Update README.md

README.md

@@ -4,11 +4,11 @@ datasets:
 - banking77
 ---
 
-# tweets_model
-This is a fine-tuned version of the [tweets_model](https://huggingface.co/Kwaku/tweets_model_finetuned) which is a finetuned version of [Distilbert](https://huggingface.co/models?other=distilbert). It's best suited for sentiment-analysis.
+# Social Media Sentiment Analysis Model (Finetuned)
+This is a fine-tuned version of the [Social Media Sentiment Analysis Model](https://huggingface.co/Kwaku/tweets_model_finetuned) which is a finetuned version of [Distilbert](https://huggingface.co/models?other=distilbert). It's best suited for sentiment-analysis.
 
 ## Model Description
-tweets_model_finetuned was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle."
+Social Media Sentiment Analysis Model was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle."
 
 ## Intended Uses and Limitations
 This model is meant for sentiment-analysis. Because it was trained on a corpus of tweets, it is familiar with social media jargons.
@@ -20,7 +20,7 @@ You can use this model directly with a pipeline for text generation:
 ```python
 >>>from transformers import pipeline
 
->>> model_name = "Kwaku/tweets_model_finetuned"
+>>> model_name = "Kwaku/social_media_sa_finetuned_1"
 >>> generator = pipeline("sentiment-analysis", model=model_name)
 >>> result = generator("I like this model")
 >>> print(result)