Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AnonymousCS/populism_model265 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_model265 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_model265")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_model265") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_model265", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cabe28a58b9babeb7b96559e4f65c16c4efadb6523fd4ff21a91532965ecd22
- Size of remote file:
- 5.37 kB
- SHA256:
- 10b2adbb6bf530407ee98c9fdac6153290205a85fa207ab4871d7ddc2a925a2b
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