readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: mt5-small-finetuned-28jan-2
    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. -->
  
  # mt5-small-finetuned-28jan-2
  
  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.5078
  - Rouge1: 18.7485
  - Rouge2: 5.8034
  - Rougel: 18.5163
  - Rougelsum: 18.4817
  
  ## 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: 9
  - eval_batch_size: 9
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
  | 5.9652        | 1.0   | 242  | 2.8048          | 14.036  | 4.037  | 13.7766 | 13.8254   |
  | 3.474         | 2.0   | 484  | 2.7485          | 16.821  | 4.8051 | 16.6168 | 16.5782   |
  | 3.2101        | 3.0   | 726  | 2.6444          | 17.2659 | 5.1077 | 16.9501 | 16.8998   |
  | 3.0555        | 4.0   | 968  | 2.6408          | 17.3002 | 4.8657 | 17.0414 | 16.9794   |
  | 2.9515        | 5.0   | 1210 | 2.5860          | 17.6468 | 5.3816 | 17.3755 | 17.3434   |
  | 2.8694        | 6.0   | 1452 | 2.5586          | 18.3932 | 5.3896 | 18.2521 | 18.0748   |
  | 2.7898        | 7.0   | 1694 | 2.5325          | 18.4954 | 5.5609 | 18.2994 | 18.2112   |
  | 2.7436        | 8.0   | 1936 | 2.5431          | 18.8172 | 5.9338 | 18.4693 | 18.4324   |
  | 2.6955        | 9.0   | 2178 | 2.5588          | 18.7895 | 6.1003 | 18.3593 | 18.3268   |
  | 2.6571        | 10.0  | 2420 | 2.5079          | 19.2525 | 5.8268 | 19.0279 | 18.9846   |
  | 2.629         | 11.0  | 2662 | 2.5118          | 18.9191 | 5.9877 | 18.6505 | 18.6      |
  | 2.5998        | 12.0  | 2904 | 2.5070          | 18.7181 | 5.9061 | 18.4432 | 18.3931   |
  | 2.5692        | 13.0  | 3146 | 2.5014          | 18.4412 | 6.1983 | 18.2394 | 18.1618   |
  | 2.5751        | 14.0  | 3388 | 2.5125          | 18.7014 | 5.9729 | 18.4366 | 18.406    |
  | 2.55          | 15.0  | 3630 | 2.5078          | 18.7485 | 5.8034 | 18.5163 | 18.4817   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  