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--- |
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license: apache-2.0 |
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base_model: distilbert-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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- recall |
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- precision |
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- f1 |
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model-index: |
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- name: hatespeech_distilbert |
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results: [] |
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widget: |
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- text: "Democrats using African-Americans again." |
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example_title: "Non-Hate Speech Example" |
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- text: "Holy fuck this girl's trash, what a cunt." |
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example_title: "Hate Speech Example" |
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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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# hatespeech_distilbert |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9977 |
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- Accuracy: 0.7737 |
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- Recall: 0.8118 |
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- Precision: 0.7526 |
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- F1: 0.7811 |
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And the following results on the test set: |
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- Loss: 1.0640 |
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- Accuracy: 0.7544 |
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- Recall: 0.7930 |
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- Precision: 0.7406 |
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- F1: 0.7659 |
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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: 8e-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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.4863 | 0.9935 | 77 | 0.4678 | 0.7701 | 0.7421 | 0.7841 | 0.7625 | |
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| 0.3935 | 2.0 | 155 | 0.4595 | 0.7834 | 0.7340 | 0.8124 | 0.7712 | |
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| 0.2792 | 2.9935 | 232 | 0.5285 | 0.7850 | 0.7291 | 0.8188 | 0.7713 | |
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| 0.1408 | 4.0 | 310 | 0.7130 | 0.7785 | 0.7940 | 0.7684 | 0.7810 | |
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| 0.0945 | 4.9935 | 387 | 0.8230 | 0.7806 | 0.7551 | 0.7937 | 0.7739 | |
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| 0.0541 | 6.0 | 465 | 0.9977 | 0.7737 | 0.8118 | 0.7526 | 0.7811 | |
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| 0.0331 | 6.9935 | 542 | 1.1107 | 0.7753 | 0.7859 | 0.7678 | 0.7768 | |
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| 0.0151 | 8.0 | 620 | 1.1703 | 0.7789 | 0.7543 | 0.7915 | 0.7724 | |
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| 0.0106 | 8.9935 | 697 | 1.2741 | 0.7785 | 0.7616 | 0.7864 | 0.7738 | |
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| 0.0051 | 9.9355 | 770 | 1.2964 | 0.7753 | 0.7851 | 0.7683 | 0.7766 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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