Instructions to use kpalczewski-displate/category_cleaning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kpalczewski-displate/category_cleaning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="kpalczewski-displate/category_cleaning") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("kpalczewski-displate/category_cleaning") model = AutoModelForZeroShotImageClassification.from_pretrained("kpalczewski-displate/category_cleaning") - Notebooks
- Google Colab
- Kaggle
category_cleaning
This model was trained from scratch on the Displate/category_cleaning dataset. It achieves the following results on the evaluation set:
- Loss: 4.5536
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.0173 | 0.26 | 100 | 4.2489 |
| 2.8146 | 0.51 | 200 | 4.2605 |
| 2.7177 | 0.77 | 300 | 4.2547 |
| 2.6338 | 1.02 | 400 | 4.3864 |
| 2.102 | 1.28 | 500 | 4.3538 |
| 2.1015 | 1.53 | 600 | 4.3395 |
| 2.0691 | 1.79 | 700 | 4.3363 |
| 1.9422 | 2.04 | 800 | 4.6331 |
| 1.4405 | 2.3 | 900 | 4.5653 |
| 1.3896 | 2.55 | 1000 | 4.5768 |
| 1.3735 | 2.81 | 1100 | 4.5610 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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