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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/LessSexistBERT |
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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/LessSexistBERT](https://huggingface.co/clincolnoz/LessSexistBERT) 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: 0.3458 |
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- F1 Macro: 0.8374 |
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- F1 Weighted: 0.8806 |
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- F1: 0.7535 |
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- Accuracy: 0.8808 |
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- Confusion Matrix: [[2794 236] |
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[ 241 729]] |
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- Confusion Matrix Norm: [[0.92211221 0.07788779] |
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[0.24845361 0.75154639]] |
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- Classification Report: precision recall f1-score support |
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0 0.920593 0.922112 0.921352 3030.00000 |
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1 0.755440 0.751546 0.753488 970.00000 |
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accuracy 0.880750 0.880750 0.880750 0.88075 |
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macro avg 0.838017 0.836829 0.837420 4000.00000 |
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weighted avg 0.880544 0.880750 0.880645 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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| 0.3253 | 1.0 | 1000 | 0.3011 | 0.8256 | 0.8748 | 0.7301 | 0.878 | [[2852 178] |
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[ 310 660]] | [[0.94125413 0.05874587] |
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[0.31958763 0.68041237]] | precision recall f1-score support |
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0 0.901961 0.941254 0.921189 3030.000 |
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1 0.787589 0.680412 0.730088 970.000 |
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accuracy 0.878000 0.878000 0.878000 0.878 |
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macro avg 0.844775 0.810833 0.825639 4000.000 |
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weighted avg 0.874226 0.878000 0.874847 4000.000 | |
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| 0.2439 | 2.0 | 2000 | 0.3122 | 0.8411 | 0.8848 | 0.7562 | 0.8865 | [[2842 188] |
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[ 266 704]] | [[0.9379538 0.0620462] |
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[0.2742268 0.7257732]] | precision recall f1-score support |
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0 0.914414 0.937954 0.926035 3030.0000 |
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1 0.789238 0.725773 0.756176 970.0000 |
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accuracy 0.886500 0.886500 0.886500 0.8865 |
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macro avg 0.851826 0.831863 0.841105 4000.0000 |
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weighted avg 0.884059 0.886500 0.884844 4000.0000 | |
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| 0.1962 | 3.0 | 3000 | 0.3458 | 0.8374 | 0.8806 | 0.7535 | 0.8808 | [[2794 236] |
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[ 241 729]] | [[0.92211221 0.07788779] |
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[0.24845361 0.75154639]] | precision recall f1-score support |
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0 0.920593 0.922112 0.921352 3030.00000 |
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1 0.755440 0.751546 0.753488 970.00000 |
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accuracy 0.880750 0.880750 0.880750 0.88075 |
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macro avg 0.838017 0.836829 0.837420 4000.00000 |
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weighted avg 0.880544 0.880750 0.880645 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 |
