| # Model Card for Model ID |
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| This model is meant to extract sentiments (positive, negative, or neutral) from a tweet text. |
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| - **Model type:** text-classification |
| - **Language(s) (NLP):** English |
| - **License:** cc |
| - **Finetuned from model:** BERT |
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| ## Training Details |
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| This model is a fine-tuned version of the BERT model. |
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| ## Training Data |
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| Trained on [tweet_eval](https://huggingface.co/datasets/tweet_eval/viewer/sentiment/train) from HuggingFace Hub. |
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| ## How to Get Started with the Model |
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| Note: model inputs were tokenized using bert-base-uncased tokenizer |
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| ``` |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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| tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") |
| model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Sentiment-Analyzer") |
| ``` |
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