---
datasets:
- tweet_eval
language:
- en
---
# Model Card for Model ID

This model is meant to extract sentiments (positive, negative, or neutral) from a tweet text.

- **Model type:** text-classification
- **Language(s) (NLP):** English
- **License:** cc
- **Finetuned from model:** BERT

## Training Details

This model is a fine-tuned version of the BERT model.

## Training Data

Trained on [tweet_eval](https://huggingface.co/datasets/tweet_eval/viewer/sentiment/train) from HuggingFace Hub.

## How to Get Started with the Model

Note: model inputs were tokenized using bert-base-uncased tokenizer

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Sentiment-Analyzer")
```