Instructions to use pritamdeb68/SentimentBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pritamdeb68/SentimentBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pritamdeb68/SentimentBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pritamdeb68/SentimentBERT") model = AutoModelForSequenceClassification.from_pretrained("pritamdeb68/SentimentBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ff8bd82c34175c76264992e01daf183720481312db7dec40123230b34106f60
- Size of remote file:
- 438 MB
- SHA256:
- 7834de08a5179197719db0b18a27538d9e1f59c816625ef0914158922d74a448
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