Text Classification
Transformers
Safetensors
English
bert
toxic
toxicity
hate speech
offensive language
multi-class-classification
multi-label-classification
text-embeddings-inference
Instructions to use minuva/MiniLMv2-toxic-jigsaw-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minuva/MiniLMv2-toxic-jigsaw-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minuva/MiniLMv2-toxic-jigsaw-lite")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minuva/MiniLMv2-toxic-jigsaw-lite") model = AutoModelForSequenceClassification.from_pretrained("minuva/MiniLMv2-toxic-jigsaw-lite") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fafde47dd38c88914465e3f20fb592d646ce3ee0ca873c1d3177bb481b8d87c8
- Size of remote file:
- 4.28 kB
- SHA256:
- 40418718ceb5ef3def8519eb28897c9753b65a05a6ac4e1bae710095a0cd772a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.