readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: sentence-compression-roberta
    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. -->
  
  # sentence-compression-roberta
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3465
  - Accuracy: 0.8473
  - F1: 0.6835
  - Precision: 0.6835
  - Recall: 0.6835
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.5312        | 1.0   | 50   | 0.5251          | 0.7591   | 0.0040 | 0.75      | 0.0020 |
  | 0.4           | 2.0   | 100  | 0.4003          | 0.8200   | 0.5341 | 0.7113    | 0.4275 |
  | 0.3355        | 3.0   | 150  | 0.3465          | 0.8473   | 0.6835 | 0.6835    | 0.6835 |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.10.0+cu113
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  