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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-r2a0d0.15-1 |
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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-r2a0d0.15-1 |
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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: 0.3633 |
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- Accuracy: 0.8396 |
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- Precision: 0.8128 |
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- Recall: 0.7890 |
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- F1: 0.7992 |
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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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| 0.5664 | 1.0 | 122 | 0.5221 | 0.7218 | 0.6580 | 0.6432 | 0.6487 | |
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| 0.5148 | 2.0 | 244 | 0.5111 | 0.7243 | 0.6758 | 0.6899 | 0.6810 | |
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| 0.4924 | 3.0 | 366 | 0.4791 | 0.7444 | 0.6884 | 0.6741 | 0.6799 | |
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| 0.4615 | 4.0 | 488 | 0.4651 | 0.7644 | 0.7148 | 0.7058 | 0.7099 | |
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| 0.4516 | 5.0 | 610 | 0.4581 | 0.7644 | 0.7214 | 0.7408 | 0.7286 | |
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| 0.4291 | 6.0 | 732 | 0.4295 | 0.7895 | 0.7462 | 0.7385 | 0.7421 | |
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| 0.4194 | 7.0 | 854 | 0.4191 | 0.7995 | 0.7581 | 0.7606 | 0.7593 | |
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| 0.3994 | 8.0 | 976 | 0.4048 | 0.8120 | 0.7745 | 0.7645 | 0.7691 | |
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| 0.3919 | 9.0 | 1098 | 0.3950 | 0.8246 | 0.7954 | 0.7659 | 0.7778 | |
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| 0.3762 | 10.0 | 1220 | 0.3881 | 0.8271 | 0.8022 | 0.7626 | 0.7777 | |
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| 0.3704 | 11.0 | 1342 | 0.3806 | 0.8271 | 0.7949 | 0.7776 | 0.7853 | |
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| 0.3642 | 12.0 | 1464 | 0.3733 | 0.8421 | 0.8122 | 0.8008 | 0.8061 | |
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| 0.3614 | 13.0 | 1586 | 0.3753 | 0.8321 | 0.8092 | 0.7687 | 0.7842 | |
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| 0.3474 | 14.0 | 1708 | 0.3695 | 0.8396 | 0.8155 | 0.7840 | 0.7969 | |
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| 0.3479 | 15.0 | 1830 | 0.3675 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | |
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| 0.3347 | 16.0 | 1952 | 0.3649 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | |
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| 0.335 | 17.0 | 2074 | 0.3653 | 0.8371 | 0.8114 | 0.7822 | 0.7943 | |
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| 0.3361 | 18.0 | 2196 | 0.3632 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | |
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| 0.3343 | 19.0 | 2318 | 0.3636 | 0.8371 | 0.8114 | 0.7822 | 0.7943 | |
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| 0.3347 | 20.0 | 2440 | 0.3633 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | |
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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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