---
license: cc-by-sa-4.0
language:
- 'no'
pipeline_tag: text-classification
---

The Entailment Model is a pre-trained classifier to generate Entailment score for fact verification purpose.

Specifically, we fine-tune NorBERT on a collection of machine translated [VitaminC](https://huggingface.co/datasets/tals/vitaminc) dataset which is designed to determine whether the evidence supports assumption and is suitable for training a model on whether the given context entails the generated texts. Then, we employ the fine-tuned model as our Entailment model.

Prompt format:
```
{article}[SEP]{positive_sample}
```
Inference format:
```
{article}[SEP]{generated_text}
```

## Run the Model
```python
import torch
from transformers import AutoTokenizer, BertForSequenceClassification

model_id = "NorGLM/Entailment"
tokenizer = AutoTokenizer.from_pretrained(model_id, fast_tokenizer=True)
tokenizer.add_special_tokens({'pad_token': '[PAD]'})

model = BertForSequenceClassification.from_pretrained(
    model_id
)
```

## Inference Example
```python
from torch.utils.data import TensorDataset, DataLoader

def entailment_score(texts, references, generated_texts):
	# Entailment: 1, Contradict: 0, Neutral: 2
	# concatinate news articles and generated summaries as input
	input_texts = [t + ' [SEP] '+ g for t,g in zip(texts, generated_texts)]
	# Set the maximum sequence length according to NorBERT config.
	MAX_LEN = 512
	batch_size = 16

	test_inputs = tokenizer(text=input_texts, add_special_tokens=True, return_attention_mask = True, return_tensors="pt", padding=True, truncation=True,  max_length=MAX_LEN)
	validation_data = TensorDataset(test_inputs['input_ids'],test_inputs['attention_mask'])
	validation_dataloader = DataLoader(validation_data,batch_size=batch_size)

	model.eval()

	results = []
	num_batches = 1
	for batch in validation_dataloader:
		# Add batch to GPU
		batch = tuple(t.to(device) for t in batch)
		# Unpack the inputs from our dataloader
		b_input_ids, b_input_mask = batch
		# Telling the model not to compute or store gradients, saving memory and speeding up validation
		with torch.no_grad():
			# Forward pass, calculate logit predictions
			logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)

		# Move logits and labels to CPU
		logits = logits[0].to('cpu').numpy()
		pred_flat = np.argmax(logits, axis=1).flatten()

		results.extend(pred_flat)
		num_batches += 1

	ent_ratio = results.count(1) / float(len(results))
	neu_ratio = results.count(2) / float(len(results))
	con_ratio = results.count(0) / float(len(results))
	print("Entailment ratio: {}; Neutral ratio: {}; Contradict ratio: {}.".format(ent_ratio, neu_ratio, con_ratio))
	return ent_ratio, neu_ratio, con_ratio

# load evaluation text
eva_file_name = <input csv file for evaluation>
eval_df = pd.read_csv(eva_file_name)

remove_str = 'Token indices sequence length is longer than 2048.'
eval_df = eval_df[eval_df!=remove_str]
eval_df = eval_df.dropna()
references = eval_df['positive_sample'].to_list()
hypo_list = eval_df['generated_text'].to_list()
articles = eval_df['article'].to_list()
ent_ratio, neu_ratio, con_ratio = entailment_score(articles, references, hypo_list)
```

## Citation Information
If you feel our work is helpful, please cite our paper:

```
@article{liu2023nlebench+,
  title={NLEBench+ NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in Norwegian},
  author={Liu, Peng and Zhang, Lemei and Farup, Terje Nissen and Lauvrak, Even W and Ingvaldsen, Jon Espen and Eide, Simen and Gulla, Jon Atle and Yang, Zhirong},
  journal={arXiv preprint arXiv:2312.01314},
  year={2023}
}
```