Sentence Similarity
sentence-transformers
Safetensors
bert
embedding
context-curation
long-context
memory-management
retrieval
text-embeddings-inference
Instructions to use SkyIsNotGreen/splinter-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SkyIsNotGreen/splinter-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SkyIsNotGreen/splinter-encoder") 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] - Notebooks
- Google Colab
- Kaggle
Download finetune_meta.json from SkyIsNotGreen/splinter-encoder: direct link, hf CLI and curl.
- Browser
- Download file 295 Bytes
-
https://huggingface.co/SkyIsNotGreen/splinter-encoder/resolve/main/finetune_meta.json
- Command line
-
hf download hf://SkyIsNotGreen/splinter-encoder/finetune_meta.json
-
curl -L -o finetune_meta.json https://huggingface.co/SkyIsNotGreen/splinter-encoder/resolve/main/finetune_meta.json
295 Bytes
| { | |
| "student": "sentence-transformers/paraphrase-MiniLM-L3-v2", | |
| "teacher": "", | |
| "examples": 561, | |
| "epochs": 1, | |
| "batch_size": 16, | |
| "lr": 5e-06, | |
| "temperature": 0.05, | |
| "alpha": 0.5, | |
| "hard_weight": 0.5, | |
| "seed": 20261020, | |
| "seconds": 82.5, | |
| "history": [ | |
| { | |
| "epoch": 0, | |
| "steps": 35 | |
| } | |
| ] | |
| } |