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%2F1/pytorch_model.bin
- Command line
-
hf download hf://quesmed/tone@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/quesmed/tone/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 90b922843dad3fe08a6e94b3f0b07c43f9a1e78a29eba8a848c77d8c9d16c416
- Size of remote file:
- 499 MB
- SHA256:
- 6446223d53c0786f4b6e2df1b7528614126633a88dd57d4034e59f974818e084
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.