Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Jios/TON_IoT_no_injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jios/TON_IoT_no_injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jios/TON_IoT_no_injection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jios/TON_IoT_no_injection") model = AutoModelForSequenceClassification.from_pretrained("Jios/TON_IoT_no_injection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71dc33c70b63b27588d8f623485963a206a9ea0f6a50c19e80521330c1b8df1e
- Size of remote file:
- 5.3 kB
- SHA256:
- ff53d12f006d7cc08292f93d35c1592bbfceb96b66f7876bb03a52c104b4be4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.