readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: roberta-tweet-context
    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. -->
  
  # roberta-tweet-context
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5340
  - Mse: 0.5340
  - Mae: 0.5438
  - R2: 0.5916
  - Accuracy: 0.5735
  
  ## 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: 4e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Accuracy | Validation Loss | Mae    | Mse    | R2     |
  |:-------------:|:-----:|:------:|:--------:|:---------------:|:------:|:------:|:------:|
  | 0.8743        | 0.02  | 2500   | 0.4705   | 0.6878          | 0.6465 | 0.6878 | 0.4740 |
  | 0.7153        | 0.05  | 5000   | 0.4838   | 0.6729          | 0.6304 | 0.6729 | 0.4853 |
  | 0.6907        | 0.07  | 7500   | 0.4900   | 0.6810          | 0.6346 | 0.6810 | 0.4792 |
  | 0.6724        | 0.1   | 10000  | 0.5022   | 0.6686          | 0.6250 | 0.6686 | 0.4886 |
  | 0.6601        | 0.12  | 12500  | 0.5221   | 0.6424          | 0.6053 | 0.6424 | 0.5087 |
  | 0.6432        | 0.15  | 15000  | 0.5040   | 0.6204          | 0.6076 | 0.6204 | 0.5255 |
  | 0.6377        | 0.17  | 17500  | 0.5280   | 0.6360          | 0.6082 | 0.6360 | 0.5136 |
  | 0.6227        | 0.2   | 20000  | 0.5059   | 0.6593          | 0.6197 | 0.6593 | 0.4958 |
  | 0.6177        | 0.22  | 22500  | 0.5246   | 0.6460          | 0.6066 | 0.6460 | 0.5060 |
  | 0.618         | 0.25  | 25000  | 0.5283   | 0.6245          | 0.5980 | 0.6245 | 0.5224 |
  | 0.6106        | 0.27  | 27500  | 0.5418   | 0.6059          | 0.5849 | 0.6059 | 0.5366 |
  | 0.6005        | 0.29  | 30000  | 0.5329   | 0.6102          | 0.5916 | 0.6102 | 0.5333 |
  | 0.5903        | 0.32  | 32500  | 0.5413   | 0.5866          | 0.5814 | 0.5866 | 0.5513 |
  | 0.5908        | 0.34  | 35000  | 0.5364   | 0.5998          | 0.5873 | 0.5998 | 0.5413 |
  | 0.585         | 0.37  | 37500  | 0.5402   | 0.5978          | 0.5874 | 0.5978 | 0.5428 |
  | 0.5865        | 0.39  | 40000  | 0.5481   | 0.5932          | 0.5782 | 0.5932 | 0.5463 |
  | 0.5744        | 0.42  | 42500  | 0.5434   | 0.5781          | 0.5751 | 0.5781 | 0.5579 |
  | 0.5758        | 0.44  | 45000  | 0.5418   | 0.5710          | 0.5734 | 0.5710 | 0.5633 |
  | 0.5633        | 0.47  | 47500  | 0.5569   | 0.5708          | 0.5663 | 0.5708 | 0.5635 |
  | 0.5695        | 0.49  | 50000  | 0.5451   | 0.5911          | 0.5786 | 0.5911 | 0.5479 |
  | 0.5572        | 0.52  | 52500  | 0.5471   | 0.5908          | 0.5754 | 0.5908 | 0.5481 |
  | 0.5569        | 0.54  | 55000  | 0.5569   | 0.5819          | 0.5717 | 0.5819 | 0.5550 |
  | 0.5459        | 0.56  | 57500  | 0.5516   | 0.5603          | 0.5653 | 0.5603 | 0.5715 |
  | 0.5518        | 0.59  | 60000  | 0.5569   | 0.5684          | 0.5662 | 0.5684 | 0.5653 |
  | 0.5341        | 0.61  | 62500  | 0.5520   | 0.5700          | 0.5675 | 0.5700 | 0.5641 |
  | 0.5422        | 0.64  | 65000  | 0.5553   | 0.5691          | 0.5636 | 0.5691 | 0.5648 |
  | 0.537         | 0.66  | 67500  | 0.5576   | 0.5721          | 0.5691 | 0.5721 | 0.5625 |
  | 0.5258        | 0.69  | 70000  | 0.5578   | 0.5742          | 0.5637 | 0.5742 | 0.5608 |
  | 0.5298        | 0.71  | 72500  | 0.5536   | 0.5727          | 0.5684 | 0.5727 | 0.5620 |
  | 0.5313        | 0.74  | 75000  | 0.5602   | 0.5560          | 0.5589 | 0.5560 | 0.5748 |
  | 0.5266        | 0.76  | 77500  | 0.5521   | 0.5481          | 0.5586 | 0.5481 | 0.5808 |
  | 0.5216        | 0.79  | 80000  | 0.5604   | 0.5497          | 0.5576 | 0.5497 | 0.5796 |
  | 0.5259        | 0.81  | 82500  | 0.5602   | 0.5509          | 0.5550 | 0.5509 | 0.5787 |
  | 0.519         | 0.83  | 85000  | 0.5622   | 0.5358          | 0.5477 | 0.5358 | 0.5903 |
  | 0.5229        | 0.86  | 87500  | 0.5667   | 0.5368          | 0.5490 | 0.5368 | 0.5895 |
  | 0.5213        | 0.88  | 90000  | 0.5626   | 0.5344          | 0.5513 | 0.5344 | 0.5913 |
  | 0.5145        | 0.91  | 92500  | 0.5639   | 0.5536          | 0.5556 | 0.5536 | 0.5766 |
  | 0.5084        | 0.93  | 95000  | 0.5666   | 0.5413          | 0.5502 | 0.5413 | 0.5860 |
  | 0.5082        | 0.96  | 97500  | 0.5662   | 0.5410          | 0.5508 | 0.5410 | 0.5862 |
  | 0.5064        | 0.98  | 100000 | 0.5681   | 0.5382          | 0.5491 | 0.5382 | 0.5884 |
  | 0.5257        | 1.01  | 102500 | 0.5765   | 0.5765          | 0.5686 | 0.5591 | 0.5531 |
  | 0.5016        | 1.0