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")# 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
- Xet hash:
- 45d3eef0dcbc79653d9a1ac610c87e1f0ed3a31cdab6243207c3a9ff760ce647
- Size of remote file:
- 4.03 kB
- SHA256:
- 892ab53565c1803e1ab66bef44a174dbb95506be506ef98a6c84e5d5bfd5eba7
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