Downloads · 30 days
0
STL-ethiollm-l-250K-stance
tadiecool29/STL-ethiollm-l-250K-stance
STL-ethiollm-l-250K-stance 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. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
Access
Public
Updated Aug 31, 2026
Parameters
560M
2.3 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.2 GB · 99%
At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026
- SHA
- bbce99ee
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-ethiollm-l-250K-stance
This model is a fine-tuned version of EthioNLP/EthioLLM-l-250K on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9751
- Stance Precision: 0.7326
- Stance Recall: 0.7394
- F1: 0.7353
- Stance Acc: 0.7282
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 | Stance Precision | Stance Recall | F1 | Stance Acc |
|---|---|---|---|---|---|---|---|
| 1.3570 | 1.0 | 377 | 1.3298 | 0.4344 | 0.4406 | 0.4173 | 0.4352 |
| 0.9639 | 2.0 | 754 | 0.8459 | 0.6700 | 0.6759 | 0.6638 | 0.6596 |
| 0.8449 | 3.0 | 1131 | 0.8375 | 0.7194 | 0.6645 | 0.6770 | 0.6683 |
| 0.7069 | 4.0 | 1508 | 0.7768 | 0.6927 | 0.6961 | 0.6879 | 0.6820 |
| 0.6190 | 5.0 | 1885 | 0.7906 | 0.7112 | 0.7333 | 0.7150 | 0.7120 |
| 0.4324 | 6.0 | 2262 | 0.8507 | 0.7173 | 0.7266 | 0.7211 | 0.7132 |
| 0.3733 | 7.0 | 2639 | 0.8745 | 0.7349 | 0.7282 | 0.7294 | 0.7219 |
| 0.3208 | 8.0 | 3016 | 0.9516 | 0.7328 | 0.7394 | 0.7349 | 0.7282 |
| 0.2949 | 9.0 | 3393 | 0.9611 | 0.7375 | 0.7383 | 0.7379 | 0.7294 |
| 0.3042 | 10.0 | 3770 | 0.9751 | 0.7326 | 0.7394 | 0.7353 | 0.7282 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- License
- mit
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026