readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: mt5-small-finetuned-19jan-5
    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-5
  
  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: 2.6411
  - Rouge1: 7.6385
  - Rouge2: 0.3333
  - Rougel: 7.4817
  - Rougelsum: 7.4859
  
  ## 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: 2e-05
  - train_batch_size: 10
  - eval_batch_size: 10
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 19.2402       | 1.0   | 60   | 8.2701          | 2.1815 | 0.1429 | 2.2246 | 2.2081    |
  | 12.7954       | 2.0   | 120  | 5.3510          | 3.3524 | 0.3929 | 3.3843 | 3.3893    |
  | 8.8288        | 3.0   | 180  | 3.5929          | 4.3158 | 0.4242 | 4.2947 | 4.2986    |
  | 6.9994        | 4.0   | 240  | 3.2479          | 4.1515 | 0.5195 | 4.1991 | 4.1535    |
  | 5.7594        | 5.0   | 300  | 3.0701          | 4.4127 | 0.4838 | 4.4044 | 4.4096    |
  | 5.075         | 6.0   | 360  | 3.0252          | 5.6953 | 0.925  | 5.6925 | 5.6771    |
  | 4.6336        | 7.0   | 420  | 2.9917          | 5.8009 | 1.1576 | 5.8699 | 5.871     |
  | 4.3993        | 8.0   | 480  | 2.9676          | 5.8763 | 1.1953 | 5.9074 | 5.8808    |
  | 4.1863        | 9.0   | 540  | 2.9213          | 6.2006 | 1.3455 | 6.2031 | 6.1713    |
  | 4.0672        | 10.0  | 600  | 2.9115          | 5.3167 | 1.2394 | 5.3518 | 5.3606    |
  | 3.9671        | 11.0  | 660  | 2.8743          | 5.2749 | 1.2394 | 5.3117 | 5.2936    |
  | 3.86          | 12.0  | 720  | 2.8472          | 5.8311 | 1.1505 | 5.9026 | 5.8415    |
  | 3.8103        | 13.0  | 780  | 2.8158          | 6.3536 | 1.1505 | 6.3989 | 6.3321    |
  | 3.7412        | 14.0  | 840  | 2.7794          | 6.4438 | 1.1505 | 6.4702 | 6.4715    |
  | 3.6757        | 15.0  | 900  | 2.7632          | 6.3778 | 0.9616 | 6.4342 | 6.417     |
  | 3.643         | 16.0  | 960  | 2.7335          | 6.2346 | 0.9616 | 6.2724 | 6.2393    |
  | 3.5952        | 17.0  | 1020 | 2.7152          | 5.9718 | 0.7727 | 6.0017 | 5.9683    |
  | 3.585         | 18.0  | 1080 | 2.6998          | 8.8466 | 0.3333 | 8.7787 | 8.7648    |
  | 3.493         | 19.0  | 1140 | 2.6982          | 8.1089 | 0.3333 | 7.95   | 7.9352    |
  | 3.4807        | 20.0  | 1200 | 2.6911          | 7.9967 | 0.3333 | 7.8437 | 7.843     |
  | 3.451         | 21.0  | 1260 | 2.6885          | 7.9967 | 0.3333 | 7.8437 | 7.843     |
  | 3.4368        | 22.0  | 1320 | 2.6945          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
  | 3.4044        | 23.0  | 1380 | 2.6909          | 8.6753 | 0.3333 | 8.5901 | 8.4835    |
  | 3.3862        | 24.0  | 1440 | 2.6899          | 8.4263 | 0.3333 | 8.2222 | 8.1901    |
  | 3.3421        | 25.0  | 1500 | 2.6897          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
  | 3.3414        | 26.0  | 1560 | 2.6801          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
  | 3.3354        | 27.0  | 1620 | 2.6772          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
  | 3.299         | 28.0  | 1680 | 2.6780          | 8.2061 | 0.3333 | 8.0333 | 8.0097    |
  | 3.3058        | 29.0  | 1740 | 2.6711          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
  | 3.2678        | 30.0  | 1800 | 2.6693          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
  | 3.2538        | 31.0  | 1860 | 2.6661          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
  | 3.2361        | 32.0  | 1920 | 2.6687          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
  | 3.2326        | 33.0  | 1980 | 2.6625          | 8.0944 | 0.3333 | 7.9019 | 7.8787    |
  | 3.2142        | 34.0  | 2040 | 2.6648          | 8.0526 | 0.3333 | 7.9026 | 7.8801    |
  | 3.1875        | 35.0  | 2100 | 2.6634          | 8.5204 | 0.3333 | 8.3199 | 8.3352    |
  | 3.1717        | 36.0  | 2160 | 2.6611          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
  | 3.1706        | 37.0  | 2220 | 2.6641          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
  | 3.1541        | 38.0  | 2280 | 2.6573          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
  | 3.1468        | 39.0  | 2340 | 2.6626          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
  | 3.1376        | 40.0  | 2400 | 2.6602          | 8.5083 | 0.3333 | 8.3228 | 8.3359    |
  | 3.1572        | 41.0  | 2460 | 2.6539          | 7.9385 | 0.3333 | 7.8019 | 7.8519    |
  | 3.147     