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# 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]))
```
{"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "extractive_reader_nq_squad_v2", "results": []}]}
transformers pytorch tensorboard bert question-answering generated_from_trainer en dataset:squad_v2 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #endpoints_compatible #region-us 




# 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


# 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
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #endpoints_compatible #region-us 
 # 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
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #endpoints_compatible #region-us 
# 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