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--- |
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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-r16a2d0.2-0 |
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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-r16a2d0.2-0 |
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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: 0.2895 |
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- Accuracy: 0.8647 |
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- Precision: 0.8352 |
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- Recall: 0.8417 |
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- F1: 0.8383 |
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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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| 0.5604 | 1.0 | 122 | 0.4996 | 0.7268 | 0.6671 | 0.6592 | 0.6627 | |
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| 0.4842 | 2.0 | 244 | 0.4520 | 0.7544 | 0.7169 | 0.7462 | 0.7247 | |
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| 0.4079 | 3.0 | 366 | 0.3749 | 0.8321 | 0.7963 | 0.8062 | 0.8009 | |
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| 0.3378 | 4.0 | 488 | 0.3624 | 0.8471 | 0.8184 | 0.8068 | 0.8122 | |
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| 0.3146 | 5.0 | 610 | 0.3620 | 0.8471 | 0.8130 | 0.8393 | 0.8235 | |
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| 0.2935 | 6.0 | 732 | 0.3518 | 0.8496 | 0.8158 | 0.8386 | 0.8253 | |
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| 0.2842 | 7.0 | 854 | 0.3307 | 0.8647 | 0.8346 | 0.8442 | 0.8391 | |
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| 0.267 | 8.0 | 976 | 0.3191 | 0.8622 | 0.8333 | 0.8350 | 0.8341 | |
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| 0.2598 | 9.0 | 1098 | 0.3174 | 0.8672 | 0.8393 | 0.8410 | 0.8402 | |
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| 0.2557 | 10.0 | 1220 | 0.3076 | 0.8647 | 0.8367 | 0.8367 | 0.8367 | |
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| 0.2341 | 11.0 | 1342 | 0.3144 | 0.8697 | 0.8411 | 0.8478 | 0.8443 | |
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| 0.2352 | 12.0 | 1464 | 0.3135 | 0.8672 | 0.8385 | 0.8435 | 0.8409 | |
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| 0.2335 | 13.0 | 1586 | 0.3035 | 0.8722 | 0.8445 | 0.8496 | 0.8470 | |
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| 0.232 | 14.0 | 1708 | 0.3012 | 0.8697 | 0.8404 | 0.8503 | 0.8451 | |
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| 0.221 | 15.0 | 1830 | 0.3050 | 0.8672 | 0.8372 | 0.8485 | 0.8425 | |
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| 0.216 | 16.0 | 1952 | 0.3016 | 0.8697 | 0.8399 | 0.8528 | 0.8458 | |
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| 0.2096 | 17.0 | 2074 | 0.2881 | 0.8697 | 0.8419 | 0.8453 | 0.8436 | |
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| 0.2184 | 18.0 | 2196 | 0.2966 | 0.8697 | 0.8393 | 0.8553 | 0.8465 | |
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| 0.2134 | 19.0 | 2318 | 0.2884 | 0.8672 | 0.8385 | 0.8435 | 0.8409 | |
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| 0.2077 | 20.0 | 2440 | 0.2895 | 0.8647 | 0.8352 | 0.8417 | 0.8383 | |
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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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