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---
license: mit
datasets:
- squad_v2
- squad
language:
- en
library_name: transformers
tags:
- deberta
- deberta-v3
- question-answering
- squad
- squad_v2
- lora
- peft
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
- task:
type: question-answering
name: Question Answering
dataset:
name: adversarial_qa
type: adversarial_qa
config: adversarialQA
split: validation
metrics:
- type: exact_match
value: 31.167
name: Exact Match
- type: f1
value: 41.787
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: 75.993
name: Exact Match
- type: f1
value: 80.495
name: F1
---
# deberta-v3-large for Extractive QA
This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) 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](https://github.com/huggingface/peft).
## 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 Transformers
This uses the merged weights (base model weights + LoRA weights) to allow for simple use in Transformers pipelines. It has the same performance as using the weights separately when using the PEFT library.
```python
import torch
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)
# {'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'
```
### Using with Peft
**NOTE**: This requires code in the PR https://github.com/huggingface/peft/pull/473 for the PEFT library.
```python
#!pip install peft
from peft import LoraConfig, PeftModelForQuestionAnswering
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model_name = "sjrhuschlee/deberta-v3-large-squad2"
``` |