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roberta-large_MBTI_I_MBTI_agg_balanced_25
DrinkIcedT/roberta-large_MBTI_I_MBTI_agg_balanced_25
roberta-large_MBTI_I_MBTI_agg_balanced_25 is a text classification model from DrinkIcedT. 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
- roberta
- Access
- Public
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026
- SHA
- c66af8a2
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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. -->
roberta-large_MBTI_I_MBTI_agg_balanced_25
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.3974
- F1: 0.6820
- Threshold: 0.49
- F1 At 05: 0.6819
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 400
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Threshold | F1 At 05 |
|---|---|---|---|---|---|---|
| 2.7293 | 0.2825 | 200 | 2.6692 | 0.5648 | 0.4400 | 0.5475 |
| 2.5669 | 0.5650 | 400 | 2.4746 | 0.6586 | 0.48 | 0.6494 |
| 2.4470 | 0.8475 | 600 | 2.3747 | 0.6705 | 0.49 | 0.6654 |
| 2.1590 | 1.1299 | 800 | 2.3351 | 0.6807 | 0.3400 | 0.6584 |
| 2.0981 | 1.4124 | 1000 | 2.3197 | 0.6860 | 0.31 | 0.6653 |
| 1.9872 | 1.6949 | 1200 | 2.4732 | 0.6810 | 0.48 | 0.6797 |
| 1.9866 | 1.9774 | 1400 | 2.4751 | 0.6869 | 0.47 | 0.6831 |
| 1.5646 | 2.2599 | 1600 | 2.8745 | 0.6816 | 0.51 | 0.6800 |
| 1.4286 | 2.5424 | 1800 | 2.9926 | 0.6868 | 0.48 | 0.6842 |
| 1.5118 | 2.8249 | 2000 | 2.8057 | 0.6888 | 0.5 | 0.6888 |
| 1.0639 | 3.1073 | 2200 | 3.5753 | 0.6860 | 0.39 | 0.6839 |
| 1.0155 | 3.3898 | 2400 | 3.6367 | 0.6826 | 0.4200 | 0.6786 |
| 1.0504 | 3.6723 | 2600 | 3.3507 | 0.6872 | 0.49 | 0.6868 |
| 1.0401 | 3.9548 | 2800 | 3.6559 | 0.6798 | 0.51 | 0.6790 |
| 0.8185 | 4.2373 | 3000 | 4.1669 | 0.6782 | 0.35 | 0.6751 |
| 0.7157 | 4.5198 | 3200 | 4.5369 | 0.6810 | 0.4500 | 0.6791 |
| 0.8082 | 4.8023 | 3400 | 4.3974 | 0.6820 | 0.49 | 0.6819 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.7.1
- Datasets 4.8.5
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026