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HateBERT-edos
clincolnoz/HateBERT-edos
HateBERT-edos is a text classification model from clincolnoz. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Oct 6, 2023
- Updated
- Dec 21, 2023
- SHA
- c4828721
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Base models
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- en
- Created
- Oct 6, 2023
- Updated
- Dec 21, 2023