| --- |
| 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") |
| ``` |
