README.md

metadata

license: mit
datasets:
  - squad_v2
  - squad
language:
  - en
library_name: transformers
tags:
  - deberta
  - deberta-v3
  - question-answering
  - squad
  - squad_v2
model-index:
  - name: sjrhuschlee/deberta-v3-large-squad2
    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: 87.956
            name: Exact Match
          - type: f1
            value: 90.776
            name: F1
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad
          type: squad
          config: plain_text
          split: validation
        metrics:
          - type: exact_match
            value: 89.29
            name: Exact Match
          - type: f1
            value: 94.985
            name: F1

deberta-v3-large for Extractive QA

This is the deberta-v3-large 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.

This model was trained using LoRA available through the PEFT library.

Overview

Language model: deberta-v3-large
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Infrastructure: 1x NVIDIA 3070

Model Usage

Using the Merged Model

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "sjrhuschlee/deberta-v3-large-squad2"

# 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)

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

Using with Peft

NOTE: This requires code in the PR https://github.com/huggingface/peft/pull/473 for the PEFT library.

#!pip install peft

from peft import LoraConfig, PeftModelForQuestionAnswering
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model_name = "sjrhuschlee/deberta-v3-large-squad2"