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---
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]))
``` |