Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
Generated from Trainer
dataset_size:700000
loss:DenoisingAutoEncoderLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bobox/E5-base-unsupervised-TSDAE-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bobox/E5-base-unsupervised-TSDAE-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bobox/E5-base-unsupervised-TSDAE-2") sentences = [ "in Freeview no extra therefore minimal Also the is wide decent, plus they and.", "Pokémon-GX (Japanese: ポケモンGX Pokémon GX), officially written Pokémon-GX, are a variant of Pokémon in the Pokémon Trading Card Game. They were first introduced in the Sun & Moon expansion (the Collection Sun and Collection Moon expansions in Japan). Pokémon-GX have a stylized. graphic on the card name.", "The Cape Colony (Dutch: Kaapkolonie) was a Dutch East India Company colony in Southern Africa, centered on the Cape of Good Hope, whence it derived its name. The original colony and its successive states that the colony was incorporated into occupied much of modern South Africa.", "Avtex is expensive, but you get built in Freeview, Freesat and built in DVD player, which means no extra boxes, and therefore minimal wiring. Also the viewing angle is wide and a decent picture quality, plus they are light and designed for mobile use." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1 opened 12 months ago
by
SFconvertbot