Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1000
loss:BatchAllTripletLoss
text-embeddings-inference
Instructions to use Snivellus789/router-embedding-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Snivellus789/router-embedding-tuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snivellus789/router-embedding-tuned") sentences = [ "x-code秦皇岛革命工程车型号,所体现出来的不同原理,车厢分为哪几类,他们的轮子和动力系统又分为哪几种类型?请详细介绍一下。", "計算費率給定的延期年金的值。", "从系统生物学的视角解读生物科技的重要性。", "新的一年于昨天开始了,请协助完成这篇600字的文章进行时事背景介绍制作,主题有关于“跨年夜上海外滩陈毅广场踩踏事件”五周年。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from Snivellus789/router-embedding-tuned: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/Snivellus789/router-embedding-tuned/resolve/main/modules.json
- Command line
-
hf download hf://Snivellus789/router-embedding-tuned/modules.json
-
curl -L -o modules.json https://huggingface.co/Snivellus789/router-embedding-tuned/resolve/main/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |