readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: gpt_trinity_2_4_3e-5_lp5_nb5
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # gpt_trinity_2_4_3e-5_lp5_nb5
  
  This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.0291
  
  ## 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: 3e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 4.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.5765        | 0.05  | 1000  | 4.1247          |
  | 3.19          | 0.09  | 2000  | 4.0578          |
  | 3.1177        | 0.14  | 3000  | 4.0708          |
  | 3.1116        | 0.19  | 4000  | 4.0654          |
  | 3.0777        | 0.24  | 5000  | 4.0857          |
  | 3.1105        | 0.28  | 6000  | 4.1127          |
  | 3.1018        | 0.33  | 7000  | 4.1410          |
  | 3.0728        | 0.38  | 8000  | 4.1834          |
  | 3.1248        | 0.42  | 9000  | 4.2058          |
  | 3.1035        | 0.47  | 10000 | 4.2048          |
  | 3.0943        | 0.52  | 11000 | 4.1892          |
  | 3.0724        | 0.57  | 12000 | 4.2063          |
  | 3.0517        | 0.61  | 13000 | 4.1923          |
  | 3.0372        | 0.66  | 14000 | 4.2112          |
  | 3.0235        | 0.71  | 15000 | 4.2043          |
  | 3.0329        | 0.76  | 16000 | 4.1630          |
  | 3.0171        | 0.8   | 17000 | 4.1631          |
  | 2.9997        | 0.85  | 18000 | 4.1563          |
  | 2.9913        | 0.9   | 19000 | 4.1616          |
  | 2.9579        | 0.94  | 20000 | 4.1494          |
  | 2.9576        | 0.99  | 21000 | 4.1367          |
  | 2.7461        | 1.04  | 22000 | 4.1593          |
  | 2.7637        | 1.09  | 23000 | 4.1453          |
  | 2.741         | 1.13  | 24000 | 4.1624          |
  | 2.7514        | 1.18  | 25000 | 4.1357          |
  | 2.755         | 1.23  | 26000 | 4.1524          |
  | 2.7365        | 1.27  | 27000 | 4.1399          |
  | 2.7356        | 1.32  | 28000 | 4.1285          |
  | 2.7386        | 1.37  | 29000 | 4.1286          |
  | 2.7489        | 1.42  | 30000 | 4.1231          |
  | 2.7518        | 1.46  | 31000 | 4.1104          |
  | 2.7317        | 1.51  | 32000 | 4.1202          |
  | 2.7378        | 1.56  | 33000 | 4.1132          |
  | 2.7309        | 1.6   | 34000 | 4.1047          |
  | 2.7791        | 1.65  | 35000 | 4.0976          |
  | 2.7427        | 1.7   | 36000 | 4.0874          |
  | 2.7184        | 1.75  | 37000 | 4.0953          |
  | 2.7107        | 1.79  | 38000 | 4.0963          |
  | 2.7122        | 1.84  | 39000 | 4.0841          |
  | 2.7172        | 1.89  | 40000 | 4.0852          |
  | 2.7126        | 1.94  | 41000 | 4.0632          |
  | 2.7063        | 1.98  | 42000 | 4.0643          |
  | 2.5311        | 2.03  | 43000 | 4.0848          |
  | 2.4496        | 2.08  | 44000 | 4.0943          |
  | 2.4597        | 2.12  | 45000 | 4.0799          |
  | 2.4472        | 2.17  | 46000 | 4.0802          |
  | 2.4628        | 2.22  | 47000 | 4.0880          |
  | 2.4508        | 2.27  | 48000 | 4.0791          |
  | 2.4743        | 2.31  | 49000 | 4.0765          |
  | 2.4692        | 2.36  | 50000 | 4.0739          |
  | 2.4651        | 2.41  | 51000 | 4.0690          |
  | 2.4885        | 2.45  | 52000 | 4.0723          |
  | 2.5023        | 2.5   | 53000 | 4.0675          |
  | 2.4651        | 2.55  | 54000 | 4.0649          |
  | 2.4774        | 2.6   | 55000 | 4.0695          |
  | 2.4717        | 2.64  | 56000 | 4.0559          |
  | 2.4856        | 2.69  | 57000 | 4.0512          |
  | 2.4572        | 2.74  | 58000 | 4.0473          |
  | 2.486         | 2.79  | 59000 | 4.0438          |
  | 2.449         | 2.83  | 60000 | 4.0385          |
  | 2.456         | 2.88  | 61000 | 4.0355          |
  | 2.4802        | 2.93  | 62000 | 4.0378          |
  | 2.4635        | 2.97  | 63000 | 4.0308          |
  | 2.3742        | 3.02  | 64000 | 4.0488          |
  | 2.2371        | 3.07  | 65000 | 4.0579          |
  | 2.2496        | 3.12  | 66000 | 4.0630          |
  | 2.2758        | 3.16  | 67000 | 4.0516          |
  | 2.2489        | 3.21  | 68000 | 4.0585          |
  | 2.2374        | 3.26  | 69000 | 4.0715          |
  | 2.2862        | 3.3   | 70000 | 4.0507          |
  | 2.2502        | 3.35  | 71000 | 4.0512          |
  | 2.238         | 3.4   | 72000 | 4.0545          |
  | 2.2407        | 3.45  | 73000 | 4.0459          |
  | 2.