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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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base_model: distilbert-base-cased |
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model-index: |
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- name: distilbert-base-cased-hate-speech |
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results: [] |
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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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# distilbert-base-cased-hate-speech |
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**Training:** The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on [hate speech](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6837 |
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- Mae: 1.9686 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mae | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.6857 | 1.0 | 3389 | 0.6471 | 1.9725 | |
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| 0.3645 | 2.0 | 6778 | 0.4359 | 1.9725 | |
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| 0.2266 | 3.0 | 10167 | 0.3664 | 1.9725 | |
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| 0.1476 | 4.0 | 13556 | 0.3253 | 1.9725 | |
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| 0.0992 | 5.0 | 16945 | 0.3047 | 1.9725 | |
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| 0.0737 | 6.0 | 20334 | 0.2869 | 1.9725 | |
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| 0.0537 | 7.0 | 23723 | 0.2709 | 1.9725 | |
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| 0.0458 | 8.0 | 27112 | 0.2667 | 1.9725 | |
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| 0.0313 | 9.0 | 30501 | 0.2589 | 1.9725 | |
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| 0.027 | 10.0 | 33890 | 0.2540 | 1.9725 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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