Text Classification
Transformers
PyTorch
English
distilbert
abuse-detection
relationship-analysis
multi-label-classification
safety
psychology
nlp
Eval Results (legacy)
text-embeddings-inference
Instructions to use SamanthaStorm/abusedetector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamanthaStorm/abusedetector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SamanthaStorm/abusedetector")# Load model directly from transformers import AutoTokenizer, AbusePatternDetector tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/abusedetector") model = AbusePatternDetector.from_pretrained("SamanthaStorm/abusedetector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89ab7e6470b25b88b7a61856075f8600f4222d1e3a5e1ee042dabbc5a3d2398a
- Size of remote file:
- 266 MB
- SHA256:
- 638808ce4a670d92461b413e406f0fb960038d313b94571e76cea15260fd96a9
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