Instructions to use privacy-tech-lab/MultitaskModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/MultitaskModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/MultitaskModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/MultitaskModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/MultitaskModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abfcb4a0c1a253bf4d2e4eba805805ac118f30f13266af4e7040841e237adc0c
- Size of remote file:
- 57.4 MB
- SHA256:
- 46674961e218ec7fa7863556b62724798241485a195ae3401ce05513f8964aa4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.