---
license: mit
language:
- en
base_model:
- facebook/bart-large-mnli
pipeline_tag: zero-shot-classification
---
🚀 **Other links:**
- 📝 [Checkout our GitHub repository](https://github.com/Herb-Lab/LLM_housing_livability)
- 🤗 [Test the model](https://huggingface.co/spaces/Herb-Lab/LLM_housing_livability) with sentiment analysis on the Huggingface Space

Additional information about this model:
- The [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) model page
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
](https://arxiv.org/abs/1910.13461)

The following instructions for model deployment is a slightly modified version from the The [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) model page.
#### With the zero-shot classification pipeline

The model can be loaded with the `zero-shot-classification` pipeline like so:

```python
from transformers.pipelines import pipeline
classifier = pipeline("zero-shot-classification",
                      model="Herb-Lab/LLM_housing_livability")
```

You can then use this pipeline to classify sequences into any of the class names you specify.

```python
sequence_to_classify = "The air conditioning was a bit noisy, and had to be turned off to sleep."
candidate_labels = ['Indoor Air Quality', 'Thermal', 'Acoustic', 'Visual']
classifier(sequence_to_classify, candidate_labels)
```

If more than one candidate label can be correct, pass `multi_label=True` to calculate each class independently:

```python
candidate_labels = ['Indoor Air Quality', 'Thermal', 'Acoustic', 'Visual']
classifier(sequence_to_classify, candidate_labels, multi_label=True)
```


#### With manual PyTorch

```python
# pose sequence as a NLI premise and label as a hypothesis
from transformers import AutoModelForSequenceClassification, AutoTokenizer
nli_model = AutoModelForSequenceClassification.from_pretrained('Herb-Lab/LLM_housing_livability')
tokenizer = AutoTokenizer.from_pretrained('Herb-Lab/LLM_housing_livability')

premise = sequence
hypothesis = f'This example is {label}.'

# run through model pre-trained on MNLI
x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
                     truncation_strategy='only_first')
logits = nli_model(x.to(device))[0]

# we throw away "neutral" (dim 1) and take the probability of
# "entailment" (2) as the probability of the label being true 
entail_contradiction_logits = logits[:,[0,2]]
probs = entail_contradiction_logits.softmax(dim=1)
prob_label_is_true = probs[:,1]
```