Instructions to use aloxatel/9WT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/9WT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/9WT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/9WT") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/9WT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a05b572948aa74abea99b8e3da6b668c86aae8dfadcf5299845765fecc2f2336
- Size of remote file:
- 669 MB
- SHA256:
- 36208addb8bbc3641b82a32e8bc161c016139afbe7fb75dc02318f1a428e3dc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.