File size: 15,618 Bytes
d835089 3c55933 d835089 3c9f67d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 | ---
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
--- |