# Nissan Project

---
license: mit
---

## Overview

This model is based on [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) model and [roberta-base-squad2 ](https://huggingface.co/deepset/roberta-base-squad2) model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squad2 is a Question Answering model, which helps us to filter which comment mentions the feature.

## Text-image matching 

### Model Input

```python
import pandas as pd
from transformers import pipeline

QA_input = {
    'question': 'Does it mention dependable?',
    'context': input("Enter your sentence:")
}
```

### Model Process

```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/roberta-base-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)

res = nlp(QA_input)

if res['score'] > 0.1:
  sentence = QA_input['context']
  classifier = pipeline("zero-shot-classification",
                      model="facebook/bart-large-mnli", device=0)
  sequence_to_classify = sentence
  candidate_labels = ['dependable', 'not dependable']
  res_2 = classifier(sequence_to_classify, candidate_labels, multi_label=False)
  score = res_2.get('scores')[0]*2-1
else:
  score = 0

print(score)
```

## Result

If the score is zero, it means it doesn't mention the feature. Others, it gets the score of the comment with the feature we select.

### Demo code (Python Notebook)
https://github.com/vanderbilt-data-science/nissan/blob/main/30-ModelFilter/question-answering.ipynb
https://github.com/vanderbilt-data-science/nissan/blob/main/31-ModelWalkthrough/label_after_filtering.ipynb
{}
has_space region:us
TAGS
#has_space #region-us 

# Nissan Project

---
license: mit
---

## Overview

This model is based on facebook/bart-large-mnli model and roberta-base-squad2  model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squad2 is a Question Answering model, which helps us to filter which comment mentions the feature.

## Text-image matching 

### Model Input



### Model Process



## Result

If the score is zero, it means it doesn't mention the feature. Others, it gets the score of the comment with the feature we select.

### Demo code (Python Notebook)
URL
URL
# Nissan Project

---
license: mit
--- ## Overview

This model is based on facebook/bart-large-mnli model and roberta-base-squad2  model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squad2 is a Question Answering model, which helps us to filter which comment mentions the feature. ## Text-image matching ### Model Input ### Model Process ## Result

If the score is zero, it means it doesn't mention the feature. Others, it gets the score of the comment with the feature we select. ### Demo code (Python Notebook)
URL
URL
TAGS
#has_space #region-us 
 # Nissan Project

---
license: mit
--- ## Overview

This model is based on facebook/bart-large-mnli model and roberta-base-squad2  model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squad2 is a Question Answering model, which helps us to filter which comment mentions the feature. ## Text-image matching ### Model Input ### Model Process ## Result

If the score is zero, it means it doesn't mention the feature. Others, it gets the score of the comment with the feature we select. ### Demo code (Python Notebook)
URL
URL
passage: TAGS
#has_space #region-us 
# Nissan Project

---
license: mit
---## Overview

This model is based on facebook/bart-large-mnli model and roberta-base-squad2  model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squad2 is a Question Answering model, which helps us to filter which comment mentions the feature.## Text-image matching### Model Input### Model Process## Result

If the score is zero, it means it doesn't mention the feature. Others, it gets the score of the comment with the feature we select.### Demo code (Python Notebook)
URL
URL