Instructions to use mayapapaya/Sentiment-Analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayapapaya/Sentiment-Analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mayapapaya/Sentiment-Analyzer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mayapapaya/Sentiment-Analyzer") model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Sentiment-Analyzer") - Notebooks
- Google Colab
- Kaggle
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 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")