Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:3872
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use HarishMaths/Hotel-Policy-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HarishMaths/Hotel-Policy-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HarishMaths/Hotel-Policy-Embedding") sentences = [ "(g) If a Member wishes to extend their stay and has enough Nightly Upgrade Award(s) to cover the extension, the Member must book a separate reservation for the additional nights and request to use Nightly Upgrade Awards on Marriott Websites or by calling Member Support; the Nightly Upgrade Award request cannot be processed at the Participating Property.", "Flexible rates cancel up to a deadline the property sets.", "DONT book a non-refundable hotel without reading the cancellation policy because you must understand the exact penalty structurewhether you forfeit one night, the full amount, or a percentageto determine your coverage needs and ensure your insurance limit is adequate.", "As for semi-flexible plans, they might require notice at least five days before check-in." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from HarishMaths/Hotel-Policy-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 90 Bytes
-
https://huggingface.co/HarishMaths/Hotel-Policy-Embedding/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://HarishMaths/Hotel-Policy-Embedding/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/HarishMaths/Hotel-Policy-Embedding/resolve/main/1_Pooling/config.json
90 Bytes
| { | |
| "embedding_dimension": 384, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |