Instructions to use tajuarAkash/xlmr-hate-speech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tajuarAkash/xlmr-hate-speech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tajuarAkash/xlmr-hate-speech")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tajuarAkash/xlmr-hate-speech") model = AutoModelForSequenceClassification.from_pretrained("tajuarAkash/xlmr-hate-speech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3af4516a0b58418acfa38dad21676cb65501b71b0c3d9d3d6ce33064c01ee8e8
- Size of remote file:
- 17.1 MB
- SHA256:
- a514807cffabd8abaf028cfaffe7ff0c4f60b97ea2db80c41f14172ae6b018ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.