Instructions to use tessier0ashpool/slop-hunter-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tessier0ashpool/slop-hunter-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tessier0ashpool/slop-hunter-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tessier0ashpool/slop-hunter-v3") model = AutoModelForTokenClassification.from_pretrained("tessier0ashpool/slop-hunter-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
slop-hunter-v3
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1351
- F1: 0.7272
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.8518 | 1.0 | 75 | 0.7363 | 0.1472 |
| 0.2907 | 2.0 | 150 | 0.2681 | 0.5707 |
| 0.2247 | 3.0 | 225 | 0.1877 | 0.6284 |
| 0.129 | 4.0 | 300 | 0.1468 | 0.6709 |
| 0.1335 | 5.0 | 375 | 0.1311 | 0.7030 |
| 0.0689 | 6.0 | 450 | 0.1421 | 0.6990 |
| 0.0637 | 7.0 | 525 | 0.1341 | 0.7081 |
| 0.0738 | 8.0 | 600 | 0.1362 | 0.7235 |
| 0.0696 | 9.0 | 675 | 0.1351 | 0.7272 |
| 0.0619 | 10.0 | 750 | 0.1364 | 0.7206 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.10.0+cu128
- Datasets 3.2.0
- Tokenizers 0.21.4
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Model tree for tessier0ashpool/slop-hunter-v3
Base model
microsoft/deberta-v3-base