Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- Question Answering
+metrics:
+- squad
+model-index:
+- name: consciousAI/question-answering-roberta-base-s-v2
+  results: []
+---
+
+# Question Answering 
+The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text, answer span & confidence score.<br>
+Model is encoder-only (deepset/roberta-base-squad2) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with **exact_match:** 84.83 & **f1:** 91.80 performance scores.
+
+[Live Demo: Question Answering Encoders vs Generative](https://huggingface.co/spaces/consciousAI/question_answering)
+
+Please follow this link for [Encoder based Question Answering V1](https://huggingface.co/consciousAI/question-answering-roberta-base-s/)
+Please follow this link for [Generative Question Answering](https://huggingface.co/consciousAI/question-answering-generative-t5-v1-base-s-q-c/)
+
+Example code:
+```
+from transformers import pipeline
+
+model_checkpoint = "consciousAI/question-answering-roberta-base-s-v2"
+
+context = """
+🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
+between them. It's straightforward to train your models with one before loading them for inference with the other.
+"""
+question = "Which deep learning libraries back 🤗 Transformers?"
+
+question_answerer = pipeline("question-answering", model=model_checkpoint)
+question_answerer(question=question, context=context)
+
+```
+
+## Training and evaluation data
+
+SQUAD Split
+
+## Training procedure
+
+Preprocessing:
+1. SQUAD Data longer chunks were sub-chunked with input context max-length 384 tokens and stride as 128 tokens.
+2. Target answers readjusted for sub-chunks, sub-chunks with no-answers or partial answers were set to target answer span as (0,0)
+
+Metrics:
+1. Adjusted accordingly to handle sub-chunking.
+2. n best = 20
+3. skip answers with length zero or higher than max answer length (30)
+
+### Training hyperparameters
+Custom Training Loop:
+The following hyperparameters were used during training:
+- learning_rate: 2e-5
+- train_batch_size: 32
+- eval_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+{'exact_match': 84.83443708609272, 'f1': 91.79987545811638}
+
+### Framework versions
+
+- Transformers 4.23.0.dev0
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.2
+- Tokenizers 0.13.0