
# Deberta v3 base model for QA (SQuAD 2.0)

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.

## Training Data
The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.

It can be used for question answering task.

## Usage and Performance
The trained model can be used like this:
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Load model & tokenizer
deberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/deberta-v3-base-squad2')
deberta_tokenizer = AutoTokenizer.from_pretrained('navteca/deberta-v3-base-squad2')

# Get predictions
nlp = pipeline('question-answering', model=deberta_model, tokenizer=deberta_tokenizer)

result = nlp({
    'question': 'How many people live in Berlin?',
    'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'
})

print(result)

#{
#  "answer": "3,520,031"
#  "end": 36,
#  "score": 0.96186668,
#  "start": 27,
#}
```

## Author
[deepset](http://deepset.ai/)

{"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3"], "datasets": ["squad_v2"], "pipeline_tag": "question-answering", "model-index": [{"name": "navteca/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}, {"type": "f1", "value": 87.41, "name": "F1", "verified": true}]}, {"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}, {"type": "f1", "value": 92.2777, "name": "F1", "verified": true}]}]}]}
transformers pytorch deberta-v2 question-answering deberta deberta-v3 en dataset:squad_v2 license:mit model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #region-us 


# Deberta v3 base model for QA (SQuAD 2.0)

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.

## Training Data
The models have been trained on the SQuAD 2.0 dataset.

It can be used for question answering task.

## Usage and Performance
The trained model can be used like this:


## Author
deepset

# Deberta v3 base model for QA (SQuAD 2.0)

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. ## Training Data
The models have been trained on the SQuAD 2.0 dataset.

It can be used for question answering task. ## Usage and Performance
The trained model can be used like this: ## Author
deepset
TAGS
#transformers #pytorch #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #region-us 
 # Deberta v3 base model for QA (SQuAD 2.0)

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. ## Training Data
The models have been trained on the SQuAD 2.0 dataset.

It can be used for question answering task. ## Usage and Performance
The trained model can be used like this: ## Author
deepset
passage: TAGS
#transformers #pytorch #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #region-us 
# Deberta v3 base model for QA (SQuAD 2.0)

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.## Training Data
The models have been trained on the SQuAD 2.0 dataset.

It can be used for question answering task.## Usage and Performance
The trained model can be used like this:## Author
deepset