# Zero shot classification with manual pytorch

**URL:** <https://discuss.huggingface.co/t/zero-shot-classification-with-manual-pytorch/9534>\
**Category:** Beginners\
**Created:** [August 27, 2021, 3:42pm UTC](https://discuss.huggingface.co/t/zero-shot-classification-with-manual-pytorch/9534 "2021-08-27T15:42:26Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Subham](https://avatars.discourse-cdn.com/v4/letter/s/ba9def/32.png) [@Subham](https://discuss.huggingface.co/u/Subham)\
**Post date:** [August 27, 2021, 3:42pm UTC](https://discuss.huggingface.co/t/zero-shot-classification-with-manual-pytorch/9534/1 "2021-08-27T15:42:26Z")

</div>

Hi

I want to run zero shot classification task without using the transformers pipeline.

I have gone through the code snippet for facebook/bart-large-mnli model, but I am unsure on how to recreate this for more than 3 labels. My dataset has 52 labels.

```auto
from transformers import AutoModelForSequenceClassification, AutoTokenizer
nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')

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]

```

Kindly help in converting above example for 52 labels.

Thanks