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---
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.<br>
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/anshoomehra/question_answering)
Please follow this link for [Generative Question Answering](https://huggingface.co/anshoomehra/question-answering-generative-t5-v1-base-s-q-c/)
Example code:
```
from transformers import pipeline
model_checkpoint = "anshoomehra/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
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