Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-9") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3e0822d17d82c86ce25dac58a0672124efb87a18469679a5d339da82bcca721
- Size of remote file:
- 268 MB
- SHA256:
- 02f505a04a9d1872f2ab78b03d066583cf8d5d697576ca122acc5d538bd0470c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.