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bert_essay
eovdedn/bert_essay
bert_essay is a text classification model from eovdedn. 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
- May 2, 2024
- Updated
- May 6, 2024
- SHA
- e592901e
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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. -->
bert_essay
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3763
- Mse: 0.3763
- Mae: 0.4747
- R2: 0.6434
- Accuracy: 0.2684
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.6577 | 1.0 | 866 | 0.5250 | 0.5250 | 0.5685 | 0.5025 | 0.2674 |
| 0.3355 | 2.0 | 1732 | 0.4174 | 0.4174 | 0.5027 | 0.6045 | 0.2615 |
| 0.2592 | 3.0 | 2598 | 0.3763 | 0.3763 | 0.4747 | 0.6434 | 0.2684 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- May 2, 2024
- Updated
- May 6, 2024