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--- |
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license: mit |
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base_model: PORTULAN/albertina-100m-portuguese-ptpt-encoder |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: LVI_albertina-100m-portuguese-ptpt-encoder |
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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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# LVI_albertina-100m-portuguese-ptpt-encoder |
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This model is a fine-tuned version of [PORTULAN/albertina-100m-portuguese-ptpt-encoder](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptpt-encoder) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1867 |
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- Accuracy: 0.9802 |
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- F1: 0.9800 |
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- Precision: 0.9905 |
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- Recall: 0.9696 |
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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: 5e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1284 | 1.0 | 3217 | 0.1454 | 0.9581 | 0.9567 | 0.9882 | 0.9272 | |
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| 0.0946 | 2.0 | 6434 | 0.1211 | 0.9737 | 0.9734 | 0.9864 | 0.9607 | |
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| 0.0575 | 3.0 | 9651 | 0.1087 | 0.9776 | 0.9774 | 0.9892 | 0.9659 | |
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| 0.0374 | 4.0 | 12868 | 0.1033 | 0.981 | 0.9809 | 0.9854 | 0.9765 | |
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| 0.0311 | 5.0 | 16085 | 0.1154 | 0.981 | 0.9808 | 0.9896 | 0.9722 | |
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| 0.0125 | 6.0 | 19302 | 0.1143 | 0.9830 | 0.9830 | 0.9833 | 0.9826 | |
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| 0.0107 | 7.0 | 22519 | 0.1562 | 0.9807 | 0.9805 | 0.9910 | 0.9702 | |
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| 0.0032 | 8.0 | 25736 | 0.1711 | 0.9808 | 0.9806 | 0.9892 | 0.9721 | |
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| 0.0036 | 9.0 | 28953 | 0.1867 | 0.9802 | 0.9800 | 0.9905 | 0.9696 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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