Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:5700
loss:TripletLoss
text-embeddings-inference
Instructions to use Alexhuou/embedder_model_STxmmluV3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alexhuou/embedder_model_STxmmluV3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alexhuou/embedder_model_STxmmluV3") sentences = [ "Suppose there is a correlation of r = 0.9 between number of hours per day students study and GPAs. Which of the following is a reasonable conclusion?", "Ulcerative Colitis\n", "Given that the sample has a standard deviation of zero, which of the following is a true statement?", "Which of the following items is not subject to the application of intraperiod income tax allocation?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Alexhuou/embedder_model_STxmmluV3: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/Alexhuou/embedder_model_STxmmluV3/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Alexhuou/embedder_model_STxmmluV3/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Alexhuou/embedder_model_STxmmluV3/resolve/main/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "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 | |
| } |