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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-r4a0d0.05-0 |
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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-r4a0d0.05-0 |
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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: 0.3486 |
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- Accuracy: 0.8396 |
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- Precision: 0.8055 |
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- Recall: 0.8115 |
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- F1: 0.8084 |
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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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| 0.5619 | 1.0 | 122 | 0.5127 | 0.7168 | 0.6536 | 0.6446 | 0.6484 | |
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| 0.5059 | 2.0 | 244 | 0.4967 | 0.7343 | 0.6956 | 0.7220 | 0.7022 | |
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| 0.4822 | 3.0 | 366 | 0.4506 | 0.7469 | 0.7006 | 0.7159 | 0.7065 | |
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| 0.4402 | 4.0 | 488 | 0.3984 | 0.8195 | 0.7876 | 0.7623 | 0.7728 | |
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| 0.4068 | 5.0 | 610 | 0.4136 | 0.7870 | 0.7473 | 0.7718 | 0.7561 | |
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| 0.3791 | 6.0 | 732 | 0.3771 | 0.8321 | 0.7972 | 0.7987 | 0.7979 | |
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| 0.3635 | 7.0 | 854 | 0.3916 | 0.8195 | 0.7822 | 0.8048 | 0.7912 | |
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| 0.3433 | 8.0 | 976 | 0.3799 | 0.8296 | 0.7934 | 0.8019 | 0.7974 | |
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| 0.3379 | 9.0 | 1098 | 0.3714 | 0.8271 | 0.7903 | 0.8026 | 0.7959 | |
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| 0.3296 | 10.0 | 1220 | 0.3635 | 0.8371 | 0.8032 | 0.8047 | 0.8040 | |
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| 0.3105 | 11.0 | 1342 | 0.3652 | 0.8296 | 0.7933 | 0.8044 | 0.7984 | |
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| 0.3024 | 12.0 | 1464 | 0.3702 | 0.8346 | 0.7988 | 0.8180 | 0.8069 | |
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| 0.309 | 13.0 | 1586 | 0.3512 | 0.8371 | 0.8032 | 0.8047 | 0.8040 | |
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| 0.3021 | 14.0 | 1708 | 0.3505 | 0.8396 | 0.8060 | 0.8090 | 0.8075 | |
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| 0.2903 | 15.0 | 1830 | 0.3553 | 0.8421 | 0.8077 | 0.8208 | 0.8136 | |
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| 0.2834 | 16.0 | 1952 | 0.3530 | 0.8396 | 0.8046 | 0.8215 | 0.8119 | |
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| 0.2811 | 17.0 | 2074 | 0.3471 | 0.8446 | 0.8120 | 0.8151 | 0.8135 | |
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| 0.288 | 18.0 | 2196 | 0.3505 | 0.8446 | 0.8107 | 0.8226 | 0.8161 | |
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| 0.277 | 19.0 | 2318 | 0.3479 | 0.8396 | 0.8055 | 0.8115 | 0.8084 | |
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| 0.2775 | 20.0 | 2440 | 0.3486 | 0.8396 | 0.8055 | 0.8115 | 0.8084 | |
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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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