Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-9 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-32-9 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-32-9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-9") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c48e66f054c2bbf017ec36962ce4f2adcc9fb56fcd25fe5d29ad8948d96c154d
- Size of remote file:
- 3.12 kB
- SHA256:
- 714075303f006bdf79172192c5bd86048c4391a0c320f8cac7287361fa10631a
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