Update README.md

# mdeberta-v3-base for QA

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

## Overview
**Language model:** mdeberta-v3-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Infrastructure**: 1x NVIDIA T4

### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "sjrhuschlee/mdeberta-v3-base-squad2"
# a) Get predictions
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)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

README.md

@@ -1,3 +1,14 @@
 ---
 license: mit
----
+datasets:
+- squad_v2
+language:
+- en
+library_name: transformers
+pipeline_tag: question-answering
+tags:
+- deberta
+- deberta-v3
+- mdeberta
+- question-answering
+---