Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv2-b14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-b14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-b14", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/tipsv2-b14", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "do_lower_case": true, | |
| "eos_token": null, | |
| "model_max_length": 64, | |
| "pad_token": "<pad>", | |
| "processor_class": "Tipsv2Processor", | |
| "token_type_ids_pattern": "all_zeros", | |
| "tokenizer_class": "Tipsv2Tokenizer", | |
| "unk_token": "<unk>" | |
| } | |