Ms Marco TinyBERT L 2
Meet the Ms Marco TinyBERT L 2, a powerful AI model designed for information retrieval tasks. What if you could quickly find the most relevant passages for a given query? This model makes it possible by encoding queries and passages, then sorting them in decreasing order. With a performance of 67.43 NDCG@10 on the TREC Deep Learning 2019 dataset and 30.15 MRR@10 on the MS Marco Dev dataset, it's clear this model is built for speed and efficiency. Plus, it can process 9,000 documents per second, making it a great choice for those who need fast and accurate results. Whether you're working with large datasets or just need to find the right information quickly, the Ms Marco TinyBERT L 2 is an excellent tool to have in your toolkit.
Table of Contents
Model Overview
The Cross-Encoder for MS Marco model is a powerful tool for information retrieval tasks. It’s trained to help you find the most relevant passages in a large text database, given a specific query.
Capabilities
Imagine you have a huge library with millions of books, and you’re looking for a specific piece of information. This model can encode your query and all the possible passages, and then sort them in order of relevance.
What can it do?
- Rank passages: Given a query, the model can encode the query with all possible passages and sort them in a decreasing order of relevance.
- Improve search results: By re-ranking passages, the model can help you find the most accurate and relevant information for your query.
How does it work?
The model uses a combination of natural language processing (NLP) and machine learning algorithms to understand the query and passages. It then calculates a score for each passage based on its relevance to the query.
What makes it special?
- High performance: The model has achieved state-of-the-art results on the TREC Deep Learning 2019 and MS Marco Passage Reranking datasets.
- Fast and efficient: It can process a large number of passages quickly, making it suitable for real-time applications.
Performance
But how does it stack up against other models? Let’s take a look at its performance metrics:
| Model Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
|---|---|---|---|
| Current Model | 69.84 | 32.56 | 9,000 |
| MiniLM-L-2-v2 | 71.01 | 34.85 | 4,100 |
| MiniLM-L-4-v2 | 73.04 | 37.70 | 2,500 |
| … | … | … | … |
Limitations
While the model is powerful, it’s not perfect. Let’s talk about some of its limitations.
Limited Context Understanding
The model is trained on a specific task, MS Marco Passage Ranking, which means it might not perform well on other tasks or datasets.
Dependence on Passage Retrieval
The model relies on a good passage retrieval system, like ElasticSearch, to provide it with relevant passages to rank.
Limited Scalability
As you can see from the performance table, the model’s performance decreases as the number of passages to rank increases.
Usage
You can use this model with popular libraries like Transformers and SentenceTransformers. Here’s an example code snippet:
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2')])
Note that the performance metrics were computed on a V100 GPU.
Format
The model accepts input in the form of tokenized text sequences. This means you’ll need to pre-process your text data before feeding it into the model.
Input Requirements
To use this model, you’ll need to provide two inputs:
- A query (e.g. “How many people live in Berlin?“)
- A passage (e.g. “Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.“)
You can provide multiple passages for a single query, and the model will rank them in order of relevance.
Output
The model outputs a score for each passage, indicating how relevant it is to the query. You can use these scores to rank the passages and select the most relevant ones.
Code Example
Here’s an example of how to use the model with the Transformers library:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'],
['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.',
'New York City is famous for the Metropolitan Museum of Art.'],
padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
