Downloads · 30 days
4
18% of all-time downloads
DeBERTaV3_model_V3_multilabel
sergiomvazq/DeBERTaV3_model_V3_multilabel
DeBERTaV3_model_V3_multilabel is a text classification model from sergiomvazq. 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
22
Public
Parameters
142M
570 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors568 MB · 96%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Jun 10, 2024
- Updated
- Jun 10, 2024
- SHA
- 8f5ca510
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. -->
DeBERTaV3_model_V3_multilabel
This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0117
- Accuracy: 0.9978
- F1: 0.9985
- Precision: 0.9970
- Recall: 1.0
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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 90 | 0.0239 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| No log | 2.0 | 180 | 0.0147 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| No log | 3.0 | 270 | 0.0131 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| No log | 4.0 | 360 | 0.0124 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| No log | 5.0 | 450 | 0.0121 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| 0.0608 | 6.0 | 540 | 0.0119 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| 0.0608 | 7.0 | 630 | 0.0117 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| 0.0608 | 8.0 | 720 | 0.0120 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| 0.0608 | 9.0 | 810 | 0.0127 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| 0.0608 | 10.0 | 900 | 0.0132 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
| 0.0608 | 11.0 | 990 | 0.0139 | 0.9970 | 0.998 | 0.9970 | 0.9990 |
| 0.0121 | 12.0 | 1080 | 0.0143 | 0.9970 | 0.998 | 0.9970 | 0.9990 |
| 0.0121 | 13.0 | 1170 | 0.0143 | 0.9970 | 0.998 | 0.9970 | 0.9990 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Jun 10, 2024
- Updated
- Jun 10, 2024