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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-lora-r4a2d0.1-0 |
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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-lora-r4a2d0.1-0 |
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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: 0.3483 |
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- Accuracy: 0.8446 |
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- Precision: 0.8111 |
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- Recall: 0.8201 |
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- F1: 0.8153 |
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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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| 0.5617 | 1.0 | 122 | 0.5117 | 0.7193 | 0.6580 | 0.6514 | 0.6543 | |
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| 0.5046 | 2.0 | 244 | 0.4917 | 0.7419 | 0.7042 | 0.7324 | 0.7112 | |
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| 0.4798 | 3.0 | 366 | 0.4466 | 0.7594 | 0.7129 | 0.7248 | 0.7179 | |
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| 0.4374 | 4.0 | 488 | 0.3994 | 0.8195 | 0.7866 | 0.7648 | 0.7741 | |
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| 0.4037 | 5.0 | 610 | 0.4150 | 0.7845 | 0.7480 | 0.7800 | 0.7575 | |
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| 0.3741 | 6.0 | 732 | 0.3737 | 0.8371 | 0.8028 | 0.8072 | 0.8049 | |
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| 0.3574 | 7.0 | 854 | 0.3776 | 0.8221 | 0.7845 | 0.7991 | 0.7909 | |
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| 0.3387 | 8.0 | 976 | 0.3654 | 0.8446 | 0.8120 | 0.8151 | 0.8135 | |
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| 0.3293 | 9.0 | 1098 | 0.3627 | 0.8371 | 0.8021 | 0.8122 | 0.8068 | |
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| 0.3209 | 10.0 | 1220 | 0.3553 | 0.8371 | 0.8032 | 0.8047 | 0.8040 | |
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| 0.2967 | 11.0 | 1342 | 0.3674 | 0.8346 | 0.7989 | 0.8130 | 0.8052 | |
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| 0.2928 | 12.0 | 1464 | 0.3707 | 0.8321 | 0.7960 | 0.8112 | 0.8027 | |
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| 0.2967 | 13.0 | 1586 | 0.3514 | 0.8471 | 0.8153 | 0.8168 | 0.8160 | |
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| 0.2934 | 14.0 | 1708 | 0.3507 | 0.8421 | 0.8083 | 0.8158 | 0.8119 | |
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| 0.2811 | 15.0 | 1830 | 0.3553 | 0.8346 | 0.7991 | 0.8105 | 0.8043 | |
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| 0.2738 | 16.0 | 1952 | 0.3555 | 0.8421 | 0.8077 | 0.8208 | 0.8136 | |
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| 0.2717 | 17.0 | 2074 | 0.3468 | 0.8496 | 0.8174 | 0.8236 | 0.8204 | |
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| 0.278 | 18.0 | 2196 | 0.3510 | 0.8421 | 0.8080 | 0.8183 | 0.8127 | |
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| 0.2701 | 19.0 | 2318 | 0.3471 | 0.8471 | 0.8142 | 0.8218 | 0.8178 | |
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| 0.2722 | 20.0 | 2440 | 0.3483 | 0.8446 | 0.8111 | 0.8201 | 0.8153 | |
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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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