Instructions to use KnutJaegersberg/sentence-data-scideberta-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KnutJaegersberg/sentence-data-scideberta-cs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KnutJaegersberg/sentence-data-scideberta-cs") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use KnutJaegersberg/sentence-data-scideberta-cs with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/sentence-data-scideberta-cs") model = AutoModel.from_pretrained("KnutJaegersberg/sentence-data-scideberta-cs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a73fe03a3967cb4e93fa8fa2ff6ed159d9c668d2616487887f7e1159daaa3f3
- Size of remote file:
- 554 MB
- SHA256:
- a6dafbe43e0b1efbe93e53e7770681cbb851116209346d9e0d877e42cc11c9da
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