Update README.md

README.md

@@ -12,4 +12,43 @@ tags:
 - question-answering
 - squad
 - squad_v2
----
+---
+
+# deberta-v3-large for Extractive QA
+
+This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) 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-large  
+**Language:** English  
+**Downstream-task:** Extractive QA  
+**Training data:** SQuAD 2.0  
+**Eval data:** SQuAD 2.0  
+**Infrastructure**: 1x NVIDIA 3070  
+
+## Model Usage
+
+### Using with Peft
+```python
+from peft import LoraConfig, PeftModelForQuestionAnswering
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+model_name = "sjrhuschlee/deberta-v3-large-squad2"
+```
+
+### Using the Merged Model
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+model_name = "sjrhuschlee/deberta-v3-large-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)
+```