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---
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
base_model: clincolnoz/LessSexistBERT
model-index:
- name: final-lr2e-5-bs16-fp16-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# final-lr2e-5-bs16-fp16-2
This model is a fine-tuned version of [clincolnoz/LessSexistBERT](https://huggingface.co./clincolnoz/LessSexistBERT) on an unknown 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
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