Instructions to use mbartolo/roberta-large-synqa-ext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbartolo/roberta-large-synqa-ext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mbartolo/roberta-large-synqa-ext", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mbartolo/roberta-large-synqa-ext") model = AutoModelForQuestionAnswering.from_pretrained("mbartolo/roberta-large-synqa-ext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
license: apache-2.0
tags:
- question-answering
datasets:
- UCLNLP/adversarial_qa
- mbartolo/synQA
- squad
metrics:
- exact_match
- f1
model-index:
- name: mbartolo/roberta-large-synqa-ext
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: adversarial_qa
type: adversarial_qa
config: adversarialQA
split: validation
metrics:
- type: exact_match
value: 53.2
name: Exact Match
verified: true
- type: f1
value: 64.6266
name: F1
verified: true
Model Overview
This is a RoBERTa-Large QA Model trained from https://huggingface.co/roberta-large in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD as well as Wikipedia passages external to SQuAD, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.00293) in a second stage of fine-tuning.
Data
Training data: SQuAD + AdversarialQA Evaluation data: SQuAD + AdversarialQA
Training Process
Approx. 1 training epoch on the synthetic data and 2 training epochs on the manually-curated data.
Additional Information
Please refer to https://arxiv.org/abs/2104.08678 for full details.