Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use RobCaamano/toxicity_weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobCaamano/toxicity_weighted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RobCaamano/toxicity_weighted")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RobCaamano/toxicity_weighted") model = AutoModelForSequenceClassification.from_pretrained("RobCaamano/toxicity_weighted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6708bcbe63c8805f0d5367cd95c641ec3c16ac68e9113aeb7486486c3cf32a9
- Size of remote file:
- 268 MB
- SHA256:
- e28e4413e83f524129243b1c5e6730b6eea1a6ce537db77d7a79ca2ef51fc808
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