readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - squad_bn
  metrics:
  - sacrebleu
  model-index:
  - name: squad-bn-qgen-banglat5
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: squad_bn
        type: squad_bn
        args: squad_bn
      metrics:
      - name: Sacrebleu
        type: sacrebleu
        value: 8.0898
  ---
  
  <!-- 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. -->
  
  # squad-bn-qgen-banglat5
  
  This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the squad_bn dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4808
  - Rouge1 Precision: 37.7366
  - Rouge1 Recall: 34.2712
  - Rouge1 Fmeasure: 34.8738
  - Rouge2 Precision: 16.2055
  - Rouge2 Recall: 14.568
  - Rouge2 Fmeasure: 14.852
  - Rougel Precision: 35.4241
  - Rougel Recall: 32.2011
  - Rougel Fmeasure: 32.7617
  - Rougelsum Precision: 35.4167
  - Rougelsum Recall: 32.1978
  - Rougelsum Fmeasure: 32.7572
  - Sacrebleu: 8.0898
  - Meteor: 0.1782
  - Gen Len: 9.8299
  
  ## 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: 5e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Sacrebleu | Meteor | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:---------:|:------:|:-------:|
  | 0.5208        | 1.0   | 16396 | 0.4683          | 38.566           | 35.5094       | 35.9216         | 17.0701          | 15.3916       | 15.6829         | 36.4433          | 33.5298       | 33.958          | 36.4637             | 33.5496          | 33.9913            | 8.6055    | 0.1799 | 9.8340  |
  | 0.479         | 2.0   | 32792 | 0.4815          | 40.7475          | 35.8163       | 37.0498         | 17.9002          | 15.2742       | 15.9601         | 38.6977          | 33.8607       | 35.1258         | 38.7261             | 33.8717          | 35.1537            | 9.0561    | 0.1835 | 9.4338  |
  | 0.4577        | 3.0   | 49188 | 0.4879          | 40.6712          | 36.2763       | 37.2775         | 18.5942          | 16.0689       | 16.7206         | 38.8546          | 34.5013       | 35.5491         | 38.8633             | 34.5255          | 35.5682            | 9.7947    | 0.1879 | 9.6324  |
  | 0.4389        | 4.0   | 65584 | 0.4881          | 41.4251          | 36.2873       | 37.6272         | 18.561           | 15.7067       | 16.5358         | 39.434           | 34.3496       | 35.7457         | 39.533              | 34.4702          | 35.8347            | 9.7612    | 0.1881 | 9.3944  |
  | 0.4321        | 5.0   | 81980 | 0.4937          | 41.1197          | 36.0568       | 37.4121         | 18.7179          | 15.8348       | 16.6644         | 39.3386          | 34.3177       | 35.7088         | 39.3171             | 34.3015          | 35.6748            | 9.8263    | 0.1887 | 9.4040  |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  