readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: mt5-small-finetuned-19jan-9
    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-19jan-9
  
  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: 2.6105
  - Rouge1: 7.7
  - Rouge2: 0.1667
  - Rougel: 7.5759
  - Rougelsum: 7.6113
  
  ## 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: 3e-05
  - train_batch_size: 12
  - eval_batch_size: 12
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 60
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 16.5659       | 1.0   | 50   | 5.8214          | 2.2745 | 0.2338 | 2.2506 | 2.2815    |
  | 10.2143       | 2.0   | 100  | 3.8680          | 4.4303 | 0.7671 | 4.4208 | 4.483     |
  | 7.4492        | 3.0   | 150  | 3.2448          | 3.6533 | 0.6857 | 3.6861 | 3.6791    |
  | 5.8239        | 4.0   | 200  | 3.0981          | 5.6287 | 0.8679 | 5.642  | 5.627     |
  | 4.9377        | 5.0   | 250  | 3.0326          | 6.1068 | 1.1621 | 5.9651 | 6.0361    |
  | 4.4824        | 6.0   | 300  | 2.9802          | 6.6496 | 1.3443 | 6.5332 | 6.5395    |
  | 4.2193        | 7.0   | 350  | 2.9484          | 6.0845 | 1.2364 | 6.1077 | 6.0827    |
  | 4.0234        | 8.0   | 400  | 2.9076          | 6.0958 | 1.3299 | 6.0806 | 6.0413    |
  | 3.9046        | 9.0   | 450  | 2.8460          | 5.6462 | 1.1644 | 5.6397 | 5.6103    |
  | 3.8087        | 10.0  | 500  | 2.8036          | 5.7538 | 1.1644 | 5.774  | 5.7442    |
  | 3.6872        | 11.0  | 550  | 2.7727          | 6.5993 | 1.3311 | 6.5773 | 6.6049    |
  | 3.6338        | 12.0  | 600  | 2.7285          | 6.0417 | 1.0778 | 6.047  | 6.089     |
  | 3.574         | 13.0  | 650  | 2.7132          | 8.7833 | 0.25   | 8.803  | 8.6985    |
  | 3.548         | 14.0  | 700  | 2.7023          | 8.9393 | 0.75   | 8.9619 | 8.8679    |
  | 3.49          | 15.0  | 750  | 2.6943          | 9.1778 | 1.0    | 9.1537 | 9.0722    |
  | 3.4098        | 16.0  | 800  | 2.6856          | 8.9167 | 0.75   | 8.9477 | 8.8597    |
  | 3.3776        | 17.0  | 850  | 2.6827          | 8.3503 | 0.1667 | 8.3179 | 8.2614    |
  | 3.3493        | 18.0  | 900  | 2.6899          | 8.6983 | 0.4524 | 8.6503 | 8.602     |
  | 3.3309        | 19.0  | 950  | 2.6833          | 8.2433 | 0.4524 | 8.1185 | 8.1429    |
  | 3.2833        | 20.0  | 1000 | 2.6785          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
  | 3.2491        | 21.0  | 1050 | 2.6817          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
  | 3.22          | 22.0  | 1100 | 2.6697          | 8.3829 | 0.4524 | 8.2852 | 8.3167    |
  | 3.2433        | 23.0  | 1150 | 2.6522          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
  | 3.1882        | 24.0  | 1200 | 2.6493          | 8.2194 | 0.4524 | 8.106  | 8.1227    |
  | 3.1622        | 25.0  | 1250 | 2.6630          | 8.3593 | 0.4524 | 8.2859 | 8.3167    |
  | 3.1396        | 26.0  | 1300 | 2.6523          | 8.3593 | 0.4524 | 8.2859 | 8.3167    |
  | 3.121         | 27.0  | 1350 | 2.6565          | 8.3593 | 0.4524 | 8.2859 | 8.3167    |
  | 3.1095        | 28.0  | 1400 | 2.6385          | 8.5833 | 0.4524 | 8.45   | 8.516     |
  | 3.1113        | 29.0  | 1450 | 2.6378          | 7.6135 | 0.3333 | 7.5385 | 7.5885    |
  | 3.0661        | 30.0  | 1500 | 2.6415          | 8.2734 | 0.3333 | 8.1583 | 8.25      |
  | 3.0316        | 31.0  | 1550 | 2.6435          | 7.6135 | 0.3333 | 7.5385 | 7.5885    |
  | 3.0468        | 32.0  | 1600 | 2.6342          | 7.6135 | 0.3333 | 7.5385 | 7.5885    |
  | 3.0323        | 33.0  | 1650 | 2.6330          | 7.8333 | 0.4167 | 7.7551 | 7.8317    |
  | 3.0031        | 34.0  | 1700 | 2.6332          | 8.1192 | 0.4167 | 8.0718 | 8.1167    |
  | 2.9904        | 35.0  | 1750 | 2.6291          | 8.2734 | 0.3333 | 8.1583 | 8.25      |
  | 2.9765        | 36.0  | 1800 | 2.6364          | 7.8667 | 0.4167 | 7.8167 | 7.8269    |
  | 2.9872        | 37.0  | 1850 | 2.6267          | 7.9984 | 0.4167 | 7.875  | 7.9843    |
  | 2.976         | 38.0  | 1900 | 2.6252          | 7.9984 | 0.4167 | 7.875  | 7.9843    |
  | 2.9528        | 39.0  | 1950 | 2.6319          | 7.701  | 0.3333 | 7.7167 | 7.6769    |
  | 2.9385        | 40.0  | 2000 | 2.6279          | 7.8667 | 0.4167 | 7.8167 | 7.8269    |
  | 2.9371        | 41.0  | 2050 | 2.6227          | 7.4658 | 0.4167 | 7.4167 | 7.4397    |
  | 2.9214       