Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AnonymousCS/populism_model283 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_model283 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_model283")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_model283") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_model283", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 105eb0a42b3452813cb0a31217a8fffbd0ea9c8860cb3fedc7fac8f020470618
- Size of remote file:
- 5.37 kB
- SHA256:
- 8b598461dea66c3db2442808f855b652aa1f6b72cc392571e33aefadf8f2ecd9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.