Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
dataset_size:1M<n<10M
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use gkudirka/crash_encoder2-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gkudirka/crash_encoder2-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gkudirka/crash_encoder2-sts") sentences = [ "B C C_L CENTER TUNNEL VERT Other XXXX GENERIC G-S", "T L ENG TO RAD SWITCH 90 Deg Front 2015 P552 VOLTS", "T RCM ENS 071 RCM ENS EFPR VOLT 90 Deg Front 2021 CX430 VOLTS", "T L ROCKER AT B PILLAR LONG 90 Deg Front 2020 V363N G-S" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle