Instructions to use Dmyadav2001/Sentimental-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dmyadav2001/Sentimental-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dmyadav2001/Sentimental-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dmyadav2001/Sentimental-Analysis") model = AutoModelForSequenceClassification.from_pretrained("Dmyadav2001/Sentimental-Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 550e4bd1dd3d8a3b766091ad9ac68dd7e348c6687d27aa9e392eed00c3ebe698
- Size of remote file:
- 268 MB
- SHA256:
- 2af9be68d93b2b03b5233d2d4857777bd309afc4217b82b44da8363140555a0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.