Instructions to use hf-internal-testing/tiny-random-SiglipForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SiglipForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "added_tokens_decoder": { | |
| "1": { | |
| "content": "</s>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "<unk>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "additional_special_tokens": [], | |
| "clean_up_tokenization_spaces": true, | |
| "do_lower_case": true, | |
| "eos_token": "</s>", | |
| "model_input_names": [ | |
| "input_ids" | |
| ], | |
| "model_max_length": 64, | |
| "pad_token": "</s>", | |
| "processor_class": "SiglipProcessor", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "SiglipTokenizer", | |
| "unk_token": "<unk>" | |
| } | |