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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- hate_speech_filipino |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-kd-from-scratch-silver-data-hate_speech_filipino-model-xlm-roberta-base |
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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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# scenario-kd-from-scratch-silver-data-hate_speech_filipino-model-xlm-roberta-base |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the hate_speech_filipino dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1354 |
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- Accuracy: 0.7665 |
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- F1: 0.7412 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 6969 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 0.32 | 100 | 2.4557 | 0.6656 | 0.6952 | |
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| No log | 0.64 | 200 | 2.0812 | 0.7063 | 0.7186 | |
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| No log | 0.96 | 300 | 1.9137 | 0.7079 | 0.7300 | |
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| No log | 1.28 | 400 | 1.8171 | 0.7172 | 0.7401 | |
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| 2.5353 | 1.6 | 500 | 1.7305 | 0.7462 | 0.6959 | |
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| 2.5353 | 1.92 | 600 | 2.3251 | 0.6645 | 0.7221 | |
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| 2.5353 | 2.24 | 700 | 1.5004 | 0.7571 | 0.7299 | |
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| 2.5353 | 2.56 | 800 | 1.7161 | 0.7431 | 0.6752 | |
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| 2.5353 | 2.88 | 900 | 1.3750 | 0.7519 | 0.7400 | |
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| 1.5143 | 3.19 | 1000 | 1.6104 | 0.7561 | 0.6968 | |
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| 1.5143 | 3.51 | 1100 | 1.4419 | 0.7561 | 0.7104 | |
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| 1.5143 | 3.83 | 1200 | 1.3306 | 0.7450 | 0.7496 | |
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| 1.5143 | 4.15 | 1300 | 1.4285 | 0.7668 | 0.7352 | |
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| 1.5143 | 4.47 | 1400 | 1.3335 | 0.7576 | 0.7552 | |
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| 1.1029 | 4.79 | 1500 | 1.3649 | 0.7394 | 0.7487 | |
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| 1.1029 | 5.11 | 1600 | 1.5830 | 0.7224 | 0.7434 | |
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| 1.1029 | 5.43 | 1700 | 1.2794 | 0.7592 | 0.7560 | |
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| 1.1029 | 5.75 | 1800 | 1.2877 | 0.7547 | 0.7165 | |
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| 1.1029 | 6.07 | 1900 | 1.2428 | 0.7637 | 0.7325 | |
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| 0.8948 | 6.39 | 2000 | 1.2774 | 0.7387 | 0.7494 | |
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| 0.8948 | 6.71 | 2100 | 1.2324 | 0.7628 | 0.7354 | |
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| 0.8948 | 7.03 | 2200 | 1.3675 | 0.7387 | 0.7505 | |
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| 0.8948 | 7.35 | 2300 | 1.2021 | 0.7670 | 0.7490 | |
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| 0.8948 | 7.67 | 2400 | 1.3012 | 0.7682 | 0.7348 | |
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| 0.7714 | 7.99 | 2500 | 1.2338 | 0.7580 | 0.7210 | |
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| 0.7714 | 8.31 | 2600 | 1.2189 | 0.7628 | 0.7519 | |
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| 0.7714 | 8.63 | 2700 | 1.2962 | 0.7410 | 0.7526 | |
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| 0.7714 | 8.95 | 2800 | 1.3151 | 0.7675 | 0.7416 | |
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| 0.7714 | 9.27 | 2900 | 1.1539 | 0.7616 | 0.7528 | |
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| 0.7096 | 9.58 | 3000 | 1.3696 | 0.7561 | 0.7523 | |
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| 0.7096 | 9.9 | 3100 | 1.2055 | 0.7514 | 0.7533 | |
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| 0.7096 | 10.22 | 3200 | 1.1354 | 0.7665 | 0.7412 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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