Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use AnonymousCS/populism_classifier_bsample_130 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_classifier_bsample_130 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_classifier_bsample_130")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_classifier_bsample_130") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_classifier_bsample_130", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e83a210e175e0215e537ea41d9b77678a5a5cecc3c4e8cd424d6212307f4f8f2
- Size of remote file:
- 2.24 GB
- SHA256:
- f9b8c253c0ed10f9ebf0a1428eb9800cb6b564ddece897118d2dd905e93ebb2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.