--- tags: - generated_from_trainer metrics: - f1 - accuracy base_model: clincolnoz/LessSexistBERT model-index: - name: final-lr2e-5-bs16-fp16-2 results: [] language: - en library_name: transformers pipeline_tag: text-classification --- # final-lr2e-5-bs16-fp16-2 This model is a fine-tuned version of [clincolnoz/LessSexistBERT](https://huggingface.co/clincolnoz/LessSexistBERT) on an https://github.com/rewire-online/edos dataset. It achieves the following results on the evaluation set: - Loss: 0.3458 - F1 Macro: 0.8374 - F1 Weighted: 0.8806 - F1: 0.7535 - Accuracy: 0.8808 - Confusion Matrix: [[2794 236] [ 241 729]] - Confusion Matrix Norm: [[0.92211221 0.07788779] [0.24845361 0.75154639]] - Classification Report: precision recall f1-score support 0 0.920593 0.922112 0.921352 3030.00000 1 0.755440 0.751546 0.753488 970.00000 accuracy 0.880750 0.880750 0.880750 0.88075 macro avg 0.838017 0.836829 0.837420 4000.00000 weighted avg 0.880544 0.880750 0.880645 4000.00000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 12345 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.3253 | 1.0 | 1000 | 0.3011 | 0.8256 | 0.8748 | 0.7301 | 0.878 | [[2852 178] [ 310 660]] | [[0.94125413 0.05874587] [0.31958763 0.68041237]] | precision recall f1-score support 0 0.901961 0.941254 0.921189 3030.000 1 0.787589 0.680412 0.730088 970.000 accuracy 0.878000 0.878000 0.878000 0.878 macro avg 0.844775 0.810833 0.825639 4000.000 weighted avg 0.874226 0.878000 0.874847 4000.000 | | 0.2439 | 2.0 | 2000 | 0.3122 | 0.8411 | 0.8848 | 0.7562 | 0.8865 | [[2842 188] [ 266 704]] | [[0.9379538 0.0620462] [0.2742268 0.7257732]] | precision recall f1-score support 0 0.914414 0.937954 0.926035 3030.0000 1 0.789238 0.725773 0.756176 970.0000 accuracy 0.886500 0.886500 0.886500 0.8865 macro avg 0.851826 0.831863 0.841105 4000.0000 weighted avg 0.884059 0.886500 0.884844 4000.0000 | | 0.1962 | 3.0 | 3000 | 0.3458 | 0.8374 | 0.8806 | 0.7535 | 0.8808 | [[2794 236] [ 241 729]] | [[0.92211221 0.07788779] [0.24845361 0.75154639]] | precision recall f1-score support 0 0.920593 0.922112 0.921352 3030.00000 1 0.755440 0.751546 0.753488 970.00000 accuracy 0.880750 0.880750 0.880750 0.88075 macro avg 0.838017 0.836829 0.837420 4000.00000 weighted avg 0.880544 0.880750 0.880645 4000.00000 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2