Instructions to use Everlyn/sentiment_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Everlyn/sentiment_model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Everlyn/sentiment_model2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Everlyn/sentiment_model2") model = AutoModelForSequenceClassification.from_pretrained("Everlyn/sentiment_model2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5aa54a546a0e4d6cd5912ca22c0bc0d66d0d4b56887029f5ea76cd5132b10d43
- Size of remote file:
- 499 MB
- SHA256:
- 4834104be59d2dc21b8a94601c462aa669a2ce16a59cc5e8525cf937d0bd01f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.