<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

extractive_reader_nq_squad_v2

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:
    print(tokenizer.decode(inputs[0][answer_start:answer_end+1]))