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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- hate_speech18 |
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widget: |
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- text: "'ok, so do we need to kill them too or are the slavs okay ? for some reason whenever i hear the word slav , the word slobber comes to mind and i picture a slobbering half breed creature like the humpback of notre dame or Igor haha" |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-small-hate-speech |
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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_speech18 |
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type: hate_speech18 |
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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: 0.916058394160584 |
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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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# DeBERTa v3 small fine-tuned on hate_speech18 dataset for Hate Speech Detection |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the hate_speech18 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2922 |
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- Accuracy: 0.9161 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.4147 | 1.0 | 650 | 0.3910 | 0.8832 | |
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| 0.2975 | 2.0 | 1300 | 0.2922 | 0.9161 | |
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| 0.2575 | 3.0 | 1950 | 0.3555 | 0.9051 | |
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| 0.1553 | 4.0 | 2600 | 0.4263 | 0.9124 | |
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| 0.1267 | 5.0 | 3250 | 0.4238 | 0.9161 | |
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### Framework versions |
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- Transformers 4.12.5 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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