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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
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts
type: squadshifts
config: amazon
split: test
metrics:
- type: exact_match
value: 66.272
name: Exact Match
- type: f1
value: 77.941
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts
type: squadshifts
config: new_wiki
split: test
metrics:
- type: exact_match
value: 81.456
name: Exact Match
- type: f1
value: 89.142
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts
type: squadshifts
config: nyt
split: test
metrics:
- type: exact_match
value: 81.739
name: Exact Match
- type: f1
value: 88.826
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts
type: squadshifts
config: reddit
split: test
metrics:
- type: exact_match
value: 61.4
name: Exact Match
- type: f1
value: 69.999
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'
```
## Metrics
```bash
# Squad v2
{
"eval_HasAns_exact": 84.83468286099865,
"eval_HasAns_f1": 90.48374860633226,
"eval_HasAns_total": 5928,
"eval_NoAns_exact": 91.0681244743482,
"eval_NoAns_f1": 91.0681244743482,
"eval_NoAns_total": 5945,
"eval_best_exact": 87.95586625115808,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 90.77635490089573,
"eval_best_f1_thresh": 0.0,
"eval_exact": 87.95586625115808,
"eval_f1": 90.77635490089592,
"eval_runtime": 623.1333,
"eval_samples": 11951,
"eval_samples_per_second": 19.179,
"eval_steps_per_second": 0.799,
"eval_total": 11873
}
# Squad
{
"eval_exact_match": 89.29044465468307,
"eval_f1": 94.9846365606959,
"eval_runtime": 553.7132,
"eval_samples": 10618,
"eval_samples_per_second": 19.176,
"eval_steps_per_second": 0.8
}
```
### 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"
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
### LoRA Config
```
{
"base_model_name_or_path": "microsoft/deberta-v3-large",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"lora_alpha": 32,
"lora_dropout": 0.1,
"modules_to_save": ["qa_outputs"],
"peft_type": "LORA",
"r": 8,
"target_modules": [
"query_proj",
"key_proj",
"value_proj",
"dense"
],
"task_type": "QUESTION_ANS"
}
```
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3