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