Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv2-so400m14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-so400m14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-so400m14", 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 AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("google/tipsv2-so400m14", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-so400m14", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from google/tipsv2-so400m14: direct link, hf CLI and curl.
- Browser
- Download file 292 Bytes
-
https://huggingface.co/google/tipsv2-so400m14/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://google/tipsv2-so400m14@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/google/tipsv2-so400m14/resolve/refs%2Fpr%2F1/tokenizer_config.json
292 Bytes
| { | |
| "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>" | |
| } | |