Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2806
loss:TripletLoss
text-embeddings-inference
Instructions to use ainativestudio/locomo-e5-large-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ainativestudio/locomo-e5-large-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ainativestudio/locomo-e5-large-finetuned") sentences = [ "What kind of interests do Joanna and Nate share?", "[4:21 pm on 16 July, 2023] John: John scored 40 points in a game last week, his highest ever.", "[2:01 pm on 23 January, 2022] Nate: Thanks! The turtles might be small, but both sure have big personalities. I really reccomend having something like these little guys for times of stress.", "[11:54 am on 2 May, 2022] Joanna: Joanna has been working on some projects and testing dairy-free dessert recipes for friends and family." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize" | |
| } | |
| ] |