Instructions to use iTroned/targeted_baseline_v4_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/targeted_baseline_v4_1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/targeted_baseline_v4_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
targeted_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: 4.6953
- Accuracy Targeted: 0.7044
- F1 Macro Targeted: 0.5475
- F1 Weighted Targeted: 0.6423
- F1 Macro Total: 0.5475
- F1 Weighted Total: 0.6423
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 Targeted | F1 Macro Targeted | F1 Weighted Targeted | F1 Macro Total | F1 Weighted Total |
|---|---|---|---|---|---|---|---|---|
| 0.7113 | 1.0 | 1100 | 2.2719 | 0.6778 | 0.4040 | 0.5476 | 0.4040 | 0.5476 |
| 1.0394 | 2.0 | 2200 | 2.2550 | 0.6778 | 0.4040 | 0.5476 | 0.4040 | 0.5476 |
| 0.9156 | 3.0 | 3300 | 2.5674 | 0.6933 | 0.4595 | 0.5859 | 0.4595 | 0.5859 |
| 0.7673 | 4.0 | 4400 | 2.7223 | 0.7067 | 0.4983 | 0.6133 | 0.4983 | 0.6133 |
| 0.6303 | 5.0 | 5500 | 3.1295 | 0.6956 | 0.4818 | 0.6002 | 0.4818 | 0.6002 |
| 0.5417 | 6.0 | 6600 | 3.7576 | 0.6911 | 0.4469 | 0.5776 | 0.4469 | 0.5776 |
| 0.3341 | 7.0 | 7700 | 3.6453 | 0.7 | 0.4943 | 0.6090 | 0.4943 | 0.6090 |
| 0.2601 | 8.0 | 8800 | 4.3086 | 0.6978 | 0.4930 | 0.6075 | 0.4930 | 0.6075 |
| 0.1736 | 9.0 | 9900 | 4.9458 | 0.6844 | 0.4985 | 0.6071 | 0.4985 | 0.6071 |
| 0.0747 | 10.0 | 11000 | 5.4903 | 0.6978 | 0.4977 | 0.6104 | 0.4977 | 0.6104 |
| 0.0801 | 11.0 | 12100 | 4.6953 | 0.7044 | 0.5475 | 0.6423 | 0.5475 | 0.6423 |
| 0.0554 | 12.0 | 13200 | 6.0291 | 0.7 | 0.4844 | 0.6029 | 0.4844 | 0.6029 |
| 0.0364 | 13.0 | 14300 | 6.3335 | 0.6978 | 0.5068 | 0.6160 | 0.5068 | 0.6160 |
| 0.0449 | 14.0 | 15400 | 6.3268 | 0.6978 | 0.5155 | 0.6212 | 0.5155 | 0.6212 |
| 0.0141 | 15.0 | 16500 | 6.4658 | 0.6889 | 0.5056 | 0.6126 | 0.5056 | 0.6126 |
| 0.0215 | 16.0 | 17600 | 6.3852 | 0.6933 | 0.4903 | 0.6047 | 0.4903 | 0.6047 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
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