Instructions to use iTroned/stance_baseline_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/stance_baseline_v3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/stance_baseline_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
stance_baseline_v3
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7193
- Accuracy Stance: 0.6689
- F1 Macro Stance: 0.4543
- F1 Weighted Stance: 0.6476
- F1 Macro Total: 0.4543
- F1 Weighted Total: 0.6476
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 |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 40 | 1.0642 | 0.4189 | 0.2040 | 0.2589 | 0.2040 | 0.2589 |
| No log | 2.0 | 80 | 1.0381 | 0.4189 | 0.1968 | 0.2474 | 0.1968 | 0.2474 |
| No log | 3.0 | 120 | 1.0219 | 0.4189 | 0.1968 | 0.2474 | 0.1968 | 0.2474 |
| No log | 4.0 | 160 | 1.0082 | 0.4932 | 0.3289 | 0.4549 | 0.3289 | 0.4549 |
| No log | 5.0 | 200 | 0.9815 | 0.5473 | 0.3761 | 0.5293 | 0.3761 | 0.5293 |
| No log | 6.0 | 240 | 0.9725 | 0.5743 | 0.3877 | 0.5531 | 0.3877 | 0.5531 |
| No log | 7.0 | 280 | 0.9607 | 0.6216 | 0.3949 | 0.5701 | 0.3949 | 0.5701 |
| No log | 8.0 | 320 | 0.9654 | 0.5946 | 0.4179 | 0.5941 | 0.4179 | 0.5941 |
| No log | 9.0 | 360 | 1.0517 | 0.6216 | 0.3984 | 0.5760 | 0.3984 | 0.5760 |
| No log | 10.0 | 400 | 1.0698 | 0.6284 | 0.4185 | 0.6026 | 0.4185 | 0.6026 |
| No log | 11.0 | 440 | 1.1862 | 0.6554 | 0.4445 | 0.6340 | 0.4445 | 0.6340 |
| No log | 12.0 | 480 | 1.3383 | 0.6419 | 0.4357 | 0.6237 | 0.4357 | 0.6237 |
| 0.7445 | 13.0 | 520 | 1.5536 | 0.6216 | 0.4219 | 0.6064 | 0.4219 | 0.6064 |
| 0.7445 | 14.0 | 560 | 1.6323 | 0.6554 | 0.4444 | 0.6347 | 0.4444 | 0.6347 |
| 0.7445 | 15.0 | 600 | 1.7193 | 0.6689 | 0.4543 | 0.6476 | 0.4543 | 0.6476 |
| 0.7445 | 16.0 | 640 | 1.8830 | 0.6486 | 0.4372 | 0.6248 | 0.4372 | 0.6248 |
| 0.7445 | 17.0 | 680 | 1.8240 | 0.6149 | 0.4266 | 0.6111 | 0.4266 | 0.6111 |
| 0.7445 | 18.0 | 720 | 2.0342 | 0.6419 | 0.4338 | 0.6217 | 0.4338 | 0.6217 |
| 0.7445 | 19.0 | 760 | 1.9920 | 0.6351 | 0.4379 | 0.6263 | 0.4379 | 0.6263 |
| 0.7445 | 20.0 | 800 | 2.0674 | 0.6486 | 0.4430 | 0.6338 | 0.4430 | 0.6338 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
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