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---
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
base_model: bert-base-uncased
model-index:
- name: final-lr2e-5-bs16-fp16-2
results: []
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
<!-- 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 [GroNLP/hateBERT](https://huggingface.co./GroNLP/hateBERT) on an https://github.com/rewire-online/edos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4219
- F1 Macro: 0.8457
- F1 Weighted: 0.8868
- F1: 0.7658
- Accuracy: 0.887
- Confusion Matrix: [[2809 221]
[ 231 739]]
- Confusion Matrix Norm: [[0.92706271 0.07293729]
[0.23814433 0.76185567]]
- Classification Report: precision recall f1-score support
0 0.924013 0.927063 0.925535 3030.000
1 0.769792 0.761856 0.765803 970.000
accuracy 0.887000 0.887000 0.887000 0.887
macro avg 0.846902 0.844459 0.845669 4000.000
weighted avg 0.886614 0.887000 0.886800 4000.000
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.3177 | 1.0 | 1000 | 0.2894 | 0.8323 | 0.8812 | 0.7373 | 0.886 | [[2904 126]
[ 330 640]] | [[0.95841584 0.04158416]
[0.34020619 0.65979381]] | precision recall f1-score support
0 0.897959 0.958416 0.927203 3030.000
1 0.835509 0.659794 0.737327 970.000
accuracy 0.886000 0.886000 0.886000 0.886
macro avg 0.866734 0.809105 0.832265 4000.000
weighted avg 0.882815 0.886000 0.881158 4000.000 |
| 0.2232 | 2.0 | 2000 | 0.3370 | 0.8405 | 0.8830 | 0.7579 | 0.8832 | [[2802 228]
[ 239 731]] | [[0.92475248 0.07524752]
[0.24639175 0.75360825]] | precision recall f1-score support
0 0.921407 0.924752 0.923077 3030.00000
1 0.762252 0.753608 0.757906 970.00000
accuracy 0.883250 0.883250 0.883250 0.88325
macro avg 0.841830 0.839180 0.840491 4000.00000
weighted avg 0.882812 0.883250 0.883023 4000.00000 |
| 0.1534 | 3.0 | 3000 | 0.4219 | 0.8457 | 0.8868 | 0.7658 | 0.887 | [[2809 221]
[ 231 739]] | [[0.92706271 0.07293729]
[0.23814433 0.76185567]] | precision recall f1-score support
0 0.924013 0.927063 0.925535 3030.000
1 0.769792 0.761856 0.765803 970.000
accuracy 0.887000 0.887000 0.887000 0.887
macro avg 0.846902 0.844459 0.845669 4000.000
weighted avg 0.886614 0.887000 0.886800 4000.000 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2 |