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--- |
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base_model: csebuetnlp/banglat5 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: banglat5-finetuned-new-method-new |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# banglat5-finetuned-new-method-new |
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This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Rouge1 Precision: 26.1936 |
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- Rouge1 Recall: 27.4839 |
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- Rouge1 Fmeasure: 26.0831 |
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- Rouge2 Precision: 10.5258 |
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- Rouge2 Recall: 11.6544 |
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- Rouge2 Fmeasure: 10.651 |
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- Rougel Precision: 24.2917 |
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- Rougel Recall: 25.5269 |
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- Rougel Fmeasure: 24.1921 |
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- Gen Len: 8.6402 |
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- Loss: 2.8106 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Gen Len | Validation Loss | |
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|:-------------:|:-----:|:----:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------:|:---------------:| |
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| 4.1475 | 1.0 | 202 | 28.4204 | 31.0145 | 28.5051 | 12.7947 | 13.7716 | 12.4938 | 26.467 | 28.917 | 26.5293 | 11.3871 | 3.2546 | |
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| 3.741 | 2.0 | 404 | 25.0778 | 26.2399 | 24.7974 | 9.4574 | 10.3436 | 9.4444 | 23.2601 | 24.4534 | 23.0588 | 8.6824 | 2.9268 | |
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| 3.2953 | 3.0 | 606 | 25.9864 | 26.6166 | 25.5881 | 10.0107 | 10.6915 | 9.9945 | 23.7579 | 24.458 | 23.4435 | 8.4144 | 2.8387 | |
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| 2.9438 | 4.0 | 808 | 26.1936 | 27.4839 | 26.0831 | 10.5258 | 11.6544 | 10.651 | 24.2917 | 25.5269 | 24.1921 | 8.6402 | 2.8106 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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