enochyearn/MLX_RoBERTa
Roberta Question Answering using MLX.
This is a developer tool. It helps machine learning engineers and data scientists build question-answering systems using the RoBERTa model on Apple silicon with MLX. You input a question and a text passage, and it outputs the exact answer extracted from the passage. This is ideal for developers creating applications that need to quickly find specific information within text.
Use this if you are a developer looking to integrate a RoBERTa-based question-answering model into your MLX-powered application, especially for deployment on Apple devices.
Not ideal if you are a non-technical user looking for a ready-to-use application to answer questions, or if you need to train new models without writing code.
Stars
24
Forks
—
Language
Python
License
MIT
Category
Last pushed
Feb 22, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/enochyearn/MLX_RoBERTa"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
patil-suraj/question_generation
Neural question generation using transformers
vinhkhuc/MemN2N-babi-python
End-To-End Memory Networks for bAbI question-answering tasks
nelson-liu/paraphrase-id-tensorflow
Various models and code (Manhattan LSTM, Siamese LSTM + Matching Layer, BiMPM) for the...
YuriyGuts/kaggle-quora-question-pairs
My solution to Kaggle Quora Question Pairs competition (Top 2%, Private LB log loss 0.13497).
krishnap25/mauve
Package to compute Mauve, a similarity score between neural text and human text. Install with...