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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-non-kd-from-scratch-data-hate_speech_filipino-model-xlm-roberta-base |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: hate_speech_filipino |
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type: hate_speech_filipino |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7247164461247637 |
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- name: F1 |
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type: f1 |
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value: 0.7256887214504355 |
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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-non-kd-from-scratch-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: 1.0612 |
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- Accuracy: 0.7247 |
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- F1: 0.7257 |
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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 | 0.6595 | 0.6307 | 0.6853 | |
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| No log | 0.64 | 200 | 0.5676 | 0.7032 | 0.6620 | |
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| No log | 0.96 | 300 | 0.5294 | 0.7358 | 0.7069 | |
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| No log | 1.28 | 400 | 0.5112 | 0.7493 | 0.7084 | |
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| 0.585 | 1.6 | 500 | 0.5554 | 0.7283 | 0.7420 | |
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| 0.585 | 1.92 | 600 | 0.5201 | 0.7349 | 0.6679 | |
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| 0.585 | 2.24 | 700 | 0.5838 | 0.7361 | 0.7415 | |
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| 0.585 | 2.56 | 800 | 0.5693 | 0.7325 | 0.7421 | |
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| 0.585 | 2.88 | 900 | 0.5469 | 0.7517 | 0.7128 | |
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| 0.3954 | 3.19 | 1000 | 0.6406 | 0.7509 | 0.7361 | |
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| 0.3954 | 3.51 | 1100 | 0.5834 | 0.7401 | 0.7158 | |
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| 0.3954 | 3.83 | 1200 | 0.6038 | 0.7538 | 0.7324 | |
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| 0.3954 | 4.15 | 1300 | 0.7079 | 0.7436 | 0.7230 | |
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| 0.3954 | 4.47 | 1400 | 0.7422 | 0.7474 | 0.7182 | |
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| 0.2591 | 4.79 | 1500 | 0.6393 | 0.75 | 0.7307 | |
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| 0.2591 | 5.11 | 1600 | 0.7890 | 0.7481 | 0.7307 | |
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| 0.2591 | 5.43 | 1700 | 1.0788 | 0.7332 | 0.6651 | |
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| 0.2591 | 5.75 | 1800 | 0.8036 | 0.7353 | 0.7157 | |
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| 0.2591 | 6.07 | 1900 | 1.0868 | 0.7474 | 0.7167 | |
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| 0.1729 | 6.39 | 2000 | 1.3150 | 0.7441 | 0.7027 | |
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| 0.1729 | 6.71 | 2100 | 1.0097 | 0.7351 | 0.7268 | |
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| 0.1729 | 7.03 | 2200 | 1.0160 | 0.7389 | 0.7074 | |
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| 0.1729 | 7.35 | 2300 | 1.0612 | 0.7247 | 0.7257 | |
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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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