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squad-bn-qgen-mt5-all-metric
This model is a fine-tuned version of google/mt5-small on the squad_bn dataset. It achieves the following results on the evaluation set:
- Loss: 0.7273
- Rouge1 Precision: 35.8589
- Rouge1 Recall: 29.7041
- Rouge1 Fmeasure: 31.6373
- Rouge2 Precision: 15.4203
- Rouge2 Recall: 12.5155
- Rouge2 Fmeasure: 13.3978
- Rougel Precision: 34.4684
- Rougel Recall: 28.5887
- Rougel Fmeasure: 30.4627
- Rougelsum Precision: 34.4252
- Rougelsum Recall: 28.5362
- Rougelsum Fmeasure: 30.4053
- Sacrebleu: 6.4143
- Meteor: 0.1416
- Gen Len: 16.7199
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.8449 | 1.0 | 16396 | 0.7340 | 31.6476 | 26.8901 | 28.2871 | 13.621 | 11.3545 | 11.958 | 30.3276 | 25.7754 | 27.1048 | 30.3426 | 25.7489 | 27.0991 | 5.9655 | 0.1336 | 16.8685 |
0.7607 | 2.0 | 32792 | 0.7182 | 33.7173 | 28.6115 | 30.1049 | 14.8227 | 12.2059 | 12.9453 | 32.149 | 27.2036 | 28.6617 | 32.2479 | 27.2261 | 28.7272 | 6.6093 | 0.138 | 16.8522 |
0.7422 | 3.0 | 49188 | 0.7083 | 34.6128 | 29.0223 | 30.7248 | 14.9888 | 12.3092 | 13.1021 | 33.2507 | 27.8154 | 29.4599 | 33.2848 | 27.812 | 29.5064 | 6.2407 | 0.1416 | 16.5806 |
0.705 | 4.0 | 65584 | 0.7035 | 34.156 | 29.0012 | 30.546 | 14.72 | 12.0251 | 12.8161 | 32.7527 | 27.6511 | 29.1955 | 32.7692 | 27.6627 | 29.231 | 6.1784 | 0.1393 | 16.7793 |
0.6859 | 5.0 | 81980 | 0.7038 | 35.1405 | 29.6033 | 31.2614 | 15.5108 | 12.6414 | 13.5059 | 33.8335 | 28.4264 | 30.0745 | 33.8782 | 28.4349 | 30.0901 | 6.5896 | 0.144 | 16.6651 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1