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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: clincolnoz/MoreSexistBERT |
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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 [clincolnoz/MoreSexistBERT](https://huggingface.co./clincolnoz/MoreSexistBERT) 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: 0.3337 |
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- F1 Macro: 0.8461 |
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- F1 Weighted: 0.8868 |
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- F1: 0.7671 |
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- Accuracy: 0.8868 |
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- Confusion Matrix: [[2801 229] |
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[ 224 746]] |
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- Confusion Matrix Norm: [[0.92442244 0.07557756] |
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[0.23092784 0.76907216]] |
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- Classification Report: precision recall f1-score support |
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0 0.925950 0.924422 0.925186 3030.00000 |
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1 0.765128 0.769072 0.767095 970.00000 |
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accuracy 0.886750 0.886750 0.886750 0.88675 |
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macro avg 0.845539 0.846747 0.846140 4000.00000 |
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weighted avg 0.886951 0.886750 0.886849 4000.00000 |
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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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| 0.3196 | 1.0 | 1000 | 0.2973 | 0.8423 | 0.8871 | 0.7554 | 0.8902 | [[2883 147] |
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[ 292 678]] | [[0.95148515 0.04851485] |
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[0.30103093 0.69896907]] | precision recall f1-score support |
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0 0.908031 0.951485 0.929251 3030.00000 |
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1 0.821818 0.698969 0.755432 970.00000 |
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accuracy 0.890250 0.890250 0.890250 0.89025 |
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macro avg 0.864925 0.825227 0.842341 4000.00000 |
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weighted avg 0.887125 0.890250 0.887100 4000.00000 | |
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| 0.2447 | 2.0 | 2000 | 0.3277 | 0.8447 | 0.8872 | 0.7623 | 0.8885 | [[2839 191] |
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[ 255 715]] | [[0.9369637 0.0630363] |
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[0.2628866 0.7371134]] | precision recall f1-score support |
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0 0.917582 0.936964 0.927172 3030.0000 |
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1 0.789183 0.737113 0.762260 970.0000 |
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accuracy 0.888500 0.888500 0.888500 0.8885 |
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macro avg 0.853383 0.837039 0.844716 4000.0000 |
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weighted avg 0.886446 0.888500 0.887181 4000.0000 | |
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| 0.2037 | 3.0 | 3000 | 0.3337 | 0.8461 | 0.8868 | 0.7671 | 0.8868 | [[2801 229] |
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[ 224 746]] | [[0.92442244 0.07557756] |
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[0.23092784 0.76907216]] | precision recall f1-score support |
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0 0.925950 0.924422 0.925186 3030.00000 |
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1 0.765128 0.769072 0.767095 970.00000 |
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accuracy 0.886750 0.886750 0.886750 0.88675 |
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macro avg 0.845539 0.846747 0.846140 4000.00000 |
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weighted avg 0.886951 0.886750 0.886849 4000.00000 | |
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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 |
