Welcome to our guide on leveraging the power of the ColBERT model for MS Marco Passage Ranking using Vespa.ai. In this tutorial, we will walk you through the process of setting up your environment and optimizing your passage search capabilities. Let’s dive in!

Understanding the Foundations

The ColBERT model stands on the shoulders of BERT, which allows it to perform efficient and effective passage search. The model utilizes a late interaction mechanism while contextualizing query results. Think of it as a smart librarian who efficiently indexes books (representing passages) and can quickly fetch relevant information when a query is made.

Prerequisites

• Python 3.6 or higher
• Pip for managing dependencies
• A working installation of Vespa.ai
• Access to MS Marco datasets

Setting Up the Model

Before we execute any code, make sure you have all the prerequisites in place. Once confirmed, let’s start setting up the ColBERT model as follows:

from transformers import BertModel, BertPreTrainedModel, BertConfig
import torch
import torch.nn as nn

class VespaColBERT(BertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.bert = BertModel(config)
        self.linear = nn.Linear(config.hidden_size, 32, bias=False)
        self.init_weights()

    def forward(self, input_ids, attention_mask):
        Q = self.bert(input_ids, attention_mask=attention_mask)[0]
        Q = self.linear(Q)
        return torch.nn.functional.normalize(Q, p=2, dim=2)

colbert_query_encoder = VespaColBERT.from_pretrained('vespa-engine/colbert-medium')

Exporting the Model to ONNX

Next, you’ll want to export the model to ONNX format for deployment in Vespa. This step allows you to serve the model efficiently without losing any performance.

# Export model to ONNX for serving in Vespa
input_names = ['input_ids', 'attention_mask']
output_names = ['contextual']

# Prepare input
input_ids = torch.ones(1, 32, dtype=torch.int64)
attention_mask = torch.ones(1, 32, dtype=torch.int64)
args = (input_ids, attention_mask)

torch.onnx.export(colbert_query_encoder,
                  args=args,
                  f='query_encoder_colbert.onnx',
                  input_names=input_names,
                  output_names=output_names,
                  dynamic_axes={
                      'input_ids': {0: 'batch'},
                      'attention_mask': {0: 'batch'},
                      'contextual': {0: 'batch'}
                  },
                  opset_version=11)

Integrating with Vespa.ai

Once you have the ONNX model, it can be easily integrated into your Vespa.ai application. This allows you to harness the model’s full capabilities for MS Marco Passage Ranking.

Performance Metrics

When utilizing ColBERT on Vespa.ai, your model can achieve the following performance metrics:

• Dev MRR@10: 0.354
• Eval MRR@10: 0.347
• BM25 Baseline on Eval: 0.16

Troubleshooting

If you encounter issues like incorrect model deployment or performance dips, consider the following troubleshooting tips:

• Ensure you are using the correct ONNX opset version compatible with Vespa.ai.
• Check input dimensions and types to match the expected format.
• Review the training routines and datasets—properly formatted and preprocessed data is critical.
• For further assistance, stay connected with **fxis.ai** for insights and collaboration on AI projects.

At **fxis.ai**, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.