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 tokenizer_config.json from HarishMaths/Hotel-Policy-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 593 Bytes
-
https://huggingface.co/HarishMaths/Hotel-Policy-Embedding/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://HarishMaths/Hotel-Policy-Embedding/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/HarishMaths/Hotel-Policy-Embedding/resolve/main/tokenizer_config.json
593 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": true, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_max_length": 128, | |
| "never_split": null, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |