tiq / .ipynb_checkpoints /README-checkpoint.md
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End of training
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metadata
license: mit
base_model: gpt2-large
tags:
  - generated_from_trainer
model-index:
  - name: tiq
    results: []

tiq

This model is a fine-tuned version of gpt2-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 5.5477

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 1
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
6.2342 0.04 100 6.1857
5.7599 0.07 200 5.7751
5.7433 0.11 300 5.7142
5.6021 0.15 400 5.6776
5.5084 0.18 500 5.6349
5.3825 0.22 600 5.6201
5.6698 0.26 700 5.5831
5.4089 0.29 800 5.5687
5.601 0.33 900 5.5574
5.4708 0.37 1000 5.5555
5.5956 0.4 1100 5.5520
5.4704 0.44 1200 5.5494
5.4824 0.47 1300 5.5502
5.589 0.51 1400 5.5478
5.5612 0.55 1500 5.5456
5.4741 0.58 1600 5.5430
5.463 0.62 1700 5.5426
5.5071 0.66 1800 5.5424
5.5469 0.69 1900 5.5419
5.4266 0.73 2000 5.5428
5.4848 0.77 2100 5.5438
5.5069 0.8 2200 5.5446
5.5885 0.84 2300 5.5469
5.4484 0.88 2400 5.5462
5.3859 0.91 2500 5.5475
5.465 0.95 2600 5.5476
5.4355 0.99 2700 5.5477

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2