Instructions to use turing552/flickr30k-pt-br-5ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use turing552/flickr30k-pt-br-5ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="turing552/flickr30k-pt-br-5ep") 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("turing552/flickr30k-pt-br-5ep") model = AutoModelForZeroShotImageClassification.from_pretrained("turing552/flickr30k-pt-br-5ep") - Notebooks
- Google Colab
- Kaggle
flickr30k-pt-br-5ep
This model is a fine-tuned version of openai/clip-vit-base-patch32 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4179
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-06
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2659 | 1.2887 | 500 | 0.5118 |
| 0.0907 | 2.5773 | 1000 | 0.4180 |
| 0.0501 | 3.8660 | 1500 | 0.4179 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 4.4.1
- Tokenizers 0.19.1
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Model tree for turing552/flickr30k-pt-br-5ep
Base model
openai/clip-vit-base-patch32