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--- |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: RobCaamano/toxicity_weighted |
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results: [] |
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--- |
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# RobCaamano/toxicity_weighted |
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This model was trained from scratch on Distilbert Base Uncased. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0240 |
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- Train Precision: 0.9522 |
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- Train Recall: 0.9190 |
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- Epoch: 11 |
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## Model description |
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Finetuned model that uses Distilbert Base Uncased to detect types of toxic text. These include: "toxic", "severe_toxic", "obscene", "threat", "insult" & "identity_hate". |
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## Intended uses & limitations |
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Intended to classify text into different types of toxicity when it is detected. Trained off a small dataset with underrepresented categories. |
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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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- optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Train Precision | Train Recall | Epoch | |
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|:----------:|:---------------:|:------------:|:-----:| |
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| 0.0440 | 0.9059 | 0.8294 | 7 | |
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| 0.0380 | 0.9223 | 0.8632 | 8 | |
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| 0.0314 | 0.9335 | 0.8838 | 9 | |
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| 0.0282 | 0.9437 | 0.9075 | 10 | |
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| 0.0240 | 0.9522 | 0.9190 | 11 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- TensorFlow 2.10.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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