Instructions to use Data-Lab/moderation_binary_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data-Lab/moderation_binary_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Data-Lab/moderation_binary_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Data-Lab/moderation_binary_classification") model = AutoModelForSequenceClassification.from_pretrained("Data-Lab/moderation_binary_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download moderation_binary_classification-encoder.npy from Data-Lab/moderation_binary_classification: direct link, hf CLI and curl.
- Browser
- Download file 144 Bytes
-
https://huggingface.co/Data-Lab/moderation_binary_classification/resolve/4ed08a2d6d82e83941ae92f55a38a757636b77e7/moderation_binary_classification-encoder.npy
- Command line
-
hf download hf://Data-Lab/moderation_binary_classification@4ed08a2d6d82e83941ae92f55a38a757636b77e7/moderation_binary_classification-encoder.npy
-
curl -L -o moderation_binary_classification-encoder.npy https://huggingface.co/Data-Lab/moderation_binary_classification/resolve/4ed08a2d6d82e83941ae92f55a38a757636b77e7/moderation_binary_classification-encoder.npy
144 Bytes
- Xet hash:
- 233ced7b9ffeab9d8f4c114289a9b28835b9ec7c78e27d7a1ad670e41f8bc33f
- Size of remote file:
- 144 Bytes
- SHA256:
- edf57b3e7cc4d837db7a3b400e84ffa2cc07b6adc347edef9feabbc11c5183cb
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