Instructions to use JFrediani/mBERT-base-offensive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JFrediani/mBERT-base-offensive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JFrediani/mBERT-base-offensive")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JFrediani/mBERT-base-offensive") model = AutoModelForSequenceClassification.from_pretrained("JFrediani/mBERT-base-offensive", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JFrediani/mBERT-base-offensive: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/JFrediani/mBERT-base-offensive/resolve/main/training_args.bin
- Command line
-
hf download hf://JFrediani/mBERT-base-offensive/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/JFrediani/mBERT-base-offensive/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 8f747492e212e87806cb588a6a8f82d74c24137cf578dedbd841461789d1f98c
- Size of remote file:
- 5.11 kB
- SHA256:
- 5dfb155b50fb3b974ed6cb6eef52c91e5ec03b9cd631dada2baa392b95fc4e3c
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