Create README.md

README.md

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+---
+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': 0.9494990110397339}]
+```
+
+### 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.