# Load model directly
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
processor = AutoProcessor.from_pretrained("cringgaard/extrapolation_year_built")
model = AutoModelForZeroShotImageClassification.from_pretrained("cringgaard/extrapolation_year_built")Quick Links
extrapolation_year_built
This model is a fine-tuned version of openai/clip-vit-large-patch14 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.5311
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: Use 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.7751 | 1.0 | 148 | 4.0394 |
| 2.4914 | 2.0 | 296 | 4.0245 |
| 1.2778 | 3.0 | 444 | 4.5482 |
| 0.3306 | 4.0 | 592 | 5.5415 |
| 0.201 | 5.0 | 740 | 6.5311 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.1
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Model tree for cringgaard/extrapolation_year_built
Base model
openai/clip-vit-large-patch14
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cringgaard/extrapolation_year_built") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )