README.md

---
language: en
license: cc-by-4.0
tags:
- deberta
- deberta-v3
datasets:
- squad_v2
base_model: microsoft/deberta-v3-base
model-index:
- name: deepset/deberta-v3-base-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: 83.8248
      name: Exact Match
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2IyZTEyYzNlOTAwZmFlNWRiZTdiNzQzMTUyM2FmZTQ3ZWQwNWZmMzc2ZDVhYWYyMzkxOTUyMGNlMWY0M2E5MiIsInZlcnNpb24iOjF9.y8KvfefMLI977BYun0X1rAq5qudmezW_UJe9mh6sYBoiWaBosDO5TRnEGR1BHzdxmv2EgPK_PSomtZvb043jBQ
    - type: f1
      value: 87.41
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWVhNjAwM2Q5N2Y3MGU4ZWY3N2Y0MmNjYWYwYmQzNTdiYWExODhkYmQ1YjIwM2I1ODEzNWIxZDI1ZWQ1YWRjNSIsInZlcnNpb24iOjF9.Jk0v1ZheLRFz6k9iNAgCMMZtPYj5eVwUCku4E76wRYc-jHPmiUuxvNiNkn6NW-jkBD8bJGMqDSjJyVpVMn9pBA
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad
      type: squad
      config: plain_text
      split: validation
    metrics:
    - type: exact_match
      value: 84.9678
      name: Exact Match
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWUxYTg4MzU3YTdmMDRmMGM0NjFjMTcwNGM3YzljM2RkMTc1ZGNhMDQwMTgwNGI0ZDE4ZGMxZTE3YjY5YzQ0ZiIsInZlcnNpb24iOjF9.KKaJ1UtikNe2g6T8XhLoWNtL9X4dHHyl_O4VZ5LreBT9nXneGc21lI1AW3n8KXTFGemzRpRMvmCDyKVDHucdDQ
    - type: f1
      value: 92.2777
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDU0ZTQwMzg4ZDY1ZWYxOGIxMzY2ODljZTBkMTNlYjA0ODBjNjcxNTg3ZDliYWU1YTdkYTM2NTIxOTg1MGM4OCIsInZlcnNpb24iOjF9.8VHg1BXx6gLw_K7MUK2QSE80Y9guiVR8n8K8nX4laGsLibxv5u_yDv9F3ahbUa1eZG_bbidl93TY2qFUiYHtAQ
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: adversarial_qa
      type: adversarial_qa
      config: adversarialQA
      split: validation
    metrics:
    - type: exact_match
      value: 30.733
      name: Exact Match
    - type: f1
      value: 44.099
      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: 79.295
      name: Exact Match
    - type: f1
      value: 86.609
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts amazon
      type: squadshifts
      config: amazon
      split: test
    metrics:
    - type: exact_match
      value: 68.680
      name: Exact Match
    - type: f1
      value: 83.832
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts new_wiki
      type: squadshifts
      config: new_wiki
      split: test
    metrics:
    - type: exact_match
      value: 80.171
      name: Exact Match
    - type: f1
      value: 90.452
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts nyt
      type: squadshifts
      config: nyt
      split: test
    metrics:
    - type: exact_match
      value: 81.570
      name: Exact Match
    - type: f1
      value: 90.644
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts reddit
      type: squadshifts
      config: reddit
      split: test
    metrics:
    - type: exact_match
      value: 66.990
      name: Exact Match
    - type: f1
      value: 80.231
      name: F1
---

# deberta-v3-base for Extractive QA 

This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) 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 Question Answering. 


## Overview
**Language model:** deberta-v3-base  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Code:**  See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)  
**Infrastructure**: 1x NVIDIA A10G

## Hyperparameters

```
batch_size = 12
n_epochs = 4
base_LM_model = "deberta-v3-base"
max_seq_len = 512
learning_rate = 2e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride = 128
max_query_length = 64
``` 

## Usage

### In Haystack
Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. 
To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/):
```python
# After running pip install haystack-ai "transformers[torch,sentencepiece]"

from haystack import Document
from haystack.components.readers import ExtractiveReader

docs = [
    Document(content="Python is a popular programming language"),
    Document(content="python ist eine beliebte Programmiersprache"),
]

reader = ExtractiveReader(model="deepset/roberta-base-squad2")
reader.warm_up()

question = "What is a popular programming language?"
result = reader.run(query=question, documents=docs)
# {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]}
```
For a complete example with an extractive question answering pipeline that scales over many documents, check out the [corresponding Haystack tutorial](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline).

### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/roberta-base-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Why is model conversion important?',
    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

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


## Authors
**Sebastian Lee:** sebastian.lee [at] deepset.ai  
**Timo Möller:** timo.moeller [at] deepset.ai  
**Malte Pietsch:** malte.pietsch [at] deepset.ai  

## About us

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[deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/).

Some of our other work: 
- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2)
- [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1)
- [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio)

## Get in touch and join the Haystack community

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We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>

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