Sentence Similarity
sentence-transformers
Safetensors
English
embedding
emotion
memory
neuroscience
vividness
companion-ai
pad-model
reconsolidation
fine-tuned
Eval Results (legacy)
Instructions to use Kronic90/all-MiniLLM-VividTuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Kronic90/all-MiniLLM-VividTuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Kronic90/all-MiniLLM-VividTuned") 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 final/modules.json from Kronic90/all-MiniLLM-VividTuned: direct link, hf CLI and curl.
- Browser
- Download file 242 Bytes
-
https://huggingface.co/Kronic90/all-MiniLLM-VividTuned/resolve/main/final/modules.json
- Command line
-
hf download hf://Kronic90/all-MiniLLM-VividTuned/final/modules.json
-
curl -L -o modules.json https://huggingface.co/Kronic90/all-MiniLLM-VividTuned/resolve/main/final/modules.json
242 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |