readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - gem
  model_index:
  - name: BART-commongen
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: gem
        type: gem
        args: common_gen
  ---
  
  <!-- 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. -->
  
  # BART-commongen
  
  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the gem dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1263
  - Spice: 0.4178
  
  ## 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: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - training_steps: 6317
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Spice  |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 9.0971        | 0.05  | 100  | 4.1336          | 0.3218 |
  | 3.5348        | 0.09  | 200  | 1.5467          | 0.3678 |
  | 1.5099        | 0.14  | 300  | 1.1280          | 0.3821 |
  | 1.2395        | 0.19  | 400  | 1.1178          | 0.3917 |
  | 1.1827        | 0.24  | 500  | 1.0919          | 0.4086 |
  | 1.1545        | 0.28  | 600  | 1.1028          | 0.4035 |
  | 1.1363        | 0.33  | 700  | 1.1021          | 0.4187 |
  | 1.1156        | 0.38  | 800  | 1.1231          | 0.4103 |
  | 1.1077        | 0.43  | 900  | 1.1221          | 0.4117 |
  | 1.0964        | 0.47  | 1000 | 1.1169          | 0.4088 |
  | 1.0704        | 0.52  | 1100 | 1.1143          | 0.4133 |
  | 1.0483        | 0.57  | 1200 | 1.1085          | 0.4058 |
  | 1.0556        | 0.62  | 1300 | 1.1059          | 0.4249 |
  | 1.0343        | 0.66  | 1400 | 1.0992          | 0.4102 |
  | 1.0123        | 0.71  | 1500 | 1.1126          | 0.4104 |
  | 1.0108        | 0.76  | 1600 | 1.1140          | 0.4177 |
  | 1.005         | 0.81  | 1700 | 1.1264          | 0.4078 |
  | 0.9822        | 0.85  | 1800 | 1.1256          | 0.4158 |
  | 0.9918        | 0.9   | 1900 | 1.1345          | 0.4118 |
  | 0.9664        | 0.95  | 2000 | 1.1087          | 0.4073 |
  | 0.9532        | 1.0   | 2100 | 1.1217          | 0.4063 |
  | 0.8799        | 1.04  | 2200 | 1.1229          | 0.4115 |
  | 0.8665        | 1.09  | 2300 | 1.1263          | 0.4178 |
  
  
  ### Framework versions
  
  - Transformers 4.9.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.1.dev0
  - Tokenizers 0.10.3
  