readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: finetuned-marktextepoch-n600
    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. -->
  
  # finetuned-marktextepoch-n600
  
  This model is a fine-tuned version of [leokai/finetuned-marktextepoch-n500](https://huggingface.co/leokai/finetuned-marktextepoch-n500) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.6814
  
  ## 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: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 182
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss |
  |:-------------:|:-----:|:------:|:---------------:|
  | 0.5332        | 1.0   | 1606   | 2.5256          |
  | 0.5315        | 2.0   | 3212   | 2.4835          |
  | 0.5181        | 3.0   | 4818   | 2.5471          |
  | 0.5318        | 4.0   | 6424   | 2.5213          |
  | 0.5398        | 5.0   | 8030   | 2.5408          |
  | 0.5474        | 6.0   | 9636   | 2.5557          |
  | 0.541         | 7.0   | 11242  | 2.5415          |
  | 0.529         | 8.0   | 12848  | 2.5729          |
  | 0.5294        | 9.0   | 14454  | 2.5533          |
  | 0.5404        | 10.0  | 16060  | 2.5414          |
  | 0.5359        | 11.0  | 17666  | 2.5316          |
  | 0.5295        | 12.0  | 19272  | 2.5985          |
  | 0.5319        | 13.0  | 20878  | 2.5644          |
  | 0.5492        | 14.0  | 22484  | 2.5594          |
  | 0.5403        | 15.0  | 24090  | 2.5830          |
  | 0.526         | 16.0  | 25696  | 2.5999          |
  | 0.5308        | 17.0  | 27302  | 2.5629          |
  | 0.516         | 18.0  | 28908  | 2.6015          |
  | 0.525         | 19.0  | 30514  | 2.5726          |
  | 0.5238        | 20.0  | 32120  | 2.5656          |
  | 0.5244        | 21.0  | 33726  | 2.5961          |
  | 0.531         | 22.0  | 35332  | 2.5809          |
  | 0.517         | 23.0  | 36938  | 2.5688          |
  | 0.5334        | 24.0  | 38544  | 2.5828          |
  | 0.505         | 25.0  | 40150  | 2.5861          |
  | 0.5136        | 26.0  | 41756  | 2.6044          |
  | 0.5228        | 27.0  | 43362  | 2.6000          |
  | 0.5066        | 28.0  | 44968  | 2.5971          |
  | 0.5183        | 29.0  | 46574  | 2.6240          |
  | 0.5076        | 30.0  | 48180  | 2.6201          |
  | 0.5059        | 31.0  | 49786  | 2.5746          |
  | 0.5033        | 32.0  | 51392  | 2.6229          |
  | 0.5041        | 33.0  | 52998  | 2.6086          |
  | 0.5132        | 34.0  | 54604  | 2.6115          |
  | 0.5007        | 35.0  | 56210  | 2.5865          |
  | 0.4947        | 36.0  | 57816  | 2.6159          |
  | 0.4997        | 37.0  | 59422  | 2.6072          |
  | 0.4988        | 38.0  | 61028  | 2.5931          |
  | 0.5001        | 39.0  | 62634  | 2.5956          |
  | 0.5027        | 40.0  | 64240  | 2.6444          |
  | 0.4969        | 41.0  | 65846  | 2.6194          |
  | 0.4916        | 42.0  | 67452  | 2.6394          |
  | 0.4986        | 43.0  | 69058  | 2.6401          |
  | 0.5           | 44.0  | 70664  | 2.6164          |
  | 0.4848        | 45.0  | 72270  | 2.6479          |
  | 0.4926        | 46.0  | 73876  | 2.6519          |
  | 0.492         | 47.0  | 75482  | 2.6359          |
  | 0.4939        | 48.0  | 77088  | 2.6211          |
  | 0.4914        | 49.0  | 78694  | 2.6536          |
  | 0.4743        | 50.0  | 80300  | 2.6434          |
  | 0.4787        | 51.0  | 81906  | 2.6396          |
  | 0.4686        | 52.0  | 83512  | 2.6337          |
  | 0.4775        | 53.0  | 85118  | 2.6352          |
  | 0.4844        | 54.0  | 86724  | 2.6382          |
  | 0.4802        | 55.0  | 88330  | 2.6473          |
  | 0.4799        | 56.0  | 89936  | 2.6284          |
  | 0.4749        | 57.0  | 91542  | 2.6371          |
  | 0.4779        | 58.0  | 93148  | 2.6264          |
  | 0.4727        | 59.0  | 94754  | 2.6506          |
  | 0.4875        | 60.0  | 96360  | 2.6677          |
  | 0.4695        | 61.0  | 97966  | 2.6507          |
  | 0.4612        | 62.0  | 99572  | 2.6600          |
  | 0.4658        | 63.0  | 101178 | 2.6442          |
  | 0.4737        | 64.0  | 102784 | 2.6593          |
  | 0.47          | 65.0  | 104390 | 2.6451          |
  | 0.4658        | 66.0  | 105996 | 2.6493          |
  | 0.4634        | 67.0  | 107602 | 2.6795          |
  | 0.4713        | 68.0  | 109208 | 2.6392          |
  | 0.4771        | 69.0  | 110814 | 2.6633          |
  | 0.4704        | 70.0  | 112420 | 2.6273          |
  | 0.458         | 71.0  | 114026 | 2.6426          |
  | 0.4577        | 72.0  | 115632 | 2.6652          |
  | 0.4585        | 73.0  | 1