Instructions to use manarea/Demaxco-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manarea/Demaxco-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="manarea/Demaxco-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("manarea/Demaxco-sst2") model = AutoModelForSequenceClassification.from_pretrained("manarea/Demaxco-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eb37e93c9aecc2be78079223c7eb0ba5f49c8446b938b76c12bc6f8b9af85f1
- Size of remote file:
- 268 MB
- SHA256:
- fb4d42e9287dc67c2ca69746dfcc1c12eb3ea599f3d8c5cd08f0d24186366507
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.