Instructions to use nikesh66/Sentiment-Detection-using-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikesh66/Sentiment-Detection-using-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikesh66/Sentiment-Detection-using-BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikesh66/Sentiment-Detection-using-BERT") model = AutoModelForSequenceClassification.from_pretrained("nikesh66/Sentiment-Detection-using-BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
Sentiment Dection Model using Bert
Sentiment Analysis Model identifies the sentiment or emotional tone expressed in a piece of text
Sentiment Analysis Model This model has total 7 labels which are as follows:
- "anger"
- "disgust"
- "fear"
- "joy"
- "neutral"
- "sadness"
- "surprise"