README.md

---
language:
- en
license: apache-2.0
tags:
- question-answering
datasets:
- adversarial_qa
- mbartolo/synQA
- squad
metrics:
- exact_match
- f1
model-index:
- name: mbartolo/roberta-large-synqa
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad
      type: squad
      config: plain_text
      split: validation
    metrics:
    - type: exact_match
      value: 89.6529
      name: Exact Match
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjM2NTJjZDk0ZWEwM2Q5Njk5NmY5Mzk4ODk4OTViMjZlODlkMTM4M2ZlM2Q0YjgwMWY4OGUzM2QwYTk0YTBhMSIsInZlcnNpb24iOjF9.ZafZxhyJS2xpjYDMhyTO8wVmeZJrwbeJmyvZypMbhUJORR194GJwgttUp150XG3MUFVFqPYQh8tuzpm_QQ6sAA
    - type: f1
      value: 94.8172
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWE3OGFlYWZmZThhNDMwYzU4OTY3NmU4NmNlYTcwODVkZmQ3N2FlZmE0NGM2Mzk3Nzc2ZmZmNzhkM2NiNzNiMCIsInZlcnNpb24iOjF9.LF4-uxpGMMr7oP_C_SAYHgKMw6I9Sz8FiRnofaD9WFkQZrGPaPR1HjvC6sWo2Nyy5uuD76bowY278Qf8kWwLBw
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: adversarial_qa
      type: adversarial_qa
      config: adversarialQA
      split: validation
    metrics:
    - type: exact_match
      value: 55.3333
      name: Exact Match
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmIwZTdmNzg4MmE5YjM2MzkxOWFmM2JmODMzZDhhZGY5YWE0Njc2MmY0YzIyNzEwMGU0MDIwOTZjZTEyZjk5YSIsInZlcnNpb24iOjF9.dNd-MElaXPRrYSgvzxcMyN87ts0iyON4mdQChv68AIspmQKAUKRVzdm7w0mhRyvzG8a7aDl7dgUFCZVxd7-FAQ
    - type: f1
      value: 66.7464
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDU4ZTE0MTliNmJjYmFjZmI4MjEwMWRiMjJmZjhjYzBkY2Q0ZGUzMzZlMTZkNmFlZThmYzMyMThjN2IwMjI3NSIsInZlcnNpb24iOjF9.A4AxMaEXNDRZaR_ZazFH3PUhi-jn0JniWv7xEXGM3oidhR6hsWNi5twqegAAuZe56YDPxCUhuoGahovcWmoaBQ
---

# 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, 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.