README.md

metadata

license: mit
datasets:
  - squad_v2
  - squad
  - mrqa
  - mbartolo/synQA
  - adversarial_qa
language:
  - en
library_name: transformers
pipeline_tag: question-answering
tags:
  - deberta
  - deberta-v3
  - question-answering
  - squad
  - squad_v2
  - mrqa
  - synQA
  - adversarial_qa
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: 85.894
            name: Exact Match
          - type: f1
            value: 91.298
            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: 44.867
            name: Exact Match
          - type: f1
            value: 55.996
            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: 80.19
            name: Exact Match
          - type: f1
            value: 85.028
            name: F1
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squadshifts
          type: squadshifts
          config: amazon
          split: test
        metrics:
          - type: exact_match
            value: 69.712
            name: Exact Match
          - type: f1
            value: 81.171
            name: F1
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squadshifts
          type: squadshifts
          config: new_wiki
          split: test
        metrics:
          - type: exact_match
            value: 81.544
            name: Exact Match
          - type: f1
            value: 89.782
            name: F1
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squadshifts
          type: squadshifts
          config: nyt
          split: test
        metrics:
          - type: exact_match
            value: 80.05
            name: Exact Match
          - type: f1
            value: 87.756
            name: F1
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squadshifts
          type: squadshifts
          config: reddit
          split: test
        metrics:
          - type: exact_match
            value: 60.481
            name: Exact Match
          - type: f1
            value: 68.686
            name: F1

deberta-v3-base for Extractive QA

This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. 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

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'

Metrics

# Squad v2
{
    "eval_HasAns_exact": 84.36234817813765,
    "eval_HasAns_f1": 90.09079905537246,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 74.61732548359966,
    "eval_NoAns_f1": 74.61732548359966,
    "eval_NoAns_total": 5945,
    "eval_best_exact": 79.45759285774446,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 82.31771724081922,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 79.48286027120358,
    "eval_f1": 82.34298465427844,
    "eval_runtime": 109.7262,
    "eval_samples": 11951,
    "eval_samples_per_second": 108.917,
    "eval_steps_per_second": 4.539,
    "eval_total": 11873
}

# Squad
{
    "eval_exact": 85.89403973509934,
    "eval_f1": 91.2982923196374,
    "eval_runtime": 96.6499,
    "eval_samples": 10618,
    "eval_samples_per_second": 109.86,
    "eval_steps_per_second": 4.584,
    "eval_total": 10570
}

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