Text Classification
Transformers
PyTorch
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use tmarkspengler/output-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmarkspengler/output-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmarkspengler/output-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmarkspengler/output-roberta") model = AutoModelForSequenceClassification.from_pretrained("tmarkspengler/output-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tmarkspengler/output-roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/tmarkspengler/output-roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tmarkspengler/output-roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tmarkspengler/output-roberta/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- ab08851430442a43928a970c1884373d326b0e2bf7030d6324de15355fd33d52
- Size of remote file:
- 499 MB
- SHA256:
- 926ba029185155a97fc59d8cbd9c1ae59ce15e78468009862ba4ae6dc3ecab75
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