readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: enlm-roberta-130
    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. -->
  
  # enlm-roberta-130
  
  This model is a fine-tuned version of [manirai91/enlm-roberta-final](https://huggingface.co/manirai91/enlm-roberta-final) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4113
  
  ## 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: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 128
  - total_train_batch_size: 8192
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
  - lr_scheduler_type: polynomial
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 1.5183        | 0.34  | 160  | 1.4159          |
  | 1.5188        | 0.69  | 320  | 1.4158          |
  | 1.5205        | 1.03  | 480  | 1.4153          |
  | 1.5213        | 1.37  | 640  | 1.4162          |
  | 1.5195        | 1.72  | 800  | 1.4168          |
  | 1.5194        | 2.06  | 960  | 1.4150          |
  | 1.5182        | 2.4   | 1120 | 1.4142          |
  | 1.5182        | 2.75  | 1280 | 1.4131          |
  | 1.5177        | 3.09  | 1440 | 1.4167          |
  | 1.5201        | 3.43  | 1600 | 1.4156          |
  | 1.5173        | 3.78  | 1760 | 1.4111          |
  | 1.52          | 4.12  | 1920 | 1.4117          |
  | 1.5184        | 4.46  | 2080 | 1.4151          |
  | 1.5198        | 4.81  | 2240 | 1.4097          |
  | 1.5202        | 5.15  | 2400 | 1.4162          |
  | 1.5166        | 5.49  | 2560 | 1.4130          |
  | 1.5184        | 5.84  | 2720 | 1.4139          |
  | 1.5174        | 6.18  | 2880 | 1.4128          |
  | 1.5161        | 6.52  | 3040 | 1.4126          |
  | 1.5175        | 6.87  | 3200 | 1.4095          |
  | 1.5169        | 7.21  | 3360 | 1.4118          |
  | 1.516         | 7.55  | 3520 | 1.4113          |
  | 1.5182        | 7.9   | 3680 | 1.4097          |
  | 1.5195        | 8.24  | 3840 | 1.4118          |
  | 1.5187        | 8.26  | 4000 | 1.4119          |
  | 1.5149        | 8.6   | 4160 | 1.4133          |
  | 1.5183        | 8.94  | 4320 | 1.4097          |
  | 1.5192        | 9.29  | 4480 | 1.4101          |
  | 1.5191        | 9.63  | 4640 | 1.4146          |
  | 1.5192        | 9.97  | 4800 | 1.4165          |
  | 1.5164        | 10.32 | 4960 | 1.4119          |
  | 1.5235        | 10.66 | 5120 | 1.4089          |
  | 1.6571        | 11.0  | 5280 | 1.4121          |
  | 1.5184        | 11.35 | 5440 | 1.4102          |
  | 1.5185        | 11.69 | 5600 | 1.4111          |
  | 1.5172        | 12.03 | 5760 | 1.4142          |
  | 1.5189        | 12.38 | 5920 | 1.4129          |
  | 1.5147        | 12.72 | 6080 | 1.4089          |
  | 1.5177        | 13.06 | 6240 | 1.4098          |
  | 1.5164        | 13.41 | 6400 | 1.4097          |
  | 1.5188        | 13.75 | 6560 | 1.4109          |
  | 1.5158        | 14.09 | 6720 | 1.4134          |
  | 1.5134        | 14.44 | 6880 | 1.4091          |
  | 1.5167        | 14.78 | 7040 | 1.4089          |
  | 1.5163        | 15.12 | 7200 | 1.4140          |
  | 1.5172        | 15.47 | 7360 | 1.4083          |
  | 1.5153        | 15.81 | 7520 | 1.4109          |
  | 1.5164        | 16.15 | 7680 | 1.4093          |
  | 1.5164        | 16.17 | 7840 | 1.4108          |
  | 1.515         | 16.51 | 8000 | 1.4102          |
  | 1.5164        | 16.86 | 8160 | 1.4090          |
  | 1.5163        | 17.2  | 8320 | 1.4110          |
  | 1.5142        | 17.54 | 8480 | 1.4122          |
  | 1.5166        | 17.89 | 8640 | 1.4092          |
  | 1.5172        | 18.23 | 8800 | 1.4058          |
  | 1.5153        | 18.57 | 8960 | 1.4112          |
  | 1.517         | 18.92 | 9120 | 1.4098          |
  | 1.5163        | 19.26 | 9280 | 1.4113          |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.11.0
  - Datasets 2.7.0
  - Tokenizers 0.13.2
  