Downloads · 30 days
10
11% of all-time downloads
xlm-roberta-base-afr-noaug
Kuongan/xlm-roberta-base-afr-noaug
xlm-roberta-base-afr-noaug is a text classification model from Kuongan. 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
92
Public
Parameters
278M
13.4 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.1 GB · 98%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Feb 25, 2025
- Updated
- Feb 25, 2025
- SHA
- c7ff338e
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. -->
xlm-roberta-base-afr-noaug
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3379
- F1: 0.3135
- Roc Auc: 0.6162
- Accuracy: 0.5102
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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.7088 | 1.0 | 39 | 0.4912 | 0.0205 | 0.4987 | 0.4082 |
| 0.4563 | 2.0 | 78 | 0.3909 | 0.0 | 0.5 | 0.4286 |
| 0.3455 | 3.0 | 117 | 0.4042 | 0.0 | 0.5 | 0.4286 |
| 0.3167 | 4.0 | 156 | 0.3929 | 0.0 | 0.5 | 0.4286 |
| 0.3077 | 5.0 | 195 | 0.3756 | 0.1222 | 0.5415 | 0.5204 |
| 0.3003 | 6.0 | 234 | 0.3405 | 0.1278 | 0.5454 | 0.4592 |
| 0.2693 | 7.0 | 273 | 0.3168 | 0.2120 | 0.5784 | 0.5306 |
| 0.2318 | 8.0 | 312 | 0.3347 | 0.3123 | 0.6135 | 0.5 |
| 0.2243 | 9.0 | 351 | 0.3117 | 0.2356 | 0.5783 | 0.5204 |
| 0.1849 | 10.0 | 390 | 0.2912 | 0.3076 | 0.6121 | 0.5306 |
| 0.1812 | 11.0 | 429 | 0.3092 | 0.2468 | 0.5825 | 0.5102 |
| 0.15 | 12.0 | 468 | 0.3379 | 0.3135 | 0.6162 | 0.5102 |
| 0.1563 | 13.0 | 507 | 0.3258 | 0.3015 | 0.6101 | 0.5714 |
| 0.1442 | 14.0 | 546 | 0.3449 | 0.2771 | 0.5958 | 0.5 |
| 0.1339 | 15.0 | 585 | 0.3430 | 0.3015 | 0.6089 | 0.5204 |
| 0.1176 | 16.0 | 624 | 0.3529 | 0.2406 | 0.5790 | 0.5204 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Feb 25, 2025
- Updated
- Feb 25, 2025