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self_iterative_v1_offensive_iteration_1
iTroned/self_iterative_v1_offensive_iteration_1
self_iterative_v1_offensive_iteration_1 is a machine learning model from iTroned. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Public
Updated May 12, 2025
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- May 10, 2025
- Updated
- May 12, 2025
- SHA
- 55c36b53
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. -->
<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/> <img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/> <img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/> <img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>
self_iterative_v1_offensive_iteration_1
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9603
- Accuracy Offensive: 0.8284
- F1 Macro Offensive: 0.8119
- F1 Weighted Offensive: 0.8276
- F1 Macro Total: 0.8119
- F1 Weighted Total: 0.8276
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: 6e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Offensive | F1 Macro Offensive | F1 Weighted Offensive | F1 Macro Total | F1 Weighted Total |
|---|---|---|---|---|---|---|---|---|
| 0.3479 | 1.0 | 14030 | 0.9603 | 0.8284 | 0.8119 | 0.8276 | 0.8119 | 0.8276 |
| 0.2641 | 2.0 | 28060 | 1.2214 | 0.8284 | 0.8107 | 0.8271 | 0.8107 | 0.8271 |
| 0.1825 | 3.0 | 42090 | 1.1738 | 0.8213 | 0.8054 | 0.8211 | 0.8054 | 0.8211 |
| 0.112 | 4.0 | 56120 | 1.4585 | 0.8110 | 0.7995 | 0.8130 | 0.7995 | 0.8130 |
| 0.0885 | 5.0 | 70150 | 1.9186 | 0.8173 | 0.8032 | 0.8181 | 0.8032 | 0.8181 |
| 0.0647 | 6.0 | 84180 | 1.8926 | 0.8173 | 0.8011 | 0.8171 | 0.8011 | 0.8171 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
- Library
- transformers
- License
- apache-2.0
- Created
- May 10, 2025
- Updated
- May 12, 2025