Instructions to use Kanit/bert-hateXplain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kanit/bert-hateXplain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kanit/bert-hateXplain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kanit/bert-hateXplain") model = AutoModelForSequenceClassification.from_pretrained("Kanit/bert-hateXplain") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c647368b647a86fc34f7bbb320ac8a5f9e77443f43b37fea379b61f6d19cec1e
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size 17553496
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