Instructions to use ncheng/learning-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncheng/learning-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncheng/learning-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncheng/learning-sentiment") model = AutoModelForSequenceClassification.from_pretrained("ncheng/learning-sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4845e551f9a85764d78424ccb817220ee34a4a5f34255b19ddf028d863035dbb
- Size of remote file:
- 3.52 kB
- SHA256:
- a4e19dea0dff654a0cc545063b412c90d237b12cc869bdea4666e4bcc37f0d56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.