# bart-base-few-shot-k-256-finetuned-squad-seed-2 Open-Source Question-Answering Model - Efficient Answering in Few-Shot Scenarios

Bart Base Few Shot K 256 Finetuned Squad Seed 2

Developed by anas-awadalla

A question-answering model based on the BART-base architecture, fine-tuned on the SQuAD dataset, suitable for few-shot learning scenarios

Question Answering System

Transformers

Open Source License:Apache-2.0 #Question Answering System Fine-tuning #Few-shot Learning #Reading Comprehension Optimization

Downloads 13

Release Time : 9/30/2022

Model Overview

This model is a fine-tuned version of facebook/bart-base on the SQuAD question-answering dataset, specifically optimized for few-shot learning scenarios with k=256 and a random seed of 2.

Model Features

Few-shot Learning Optimization

Specially fine-tuned for few-shot learning scenarios with k=256

Based on SQuAD Dataset

Trained on the high-quality SQuAD question-answering dataset, possessing excellent question-answering capabilities

Reproducibility

Trained with a fixed random seed (seed=2) to ensure reproducible results

Model Capabilities

Question Answering Generation

Text Understanding

Few-shot Learning

Use Cases

Education

Automated Answering System

Used in educational fields for automated question-answering systems to respond to student inquiries

Customer Service

Intelligent Customer Service Q&A

Building a customer service Q&A system based on few examples

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Bart Base Few Shot K 256 Finetuned Squad Seed 2

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 bart-base-few-shot-k-256-finetuned-squad-seed-2

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Training and evaluation data

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