Instructions to use Hate-speech-CNERG/bert-base-uncased-hatexplain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hate-speech-CNERG/bert-base-uncased-hatexplain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hate-speech-CNERG/bert-base-uncased-hatexplain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain") model = AutoModelForSequenceClassification.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Hate-speech-CNERG/bert-base-uncased-hatexplain: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Hate-speech-CNERG/bert-base-uncased-hatexplain/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d068e5565048e6236daa499542e719d961883dbd3218066ee7a55b5d90954cfb
- Size of remote file:
- 438 MB
- SHA256:
- 7939d499f5c2ec887db9c09d9e790f33321686a4993c6b3da7d89db059a7452e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.