Text Classification
setfit
Safetensors
sentence-transformers
new
generated_from_setfit_trainer
custom_code
text-embeddings-inference
Instructions to use tmp-org/dm_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use tmp-org/dm_v1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("tmp-org/dm_v1") - sentence-transformers
How to use tmp-org/dm_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tmp-org/dm_v1", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
File size: 793 Bytes
9ae2cf4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"normalize_embeddings": false,
"labels": [
"Angebote_Angebote",
"Angebote_Loading",
"Coupons_Coupons",
"Coupons_Loading",
"Karte + Zahlen_Coupons",
"Karte + Zahlen_Karte + Zahlen",
"Karte + Zahlen_Loading",
"Karte + Zahlen_Nur Karte",
"Online-Shop_Loading",
"Online-Shop_Online-Shop",
"Other_Adventskalender",
"Other_Angebote details",
"Other_Code einl\u00f6sen",
"Other_Coupon details",
"Other_Einkaufsliste",
"Other_Gewinnspiel",
"Other_Information",
"Other_Loading",
"Other_Mein PAYBACK",
"Other_Meine Funktionen",
"Other_Meine digitalen Kassenbons",
"Other_Neuigkeiten",
"Other_Prospekte",
"Other_Rezepte",
"Other_Unknown",
"Startseite_Loading",
"Startseite_Startseite"
]
} |