readme: ---
  license: mit
  language: 
   - en
  datasets: climate_fever
  tags:
   - fact-checking
   - climate
   - text entailment
  ---
  
  This model fine-tuned [ClimateBert](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the textual entailment task using Climate FEVER data. Given (claim, evidence) pairs, the model predicts support (entailment), refute (contradict), or not enough info (neutral). The model has 67% validation accuracy.
   
   ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  model = AutoModelForSequenceClassification.from_pretrained("amandakonet/climatebert-fact-checking")
  tokenizer = AutoTokenizer.from_pretrained("amandakonet/climatebert-fact-checking")
  
  features = tokenizer(['Beginning in 2005, however, polar ice modestly receded for several years'], 
                      ['Polar Discovery "Continued Sea Ice Decline in 2005'],  
                      padding='max_length', truncation=True, return_tensors="pt", max_length=512)
  
  model.eval()
  with torch.no_grad():
      scores = model(**features).logits
      label_mapping = ['contradiction', 'entailment', 'neutral']
      labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
      print(labels)
   ```