# jina-embeddings-v2-base-en

Source: https://zilliz.com/ai-models/jina-embeddings-v2-base-en
Published: 2024-08-12T21:30:47.501Z

## Model attributes

| Attribute | Value |
| --- | --- |
| Provider | Jina AI |
| Integration | Milvus Integrated |
| Task | Embedding |
| Modality | Text |
| Similarity metric | Any (Normalized) |
| License | Apache 2.0 |
| Dimensions | 768 |
| Max input tokens | 8192 |
| Price | Free |

## Introduction to Jina Embedding v2 Models

Jina Embeddings v2 models are designed to handle long documents with an expanded max input size of 8,192 tokens. As of October 2024, Jina AI Embedding V2 has the following variants, each catering to different embedding needs:

- [jina-embeddings-v2-small-en](https://zilliz.com/ai-models/jina-embeddings-v2-small-en)
- [jina-embeddings-v2-base-en](https://zilliz.com/ai-models/jina-embeddings-v2-base-en)
- [jina-embeddings-v2-base-zh](https://zilliz.com/ai-models/jina-embeddings-v2-base-zh)
- [jina-embeddings-v2-base-de](https://zilliz.com/ai-models/jina-embeddings-v2-base-de)
- [jina-embeddings-v2-base-code](https://zilliz.com/ai-models/jina-embeddings-v2-base-code)


## Introduction to jina-embeddings-v2-base-en

**jina-embeddings-v2-base-en** is an English monolingual embedding model for a sequence length of up to 8192 tokens. It is the medium-sized or basic variant in the Jina Embeddings v2 family, which has been trained with 137 million parameters and generates 768-dimensional embeddings.

Comparing jina-embeddings-v2-small-en with other Jina embedding models. 

| Model                        | Parameter Size | Embedding Dimension                     | Text                                                        |
| ---------------------------- | -------------- | --------------------------------------- | ----------------------------------------------------------- |
| [jina-embeddings-v3](https://zilliz.com/ai-models/jina-embeddings-v3)           | 570M            | flexible embedding size (Default: 1024) | multilingual text embeddings; supports 94 language in total |
| [jina-embeddings-v2-small-en](https://zilliz.com/ai-models/jina-embeddings-v2-small-en) | 33M            | 512                                     | English monolingual embeddings                              |
| [jina-embeddings-v2-base-en](https://zilliz.com/ai-models/jina-embeddings-v2-base-en) | 137M           | 768                                     | English monolingual embeddings                              |
| [jina-embeddings-v2-base-zh](https://zilliz.com/ai-models/jina-embeddings-v2-base-zh)  | 161M           | 768                                     | Chinese-English Bilingual embeddings                        |
| [jina-embeddings-v2-base-de](https://zilliz.com/ai-models/jina-embeddings-v2-base-de) | 161M           | 768                                     | German-English Bilingual embeddings                         |
| [jina-embeddings-v2-base-code](https://zilliz.com/ai-models/jina-embeddings-v2-base-code) | 161M           | 768                                     | English and programming languages                           |


## How to create embeddings with jina-embeddings-v2-base-en

There are two primary ways to use the `jina-embeddings-v2-base-en` model to generate vector embeddings:

1. [PyMilvus](https://github.com/milvus-io/pymilvus): the Python SDK for [Milvus](https://zilliz.com/what-is-milvus) that seamlessly integrates the `jina-embeddings-v2-base-en` model.
2. SentenceTransformer library: the python library `sentence-transformer`.

### 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("jinaai/jina-embeddings-v2-base-en", trust_remote_code=True)

docs = [
   "Artificial intelligence was founded as an academic discipline in 1956.",
   "Alan Turing was the first person to conduct substantial research in AI.",
   "Born in Maida Vale, London, Turing was raised in southern England."
]
# Generate embeddings for documents
docs_embeddings = ef(docs)

queries = ["When was artificial intelligence founded",
          "Where was Alan Turing born?"]
# 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("jinaai/jina-embeddings-v2-base-en", trust_remote_code=True)

docs = [
   "Artificial intelligence was founded as an academic discipline in 1956.",
   "Alan Turing was the first person to conduct substantial research in AI.",
   "Born in Maida Vale, London, Turing was raised in southern England."
]
# 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=768,
    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).