Sentence Similarity
sentence-transformers
Safetensors
Chinese
bert
feature-extraction
Generated from Trainer
dataset_size:225000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use slxhere/modern_ancientpoem_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use slxhere/modern_ancientpoem_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("slxhere/modern_ancientpoem_encoder") sentences = [ "下班后和同事直奔常去的那家火锅店,热热闹闹地涮了一晚上。", "联延掩四远,赫弈成洪炉。", "把酒仰问天,古今谁不死。", "骑出平阳里,筵开卫尉家。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from slxhere/modern_ancientpoem_encoder: direct link, hf CLI and curl.
- Browser
- Download file 341 Bytes
-
https://huggingface.co/slxhere/modern_ancientpoem_encoder/resolve/main/modules.json
- Command line
-
hf download hf://slxhere/modern_ancientpoem_encoder/modules.json
-
curl -L -o modules.json https://huggingface.co/slxhere/modern_ancientpoem_encoder/resolve/main/modules.json
341 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_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| } | |
| ] |