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our_baseline_with_hs_and_sentiment
iTroned/our_baseline_with_hs_and_sentiment
our_baseline_with_hs_and_sentiment 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 2, 2025
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- May 2, 2025
- Updated
- May 2, 2025
- SHA
- aed25194
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. -->
our_baseline_with_hs_and_sentiment
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: 0.5505
- Accuracy Offensive: 0.8244
- F1 Macro Offensive: 0.7964
- F1 Weighted Offensive: 0.8298
- F1 Macro Total: 0.7964
- F1 Weighted Total: 0.8298
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Offensive | F1 Macro Offensive | F1 Weighted Offensive | F1 Macro Total | F1 Weighted Total |
|---|---|---|---|---|---|---|---|---|
| 0.5667 | 1.0 | 3310 | 0.4502 | 0.7209 | 0.4189 | 0.6040 | 0.4189 | 0.6040 |
| 0.6199 | 2.0 | 6620 | 0.4474 | 0.8035 | 0.7769 | 0.8109 | 0.7769 | 0.8109 |
| 0.6099 | 3.0 | 9930 | 0.6295 | 0.8442 | 0.7941 | 0.8390 | 0.7941 | 0.8390 |
| 0.5149 | 4.0 | 13240 | 0.5505 | 0.8244 | 0.7964 | 0.8298 | 0.7964 | 0.8298 |
| 0.5546 | 5.0 | 16550 | 0.8058 | 0.8221 | 0.7889 | 0.8259 | 0.7889 | 0.8259 |
| 0.4912 | 6.0 | 19860 | 0.9587 | 0.8337 | 0.7957 | 0.8346 | 0.7957 | 0.8346 |
| 0.4298 | 7.0 | 23170 | 1.0629 | 0.8267 | 0.7861 | 0.8273 | 0.7861 | 0.8273 |
| 0.4101 | 8.0 | 26480 | 1.1793 | 0.8279 | 0.7834 | 0.8268 | 0.7834 | 0.8268 |
| 0.342 | 9.0 | 29790 | 1.2443 | 0.8070 | 0.7732 | 0.8119 | 0.7732 | 0.8119 |
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 2, 2025
- Updated
- May 2, 2025