Downloads · 30 days
8
15% of all-time downloads
Twitter_concatenatewithPrompttrainval_1e-fold4
aomar85/Twitter_concatenatewithPrompttrainval_1e-fold4
Twitter_concatenatewithPrompttrainval_1e-fold4 is a text classification model from aomar85. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
54
Public
Parameters
135M
4.3 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors541 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Feb 13, 2026
- Updated
- Feb 13, 2026
- SHA
- 879376e5
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. -->
Twitter_concatenatewithPrompttrainval_1e-fold4
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02-twitter on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4313
- Accuracy: 0.8512
- Macro F1: 0.8516
- Weighted F1: 0.8515
- F1 Pro: 0.8772
- F1 Against: 0.8293
- F1 Neutral: 0.8485
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral |
|---|---|---|---|---|---|---|---|---|---|
| 1.0264 | 1.1628 | 50 | 0.8356 | 0.7143 | 0.7167 | 0.7150 | 0.7021 | 0.6861 | 0.7619 |
| 0.7735 | 2.3256 | 100 | 0.6017 | 0.7857 | 0.7827 | 0.7839 | 0.8276 | 0.7812 | 0.7391 |
| 0.5909 | 3.4884 | 150 | 0.4841 | 0.8333 | 0.8342 | 0.8339 | 0.8596 | 0.8095 | 0.8333 |
| 0.4880 | 4.6512 | 200 | 0.4384 | 0.8452 | 0.8458 | 0.8456 | 0.8649 | 0.8264 | 0.8462 |
| 0.3898 | 5.8140 | 250 | 0.4315 | 0.8512 | 0.8516 | 0.8515 | 0.8772 | 0.8293 | 0.8485 |
| 0.3502 | 6.9767 | 300 | 0.4303 | 0.8452 | 0.8446 | 0.8452 | 0.8673 | 0.8430 | 0.8235 |
| 0.2954 | 8.1395 | 350 | 0.4261 | 0.8452 | 0.8444 | 0.8450 | 0.8772 | 0.8361 | 0.82 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Feb 13, 2026
- Updated
- Feb 13, 2026