Text Classification
Transformers
PyTorch
Italian
camembert
cross-encoder
sentence-similarity
text-embeddings-inference
Instructions to use efederici/cross-encoder-umberto-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efederici/cross-encoder-umberto-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="efederici/cross-encoder-umberto-stsb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("efederici/cross-encoder-umberto-stsb") model = AutoModelForSequenceClassification.from_pretrained("efederici/cross-encoder-umberto-stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85b76425b8b7f9b962d7e896ba560c7be5066d733030c219b4a2dc3f4e1ba9f8
- Size of remote file:
- 443 MB
- SHA256:
- 841fd18075afdd5ff75471603797be04304e651d5d05e4f8ebab5daa0a88da81
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