Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:48972
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ishandotsh/logembed_a1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ishandotsh/logembed_a1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ishandotsh/logembed_a1") sentences = [ "[instance: <*>] Terminating instance", "pam_unix(sshd:session): session opened for user <*> by (uid=<*>)", "[instance: <*>] Terminating instance", "[instance: <*>] Creating image" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |