Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use scottstots/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 scottstots/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="scottstots/roberta-base-prop-16-train-set")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scottstots/roberta-base-prop-16-train-set") model = AutoModelForSequenceClassification.from_pretrained("scottstots/roberta-base-prop-16-train-set", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from scottstots/roberta-base-prop-16-train-set: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/scottstots/roberta-base-prop-16-train-set/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://scottstots/roberta-base-prop-16-train-set/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/scottstots/roberta-base-prop-16-train-set/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 4f9bb47e6eccebf032cb45c371539bed851bb7f613492e7cb4737efb81a949ca
- Size of remote file:
- 499 MB
- SHA256:
- 6f0079e8bcdac6a9ee39e7fad5c895333a31f3c741c00b1f8b8c3d64cbe607d3
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