# instructor-base

Source: https://zilliz.com/ai-models/instructor-base
Published: 2024-08-25T21:44:52.475Z

## Model attributes

| Attribute | Value |
| --- | --- |
| Provider | HKU NLP |
| Integration | Milvus Integrated |
| Task | Embedding |
| Modality | Text |
| Similarity metric | Cosine |
| License | Apache 2.0 |
| Dimensions | 768 |
| Max input tokens | 512 |
| Price | Free |

## Introduction to the Instructor Model Family

The Instructor model by NKU NLP is a text embedding model fine-tuned with instructions. It creates task-specific embeddings (for classification, retrieval, clustering, text evaluation, etc.) across various domains (like science and finance) by just providing task instructions—no extra fine-tuning is needed. 

![Figure How the Instructor Model works](https://assets.zilliz.com/Figure_How_the_Instructor_Model_works_2b174dc8aa.png)

Figure: How the Instructor Model works (image by NKU NLP) 

The Instructor model comes in three variations: **[instructor-base](instructor-base), [instructor-xl](https://zilliz.com/ai-models/instructor-xl), and [instructor-large](https://zilliz.com/ai-models/instructor-large).** Each version offers different levels of performance and scalability to suit various embedding needs.


## Introduction to instructor-base

**instructor-base** is the smallest variant of the Instructor model family, designed to balance efficiency and performance. It’s ideal for tasks that require high-quality text embeddings with a lower computational footprint.


### Comparing **instructor-base, instructor-xl, and instructor-large**

| **Feature**             | **instructor-base** | **instructor-large** | **instructor-xl** |
| ----------------------- | ------------------- | -------------------- | ----------------- |
| **Parameter Size**      | 86 million          | 335 million          | 1.5 billion       |
| **Embedding Dimension** | 768                 | 768                  | 768               |
| **Avg. MTEB Score**     | 55.9                | 58.4                 | 58.8              |


## How to create embeddings with **instructor-base**

There are two primary ways to generate vector embeddings:

- [PyMilvus](https://github.com/milvus-io/pymilvus): the Python SDK for [Milvus](https://zilliz.com/what-is-milvus) that seamlessly integrates the `instructor-base` model.
- Instructor library: the Python library `InstructorEmbedding`.

Once the vector embeddings are created, they can be stored in [Zilliz Cloud](https://zilliz.com/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:

- [Sign up](https://cloud.zilliz.com/signup) for a Zilliz Cloud account for free.
- [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).
- Create a vector collection and insert your vector embeddings.
- Run a semantic search on the stored embeddings.


### Create embeddings via PyMilvus 

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

ef = InstructorEmbeddingFunction(
    "hkunlp/instructor-base", 
    query_instruction="Represent the Wikipedia question for retrieving supporting documents:", 
    doc_instruction="Represent the Wikipedia document for retrieval:")

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.encode_documents(docs)

queries = ["When was artificial intelligence founded",
          "Where was Alan Turing born?"]

# Generate embeddings for queries
query_embeddings = ef.encode_queries(queries)

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"])
```


### Generate vector embeddings via InstructorEmbedding Library

```
from InstructorEmbedding import INSTRUCTOR
from pymilvus import MilvusClient

model = INSTRUCTOR('hkunlp/instructor-base')

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


queries = [["Represent the Wikipedia question for retrieving supporting documents: ", "When was artificial intelligence founded"],
           ["Represent the Wikipedia question for retrieving supporting documents: ", "Where was Alan Turing born?"]]
# 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"])
```