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Model3_Marabertv2_T1_WOS
SMG0/Model3_Marabertv2_T1_WOS
Model3_Marabertv2_T1_WOS is a text classification model from SMG0. 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
- Aug 15, 2023
- Updated
- Aug 15, 2023
- SHA
- cacebed6
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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. -->
Model3_Marabertv2_T1_WOS
This model is a fine-tuned version of UBC-NLP/MARBERTv2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2308
- F1: 0.8430
- F1 Macro: 0.7804
- Roc Auc: 0.9048
- Accuracy: 0.8142
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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F1 Macro | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2194 | 1.0 | 507 | 0.1556 | 0.8330 | 0.7507 | 0.8909 | 0.7947 |
| 0.1166 | 2.0 | 1014 | 0.1850 | 0.8269 | 0.7439 | 0.8920 | 0.8010 |
| 0.0747 | 3.0 | 1521 | 0.1915 | 0.8368 | 0.7724 | 0.8992 | 0.8115 |
| 0.0445 | 4.0 | 2028 | 0.2034 | 0.8398 | 0.7695 | 0.9014 | 0.8149 |
| 0.0301 | 5.0 | 2535 | 0.2308 | 0.8430 | 0.7804 | 0.9048 | 0.8142 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Aug 15, 2023
- Updated
- Aug 15, 2023