Instructions to use Etelis/TSE_XLNET_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/TSE_XLNET_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/TSE_XLNET_5E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/TSE_XLNET_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/TSE_XLNET_5E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6662bba744bc210445b8b09a83be07bf3c08f814fd6e0b396babc94ac7a97e60
- Size of remote file:
- 469 MB
- SHA256:
- 4fbb937b15b160bb835f7a313eb3f4aebffcbbf32aee3d7f7adbb4c8b2f874d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.