metadata
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
base_model: clincolnoz/MoreSexistBERT
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/MoreSexistBERT on an https://github.com/rewire-online/edos dataset. It achieves the following results on the evaluation set:
- Loss: 0.3337
- F1 Macro: 0.8461
- F1 Weighted: 0.8868
- F1: 0.7671
- Accuracy: 0.8868
- Confusion Matrix: [[2801 229] [ 224 746]]
- Confusion Matrix Norm: [[0.92442244 0.07557756] [0.23092784 0.76907216]]
- Classification Report: precision recall f1-score support 0 0.925950 0.924422 0.925186 3030.00000
1 0.765128 0.769072 0.767095 970.00000 accuracy 0.886750 0.886750 0.886750 0.88675 macro avg 0.845539 0.846747 0.846140 4000.00000 weighted avg 0.886951 0.886750 0.886849 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3196 | 1.0 | 1000 | 0.2973 | 0.8423 | 0.8871 | 0.7554 | 0.8902 | [[2883 147] | ||
| [ 292 678]] | [[0.95148515 0.04851485] | |||||||||
| [0.30103093 0.69896907]] | precision recall f1-score support | |||||||||
| 0 0.908031 0.951485 0.929251 3030.00000 | ||||||||||
| 1 0.821818 0.698969 0.755432 970.00000 | ||||||||||
| accuracy 0.890250 0.890250 0.890250 0.89025 | ||||||||||
| macro avg 0.864925 0.825227 0.842341 4000.00000 | ||||||||||
| weighted avg 0.887125 0.890250 0.887100 4000.00000 | ||||||||||
| 0.2447 | 2.0 | 2000 | 0.3277 | 0.8447 | 0.8872 | 0.7623 | 0.8885 | [[2839 191] | ||
| [ 255 715]] | [[0.9369637 0.0630363] | |||||||||
| [0.2628866 0.7371134]] | precision recall f1-score support | |||||||||
| 0 0.917582 0.936964 0.927172 3030.0000 | ||||||||||
| 1 0.789183 0.737113 0.762260 970.0000 | ||||||||||
| accuracy 0.888500 0.888500 0.888500 0.8885 | ||||||||||
| macro avg 0.853383 0.837039 0.844716 4000.0000 | ||||||||||
| weighted avg 0.886446 0.888500 0.887181 4000.0000 | ||||||||||
| 0.2037 | 3.0 | 3000 | 0.3337 | 0.8461 | 0.8868 | 0.7671 | 0.8868 | [[2801 229] | ||
| [ 224 746]] | [[0.92442244 0.07557756] | |||||||||
| [0.23092784 0.76907216]] | precision recall f1-score support | |||||||||
| 0 0.925950 0.924422 0.925186 3030.00000 | ||||||||||
| 1 0.765128 0.769072 0.767095 970.00000 | ||||||||||
| accuracy 0.886750 0.886750 0.886750 0.88675 | ||||||||||
| macro avg 0.845539 0.846747 0.846140 4000.00000 | ||||||||||
| weighted avg 0.886951 0.886750 0.886849 4000.00000 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2