Ms Marco MiniLM L 6 V2
Are you looking for a model that can efficiently handle information retrieval tasks? The Ms Marco MiniLM L 6 V2 model is designed to do just that. Trained on the MS Marco Passage Ranking task, this model can encode queries and passages, then sort them in decreasing order, making it perfect for tasks like search and ranking. With a performance of 74.30 NDCG@10 on the TREC Deep Learning 2019 dataset and 39.01 MRR@10 on the MS Marco Passage Reranking dataset, this model is both accurate and fast, processing around 1800 documents per second on a V100 GPU. Its unique architecture makes it a great choice for those looking for a reliable and efficient model for information retrieval tasks.
Table of Contents
Model Overview
The Current Model is a powerful tool for Information Retrieval tasks. It’s designed to find the most relevant passages for a given query.
Capabilities
So, what can it do? Well, it can:
- Take a query and a list of possible passages as input
- Encode the query and passages together
- Sort the passages in a decreasing order of relevance
- Return the top-ranked passages for your query
Let’s take a look at an example. Suppose you’re searching for information on a specific topic, and you have a list of possible passages that might be relevant. You can use the Current Model to rank these passages in order of relevance, so you can quickly find the most useful information.
How it Works
You can use the Current Model with popular libraries like Transformers or SentenceTransformers. Here’s an example with SentenceTransformers:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2'), ('Query', 'Paragraph3')])
This code takes a query and a list of possible passages as input, and returns a list of scores that indicate how relevant each passage is to the query.
Performance
So, how well does it perform? The Current Model has been fine-tuned on various datasets, including TREC Deep Learning 2019 and MS Marco Passage Reranking. Here are some performance metrics:
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
|---|---|---|---|
| Current Model | 74.30 | 39.01 | 1800 |
As you can see, the Current Model achieves high scores on both datasets, indicating that it’s effective at finding relevant passages.
What’s Next?
Want to try out the Current Model for yourself? You can use the code snippet above as a starting point. Just remember to preprocess your input data using a tokenizer, and you’re good to go!
Limitations
While the Current Model is a powerful tool, it’s not perfect. One limitation is that it relies on a retrieval system to provide a list of possible passages. If the retrieval system is not effective, the Current Model may not be able to find the most relevant passages.
Another limitation is that the Current Model requires significant computational resources, particularly for larger models. This can make it challenging to deploy in resource-constrained environments.
Format
The Current Model uses a Cross-Encoder architecture, which is a type of transformer model. It’s designed to work with two inputs: a query and a passage. The model takes these two inputs, encodes them together, and outputs a score that indicates how relevant the passage is to the query.
The input format is simple: just provide a query and a list of possible passages, and the Current Model will take care of the rest. The output format is also straightforward: the model returns a list of scores that indicate how relevant each passage is to the query.
