Instructions to use Sumedhzz/Sentiment-Analyzer-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sumedhzz/Sentiment-Analyzer-Quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sumedhzz/Sentiment-Analyzer-Quantized")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sumedhzz/Sentiment-Analyzer-Quantized") model = AutoModelForSequenceClassification.from_pretrained("Sumedhzz/Sentiment-Analyzer-Quantized") - Notebooks
- Google Colab
- Kaggle
| from transformers import AutoTokenizer, pipeline | |
| from optimum.onnxruntime import ORTModelForSequenceClassification | |
| def init(): | |
| global classifier | |
| model_id = "." | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = ORTModelForSequenceClassification.from_pretrained(model_id) | |
| classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) | |
| def __call__(data): | |
| return classifier(data["inputs"]) |