Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AnonymousCS/populism_model307 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_model307 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_model307")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_model307") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_model307", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac578ac663482ed03d4b7f7006b1a35222edb49ba66ddc94b810043e7a8d25d3
- Size of remote file:
- 5.37 kB
- SHA256:
- 85b2f8d54f242872e9baa6518e79399bdd1bd9048fa79cee759e776e4c6fbaae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.