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KnutJaegersberg
/
topic-classification-IPTC-subject-labels

Text Classification
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
SetFit
News
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use KnutJaegersberg/topic-classification-IPTC-subject-labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    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]
  • Transformers

    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")
  • Notebooks
  • Google Colab
  • Kaggle
topic-classification-IPTC-subject-labels / 1_Pooling
190 Bytes
Ctrl+K
Ctrl+K
  • 2 contributors
History: 1 commit
KnutJaegersberg's picture
KnutJaegersberg
Add SetFit model
ef61d77 over 3 years ago
  • config.json
    190 Bytes
    Add SetFit model over 3 years ago