Instructions to use Cameron/BERT-SBIC-offensive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cameron/BERT-SBIC-offensive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cameron/BERT-SBIC-offensive")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cameron/BERT-SBIC-offensive") model = AutoModelForSequenceClassification.from_pretrained("Cameron/BERT-SBIC-offensive", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Cameron/BERT-SBIC-offensive: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/Cameron/BERT-SBIC-offensive/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Cameron/BERT-SBIC-offensive/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Cameron/BERT-SBIC-offensive/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 30d1455aa8aa6ef039210f949db38c9d1f07cdfe87351b45384844f837a5bd7c
- Size of remote file:
- 433 MB
- SHA256:
- b7f32cc7a1790b5124373903c10bbd1855a29ac094c0eb1310e5f363c2b533dd
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