Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use quesmed/tone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quesmed/tone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="quesmed/tone")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("quesmed/tone") model = AutoModelForSequenceClassification.from_pretrained("quesmed/tone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from quesmed/tone: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/quesmed/tone/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://quesmed/tone@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/quesmed/tone/resolve/refs%2Fpr%2F2/pytorch_model.bin
499 MB
- Xet hash:
- 8a6e1f6d5f8fd88b31563f849d6e372d2566b4ef21e30451d50b4686a37a3bb1
- Size of remote file:
- 499 MB
- SHA256:
- 15defadc68d4660f0e2ded16aebf1b2116b446e481b898fba2fcf7a61e1f9d18
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