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This model is a fine-tuned version of ToluClassics/extractive_reader_nq on the squad_v2 dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5.0

Training results

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.0
  • Tokenizers 0.13.2

Code Examples

import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("ToluClassics/extractive_reader_nq_squad_v2")

model = AutoModelForQuestionAnswering.from_pretrained("ToluClassics/extractive_reader_nq_squad_v2")

question = ""
context = ""

inputs = tokenizer.encode(question, context, add_special_tokens=True, return_tensors="pt")

output = model(inputs)

answer_start = torch.argmax(output.start_logits)
answer_end = torch.argmax(output.end_logits)
if answer_end >= answer_start:
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Question Answering
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Dataset used to train ToluClassics/extractive_reader_nq_squad_v2