Instructions to use Srjnnnn/distilbert-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Srjnnnn/distilbert-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Srjnnnn/distilbert-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Srjnnnn/distilbert-sentiment") model = AutoModelForSequenceClassification.from_pretrained("Srjnnnn/distilbert-sentiment") - Notebooks
- Google Colab
- Kaggle
Distilbert-Sentiment
- This is a bert based sentiment analysis model. The head has been changed to make the classification task and it relies on Transformers.
- It checks; saddness, joy, fear, anger, surprise and joy.
- Downloads last month
- 2
Model tree for Srjnnnn/distilbert-sentiment
Base model
distilbert/distilbert-base-uncased