---
language:
- en
license: mit
library_name: transformers
tags:
- deberta
- deberta-v3
- question-answering
- squad
- squad_v2
- mrqa
- synQA
- adversarial_qa
datasets:
- squad_v2
- squad
- mrqa
- mbartolo/synQA
- UCLNLP/adversarial_qa
- newsqa
- trivia_qa
- search_qa
- hotpot_qa
- natural_questions
pipeline_tag: question-answering
base_model: microsoft/deberta-v3-base
model-index:
- name: sjrhuschlee/deberta-v3-base-squad2-ext-v1
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_v2
      type: squad_v2
      config: squad_v2
      split: validation
    metrics:
    - type: exact_match
      value: 79.483
      name: Exact Match
    - type: f1
      value: 82.343
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad
      type: squad
      config: plain_text
      split: validation
    metrics:
    - type: exact_match
      value: 87.985
      name: Exact Match
    - type: f1
      value: 93.651
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: adversarial_qa
      type: adversarial_qa
      config: adversarialQA
      split: validation
    metrics:
    - type: exact_match
      value: 47.533
      name: Exact Match
    - type: f1
      value: 59.838
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_adversarial
      type: squad_adversarial
      config: AddOneSent
      split: validation
    metrics:
    - type: exact_match
      value: 84.723
      name: Exact Match
    - type: f1
      value: 89.78
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts amazon
      type: squadshifts
      config: amazon
      split: test
    metrics:
    - type: exact_match
      value: 74.851
      name: Exact Match
    - type: f1
      value: 87.448
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts new_wiki
      type: squadshifts
      config: new_wiki
      split: test
    metrics:
    - type: exact_match
      value: 83.396
      name: Exact Match
    - type: f1
      value: 91.996
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts nyt
      type: squadshifts
      config: nyt
      split: test
    metrics:
    - type: exact_match
      value: 83.934
      name: Exact Match
    - type: f1
      value: 92.234
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts reddit
      type: squadshifts
      config: reddit
      split: test
    metrics:
    - type: exact_match
      value: 75.008
      name: Exact Match
    - type: f1
      value: 86.12
      name: F1
---

# deberta-v3-base for Extractive QA

This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) model, fine-tuned using the SQuAD 2.0, MRQA, AdversarialQA, and SynQA datasets. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.

## Overview
**Language model:** deberta-v3-base  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0, MRQA, AdversarialQA, SynQA  
**Eval data:** SQuAD 2.0  
**Infrastructure**: 1x NVIDIA 3070  

## Model Usage
```python
import torch
from transformers import(
  AutoModelForQuestionAnswering,
  AutoTokenizer,
  pipeline
)
model_name = "sjrhuschlee/deberta-v3-base-squad2-ext-v1"

# a) Using pipelines
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
qa_input = {
'question': 'Where do I live?',
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# {'score': 0.984, 'start': 30, 'end': 37, 'answer': ' London'}

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

question = 'Where do I live?'
context = 'My name is Sarah and I live in London'
encoding = tokenizer(question, context, return_tensors="pt")
start_scores, end_scores = model(
  encoding["input_ids"],
  attention_mask=encoding["attention_mask"],
  return_dict=False
)

all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
# 'London'
```

## Dataset Preparation

The MRQA dataset was updated to fix some errors and formatting to work with the `run_qa.py` example script provided in the Hugging Face Transformers library.
The changes included
- Updating incorrect answer starts locations (usually off by a few characters)
- Updating the answer text to exactly match the text found in the context

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Framework versions

- Transformers 4.31.0.dev0