Instructions to use Etelis/amazonPolarity_XLNET_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/amazonPolarity_XLNET_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/amazonPolarity_XLNET_5E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/amazonPolarity_XLNET_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/amazonPolarity_XLNET_5E") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95f4d3ac9dca073377000b393739190d8284797f34aab31e0bc7b733c6d97479
- Size of remote file:
- 469 MB
- SHA256:
- a47bd0acda97f5fb6ea095dbce35b50c4e2b39fe8abfae00ec16f085bab8036d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.