Instructions to use SetFit/distilbert-base-uncased__ethos_binary__all-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/distilbert-base-uncased__ethos_binary__all-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/distilbert-base-uncased__ethos_binary__all-train")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__ethos_binary__all-train") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__ethos_binary__all-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 579fb09ac38f98ae00b79e869619952091fe053b8e338ed1171bea4758df94c3
- Size of remote file:
- 268 MB
- SHA256:
- 3ca9dd1bbe1adbad28c94e1d4d8a36c5bc50288510612ff11f06abf49ec6e4be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.