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
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert
This new file is equivalent to pytorch_model.bin but safe in the sense that
no arbitrary code can be put into it.
These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.
If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions
Feel free to ignore this PR.