How to use KnutJaegersberg/topic-classification-IPTC-subject-labels with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KnutJaegersberg/topic-classification-IPTC-subject-labels") 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]
How to use KnutJaegersberg/topic-classification-IPTC-subject-labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KnutJaegersberg/topic-classification-IPTC-subject-labels")
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/topic-classification-IPTC-subject-labels") model = AutoModel.from_pretrained("KnutJaegersberg/topic-classification-IPTC-subject-labels")
What is a pickle import?
