Downloads · 30 days
0
STL-rasyosef-bert-medium-amharic-sentiment
tadiecool29/STL-rasyosef-bert-medium-amharic-sentiment
STL-rasyosef-bert-medium-amharic-sentiment is a machine learning model from tadiecool29. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
Access
Public
Updated Sep 1, 2026
Parameters
40.4M
162 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors162 MB · 100%
At a glance
- Library
- transformers
- Access
- Public
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026
- SHA
- bbf90a21
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. -->
STL-rasyosef-bert-medium-amharic-sentiment
This model is a fine-tuned version of rasyosef/bert-medium-amharic on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7579
- Sentiment Precision: 0.6921
- Sentiment Recall: 0.6954
- F1: 0.6918
- Sentiment Acc: 0.6995
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: 32
- 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: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Sentiment Precision | Sentiment Recall | F1 | Sentiment Acc |
|---|---|---|---|---|---|---|---|
| 0.8272 | 1.0 | 377 | 0.7678 | 0.6527 | 0.6522 | 0.6523 | 0.6571 |
| 0.6971 | 2.0 | 754 | 0.7301 | 0.6812 | 0.6759 | 0.6668 | 0.6783 |
| 0.6232 | 3.0 | 1131 | 0.7282 | 0.6891 | 0.6876 | 0.6782 | 0.6908 |
| 0.5508 | 4.0 | 1508 | 0.7142 | 0.6995 | 0.6990 | 0.6981 | 0.7007 |
| 0.5353 | 5.0 | 1885 | 0.7224 | 0.6848 | 0.6885 | 0.6844 | 0.6933 |
| 0.4981 | 6.0 | 2262 | 0.7598 | 0.6897 | 0.6913 | 0.6860 | 0.6933 |
| 0.4942 | 7.0 | 2639 | 0.7579 | 0.6921 | 0.6954 | 0.6918 | 0.6995 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1
- Library
- transformers
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026