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