consciousAI_-_question-answering-roberta-base-s-v2-4bits

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question-answering-roberta-base-s-v2 - bnb 4bits

• Model creator: https://huggingface.co/consciousAI/
• Original model: https://huggingface.co/consciousAI/question-answering-roberta-base-s-v2/

Original model description:

license: apache-2.0 tags:

• Question Answering metrics:
• squad model-index:
• name: consciousAI/question-answering-roberta-base-s-v2 results: []

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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

Please follow this link for Encoder based Question Answering V1 <br>Please follow this link for Generative Question Answering

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