Update README.md

README.md

@@ -1,3 +1,43 @@
 ---
 license: mit
+datasets:
+- squad_v2
+language:
+- en
+library_name: transformers
+pipeline_tag: question-answering
+tags:
+- deberta
+- deberta-v3
+- question-answering
 ---
+
+# 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 Extractive Question Answering.
+
+## Overview
+**Language model:** deberta-v3-base  
+**Language:** English  
+**Downstream-task:** Extractive QA  
+**Training data:** SQuAD 2.0  
+**Eval data:** SQuAD 2.0  
+**Infrastructure**: 1x NVIDIA 3070  
+
+### Model Usage
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+model_name = "sjrhuschlee/deberta-v3-base-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)
+
+# b) Load model & tokenizer
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```