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finetune_model
attardan/finetune_model
finetune_model is a token classification model from attardan. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as bigscience-openrail-m.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- bigscience-openrail-m
- Model type
- roberta
- Access
- Public
- Created
- Apr 7, 2025
- Updated
- Apr 8, 2025
- SHA
- 0a8967c6
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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. -->
finetune_model
This model is a fine-tuned version of ehsanaghaei/SecureBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0492
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8286
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: 8
- eval_batch_size: 8
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 8 | 2.7037 | 0.0 | 0.0 | 0.0 | 0.8286 |
| No log | 2.0 | 16 | 2.0570 | 0.0 | 0.0 | 0.0 | 0.8286 |
| No log | 3.0 | 24 | 1.0492 | 0.0 | 0.0 | 0.0 | 0.8286 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- bigscience-openrail-m
- Created
- Apr 7, 2025
- Updated
- Apr 8, 2025