[Question]: How to deal with utf-8 language?

[284nnuS]

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Question

Hello,

I'm trying to test accuracy in phase "find the right document" over than 50 sequence model with llama index in Vietnamese with xsquad_dataset but the accuracy highest is 30%.
Can you give me some advise to enhance the accuracy with utf-8 ? :(

Thanks

[Disiok]

Could you share some sample documents and queries? Would help us reproduce any issue.

[284nnuS]

Sure, I will give it for you

[dosubot[bot]]

🤖

Hello,

Thank you for reaching out and for your interest in improving the accuracy of the LlamaIndex with Vietnamese language processing. Here are some suggestions that might help:

1. Language-Specific Model: If there's a Vietnamese-specific model available from OpenAI or another provider, consider using it instead of the general-purpose "gpt-3.5-turbo" model.

2. Increase Training Data: If the xsquad_dataset for Vietnamese is small, you might want to augment it with additional Vietnamese language data.

3. Fine-tuning: Fine-tune the model on the specific task of Vietnamese language processing. The current code does not seem to include any fine-tuning steps.

4. Adjust Parameters: You could experiment with different parameters. For example, try increasing the top_k parameter in the generate_ce_fine_tuning_dataset function, which controls the number of retrieved nodes. You could also experiment with the temperature parameter in the OpenAI function, which controls the randomness of the model's output.

5. Improve Preprocessing: Enhance the preprocessing of the data. This could include better tokenization, handling of Vietnamese diacritics, etc.

6. Improve Keyword Extraction: The LlamaIndex uses a keyword-table based index. It extracts keywords from the source text and the query text, and then tries to match those keywords. Improving the keyword extraction process could potentially increase the accuracy of the LlamaIndex. This could involve using more sophisticated methods for keyword extraction or fine-tuning the keyword extraction process for the Vietnamese language. You can also experiment with different retriever modes (DEFAULT, SIMPLE, RAKE) in the as_retriever method of the BaseKeywordTableIndex class.

For the keyword extraction, the current method in the LlamaIndex uses a simpler technique that doesn't involve GPT - it uses regex instead. The _extract_keywords method in the SimpleKeywordTableIndex class uses the simple_extract_keywords function to extract keywords from the text. To improve this for Vietnamese language processing, you could consider enhancing the regex patterns used in the simple_extract_keywords function to better capture Vietnamese keywords. This could involve using Vietnamese-specific patterns or adjusting the existing patterns to better suit the structure and syntax of the Vietnamese language.

Please note that these are general suggestions and their effectiveness may vary depending on the specific characteristics of the xsquad_dataset and the nature of the Vietnamese language processing task.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Best regards,
Dosu

Sources

• llama_index/finetuning/cross_encoders/dataset_gen.py
• llama_index/indices/knowledge_graph/base.py
• llama_index/indices/keyword_table/base.py
• llama_index/indices/keyword_table/simple_base.py

---

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[dosubot[bot]]

🤖

Hello,

To improve the accuracy of the LlamaIndex when using it with the xsquad_dataset for Vietnamese language processing, you can consider the following steps:

1. Language-Specific Model: Use a language-specific model for Vietnamese if available. The current code uses the "gpt-3.5-turbo" model which is a general-purpose model. If OpenAI or another provider offers a Vietnamese-specific model, it might improve the accuracy.

2. Increase Training Data: Increase the amount of training data. If the xsquad_dataset for Vietnamese is small, consider augmenting it with additional Vietnamese language data.

3. Fine-tuning: Fine-tune the model on the specific task of Vietnamese language processing. The current code does not seem to include any fine-tuning steps.

4. Adjust Parameters: Experiment with different parameters. For example, you could try increasing the top_k parameter in the generate_ce_fine_tuning_dataset function, which controls the number of retrieved nodes. You could also experiment with the temperature parameter in the OpenAI function, which controls the randomness of the model's output.

5. Improve Preprocessing: Improve the preprocessing of the data. This could include better tokenization, handling of Vietnamese diacritics, etc.

6. Keyword Extraction: In the context shared, the LlamaIndex uses a GPT model to extract keywords from the text. You could experiment with different keyword extraction methods or adjust the max_keywords_per_chunk parameter in the BaseKeywordTableIndex class. You could also try different retriever modes such as KeywordTableRetrieverMode.SIMPLE or KeywordTableRetrieverMode.RAKE in the as_retriever method.

7. Knowledge Graph Index: In the context shared, the LlamaIndex also uses a Knowledge Graph Index. This index extracts triplets from the text and uses them to build a knowledge graph. You could experiment with the max_triplets_per_chunk parameter, which controls the maximum number of triplets to extract. You could also experiment with the include_embeddings parameter, which controls whether to include embeddings in the index. If the kg_triplet_extract_fn function is provided, it will be used for extracting triplets, otherwise, the _llm_extract_triplets method will be used.

Please note that these are general suggestions and their effectiveness may vary depending on the specific characteristics of the xsquad_dataset and the nature of the Vietnamese language processing task.

Unfortunately, without more specific information about the nature of the inaccuracies and the specific task, it's difficult to provide more targeted advice.

Sources:

• llama_index/finetuning/cross_encoders/dataset_gen.py
• llama_index/indices/keyword_table/base.py
• llama_index/indices/knowledge_graph/base.py

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Best,
Dosu

Sources

• llama_index/finetuning/cross_encoders/dataset_gen.py
• llama_index/indices/keyword_table/base.py
• llama_index/indices/knowledge_graph/base.py
• llama_index/text_splitter/token_splitter.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.