# multilingual-e5-large

Source: https://zilliz.com/ai-models/multilingual-e5-large
Published: 2024-08-05T20:30:45.005Z

## Model attributes

| Attribute | Value |
| --- | --- |
| Provider | Microsoft |
| Integration | Milvus Integrated |
| Task | Embedding |
| Modality | Text |
| Similarity metric | Any (Normalized) |
| License | Mit |
| Dimensions | 1024 |
| Max input tokens | 512 |
| Price | Free |

## Introduction to the multilingual-e5-large embedding model  

- Tailored for multilingual documents; supports 100+ languages; ideal for multilingual information retrieval and [semantic search](https://zilliz.com/glossary/semantic-search) tasks.

The `multilingual-e5-large` model is a state-of-the-art text embedding model developed by Microsoft based on the XLM-RoBERTa-large architecture. With its 24-layer structure and 560 million parameters, the `multilingual-e5-large` model generates 1024-dimensional embeddings and supports 100 languages, offering robust performance even in multilingual contexts.

Trained on a billion weakly supervised text pairs and fine-tuned on specific datasets, the model excels in multilingual information retrieval and semantic search tasks. It processes text inputs prefixed with "query:" or "passage:" to create embeddings that accurately reflect semantic content. This model demonstrates superior performance in multilingual benchmarks, surpassing smaller models and traditional methods, making it ideal for cross-lingual text analysis, clustering, and similarity comparisons. 


## How to create vector embeddings with the multilingual-e5-large model

There are two primary ways to create vector embeddings with the `multilingual-e5-large` model:

1. [PyMilvus](https://github.com/milvus-io/pymilvus): the Python SDK for [Milvus](https://zilliz.com/what-is-milvus) that seamlessly integrates with the `multilingual-e5-large` model.
2. SentenceTransformer library: the Python library of `sentence-transformer`.

Once the vector embeddings are generated, they can be stored in Zilliz Cloud (a fully managed vector database service powered by Milvus) and used for [semantic similarity search](https://zilliz.com/glossary/semantic-search). Here are four key steps:

1. [**Sign up**](https://cloud.zilliz.com/signup) for a Zilliz Cloud account for free.
2. **[Set up a serverless cluster](https://docs.zilliz.com/docs/create-cluster#set-up-a-free-cluster)** and obtain the [Public Endpoint and API Key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details).
3. **Create a vector collection** and insert your vector embeddings.
4. **Run a semantic search** on the stored embeddings.


### Generate vector embeddings via PyMilvus and insert them into Zilliz Cloud for semantic search 

```
from pymilvus.model.dense import SentenceTransformerEmbeddingFunction
from pymilvus import MilvusClient

ef = SentenceTransformerEmbeddingFunction("intfloat/multilingual-e5-large")
    
docs = [
   "passage: Artificial intelligence was founded as an academic discipline in 1956.",
   "passage: Alan Turing war die erste Person, die umfassende Forschungen im Bereich der künstlichen Intelligenz durchgeführt hat.",
   "passage: 图灵出生在伦敦的梅达维尔，他在英格兰南部长大。"
]
    
# Generate embeddings for documents
docs_embeddings = ef(docs)
    
queries = ["query: When was artificial intelligence founded",
           "query: Wo wurde Alan Turing geboren?"] 
               
# Generate embeddings for queries
query_embeddings = ef(queries)

# Connect to Zilliz Cloud with Public Endpoint and API Key
client = MilvusClient(
    uri=ZILLIZ_PUBLIC_ENDPOINT,
    token=ZILLIZ_API_KEY)

COLLECTION = "documents"
if client.has_collection(collection_name=COLLECTION):
    client.drop_collection(collection_name=COLLECTION)
client.create_collection(
    collection_name=COLLECTION,
    dimension=ef.dim,
    auto_id=True)

for doc, embedding in zip(docs, docs_embeddings):
    client.insert(COLLECTION, {"text": doc, "vector": embedding})
    
results = client.search(
    collection_name=COLLECTION,
    data=query_embeddings,
    consistency_level="Strong",
    output_fields=["text"])
```


For more information, refer to our [PyMilvus Embedding Model documentation](https://milvus.io/docs/embeddings.md). 


### Generate vector embeddings via SentenceTransformer and insert them into Zilliz Cloud for semantic search 


```
from sentence_transformers import SentenceTransformer
from pymilvus import MilvusClient

model = SentenceTransformer("intfloat/multilingual-e5-large")

docs = [
   "passage: Artificial intelligence was founded as an academic discipline in 1956.",
   "passage: Alan Turing war die erste Person, die umfassende Forschungen im Bereich der künstlichen Intelligenz durchgeführt hat.",
   "passage: 图灵出生在伦敦的梅达维尔，他在英格兰南部长大。"
]
# Generate embeddings for documents
docs_embeddings = model.encode(docs, normalize_embeddings=True)

queries = ["query: When was artificial intelligence founded",
           "query: Wo wurde Alan Turing geboren?" ] 
# Generate embeddings for queries
query_embeddings = model.encode(queries, normalize_embeddings=True)

# Connect to Zilliz Cloud with Public Endpoint and API Key
client = MilvusClient(
    uri=ZILLIZ_PUBLIC_ENDPOINT,
    token=ZILLIZ_API_KEY)

COLLECTION = "documents"
if client.has_collection(collection_name=COLLECTION):
    client.drop_collection(collection_name=COLLECTION)
client.create_collection(
    collection_name=COLLECTION,
    dimension=1024,
    auto_id=True)

for doc, embedding in zip(docs, docs_embeddings):
    client.insert(COLLECTION, {"text": doc, "vector": embedding})
    
results = client.search(
    collection_name=COLLECTION,
    data=query_embeddings,
    consistency_level="Strong",
    output_fields=["text"])
```




For more information, refer to [SentenceTransformer documentation](https://sbert.net/docs/sentence_transformer/pretrained_models.html).