Text Classification
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
SetFit
News
text-embeddings-inference
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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