Text Classification
Transformers
ONNX
Safetensors
English
bert
toxic
toxicity
offensive language
hate speech
text-embeddings-inference
Instructions to use minuva/MiniLMv2-toxic-jigsaw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minuva/MiniLMv2-toxic-jigsaw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minuva/MiniLMv2-toxic-jigsaw")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minuva/MiniLMv2-toxic-jigsaw") model = AutoModelForSequenceClassification.from_pretrained("minuva/MiniLMv2-toxic-jigsaw") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d08ae7a07e8c6b91842bcdae1de6b034836911d649990c2f44e89aefd9dacb71
- Size of remote file:
- 182 MB
- SHA256:
- 4023fd2010f265e2d33bf01816c93464ec88f8f45b29e3256297cbb3ae796152
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.