readme: ---
  language: eng
  datasets:
  - banking77
  ---
  
  # Social Media Sentiment Analysis Model (Finetuned 2)
  This is an updated fine-tuned version of the [Social Media Sentiment Analysis Model](https://huggingface.co/Kwaku/social_media_sa) which is a finetuned version of [Distilbert](https://huggingface.co/models?other=distilbert). It's best suited for sentiment-analysis.
  
  ## Model Description
  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.
  
  ### How to use
  
  You can use this model directly with a pipeline for text generation:
  
  ```python
  >>>from transformers import pipeline
  
  >>> model_name = "Kwaku/social_media_sa_finetuned_2"
  >>> generator = pipeline("sentiment-analysis", model=model_name)
  >>> result = generator("I like this model")
  >>> print(result)
  
  Generated output: [{'label': 'positive', 'score': 0.9992923736572266}]
  ```
  
  ### Limitations and bias
  
  This model inherits the bias of its parent, [Distilbert](https://huggingface.co/models?other=distilbert).
  Besides that, it was trained on only 1000 randomly selected sequences, and thus does not achieve a high probability rate.
  It does fairly well nonetheless.