
# deberta-v3-base for 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 QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)  
**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 NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/deberta-v3-base-squad2")
# or 
reader = TransformersReader(model_name_or_path="deepset/deberta-v3-base-squad2",tokenizer="deepset/deberta-v3-base-squad2")
```

### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/deberta-v3-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
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/>
     </div>
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/>
     </div>
</div>

[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.


Some of our other work: 
- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)

## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. 

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

[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) 
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transformers pytorch safetensors deberta-v2 question-answering deberta deberta-v3 en dataset:squad_v2 base_model:microsoft/deberta-v3-base license:cc-by-4.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #base_model-microsoft/deberta-v3-base #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 


# deberta-v3-base for QA 

This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 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 QA pipeline on Haystack  
Infrastructure: 1x NVIDIA A10G

## Hyperparameters

 

## Usage

### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:


### In Transformers


## Authors
Sebastian Lee: URL [at] URL  
Timo Möller: timo.moeller [at] URL  
Malte Pietsch: malte.pietsch [at] URL  

## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
</div>

deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.


Some of our other work: 
- Distilled roberta-base-squad2 (aka "tinyroberta-squad2")
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")

## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p>

Twitter | LinkedIn | Discord | GitHub Discussions | Website 
# deberta-v3-base for QA 

This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 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 QA pipeline on Haystack  
Infrastructure: 1x NVIDIA A10G ## Hyperparameters ## Usage ### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack: ### In Transformers ## Authors
Sebastian Lee: URL [at] URL  
Timo Möller: timo.moeller [at] URL  
Malte Pietsch: malte.pietsch [at] URL ## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
</div>

deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.


Some of our other work: 
- Distilled roberta-base-squad2 (aka "tinyroberta-squad2")
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p>

Twitter | LinkedIn | Discord | GitHub Discussions | Website
TAGS
#transformers #pytorch #safetensors #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #base_model-microsoft/deberta-v3-base #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
 # deberta-v3-base for QA 

This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 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 QA pipeline on Haystack  
Infrastructure: 1x NVIDIA A10G ## Hyperparameters ## Usage ### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack: ### In Transformers ## Authors
Sebastian Lee: URL [at] URL  
Timo Möller: timo.moeller [at] URL  
Malte Pietsch: malte.pietsch [at] URL ## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
</div>

deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.


Some of our other work: 
- Distilled roberta-base-squad2 (aka "tinyroberta-squad2")
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p>

Twitter | LinkedIn | Discord | GitHub Discussions | Website
passage: TAGS
#transformers #pytorch #safetensors #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #base_model-microsoft/deberta-v3-base #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
# deberta-v3-base for QA 

This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 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 QA pipeline on Haystack  
Infrastructure: 1x NVIDIA A10G## Hyperparameters## Usage### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:### In Transformers## Authors
Sebastian Lee: URL [at] URL  
Timo Möller: timo.moeller [at] URL  
Malte Pietsch: malte.pietsch [at] URL