Zero-Shot Image Classification
Transformers
ONNX
Chinese
English
m2_encoder
feature-extraction
multimodal
image-text-retrieval
bilingual
chinese
english
vision-language
custom-code
custom_code
Eval Results (legacy)
Instructions to use malusama/M2-Encoder-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malusama/M2-Encoder-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="malusama/M2-Encoder-1B", 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("malusama/M2-Encoder-1B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 237 Bytes
ea0524d | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"processor_class": "M2EncoderProcessor",
"image_processor_type": "M2EncoderImageProcessor",
"auto_map": {
"AutoProcessor": "processing_m2_encoder.M2EncoderProcessor"
},
"size": {
"height": 224,
"width": 224
}
}
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