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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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- tweets_hate_speech_detection |
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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: FirstTry |
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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: tweets_hate_speech_detection |
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type: tweets_hate_speech_detection |
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config: default |
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split: train |
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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: 0.9821679962458939 |
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- name: F1 |
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type: f1 |
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value: 0.8692660550458716 |
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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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# FirstTry |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweets_hate_speech_detection dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0847 |
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- Accuracy: 0.9822 |
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- F1: 0.8693 |
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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: 15 |
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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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| 0.1159 | 1.0 | 1599 | 0.1019 | 0.9759 | 0.8270 | |
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| 0.0727 | 2.0 | 3198 | 0.0965 | 0.9795 | 0.8424 | |
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| 0.044 | 3.0 | 4797 | 0.0847 | 0.9822 | 0.8693 | |
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| 0.0301 | 4.0 | 6396 | 0.1121 | 0.9811 | 0.8660 | |
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| 0.0206 | 5.0 | 7995 | 0.1718 | 0.9700 | 0.8110 | |
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| 0.0176 | 6.0 | 9594 | 0.1453 | 0.9811 | 0.8591 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.3 |
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