Instructions to use anismahmahi/LLMLingua2_span_propaganda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anismahmahi/LLMLingua2_span_propaganda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="anismahmahi/LLMLingua2_span_propaganda")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("anismahmahi/LLMLingua2_span_propaganda") model = AutoModelForTokenClassification.from_pretrained("anismahmahi/LLMLingua2_span_propaganda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5654c4db4760edf5b4c38055d165904c4f163ab85aa552f930859655f8f6ed2
- Size of remote file:
- 4.41 kB
- SHA256:
- bcc5850a155dbe9d0d30667b7271b7038ff1e57ed16af708c7b66caca4ba5e18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.