Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Cross-lingual-nlp
zero-shot-transfer
toxicity-analysis
abuse-detection
flag-user
block-user
multilinguality
XLM-R
Instructions to use Jayveersinh-Raj/PolyGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jayveersinh-Raj/PolyGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jayveersinh-Raj/PolyGuard")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jayveersinh-Raj/PolyGuard") model = AutoModelForSequenceClassification.from_pretrained("Jayveersinh-Raj/PolyGuard") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6d4274959a442fbfd6e8ef6320d9e91353f4ac85f9a50ccdc8d9d7954c659ce
- Size of remote file:
- 1.11 GB
- SHA256:
- f07c506682cbbd1e3032e0d7fdb6e2d0d1ee6a96686541f0390e35983f6662d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.