Text Classification
Transformers
Safetensors
PyTorch
English
bert
multi-label-classification
toxicity-detection
Eval Results (legacy)
text-embeddings-inference
Instructions to use Koushim/bert-multilabel-jigsaw-toxic-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Koushim/bert-multilabel-jigsaw-toxic-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Koushim/bert-multilabel-jigsaw-toxic-classifier")# Load model directly from transformers import AutoTokenizer, CustomBertForMultiLabel tokenizer = AutoTokenizer.from_pretrained("Koushim/bert-multilabel-jigsaw-toxic-classifier") model = CustomBertForMultiLabel.from_pretrained("Koushim/bert-multilabel-jigsaw-toxic-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cbc7c3034049b87223ce6615ce123b0f93de7f67ff0ccc926d983b76159ac44
- Size of remote file:
- 5.3 kB
- SHA256:
- ffa1279adafa9ac7a05f36daf449c5fd9e997714dccc7e38ce0a57a1045450bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.