Update README.md

README.md

@@ -6,23 +6,26 @@ datasets: climatebert/distilroberta-base-climate-f
 tags:
  - fact-checking
  - climate
+ - text entailment
 ---
 
 This model fine-tuned ClimateBert on the textual entailment task. Given (claim, evidence) pairs, the model predicts support (entailment), refute (contradict), or not enough info (neutral).
  
+ ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('lighteternal/nli-xlm-r-greek')
-tokenizer = AutoTokenizer.from_pretrained('lighteternal/nli-xlm-r-greek')
+model = AutoModelForSequenceClassification.from_pretrained("amandakonet/climatebert-fact-checking", use_auth_token=True)
+tokenizer = AutoTokenizer.from_pretrained("amandakonet/climatebert-fact-checking", use_auth_token=True)
 
-features = tokenizer(['Δύο άνθρωποι συναντιούνται στο δρόμο', 'Ο δρόμος έχει κόσμο'], 
-                    ['Ένα μαύρο αυτοκίνητο ξεκινάει στη μέση του πλήθους.', 'Ένας άντρας οδηγάει σε ένα μοναχικό δρόμο.'],  
-                    padding=True, truncation=True, return_tensors="pt")
+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)`
+    print(labels)
+ ```