readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - itihasa
  metrics:
  - bleu
  model-index:
  - name: my_sanskrit_model
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: itihasa
        type: itihasa
        config: Itihasa
        split: train
        args: Itihasa
      metrics:
      - name: Bleu
        type: bleu
        value: 0.2607
  ---
  
  <!-- 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. -->
  
  # my_sanskrit_model
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the itihasa dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.5101
  - Bleu: 0.2607
  - Gen Len: 18.9973
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Bleu   | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
  | 3.9557        | 1.0   | 4698  | 3.7191          | 0.3291 | 18.9973 |
  | 3.8243        | 2.0   | 9396  | 3.6068          | 0.2728 | 18.9973 |
  | 3.7562        | 3.0   | 14094 | 3.5503          | 0.2911 | 18.9973 |
  | 3.7306        | 4.0   | 18792 | 3.5207          | 0.2404 | 18.9973 |
  | 3.7003        | 5.0   | 23490 | 3.5101          | 0.2607 | 18.9973 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  