Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Rishi-19/Profanity_Check_CustomData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rishi-19/Profanity_Check_CustomData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rishi-19/Profanity_Check_CustomData")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rishi-19/Profanity_Check_CustomData") model = AutoModelForSequenceClassification.from_pretrained("Rishi-19/Profanity_Check_CustomData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20b63c881677e8f3e42822d5c2b07174d8a0f209006b4e7b75eb877291339bea
- Size of remote file:
- 268 MB
- SHA256:
- ecdea855af8f7d8f31a832ddb951091cce8ca68fdb2ada7b3097d4895e4abb25
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