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--- |
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license: mit |
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base_model: indobenchmark/indobert-base-p2 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: story_model |
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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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# story_model |
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This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2923 |
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- Accuracy: 0.9409 |
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- F1: 0.9043 |
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- Precision: 0.9087 |
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- Recall: 0.9 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 0.53 | 50 | 0.5324 | 0.8978 | 0.8032 | 0.9009 | 0.7572 | |
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| No log | 1.06 | 100 | 0.2795 | 0.9355 | 0.8967 | 0.8967 | 0.8967 | |
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| No log | 1.6 | 150 | 0.2561 | 0.9194 | 0.8772 | 0.8608 | 0.8972 | |
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| No log | 2.13 | 200 | 0.3274 | 0.9194 | 0.8635 | 0.8871 | 0.8444 | |
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| No log | 2.66 | 250 | 0.2756 | 0.9247 | 0.8819 | 0.8745 | 0.89 | |
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| No log | 3.19 | 300 | 0.4554 | 0.9032 | 0.8302 | 0.8696 | 0.8028 | |
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| No log | 3.72 | 350 | 0.2333 | 0.9462 | 0.9157 | 0.9075 | 0.9244 | |
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| No log | 4.26 | 400 | 0.4101 | 0.9247 | 0.8711 | 0.9013 | 0.8478 | |
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| No log | 4.79 | 450 | 0.2826 | 0.9409 | 0.9063 | 0.9021 | 0.9106 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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