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MTL-roberta-base-amharic-finetuned
tadiecool29/MTL-roberta-base-amharic-finetuned
MTL-roberta-base-amharic-finetuned 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. --
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Public
Updated Aug 29, 2026
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At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Aug 29, 2026
- Updated
- Aug 29, 2026
- SHA
- 5a56bbe6
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-roberta-base-amharic-finetuned
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6333
- Stance F1: 0.7179
- Sentiment F1: 0.7022
- F1: 0.7100
- Stance Acc: 0.7107
- Sentiment Acc: 0.7070
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 F1 | Sentiment F1 | F1 | Stance Acc | Sentiment Acc |
|---|---|---|---|---|---|---|---|---|
| 1.9600 | 1.0 | 377 | 1.7254 | 0.6136 | 0.6368 | 0.6252 | 0.6085 | 0.6409 |
| 1.6271 | 2.0 | 754 | 1.4612 | 0.7060 | 0.6891 | 0.6975 | 0.6983 | 0.6945 |
| 1.3967 | 3.0 | 1131 | 1.4895 | 0.7074 | 0.7004 | 0.7039 | 0.6995 | 0.7095 |
| 1.1703 | 4.0 | 1508 | 1.4869 | 0.7036 | 0.7066 | 0.7051 | 0.6983 | 0.7132 |
| 1.1074 | 5.0 | 1885 | 1.4851 | 0.7102 | 0.7146 | 0.7124 | 0.7070 | 0.7182 |
| 0.9625 | 6.0 | 2262 | 1.5126 | 0.7322 | 0.7045 | 0.7184 | 0.7257 | 0.7082 |
| 0.8629 | 7.0 | 2639 | 1.5545 | 0.7254 | 0.7077 | 0.7165 | 0.7170 | 0.7120 |
| 0.8544 | 8.0 | 3016 | 1.5896 | 0.7285 | 0.7117 | 0.7201 | 0.7207 | 0.7157 |
| 0.7674 | 9.0 | 3393 | 1.6298 | 0.7254 | 0.7016 | 0.7135 | 0.7182 | 0.7057 |
| 0.7501 | 10.0 | 3770 | 1.6333 | 0.7179 | 0.7022 | 0.7100 | 0.7107 | 0.7070 |
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 29, 2026
- Updated
- Aug 29, 2026