---
language:
- en
---
# entailment-verifier-xxl

## Model description
Entailment-verifier-xxl is based on [flan-t5-xxl model](https://huggingface.co/google/flan-t5-xxl) and finetuned with a ranking objective (rank the most supported hypothesis from a given pair of hypotheses for a given premise). Please refer to our paper [Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification](https://arxiv.org/abs/2402.03686) for more detals.

It is built to verify whether a given premise entails (or supports) a hypothesis or not. It works for both NLI-style of datasets and CoT rationales.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

def get_score(model, tokenizer, input_ids):
    pos_ids = tokenizer('Yes').input_ids
    neg_ids = tokenizer('No').input_ids
    pos_id = pos_ids[0]
    neg_id = neg_ids[0]
    
    logits = model(input_ids, decoder_input_ids=torch.zeros((input_ids.size(0), 1), dtype=torch.long)).logits
    pos_logits = logits[:, 0, pos_id]
    neg_logits = logits[:, 0, neg_id]
    posneg_logits = torch.cat([pos_logits.unsqueeze(-1), neg_logits.unsqueeze(-1)], dim=1)
    scores = torch.nn.functional.softmax(posneg_logits, dim=1)[:, 0]
    return scores

tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-xxl')
model = AutoModelForSeq2SeqLM.from_pretrained('soumyasanyal/entailment-verifier-xxl')

premise = "A fossil fuel is a kind of natural resource. Coal is a kind of fossil fuel."
hypothesis = "Coal is a kind of natural resource."
prompt = f"Premise: {premise}\nHypothesis: {hypothesis}\nGiven the premise, is the hypothesis correct?\nAnswer:"

input_ids = tokenizer(prompt, return_tensors='pt').input_ids

scores = get_score(model, tokenizer, input_ids)
print(f'Hypothesis entails the premise: {bool(scores >= 0.5)}')
```
> `['Hypothesis entails the premise: False']`