Ms Marco TinyBERT L 2 V2
Ms Marco TinyBERT L 2 V2 is a powerful AI model designed for Information Retrieval tasks. It's trained on the MS Marco Passage Ranking task, which enables it to effectively rank passages based on their relevance to a given query. With a remarkable performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset, this model achieves an NDCG@10 score of 69.84 and an MRR@10 score of 32.56. What's impressive is its speed, processing up to 9,000 documents per second, making it a great choice for applications that require fast and accurate results. By leveraging the power of TinyBERT, this model offers a unique combination of efficiency and performance, making it an excellent option for those looking to improve their information retrieval capabilities.
Table of Contents
Model Overview
The Current Model is designed to help you find the most relevant information. It’s like having a super-smart librarian who can sort through a huge library of passages and give you the best matches for your query.
Capabilities
What can it do?
- Information Retrieval: Given a question or query, the model can encode it along with all possible passages and then rank them in order of relevance.
- Passage Ranking: It can help you find the most relevant passages from a large collection, making it perfect for tasks like search engines or question-answering systems.
How does it work?
Imagine you have a query, like “How many people live in Berlin?” and a large collection of passages. The Current Model can help you sort these passages in order of relevance. It does this by encoding the query and each passage, and then comparing them to determine which passages are most relevant.
Performance
The Current Model is designed to excel in Information Retrieval tasks, and its performance is a testament to its capabilities. Let’s dive into the numbers and see how it stacks up against other models.
| Model | Docs / Sec |
|---|---|
| Current Model | 9,000 |
| MiniLM-L-2-v2 | 4,100 |
| MiniLM-L-4-v2 | 2,500 |
| MiniLM-L-6-v2 | 1,800 |
As you can see, the Current Model outperforms many other models in terms of speed.
Accuracy
But speed is only half the story. How accurate is the Current Model in retrieving relevant passages? The answer is: very accurate! It achieves an NDCG@10 score of 69.84 on the TREC Deep Learning 2019 dataset, and an MRR@10 score of 32.56 on the MS Marco Passage Reranking dataset.
Comparison to Other Models
How does the Current Model compare to other models? Let’s take a look:
| Model | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) |
|---|---|---|
| Current Model | 69.84 | 32.56 |
| ==nboost/pt-bert-base-uncased-msmarco== | 70.94 | 34.75 |
| ==Capreolus/electra-base-msmarco== | 71.23 | 36.89 |
As you can see, the Current Model is not always the top-performing model. Depending on the specific task and dataset, other models might be a better choice.
Limitations
While the Current Model is a powerful tool, it’s not perfect. Let’s take a closer look at some of its limitations.
Limited Context Understanding
While the Current Model can understand the context of a query and a passage, it may not always grasp the nuances of human language. For example, if a query is phrased in a way that’s open to interpretation, the Current Model might not always provide the most accurate results.
Getting Started
To use the Current Model, you can use the following code:
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)
Alternatively, you can use the SentenceTransformers library for an easier integration:
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2'), ('Query', 'Paragraph3')])
