Instructions to use tessier0ashpool/slop-hunter-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tessier0ashpool/slop-hunter-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tessier0ashpool/slop-hunter-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tessier0ashpool/slop-hunter-v1") model = AutoModelForTokenClassification.from_pretrained("tessier0ashpool/slop-hunter-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
slop-hunter-v1
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: 0.1861
- F1: 0.6686
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 |
|---|---|---|---|---|
| 1.9518 | 1.0 | 40 | 1.7293 | 0.0 |
| 0.5408 | 2.0 | 80 | 0.4746 | 0.5074 |
| 0.3273 | 3.0 | 120 | 0.2757 | 0.5887 |
| 0.2013 | 4.0 | 160 | 0.2211 | 0.6352 |
| 0.1323 | 5.0 | 200 | 0.2036 | 0.6260 |
| 0.1305 | 6.0 | 240 | 0.1849 | 0.6474 |
| 0.1291 | 7.0 | 280 | 0.2020 | 0.6447 |
| 0.0658 | 8.0 | 320 | 0.1872 | 0.6542 |
| 0.0778 | 9.0 | 360 | 0.1861 | 0.6686 |
| 0.0806 | 10.0 | 400 | 0.1899 | 0.6644 |
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-v1
Base model
microsoft/deberta-v3-base