Downloads · 30 days
0
MTL-afriberta-large-stance-sentiment
tadiecool29/MTL-afriberta-large-stance-sentiment
MTL-afriberta-large-stance-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. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
Access
Public
Updated Aug 27, 2026
Parameters
126M
503 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors503 MB · 99%
At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Aug 27, 2026
- Updated
- Aug 27, 2026
- SHA
- ae4b8dba
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. -->
MTL-afriberta-large-stance-sentiment
This model is a fine-tuned version of castorini/afriberta_large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4959
- Stance F1: 0.7543
- Sentiment F1: 0.6939
- F1: 0.7241
- Stance Acc: 0.7469
- Sentiment Acc: 0.6970
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: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Stance F1 | Sentiment F1 | F1 | Stance Acc | Sentiment Acc |
|---|---|---|---|---|---|---|---|---|
| 1.5877 | 1.0 | 402 | 1.5150 | 0.6935 | 0.6602 | 0.6768 | 0.6908 | 0.6721 |
| 1.3488 | 2.0 | 804 | 1.3662 | 0.7478 | 0.6774 | 0.7126 | 0.7394 | 0.6833 |
| 1.0682 | 3.0 | 1206 | 1.3656 | 0.7477 | 0.7218 | 0.7348 | 0.7406 | 0.7269 |
| 0.8300 | 4.0 | 1608 | 1.4897 | 0.7483 | 0.6743 | 0.7113 | 0.7394 | 0.6783 |
| 0.6654 | 5.0 | 2010 | 1.4969 | 0.7493 | 0.6887 | 0.7190 | 0.7406 | 0.6920 |
| 0.6207 | 6.0 | 2412 | 1.4959 | 0.7543 | 0.6939 | 0.7241 | 0.7469 | 0.6970 |
Framework versions
- Transformers 5.15.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- License
- mit
- Created
- Aug 27, 2026
- Updated
- Aug 27, 2026