Update README.md

README.md

@@ -37,8 +37,8 @@ This may be due to the base model being trained for emoji classification and the
 This model is better if emojis are to be also included for sentiment analysis.
 No Evaluation is done for data with only text and no emojis.
 
-The model was fine-tuned with dataset: mteb/tweet_sentiment_extraction from huggingface
-converted to hinglish text.
+The model was fine-tuned with the dataset: mteb/tweet_sentiment_extraction from hugging face
+converted to Hinglish text.
 
 The model has a test loss of 0.6 and an f1 score of 0.74 on the unseen data from the dataset.
 
@@ -78,4 +78,7 @@ Text: tu mujhe pasandh heh
 Negative: 0.01
 Neutral: 0.22
 Positive: 0.76
-```
+```
+Possible Future Direction:
+
+1. Pre-train the Hinglish model with both Hindi, Hinglish, and English datasets. Current tokens for hinlish have very small sizes i.e. low-priority vocabs are used mostly.