As we delve into the realm of Japanese language models, it’s essential to evaluate their performance to ensure that they meet contemporary standards. In this article, we’ll walk you through a straightforward process of evaluating various models using the MIRACL and hotchpotchJQaRA datasets. We’ll also include an analogy to help you better understand the code implementation.

Understanding the Evaluation Metrics

• nDCG@10: This measures the rank of the correctly predicted items at the top 10 positions.
• Recall: It indicates the ability of the model to find all relevant instances from the dataset.
• MRR: Mean Reciprocal Rank assesses the average rank of the first correct answer.

Results Summary

Here is a summary of the performance metrics for the models evaluated on the MIRACL and hotchpotchJQaRA datasets:

Model                                      nDCG@10      Recall@1000    Recall@5    Recall@30
--------------------------------------------------------------
BM25                                       0.369         0.931          -           -
splade-japanese                            0.405         0.931          0.406       0.663
splade-japanese-efficient                   0.408         0.954          0.419       0.718
splade-japanese-v2                         0.580         0.967          0.629       0.844
splade-japanese-v2-doc                     0.478         0.930          0.514       0.759
splade-japanese-v3                          0.604         0.979          0.647       0.877

The Coding Analogy: Crafting Recipes

Imagine you are a chef, looking to create the perfect dish. Each model we evaluate can be compared to a unique recipe. The ingredients are the data, the process is the code, and the final dish represents the model’s output. Just as a recipe requires precise measurements and techniques, our model requires specific input data and steps to yield optimal results.

Let’s break down the coding process in our evaluation:

• You start by gathering your ingredients (data) using the required libraries.
• Next, you prep the ingredients — this corresponds to encoding the query.
• Finally, you follow the cooking instructions to see how well your dish turns out (model evaluation).

Running the Code for Query Expansion

If you’re itching to dive deeper, here’s how to run the code for expanding queries or documents:

!pip install fugashi ipadic unidic-lite

from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch
import numpy as np

model = AutoModelForMaskedLM.from_pretrained("aken12/splade-japanese-v3")
tokenizer = AutoTokenizer.from_pretrained("aken12/splade-japanese-v3")
vocab_dict = {v: k for k, v in tokenizer.get_vocab().items()}

def encode_query(query):
    query = tokenizer(query, return_tensors='pt')
    output = model(**query, return_dict=True).logits
    output, _ = torch.max(torch.log(1 + torch.relu(output)) * query['attention_mask'].unsqueeze(-1), dim=1)
    return output

with torch.no_grad():
    model_output = encode_query(query="Please enter your query here.")
    reps = model_output
    idx = torch.nonzero(reps[0], as_tuple=False)
    dict_splade = {}
    for i in idx:
        token_value = reps[0][i[0]].item()
        if token_value > 0:
            token = vocab_dict[int(i[0])]
            dict_splade[token] = float(token_value)

sorted_dict_splade = sorted(dict_splade.items(), key=lambda item: item[1], reverse=True)

for token, value in sorted_dict_splade:
    print(token, value)

Troubleshooting Tips

If you encounter issues during evaluation or running the code, here are some troubleshooting ideas:

• Ensure Libraries are Installed: Double-check that all necessary libraries are installed properly.
• Correct Model Names: Verify you are using the correct model names to prevent errors.
• Input Data Format: Make sure the input data is formatted correctly for the model to understand.

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Conclusion

Utilizing datasets like MIRACL and hotchpotchJQaRA to evaluate Japanese language models not only propels our understanding but also enhances the efficiency of AI applications. 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.