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transformers/docs/source/en/model_doc/metaclip_2.md
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transformers/docs/source/en/model_doc/metaclip_2.md
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on {release_date} and added to Hugging Face Transformers on 2025-08-20.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# MetaCLIP 2
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## Overview
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MetaCLIP 2 is a replication of the original CLIP model trained on 300+ languages. It achieves state-of-the-art (SOTA) results on multilingual benchmarks (e.g., XM3600, CVQA, Babel‑ImageNet), surpassing previous SOTA such as [mSigLIP](siglip) and [SigLIP‑2](siglip2). The authors show that English and non-English worlds can mutually benefit and elevate each other.
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This model was contributed by [nielsr](https://huggingface.co/nielsr).
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The original code can be found [here](https://github.com/facebookresearch/MetaCLIP).
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You can find all the MetaCLIP 2 checkpoints under the [Meta](https://huggingface.co/facebook/models?search=metaclip-2) organization.
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> [!TIP]
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> Click on the MetaCLIP 2 models in the right sidebar for more examples of how to apply MetaCLIP 2 to different image and language tasks.
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The example below demonstrates how to calculate similarity scores between multiple text descriptions and an image with [`Pipeline`] or the [`AutoModel`] class. Usage of the MetaCLIP 2 models is identical to the CLIP models, you just need the `MetaClip2Model` class instead of `CLIPModel`.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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clip = pipeline(
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task="zero-shot-image-classification",
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model="facebook/metaclip-2-worldwide-huge-quickgelu",
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dtype=torch.bfloat16,
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device=0
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)
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labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
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clip("http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=labels)
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import requests
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import torch
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from PIL import Image
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from transformers import AutoProcessor, AutoModel
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model = AutoModel.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu", dtype=torch.bfloat16, attn_implementation="sdpa")
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processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
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inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image
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probs = logits_per_image.softmax(dim=1)
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most_likely_idx = probs.argmax(dim=1).item()
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most_likely_label = labels[most_likely_idx]
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print(f"Most likely label: {most_likely_label} with probability: {probs[0][most_likely_idx].item():.3f}")
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```
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</hfoption>
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</hfoptions>
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## MetaClip2Config
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[[autodoc]] MetaClip2Config
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- from_text_vision_configs
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## MetaClip2TextConfig
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[[autodoc]] MetaClip2TextConfig
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## MetaClip2VisionConfig
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[[autodoc]] MetaClip2VisionConfig
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## MetaClip2Model
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[[autodoc]] MetaClip2Model
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- forward
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- get_text_features
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- get_image_features
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## MetaClip2TextModel
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[[autodoc]] MetaClip2TextModel
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- forward
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## MetaClip2TextModelWithProjection
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[[autodoc]] MetaClip2TextModelWithProjection
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- forward
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## MetaClip2VisionModelWithProjection
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[[autodoc]] MetaClip2VisionModelWithProjection
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- forward
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## MetaClip2VisionModel
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[[autodoc]] MetaClip2VisionModel
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- forward
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## MetaClip2ForImageClassification
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[[autodoc]] MetaClip2ForImageClassification
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- forward
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