rate: 0.0

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: 0.001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 600
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Rate
No log 0.9942 86 1.0521 0.5770 0.0001
No log 2.0 173 1.0938 0.6023 0.0003
No log 2.9942 259 1.1901 0.6153 0.0004
No log 4.0 346 1.1094 0.6056 0.0006
No log 4.9942 432 1.2608 0.4934 0.0007
1.1523 6.0 519 1.2942 0.5451 0.0009
1.1523 6.9942 605 1.2737 0.5362 0.001
1.1523 8.0 692 1.2844 0.4450 0.0010
1.1523 8.9942 778 1.6545 0.2937 0.0009
1.1523 10.0 865 1.4726 0.3773 0.0009
1.1523 10.9942 951 1.5591 0.3796 0.0008
1.367 12.0 1038 1.4358 0.4459 0.0007
1.367 12.9942 1124 1.4144 0.4272 0.0006
1.367 14.0 1211 1.3512 0.4938 0.0004
1.367 14.9942 1297 1.2704 0.5067 0.0003
1.367 16.0 1384 1.2057 0.5329 0.0002
1.367 16.9942 1470 1.1236 0.5658 0.0001
1.206 18.0 1557 1.1138 0.5826 0.0001
1.206 18.9942 1643 1.0703 0.6122 0.0000
1.206 19.8844 1720 1.0771 0.6069 0.0

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.19.1
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