Instructions to use knkarthick/Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knkarthick/Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="knkarthick/Sentiment-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("knkarthick/Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("knkarthick/Sentiment-Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b6393874328d6226426292a7b05b9f8b29b55a87c828020a2d72d80d3b5ffa5
- Size of remote file:
- 499 MB
- SHA256:
- 682358ffb3869b08a144d5e59325534335729720fe64d5f2b3a543f8e5d14a9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.