Text Classification
Transformers
PyTorch
Italian
bert
cross-encoder
sentence-similarity
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("efederici/cross-encoder-bert-base-stsb")
model = AutoModelForSequenceClassification.from_pretrained("efederici/cross-encoder-bert-base-stsb")Quick Links
Cross-Encoder
This model was trained using SentenceTransformers Cross-Encoder class.
Edouard Vuillard, Sunlit Interior
Training Data
This model was trained on stsb. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
Usage and Performance
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
The model will predict scores for the pairs ('Sentence 1', 'Sentence 2') and ('Sentence 3', 'Sentence 4').
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="efederici/cross-encoder-bert-base-stsb")