readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: final-squad-bn-qgen-banglat5-all-metric-v3
    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. -->
  
  # final-squad-bn-qgen-banglat5-all-metric-v3
  
  This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4231
  - Rouge1 Precision: 39.5263
  - Rouge1 Recall: 37.168
  - Rouge1 Fmeasure: 37.1806
  - Rouge2 Precision: 17.7035
  - Rouge2 Recall: 16.508
  - Rouge2 Fmeasure: 16.5336
  - Rougel Precision: 37.135
  - Rougel Recall: 34.9177
  - Rougel Fmeasure: 34.9266
  - Rougelsum Precision: 37.1205
  - Rougelsum Recall: 34.8982
  - Rougelsum Fmeasure: 34.9129
  - Bleu-1: 36.4356
  - Bleu-2: 22.3217
  - Bleu-3: 14.7682
  - Bleu-4: 10.0865
  - Meteor: 0.2051
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### 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 | Bleu-1  | Bleu-2  | Bleu-3  | Bleu-4  | Meteor |
  |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:-------:|:-------:|:-------:|:-------:|:------:|
  | 0.5901        | 1.0   | 6769  | 0.4756          | 32.2563          | 31.3211       | 30.7652         | 12.2914          | 11.9567       | 11.6739         | 29.1321          | 28.3925       | 27.84           | 29.1291             | 28.3832          | 27.8378            | 31.9366 | 17.9528 | 10.9479 | 6.8658  | 0.1715 |
  | 0.5094        | 2.0   | 13538 | 0.4343          | 37.5727          | 35.6711       | 35.4661         | 16.3104          | 15.4196       | 15.3046         | 35.2059          | 33.4452       | 33.2559         | 35.1882             | 33.4395          | 33.24              | 35.1532 | 21.1183 | 13.7297 | 9.2128  | 0.1955 |
  | 0.4866        | 3.0   | 20307 | 0.4267          | 38.6402          | 36.2947       | 36.2796         | 16.8569          | 15.7129       | 15.7114         | 36.2902          | 34.0855       | 34.0734         | 36.2733             | 34.0723          | 34.0661            | 35.6286 | 21.554  | 14.098  | 9.5506  | 0.1996 |
  | 0.4732        | 4.0   | 27076 | 0.4235          | 39.3469          | 36.7357       | 36.8598         | 17.4835          | 16.2062       | 16.2677         | 36.9883          | 34.5422       | 34.6543         | 36.9783             | 34.5352          | 34.6594            | 35.9917 | 21.9745 | 14.4922 | 9.884   | 0.203  |
  | 0.4646        | 5.0   | 33845 | 0.4224          | 39.4223          | 37.0956       | 37.0893         | 17.6277          | 16.4682       | 16.4692         | 37.0994          | 34.896        | 34.8991         | 37.0885             | 34.8691          | 34.8811            | 36.3637 | 22.2704 | 14.7068 | 10.021  | 0.2049 |
  | 0.4517        | 6.0   | 40614 | 0.4231          | 39.5263          | 37.168        | 37.1806         | 17.7035          | 16.508        | 16.5336         | 37.135           | 34.9177       | 34.9266         | 37.1205             | 34.8982          | 34.9129            | 36.4356 | 22.3217 | 14.7682 | 10.0865 | 0.2051 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  