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finetuned_model_emotion_detection_ej2
carmengoar/finetuned_model_emotion_detection_ej2
finetuned_model_emotion_detection_ej2 is a text classification model from carmengoar. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- modernbert
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Mar 28, 2026
- SHA
- d0a104dc
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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. -->
finetuned_model_emotion_detection_ej2
This model is a fine-tuned version of jhu-clsp/mmBERT-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4358
- F1 Macro: 0.4673
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: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro |
|---|---|---|---|---|
| No log | 1.0 | 223 | 0.2854 | 0.3579 |
| No log | 2.0 | 446 | 0.2680 | 0.4072 |
| 0.2880 | 3.0 | 669 | 0.3198 | 0.4687 |
| 0.2880 | 4.0 | 892 | 0.3973 | 0.4930 |
| 0.0699 | 5.0 | 1115 | 0.4358 | 0.4673 |
Framework versions
- Transformers 5.4.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- modernbert
- License
- mit
- Created
- Mar 28, 2026
- Updated
- Mar 28, 2026