readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: distilroberta-base-finetuned-marktextepoch_n200
    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. -->
  
  # distilroberta-base-finetuned-marktextepoch_n200
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.0531
  
  ## 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: 200
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss |
  |:-------------:|:-----:|:------:|:---------------:|
  | 2.2313        | 1.0   | 1500   | 2.1592          |
  | 2.1731        | 2.0   | 3000   | 2.1277          |
  | 2.153         | 3.0   | 4500   | 2.1144          |
  | 2.1469        | 4.0   | 6000   | 2.1141          |
  | 2.1281        | 5.0   | 7500   | 2.1374          |
  | 2.1043        | 6.0   | 9000   | 2.1069          |
  | 2.0834        | 7.0   | 10500  | 2.0993          |
  | 2.0602        | 8.0   | 12000  | 2.0817          |
  | 2.024         | 9.0   | 13500  | 2.0918          |
  | 2.0261        | 10.0  | 15000  | 2.0793          |
  | 1.9889        | 11.0  | 16500  | 2.0567          |
  | 1.9915        | 12.0  | 18000  | 2.0700          |
  | 1.9532        | 13.0  | 19500  | 2.0436          |
  | 1.9362        | 14.0  | 21000  | 2.0596          |
  | 1.9024        | 15.0  | 22500  | 2.0189          |
  | 1.9262        | 16.0  | 24000  | 2.0435          |
  | 1.8883        | 17.0  | 25500  | 2.0430          |
  | 1.8867        | 18.0  | 27000  | 2.0416          |
  | 1.8807        | 19.0  | 28500  | 2.0051          |
  | 1.8517        | 20.0  | 30000  | 2.0338          |
  | 1.8357        | 21.0  | 31500  | 2.0166          |
  | 1.8241        | 22.0  | 33000  | 2.0355          |
  | 1.7985        | 23.0  | 34500  | 2.0073          |
  | 1.8061        | 24.0  | 36000  | 2.0473          |
  | 1.7996        | 25.0  | 37500  | 2.0446          |
  | 1.7786        | 26.0  | 39000  | 2.0086          |
  | 1.771         | 27.0  | 40500  | 2.0294          |
  | 1.7549        | 28.0  | 42000  | 2.0127          |
  | 1.7726        | 29.0  | 43500  | 2.0191          |
  | 1.7275        | 30.0  | 45000  | 2.0182          |
  | 1.708         | 31.0  | 46500  | 2.0130          |
  | 1.7345        | 32.0  | 48000  | 2.0155          |
  | 1.7044        | 33.0  | 49500  | 1.9898          |
  | 1.7126        | 34.0  | 51000  | 2.0166          |
  | 1.698         | 35.0  | 52500  | 1.9879          |
  | 1.6637        | 36.0  | 54000  | 2.0311          |
  | 1.6854        | 37.0  | 55500  | 2.0355          |
  | 1.6585        | 38.0  | 57000  | 2.0094          |
  | 1.6418        | 39.0  | 58500  | 2.0042          |
  | 1.667         | 40.0  | 60000  | 2.0116          |
  | 1.6507        | 41.0  | 61500  | 2.0095          |
  | 1.622         | 42.0  | 63000  | 2.0158          |
  | 1.6381        | 43.0  | 64500  | 2.0339          |
  | 1.6099        | 44.0  | 66000  | 2.0082          |
  | 1.6076        | 45.0  | 67500  | 2.0207          |
  | 1.5805        | 46.0  | 69000  | 2.0172          |
  | 1.5862        | 47.0  | 70500  | 2.0132          |
  | 1.5806        | 48.0  | 72000  | 2.0198          |
  | 1.574         | 49.0  | 73500  | 2.0181          |
  | 1.5718        | 50.0  | 75000  | 2.0086          |
  | 1.5591        | 51.0  | 76500  | 1.9832          |
  | 1.5468        | 52.0  | 78000  | 2.0167          |
  | 1.5637        | 53.0  | 79500  | 2.0118          |
  | 1.5117        | 54.0  | 81000  | 2.0290          |
  | 1.5363        | 55.0  | 82500  | 2.0011          |
  | 1.4976        | 56.0  | 84000  | 2.0160          |
  | 1.5129        | 57.0  | 85500  | 2.0224          |
  | 1.4964        | 58.0  | 87000  | 2.0219          |
  | 1.4906        | 59.0  | 88500  | 2.0212          |
  | 1.4941        | 60.0  | 90000  | 2.0255          |
  | 1.4876        | 61.0  | 91500  | 2.0116          |
  | 1.4837        | 62.0  | 93000  | 2.0176          |
  | 1.4661        | 63.0  | 94500  | 2.0388          |
  | 1.4634        | 64.0  | 96000  | 2.0165          |
  | 1.4449        | 65.0  | 97500  | 2.0185          |
  | 1.468         | 66.0  | 99000  | 2.0246          |
  | 1.4567        | 67.0  | 100500 | 2.0244          |
  | 1.4367        | 68.0  | 102000 | 2.0093          |
  | 1.4471        | 69.0  | 103500 | 2.0101          |
  | 1.4255        | 70.0  | 105000 | 2.0248          |
  | 1.4203        | 71.0  | 106500 | 2.0224          |
  | 1.42          | 72.0  | 108000 | 2.0279          |
  | 1.4239        | 73.0 