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 1_Pooling/config.json from slxhere/modern_ancientpoem_encoder: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/slxhere/modern_ancientpoem_encoder/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://slxhere/modern_ancientpoem_encoder/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/slxhere/modern_ancientpoem_encoder/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |