Update README.md

Updated to include this model's purpose, as well as to clarify intended uses.

README.md

@@ -17,7 +17,7 @@ model-index:
 
 # distilbert-depression-mixed
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression.
 It achieves the following results on the evaluation set:
 - Evaluation Loss: 0.71
 - Accuracy: 0.63
@@ -29,7 +29,7 @@ It achieves the following results on the evaluation set:
 
 ## Intended uses & limitations
 
-Feed a corpus of tweets to the model to generate label if input is indicative of depression or not. Label 1 is depression, Label 0 is not.
+Feed a corpus of tweets to the model to generate label if input is indicative of a depressed user or not. Label 1 is depressed, Label 0 is not depressed.
 
 Limitation: All token sequences longer than 512 are automatically truncated. Also, training and test data may be contaminated with mislabeled users.
 
@@ -46,6 +46,7 @@ You can use this model directly with a pipeline for sentiment analysis:
 >>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 >>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
 >>> result=classifier('pain peko',**tokenizer_kwargs) #For truncation to apply in the pipeline
+>>> #Should note that the string passed as the input can be a corpus of tweets concatenated together into one document.
 
 
 [{'label': 'LABEL_1', 'score': 0.5048992037773132}]