Instructions to use iTroned/stance_baseline_v4_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/stance_baseline_v4_1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/stance_baseline_v4_1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
stance_baseline_v4_1
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9901
- Accuracy Stance: 0.7377
- F1 Macro Stance: 0.6467
- F1 Weighted Stance: 0.7324
- F1 Macro Total: 0.6467
- F1 Weighted Total: 0.7324
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: 6e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Stance | F1 Macro Stance | F1 Weighted Stance | F1 Macro Total | F1 Weighted Total |
|---|---|---|---|---|---|---|---|---|
| 1.0014 | 1.0 | 969 | 0.7772 | 0.7672 | 0.5400 | 0.7227 | 0.5400 | 0.7227 |
| 0.8843 | 2.0 | 1938 | 0.8486 | 0.7377 | 0.5180 | 0.6935 | 0.5180 | 0.6935 |
| 0.7763 | 3.0 | 2907 | 1.0354 | 0.7377 | 0.5985 | 0.7284 | 0.5985 | 0.7284 |
| 0.7262 | 4.0 | 3876 | 1.8835 | 0.7246 | 0.6047 | 0.7092 | 0.6047 | 0.7092 |
| 0.6625 | 5.0 | 4845 | 2.0025 | 0.7311 | 0.6026 | 0.7169 | 0.6026 | 0.7169 |
| 0.5578 | 6.0 | 5814 | 1.9901 | 0.7377 | 0.6467 | 0.7324 | 0.6467 | 0.7324 |
| 0.4707 | 7.0 | 6783 | 2.1432 | 0.7377 | 0.6392 | 0.7328 | 0.6392 | 0.7328 |
| 0.3985 | 8.0 | 7752 | 3.1126 | 0.6557 | 0.5367 | 0.6504 | 0.5367 | 0.6504 |
| 0.278 | 9.0 | 8721 | 2.8917 | 0.7148 | 0.5852 | 0.7021 | 0.5852 | 0.7021 |
| 0.1985 | 10.0 | 9690 | 3.2367 | 0.6885 | 0.5706 | 0.6839 | 0.5706 | 0.6839 |
| 0.1111 | 11.0 | 10659 | 3.7015 | 0.6820 | 0.5600 | 0.6765 | 0.5600 | 0.6765 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
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