Downloads · 30 days
10
21% of all-time downloads
mdeberta-domain_EN_fold5
g-assismoraes/mdeberta-domain_EN_fold5
mdeberta-domain_EN_fold5 is a text classification model from g-assismoraes. 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. --
All-time downloads
47
Public
Parameters
184M
3 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors738 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Oct 28, 2024
- Updated
- Oct 28, 2024
- SHA
- 78a97f55
Try a prompt
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. -->
mdeberta-domain_EN_fold5
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: 0.6015
- Accuracy: 0.7724
- Precision: 0.6879
- Recall: 0.6691
- F1: 0.6745
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: 32
- eval_batch_size: 32
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.0479 | 1.0 | 19 | 0.8035 | 0.5931 | 0.8644 | 0.3333 | 0.2482 |
| 0.7913 | 2.0 | 38 | 0.6564 | 0.7655 | 0.8367 | 0.6111 | 0.5514 |
| 0.6632 | 3.0 | 57 | 0.6348 | 0.7793 | 0.8420 | 0.6333 | 0.5642 |
| 0.5637 | 4.0 | 76 | 0.6365 | 0.7862 | 0.8322 | 0.6673 | 0.6193 |
| 0.4626 | 5.0 | 95 | 0.6057 | 0.7862 | 0.6848 | 0.6456 | 0.6177 |
| 0.3666 | 6.0 | 114 | 0.5677 | 0.7724 | 0.6543 | 0.6607 | 0.6476 |
| 0.3581 | 7.0 | 133 | 0.5483 | 0.8138 | 0.7751 | 0.6980 | 0.6768 |
| 0.3113 | 8.0 | 152 | 0.5897 | 0.8 | 0.7199 | 0.6838 | 0.6810 |
| 0.2037 | 9.0 | 171 | 0.5560 | 0.8 | 0.7231 | 0.6983 | 0.6895 |
| 0.2002 | 10.0 | 190 | 0.6015 | 0.7724 | 0.6879 | 0.6691 | 0.6745 |
Framework versions
- Transformers 4.46.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.20.1
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Oct 28, 2024
- Updated
- Oct 28, 2024