Question

How to use @tensorflow-models/qna in Spanish

Posted on September 10, 2024
Machine Learning Spanish Deep Learning
Asked by EatingAquaShark

I’m interested in using the @tensorflow-models/qna model for a project, but I need it to work with Spanish text. Does anyone know if this model can handle Spanish inputs directly? I’m calling the model with a question and a context in Spanish but the model doesn’t answer, and I’m wondering if I’m doing something wrong. If not, is there a recommended way to train it on a Spanish language dataset for question answering? Any advice or resources would be greatly appreciated! Thanks in advance for your help.

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KFSys

Site Moderator

• September 12, 2024

Heya,

The @tensorflow-models/qna model, which is based on the BERT architecture, is primarily trained on English text and may not perform well with Spanish inputs directly. This model is designed for question answering tasks in English and, as you’ve noticed, it doesn’t handle Spanish inputs effectively out of the box.

For question answering in Spanish, you should consider using a model that is specifically trained on Spanish data. One popular option is the Spanish BERT model (bert-base-spanish), which can be used for various NLP tasks, including question answering.
Multilingual Models: Another option is to use a multilingual model like mBERT (multilingual BERT) or XLM-RoBERTa. These models are trained on multiple languages, including Spanish, and can handle inputs in various languages better than a model trained exclusively on English data.

Example Code for Using a Multilingual Model

Here’s a quick example of how you might use a multilingual model like XLM-RoBERTa for question answering:

from transformers import pipeline

# Load the multilingual question answering model
qa_pipeline = pipeline("question-answering", model="xlm-roberta-base", tokenizer="xlm-roberta-base")

# Define your context and question in Spanish
context = "El sol es una estrella que está en el centro del sistema solar."
question = "¿Qué es el sol?"

# Get the answer from the model
result = qa_pipeline(question=question, context=context)
print(result['answer'])