| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:3872 |
| - loss:MultipleNegativesRankingLoss |
| widget: |
| - source_sentence: (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. |
| sentences: |
| - 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. |
| - source_sentence: Checking out late at a hotel isnt guaranteed, especially when it |
| comes to complimentary late check-out. |
| sentences: |
| - However, the late check-out policy will vary based on the specific hotels policy. |
| - The hotel guest damage clause is a crucial aspect of your reservation agreement |
| that aims to protect both the hotels property and the guests interests. |
| - Yes, if a clean room is available. |
| - source_sentence: refund terms and conditions |
| sentences: |
| - CANCELLATION OR MODIFICATION OF A SERVICE RESERVATION |
| - Elite status doesn't always change the written policy, but it can give you leverage |
| with customer service if you need an exception. |
| - 'Trick #2 Resell the nonrefundable hotel room' |
| - source_sentence: occupancy rules guidelines for guests |
| sentences: |
| - Most hotel insurance policies provide coverage for theft, damage, or loss of personal |
| property under certain conditions. |
| - Choose designated smoking areas outside the hotel. |
| - 'Semi-Flexible Rates : Some properties offer rates that allow cancellation with |
| a fee (e.g., $50) or partial refund up to a certain point.' |
| - source_sentence: Smoking or vaping is allowed in designated rooms. |
| sentences: |
| - Some upgrades to Premium Rooms require payment in local currency and cannot be |
| purchased with Points. |
| - Marriott is committed to providing its guests and associates with a smoke-free |
| environment, and is proud to boast one of the most comprehensive smoke-free hotel |
| policies in the industry. |
| - Participating Properties outside the United States may provide alternative services |
| and benefits to the Elite membership benefits set forth in these Program Rules, |
| depending on local law and policy. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: val |
| type: val |
| metrics: |
| - type: pearson_cosine |
| value: 0.6244156998181909 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.6463453957364179 |
| name: Spearman Cosine |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained for semantic text understanding. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) |
| (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True}) |
| (2): Normalize({}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("HarishMaths/Hotel-Policy-Embedding") |
| # Run inference |
| sentences = [ |
| 'Smoking or vaping is allowed in designated rooms.', |
| 'Marriott is committed to providing its guests and associates with a smoke-free environment, and is proud to boast one of the most comprehensive smoke-free hotel policies in the industry.', |
| 'Some upgrades to Premium Rooms require payment in local currency and cannot be purchased with Points.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.9151, 0.2602], |
| # [0.9151, 1.0000, 0.3726], |
| # [0.2602, 0.3726, 1.0000]]) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
|
|
| * Dataset: `val` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.6244 | |
| | **spearman_cosine** | **0.6463** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 3,872 training samples |
| * Columns: <code>sentence_0</code> and <code>sentence_1</code> |
| * Approximate statistics based on the first 100 samples: |
| | | sentence_0 | sentence_1 | |
| |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | |
| | modality | text | text | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 24.39 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.12 tokens</li><li>max: 103 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | |
| |:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>Three months after booking and 20 days after purchasing insurance, the tour operator files bankruptcy and ceases all operations.</code> | <code>California SB 644 requires hotels and third-party booking sites to give a full refund when a guest cancels within 24 hours of booking, as long as the reservation was made at least 72 hours before check-in.</code> | |
| | <code>One of the easiest ways to avoid resort fees is by booking an award stay.</code> | <code>For a typical domestic Hilton hotel, it will show you the points options and cash rates all on one screen.</code> | |
| | <code>The General Contractor went out to several Home Depot locations around the City to find over 250 battery-operated smoke detectors.</code> | <code>Modern sensors detect vapour, and the resulting charge is identical to a cigarette violation.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 32 |
| - `num_train_epochs`: 25 |
| - `fp16`: True |
| - `per_device_eval_batch_size`: 32 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 32 |
| - `num_train_epochs`: 25 |
| - `max_steps`: -1 |
| - `learning_rate`: 5e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: False |
| - `fp16`: True |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: None |
| - `trackio_bucket_id`: None |
| - `trackio_static_space_id`: None |
| - `per_device_eval_batch_size`: 32 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `dataloader_multiprocessing_context`: None |
| - `dataloader_in_order`: True |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_static_graph`: None |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: None |
| - `fsdp_config`: None |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| - `warmup_ratio`: None |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | val_spearman_cosine | |
| |:-------:|:----:|:-------------:|:-------------------:| |
| | -1 | -1 | - | 0.1722 | |
| | 0.8264 | 100 | - | 0.2700 | |
| | 1.0 | 121 | - | 0.2967 | |
| | 1.6529 | 200 | - | 0.4105 | |
| | 2.0 | 242 | - | 0.4552 | |
| | 2.4793 | 300 | - | 0.4991 | |
| | 3.0 | 363 | - | 0.5376 | |
| | 3.3058 | 400 | - | 0.5566 | |
| | 4.0 | 484 | - | 0.5790 | |
| | 4.1322 | 500 | 3.5721 | 0.5825 | |
| | 4.9587 | 600 | - | 0.6156 | |
| | 5.0 | 605 | - | 0.6162 | |
| | 5.7851 | 700 | - | 0.6144 | |
| | 6.0 | 726 | - | 0.6166 | |
| | 6.6116 | 800 | - | 0.6266 | |
| | 7.0 | 847 | - | 0.6269 | |
| | 7.4380 | 900 | - | 0.6348 | |
| | 8.0 | 968 | - | 0.6276 | |
| | 8.2645 | 1000 | 2.4308 | 0.6329 | |
| | 9.0 | 1089 | - | 0.6336 | |
| | 9.0909 | 1100 | - | 0.6332 | |
| | 9.9174 | 1200 | - | 0.6399 | |
| | 10.0 | 1210 | - | 0.6381 | |
| | 10.7438 | 1300 | - | 0.6390 | |
| | 11.0 | 1331 | - | 0.6397 | |
| | 11.5702 | 1400 | - | 0.6446 | |
| | 12.0 | 1452 | - | 0.6418 | |
| | 12.3967 | 1500 | 2.1294 | 0.6463 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.13.15 |
| - Sentence Transformers: 5.7.0 |
| - Transformers: 5.16.1 |
| - PyTorch: 2.11.0+cu128 |
| - Accelerate: 1.14.0 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.23.1 |
| --- |
