Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use michaellutz/roberta-base-prop-16-train-set with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaellutz/roberta-base-prop-16-train-set with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="michaellutz/roberta-base-prop-16-train-set")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("michaellutz/roberta-base-prop-16-train-set") model = AutoModelForSequenceClassification.from_pretrained("michaellutz/roberta-base-prop-16-train-set") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90bb1cccbab00434995a3b349328590ffdff27c59da09e14c2018067edc86556
- Size of remote file:
- 499 MB
- SHA256:
- e92a800369973cf1536dca3dfa0dc2f588e95c212479f10af44869f8adbbe2f8
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