---
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: extractive_reader_nq_squad_v2
  results: []
language:
- en
---

<!-- 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](https://huggingface.co/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

```python
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]))
```