clip-ViT-B-32-multilingual-v1 / README.md

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	---
	language: multilingual
	license: apache-2.0
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	pipeline_tag: sentence-similarity
	---

	# sentence-transformers/clip-ViT-B-32-multilingual-v1

	This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model. You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close. This model can be used for image search (users search through a large collection of images) and for multi-lingual zero-shot image classification (image labels are defined as text).


	## Usage (Sentence-Transformers)

	Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

	```
	pip install -U sentence-transformers
	```

	Then you can use the model like this:

	```python
	from sentence_transformers import SentenceTransformer, util
	from PIL import Image, ImageFile
	import requests
	import torch

	# We use the original clip-ViT-B-32 for encoding images
	img_model = SentenceTransformer('clip-ViT-B-32')

	# Our text embedding model is aligned to the img_model and maps 50+
	# languages to the same vector space
	text_model = SentenceTransformer('sentence-transformers/clip-ViT-B-32-multilingual-v1')


	# Now we load and encode the images
	def load_image(url_or_path):
	if url_or_path.startswith("http://") or url_or_path.startswith("https://"):
	return Image.open(requests.get(url_or_path, stream=True).raw)
	else:
	return Image.open(url_or_path)

	# We load 3 images. You can either pass URLs or
	# a path on your disc
	img_paths = [
	# Dog image
	"https://unsplash.com/photos/QtxgNsmJQSs/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjM1ODQ0MjY3&w=640",

	# Cat image
	"https://unsplash.com/photos/9UUoGaaHtNE/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8Mnx8Y2F0fHwwfHx8fDE2MzU4NDI1ODQ&w=640",

	# Beach image
	"https://unsplash.com/photos/Siuwr3uCir0/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8NHx8YmVhY2h8fDB8fHx8MTYzNTg0MjYzMg&w=640"
	]

	images = [load_image(img) for img in img_paths]

	# Map images to the vector space
	img_embeddings = img_model.encode(images)

	# Now we encode our text:
	texts = [
	"A dog in the snow",
	"Eine Katze", # German: A cat
	"Una playa con palmeras." # Spanish: a beach with palm trees
	]

	text_embeddings = text_model.encode(texts)

	# Compute cosine similarities:
	cos_sim = util.cos_sim(text_embeddings, img_embeddings)

	for text, scores in zip(texts, cos_sim):
	max_img_idx = torch.argmax(scores)
	print("Text:", text)
	print("Score:", scores[max_img_idx] )
	print("Path:", img_paths[max_img_idx], "\n")

	```

	## Multilingual Image Search - Demo
	For a demo of multilingual image search, have a look at: [Image_Search-multilingual.ipynb](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications/image-search/Image_Search-multilingual.ipynb) ( [Colab version](https://colab.research.google.com/drive/1N6woBKL4dzYsHboDNqtv-8gjZglKOZcn?usp=sharing) )

	For more details on image search and zero-shot image classification, have a look at the documentation on [SBERT.net](https://www.sbert.net/examples/applications/image-search/README.html).


	## Training
	This model has been created using [Multilingual Knowledge Distillation](https://arxiv.org/abs/2004.09813). As teacher model, we used the original `clip-ViT-B-32` and then trained a [multilingual DistilBERT](https://huggingface.co/distilbert-base-multilingual-cased) model as student model. Using parallel data, the multilingual student model learns to align the teachers vector space across many languages. As a result, you get an text embedding model that works for 50+ languages.

	The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images.

	Have a look at the [SBERT.net - Multilingual-Models documentation](https://www.sbert.net/examples/training/multilingual/README.html) on more details and for training code.

	We used the following 50+ languages to align the vector spaces: ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, fr-ca, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, pt, pt-br, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh-cn, zh-tw.

	The original multilingual DistilBERT supports 100+ lanugages. The model also work for these languages, but might not yield the best results.

	## Full Model Architecture
	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
	(2): Dense({'in_features': 768, 'out_features': 512, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
	)
	```

	## Citing & Authors

	This model was trained by [sentence-transformers](https://www.sbert.net/).

	If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "http://arxiv.org/abs/1908.10084",
	}
	```