Multi Qa MiniLM L6 Cos V1
The Multi Qa MiniLM L6 Cos V1 model is designed for semantic search, encoding queries and text paragraphs into a dense vector space to find relevant documents. Trained on 215 million question-answer pairs from diverse sources, it uses a contrastive learning objective to predict which sentences are paired together. With a mean pooling method and cosine-similarity as a similarity function, it produces normalized embeddings. While it's intended for semantic search, it has limitations, such as a 512-word piece limit and potential issues with longer texts. How will you use this model to improve your search capabilities?
Table of Contents
Model Overview
The multi-qa-MiniLM-L6-cos-v1 model is a powerful tool for semantic search. It maps sentences and paragraphs to a 384 dimensional dense vector space, making it easy to find relevant documents for a given query.
Imagine you have a huge library with millions of books. Each book represents a sentence or a paragraph. This model helps you find the most relevant books (sentences or paragraphs) for a given query (question or sentence). It does this by converting each book into a unique vector, like a fingerprint, and then comparing these vectors to find the best matches.
Capabilities
- Encode queries and documents: The model can take in a query and a set of documents, and encode them into dense vectors.
- Find relevant documents: By comparing the encoded query and document vectors, the model can find the most relevant documents for a given query.
- Handle large datasets: The model was trained on a massive dataset of
215M(question, answer) pairs, making it well-suited for handling large-scale semantic search tasks.
How does it work?
- Mean pooling: The model uses mean pooling to aggregate the contextualized word embeddings into a single vector representation for each sentence or paragraph.
- Cosine similarity: The model uses cosine similarity to compare the encoded query and document vectors, allowing it to find the most relevant documents.
Key Features
| Feature | Value |
|---|---|
| Dimensions | 384 |
| Normalized embeddings | Yes |
| Pooling method | Mean pooling |
| Suitable score functions | Dot-product, cosine-similarity, euclidean distance |
Performance
The model is designed for semantic search tasks, such as finding relevant documents for a given query. It can handle up to 512 word pieces in a single input, but it’s worth noting that it was only trained on input text up to 250 word pieces.
Speed
The model can process text quickly, making it suitable for applications where speed is critical.
Accuracy
The model has been fine-tuned on a large dataset of (question, answer) pairs, which allows it to learn the relationships between words and phrases. This makes it particularly good at semantic search tasks.
Efficiency
The model uses a mean pooling method to reduce the dimensionality of the input text, which makes it faster and more efficient.
Limitations
While the model is powerful, it has its limitations. For example, it may struggle with more complex or nuanced contexts, and it may not be able to understand sarcasm, idioms, or figurative language.
Limited Contextual Understanding
The model may not be able to understand the context of a sentence or paragraph, which can lead to inaccurate results.
Limited Knowledge Domain
The model has been trained on a large dataset of text, but it may not have knowledge in specific domains or areas of expertise.
Limited Handling of Long Text
The model has a limit of 512 word pieces, which means that it may not be able to handle long documents or texts.
Format
The model uses a transformer architecture and accepts input in the form of tokenized text sequences.
Supported Data Formats
The model supports text data in the form of sentences or paragraphs. You can input a single sentence or a list of sentences, and the model will output a dense vector representation for each input.
Special Requirements
When using the model, keep in mind that:
- The input text should be tokenized, which means breaking down the text into individual words or subwords.
- The model has a limit of
512word pieces, so text longer than that will be truncated. - The model was trained on input text up to
250word pieces, so it might not work well for longer text.
Handling Inputs and Outputs
Here’s an example of how to use the model with the sentence-transformers library:
from sentence_transformers import SentenceTransformer, util
# Load the model
model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
# Define your input text
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
# Encode the input text
query_emb = model.encode(query)
doc_emb = model.encode(docs)
# Compute the dot score between the query and document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
# Combine the documents and scores
doc_score_pairs = list(zip(docs, scores))
# Sort the documents by score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output the passages and scores
for doc, score in doc_score_pairs:
print(score, doc)
