Instructions to use Cameron/BERT-jigsaw-identityhate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cameron/BERT-jigsaw-identityhate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cameron/BERT-jigsaw-identityhate")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cameron/BERT-jigsaw-identityhate") model = AutoModelForSequenceClassification.from_pretrained("Cameron/BERT-jigsaw-identityhate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Cameron/BERT-jigsaw-identityhate: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/Cameron/BERT-jigsaw-identityhate/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Cameron/BERT-jigsaw-identityhate/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Cameron/BERT-jigsaw-identityhate/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 4ab4f72a28b1eb04e8a28d8b4359746cfafc169132d3b7ccfe91153fe1e15331
- Size of remote file:
- 433 MB
- SHA256:
- e9ec5249049988573179427c7cb5c7b040d04577b272ea74d390a10e9be4dc73
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.