Instructions to use mayapapaya/Sentiment-Analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayapapaya/Sentiment-Analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mayapapaya/Sentiment-Analyzer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mayapapaya/Sentiment-Analyzer") model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Sentiment-Analyzer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d889003ca3ddc418f7d3f900f2b412d4ce9a53efb5367d94b9464fd00d4003a
- Size of remote file:
- 438 MB
- SHA256:
- b512cf3a1f899cbbe1cf52159677074a05cf7fd2c38db1efb4a71d6d301e1ce8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.