README.md

---
language:
- en
license: mit
library_name: transformers
tags:
- deberta
- deberta-v3
- question-answering
- squad
- squad_v2
- lora
- peft
datasets:
- squad_v2
- squad
base_model: microsoft/deberta-v3-large
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