--- license: apache-2.0 tags: - Question Answering metrics: - squad model-index: - name: question-answering-roberta-base-s 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.
Model is encoder-only (roberta-base) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with **exact_match:** 86.14 & **f1:** 92.330 performance scores. [Live Demo: Question Answering Encoders vs Generative](https://huggingface.co/spaces/consciousAI/question_answering) 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" 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 | Epoch | F1 | Exact Match | |:-----:|:--------:|:-----------:| | 1.0 | 91.3085 | 84.5412 | | 2.0 | 92.3304 | 86.1400 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.0