metadata
license: apache-2.0
Stance detection model distilled from a news dataset label by a larger model. The larger model was trained on a combination of stance datasets in the literature:
FNC-1 (Pomerleau and Rao, 2017), Perspectrum (Chen et al., 2019), ARC (Habernal et al., 2017), Emergent (Ferreira and Vlachos, 2016) NewsClaims (Reddy et al., 2021)7.
Achieves a .0.5712643678160919 f1-score on hand labeled indomain news data
To run:
from transformers import AutoTokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained('alex2awesome/stance-detection-t5')
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
tokenizer_with_prefix_space = AutoTokenizer.from_pretrained(model_name_or_path, add_prefix_space=True)
def get_tokens_as_tuple(word):
return tuple(tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0])
input_ids = tokenizer(text, return_tensors="pt").input_ids
y_pred_gen_output = model.generate(
input_ids,
renormalize_logits=True,
sequence_bias= {
get_tokens_as_tuple('__Affirm__'): 0.143841,
get_tokens_as_tuple('__Discuss__'): -0.294732,
get_tokens_as_tuple('__Neutral__'): -0.103820,
get_tokens_as_tuple('__Refute__'): 0.637734,
},
)
You can tweak the class weights yourself if you want.