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 training_args.bin from tmarkspengler/output-roberta: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/tmarkspengler/output-roberta/resolve/main/training_args.bin
- Command line
-
hf download hf://tmarkspengler/output-roberta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tmarkspengler/output-roberta/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 589121ba15cdd69a1171ad07b35f5f70d24fe6757cb01121b279ae515bb80ed0
- Size of remote file:
- 5.78 kB
- SHA256:
- 8e916ca373cd90751e68215b60895a408038c507597d3e86b83ac6268b0f5694
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