Instructions to use NathanZhu/GabHateCorpusTrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NathanZhu/GabHateCorpusTrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NathanZhu/GabHateCorpusTrained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NathanZhu/GabHateCorpusTrained") model = AutoModelForSequenceClassification.from_pretrained("NathanZhu/GabHateCorpusTrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NathanZhu/GabHateCorpusTrained: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/NathanZhu/GabHateCorpusTrained/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NathanZhu/GabHateCorpusTrained/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NathanZhu/GabHateCorpusTrained/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- a7c9b3f3610ab10679d090ad1dc57127e68d1d78a28b3e835ca29949d89dae6d
- Size of remote file:
- 438 MB
- SHA256:
- 9c14fdfddaeeb74d0ad94c74a0453724dbb545cdad841b19757361195b19587c
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