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
| 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" |