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deberta-v3-large-finetuned-dagpap22-only
domenicrosati/deberta-v3-large-finetuned-dagpap22-only
deberta-v3-large-finetuned-dagpap22-only is a text classification model from domenicrosati. 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
- deberta-v2
- Access
- Public
- Created
- Jul 29, 2022
- Updated
- Jul 29, 2022
- SHA
- 8f48df28
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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. -->
deberta-v3-large-finetuned-dagpap22-only
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0037
- F1: 0.9995
- Precision: 0.9992
- Recall: 0.9997
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: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.1804 | 1.0 | 669 | 0.0222 | 0.9971 | 0.9975 | 0.9967 |
| 0.0402 | 2.0 | 1338 | 0.0069 | 0.9990 | 0.9992 | 0.9989 |
| 0.0046 | 3.0 | 2007 | 0.0037 | 0.9995 | 0.9992 | 0.9997 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Jul 29, 2022
- Updated
- Jul 29, 2022