Instructions to use mbkim/LifeTox_Moderator_350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbkim/LifeTox_Moderator_350M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mbkim/LifeTox_Moderator_350M", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mbkim/LifeTox_Moderator_350M") model = AutoModelForSequenceClassification.from_pretrained("mbkim/LifeTox_Moderator_350M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57ecdf3686629ab4016663d4ca7cc76a01bcc4335c480c394269ad2986390e05
- Size of remote file:
- 1.42 GB
- SHA256:
- 51906d95a67d11b10c829a48be7772a277b2c40660b1834bd6edb31dde7a9de1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.