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--- |
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language: |
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- id |
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license: mit |
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base_model: indolem/indobert-base-uncased |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment-pt-pl20-0 |
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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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# sentiment-pt-pl20-0 |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2809 |
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- Accuracy: 0.9023 |
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- Precision: 0.8758 |
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- Recall: 0.8983 |
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- F1: 0.8857 |
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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-05 |
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- train_batch_size: 30 |
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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: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5417 | 1.0 | 122 | 0.4692 | 0.7368 | 0.6763 | 0.6438 | 0.6531 | |
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| 0.4301 | 2.0 | 244 | 0.4378 | 0.7769 | 0.7547 | 0.8022 | 0.7593 | |
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| 0.3347 | 3.0 | 366 | 0.3451 | 0.8446 | 0.8158 | 0.8026 | 0.8086 | |
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| 0.2954 | 4.0 | 488 | 0.3337 | 0.8647 | 0.8377 | 0.8342 | 0.8359 | |
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| 0.2632 | 5.0 | 610 | 0.3356 | 0.8571 | 0.8248 | 0.8414 | 0.8321 | |
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| 0.2492 | 6.0 | 732 | 0.3261 | 0.8446 | 0.8110 | 0.8451 | 0.8231 | |
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| 0.227 | 7.0 | 854 | 0.2978 | 0.8797 | 0.8496 | 0.8749 | 0.8602 | |
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| 0.2189 | 8.0 | 976 | 0.2742 | 0.8947 | 0.8789 | 0.8630 | 0.8704 | |
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| 0.2068 | 9.0 | 1098 | 0.2875 | 0.8922 | 0.8673 | 0.8763 | 0.8716 | |
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| 0.1935 | 10.0 | 1220 | 0.2693 | 0.9073 | 0.8904 | 0.8844 | 0.8873 | |
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| 0.1729 | 11.0 | 1342 | 0.2715 | 0.9073 | 0.8840 | 0.8969 | 0.8900 | |
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| 0.1639 | 12.0 | 1464 | 0.2755 | 0.8997 | 0.8733 | 0.8941 | 0.8825 | |
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| 0.1564 | 13.0 | 1586 | 0.2662 | 0.9023 | 0.8828 | 0.8808 | 0.8818 | |
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| 0.1495 | 14.0 | 1708 | 0.2973 | 0.8997 | 0.8722 | 0.8991 | 0.8835 | |
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| 0.1487 | 15.0 | 1830 | 0.2732 | 0.9098 | 0.8865 | 0.9012 | 0.8932 | |
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| 0.141 | 16.0 | 1952 | 0.2842 | 0.9048 | 0.8784 | 0.9026 | 0.8888 | |
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| 0.1276 | 17.0 | 2074 | 0.2794 | 0.9048 | 0.8798 | 0.8976 | 0.8878 | |
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| 0.1383 | 18.0 | 2196 | 0.2787 | 0.9073 | 0.8823 | 0.9019 | 0.8910 | |
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| 0.1371 | 19.0 | 2318 | 0.2780 | 0.9023 | 0.8758 | 0.8983 | 0.8857 | |
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| 0.1248 | 20.0 | 2440 | 0.2809 | 0.9023 | 0.8758 | 0.8983 | 0.8857 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.2 |
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