Update README.md

README.md

@@ -12,7 +12,7 @@ license: mit
 
 Using Covid-Twitter-BERT-v2 (https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) as the starting model, we continued domain-adaptive pre-training on a corpus of firm tweets between 2007 and 2020. The model was then fine-tuned on the downstream task to classify whether a given tweet is related to climate change topics.
 
-The model provides a label and probability score, indicating whether a given tweet is related to climate change topics (label = 1) or not (label = 0).
+The model provides a label and probability score, indicating whether a given tweet is related to climate change topics (label = 'Climate') or not (label = 'Non-climate').
 
 ## Performance metrics:
 
@@ -30,17 +30,17 @@ Based on the test set, the model achieves the following results:
 ```python
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
-task_name = 'binary'
-model_name = Climate-TwitterBERT/ Climate-TwitterBERT-step1'
+task_name = 'text-classification'
+model_name = 'Climate-TwitterBERT/ Climate-TwitterBERT-step1'
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-pipe = pipeline(task=‘binary‘, model=model, tokenizer=tokenizer)
+pipe = pipeline(task=task_name, model=model, tokenizer=tokenizer)
 
 tweet = "We are committed to significantly cutting our carbon emissions by 30% before 2030."
 result = pipe(tweet)
-# The 'result' variable will contain the classification output: 0 = non-climate tweet, 1= climate tweet
+# The 'result' variable will contain the classification output: 'Climate' or 'Non-climate'.
 ```
 
 ## Citation: