Instructions to use tessier0ashpool/slop-hunter-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tessier0ashpool/slop-hunter-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tessier0ashpool/slop-hunter-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tessier0ashpool/slop-hunter-v2") model = AutoModelForTokenClassification.from_pretrained("tessier0ashpool/slop-hunter-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
slop-hunter-v2
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: 0.1956
- F1: 0.6910
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 |
|---|---|---|---|---|
| 1.5453 | 1.0 | 54 | 1.2372 | 0.0206 |
| 0.3545 | 2.0 | 108 | 0.3527 | 0.5453 |
| 0.2361 | 3.0 | 162 | 0.2563 | 0.6046 |
| 0.1985 | 4.0 | 216 | 0.2411 | 0.6409 |
| 0.1199 | 5.0 | 270 | 0.2080 | 0.6701 |
| 0.1191 | 6.0 | 324 | 0.2018 | 0.6722 |
| 0.098 | 7.0 | 378 | 0.1956 | 0.6910 |
| 0.0753 | 8.0 | 432 | 0.1993 | 0.6765 |
| 0.0874 | 9.0 | 486 | 0.2033 | 0.6789 |
| 0.0729 | 10.0 | 540 | 0.1985 | 0.6906 |
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-v2
Base model
microsoft/deberta-v3-base