Text Classification
Transformers
PyTorch
Safetensors
setfit
sentence-transformers
Italian
bert
feature-extraction
hate speech
text-embeddings-inference
Instructions to use nickprock/setfit-italian-hate-speech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickprock/setfit-italian-hate-speech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/setfit-italian-hate-speech")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nickprock/setfit-italian-hate-speech") model = AutoModel.from_pretrained("nickprock/setfit-italian-hate-speech", device_map="auto") - setfit
How to use nickprock/setfit-italian-hate-speech with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("nickprock/setfit-italian-hate-speech") - sentence-transformers
How to use nickprock/setfit-italian-hate-speech with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nickprock/setfit-italian-hate-speech") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d070319d68763f2d7561bf27df3d4fdd93ca5ed78b21ef7f51983946d4864b4
- Size of remote file:
- 440 MB
- SHA256:
- e45e55b813acb8f637c7ff8c22afa24e581e5520b2744735dd4176747d3ba009
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