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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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base_model: bert-base-uncased |
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model-index: |
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- name: final-lr2e-5-bs16-fp16-2 |
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results: [] |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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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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# final-lr2e-5-bs16-fp16-2 |
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This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co./GroNLP/hateBERT) on an https://github.com/rewire-online/edos dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4219 |
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- F1 Macro: 0.8457 |
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- F1 Weighted: 0.8868 |
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- F1: 0.7658 |
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- Accuracy: 0.887 |
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- Confusion Matrix: [[2809 221] |
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[ 231 739]] |
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- Confusion Matrix Norm: [[0.92706271 0.07293729] |
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[0.23814433 0.76185567]] |
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- Classification Report: precision recall f1-score support |
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0 0.924013 0.927063 0.925535 3030.000 |
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1 0.769792 0.761856 0.765803 970.000 |
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accuracy 0.887000 0.887000 0.887000 0.887 |
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macro avg 0.846902 0.844459 0.845669 4000.000 |
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weighted avg 0.886614 0.887000 0.886800 4000.000 |
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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: 12345 |
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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: 3.0 |
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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 | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.3177 | 1.0 | 1000 | 0.2894 | 0.8323 | 0.8812 | 0.7373 | 0.886 | [[2904 126] |
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[ 330 640]] | [[0.95841584 0.04158416] |
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[0.34020619 0.65979381]] | precision recall f1-score support |
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0 0.897959 0.958416 0.927203 3030.000 |
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1 0.835509 0.659794 0.737327 970.000 |
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accuracy 0.886000 0.886000 0.886000 0.886 |
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macro avg 0.866734 0.809105 0.832265 4000.000 |
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weighted avg 0.882815 0.886000 0.881158 4000.000 | |
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| 0.2232 | 2.0 | 2000 | 0.3370 | 0.8405 | 0.8830 | 0.7579 | 0.8832 | [[2802 228] |
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[ 239 731]] | [[0.92475248 0.07524752] |
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[0.24639175 0.75360825]] | precision recall f1-score support |
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0 0.921407 0.924752 0.923077 3030.00000 |
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1 0.762252 0.753608 0.757906 970.00000 |
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accuracy 0.883250 0.883250 0.883250 0.88325 |
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macro avg 0.841830 0.839180 0.840491 4000.00000 |
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weighted avg 0.882812 0.883250 0.883023 4000.00000 | |
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| 0.1534 | 3.0 | 3000 | 0.4219 | 0.8457 | 0.8868 | 0.7658 | 0.887 | [[2809 221] |
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[ 231 739]] | [[0.92706271 0.07293729] |
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[0.23814433 0.76185567]] | precision recall f1-score support |
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0 0.924013 0.927063 0.925535 3030.000 |
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1 0.769792 0.761856 0.765803 970.000 |
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accuracy 0.887000 0.887000 0.887000 0.887 |
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macro avg 0.846902 0.844459 0.845669 4000.000 |
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weighted avg 0.886614 0.887000 0.886800 4000.000 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
