| | --- |
| | 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/consciousAI/question_answering) |
| | |
| | Please follow this link for [Encoder based Question Answering V2](https://huggingface.co/consciousAI/question-answering-roberta-base-s-v2/) |
| | <br>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 |
| | |
