
# Model description

This model corresponds to the paper "A Domain-adaptive Pre-training Approach for Language Bias Detection in News" (Krieger et al.,2022): https://github.com/Media-Bias-Group/A-Domain-adaptive-Pre-training-Approach-for-Language-BiasDetection-in-News

The model can be used for sequence classification tasks of biased and non-biased language in news and media. It is initialized with *roberta-base* weights and fine-tuned on the *Wiki Neutrality Corpus* (Pryzant et al., 2020). More details on the training setup and experiments can be found in our paper.

# How to use
You can use the model with the PyTorch framework

```
#imports
!pip install transformers
!pip install openpyxl
import torch
import torch.nn as nn
import numpy as np
from transformers import RobertaTokenizer,RobertaModel

#define model class including binary classification layer 
class RobertaClass(torch.nn.Module):
    def __init__(self):
        super(RobertaClass, self).__init__()
        self.roberta = RobertaModel.from_pretrained("roberta-base")
        self.vocab_transform = torch.nn.Linear(768, 768)
        self.dropout = torch.nn.Dropout(0.2)
        self.classifier1 = torch.nn.Linear(768,2)

    def forward(self, input_ids, attention_mask):
        output_1 = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
        hidden_state = output_1[0]
        pooler = hidden_state[:, 0]
        pooler = self.vocab_transform(pooler)
        pooler = self.dropout(pooler)
        output = self.classifier1(pooler)

        return output

#load model parameters
weight_dict = torch.load('DA-Roberta.bin')
                         
#initialize model with fine-tuned parameters
model = RobertaClass()
model.load_state_dict(weight_dict)

#exemplary bias classification with instance extracted from BABE dataset (Spinde et al.,2021)
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
inputs = tokenizer("A cop shoots a Black man, and a police union flexes its muscle", return_tensors="pt")
outputs = model(**inputs)

if int(torch.argmax(outputs)) == 1:
    print("Biased")
else:
    print("Non-biased")
```

# Cite as 

```
@InProceedings{Krieger2022,
  author={Krieger, David and Spinde, Timo and Ruas, Terry and Kulshrestha, Juhi and Gipp, Bela},
  booktitle={2022 ACM/IEEE Joint Conference on Digital Libraries (JCDL)}, 
  title={A Domain-adaptive Pre-training Appraoch for Language Bias Detection in News}, 
  year={2022},
  address = "Cologne,Germany"
  }
```
{"license": "apache-2.0"}
license:apache-2.0 region:us
TAGS
#license-apache-2.0 #region-us 


# Model description

This model corresponds to the paper "A Domain-adaptive Pre-training Approach for Language Bias Detection in News" (Krieger et al.,2022): URL

The model can be used for sequence classification tasks of biased and non-biased language in news and media. It is initialized with *roberta-base* weights and fine-tuned on the *Wiki Neutrality Corpus* (Pryzant et al., 2020). More details on the training setup and experiments can be found in our paper.

# How to use
You can use the model with the PyTorch framework



# Cite as 


# Model description

This model corresponds to the paper "A Domain-adaptive Pre-training Approach for Language Bias Detection in News" (Krieger et al.,2022): URL

The model can be used for sequence classification tasks of biased and non-biased language in news and media. It is initialized with *roberta-base* weights and fine-tuned on the *Wiki Neutrality Corpus* (Pryzant et al., 2020). More details on the training setup and experiments can be found in our paper. # How to use
You can use the model with the PyTorch framework # Cite as
TAGS
#license-apache-2.0 #region-us 
 # Model description

This model corresponds to the paper "A Domain-adaptive Pre-training Approach for Language Bias Detection in News" (Krieger et al.,2022): URL

The model can be used for sequence classification tasks of biased and non-biased language in news and media. It is initialized with *roberta-base* weights and fine-tuned on the *Wiki Neutrality Corpus* (Pryzant et al., 2020). More details on the training setup and experiments can be found in our paper. # How to use
You can use the model with the PyTorch framework # Cite as
passage: TAGS
#license-apache-2.0 #region-us 
# Model description

This model corresponds to the paper "A Domain-adaptive Pre-training Approach for Language Bias Detection in News" (Krieger et al.,2022): URL

The model can be used for sequence classification tasks of biased and non-biased language in news and media. It is initialized with *roberta-base* weights and fine-tuned on the *Wiki Neutrality Corpus* (Pryzant et al., 2020). More details on the training setup and experiments can be found in our paper.# How to use
You can use the model with the PyTorch framework# Cite as