metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:672
- loss:ContrastiveLoss
base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1
widget:
- source_sentence: >-
Animals may not be allowed onto beds or other furniture, which serves for
guests. It is not permitted to use baths, showers or washbasins for
bathing or
washing animals.
sentences:
- >-
Please advise of any special needs such as high-chairs and sleeping
cots.
- >-
Animals may not be allowed onto beds or other furniture, which serves
for
guests. It is not permitted to use baths, showers or washbasins for
bathing or
washing animals.
- >-
It is strongly advised that you arrange adequate insurance cover such as
cancellation due to illness,
accident or injury, personal accident and personal liability, loss of or
damage to baggage and sport
equipment (Note that is not an exhaustive list). We will not be
responsible or liable if you fail to take
adequate insurance cover or none at all.
- source_sentence: >-
Owners are responsible for ensuring that animals are kept quiet between
the
hours of 10:00 pm and 06:00 am. In the case of failure to abide by this
regulation the guest may be asked to leave the hotel without a refund of
the
price of the night's accommodation.
sentences:
- >-
Visitors are not allowed in the rooms and must be entertained in the
lounges and/or other public areas
provided.
- >-
To ensure the safety and comfort of everyone in the hotel, the
Management
reserves the right to terminate the accommodation of guests who fail to
comply
with the following rules and regulations.
- >-
Owners are responsible for ensuring that animals are kept quiet between
the
hours of 10:00 pm and 06:00 am. In the case of failure to abide by this
regulation the guest may be asked to leave the hotel without a refund of
the
price of the night's accommodation.
- source_sentence: >-
We ask all guests to behave in such a way that they do not disturb other
guests and the neighborhood.
The hotel staff is authorized to refuse services to a person who violates
this rule.
sentences:
- >-
Please take note of the limitation specified for the room you have
booked.
If such number is exceeded, whether temporarily or over-night, we
reserve the right to do one or more of
the following: cancel your booking; retain all the monies you've paid;
request you to vacate your room(s)
forthwith, charge a higher rate for the room or recover all monies due.
- >-
We ask all guests to behave in such a way that they do not disturb other
guests and the neighborhood.
The hotel staff is authorized to refuse services to a person who
violates this rule.
- >-
We will only deal with your information as indicated in the
booking/reservation and we will only process your
personal information (both terms as defined in the Protection of
Personal Information Act, act 4 of 2013 ['the
POPIA'] and the European Union General Data Protection Regulation –
('GDPR') and any Special Personal
Information (as defined in the GDPR & POPIA), which processing includes
amongst others the 'collecting,
storing and dissemination' of your personal information (as defined in
GDPR & POPIA).
- source_sentence: >-
All articles stored in the luggage storage room are received at the
owner’s own risk.
sentences:
- >2-
Unregistered visitors are not permitted to enter guest rooms or other areas of
the hotel. An additional fee for unregistered guests will be charged to
the
account of the guest(s) registered to the room.
- >-
Please advise us if you anticipate arriving late as bookings will be
cancelled by 17:00 on the day of arrival,
unless we have been so notified.
- >-
All articles stored in the luggage storage room are received at the
owner’s own risk.
- source_sentence: >2-
In the event of a disturbance, one polite request (warning) will
be given to reduce the noise. If our request is not followed, the guest
will be asked to leave
the hotel without refund and may be charged Guest Compensation Disturbance
Fee.
sentences:
- >-
Without limiting the generality of the aforementioned, it applies to
pay-to-view TV programmes or videos, as
well as telephone calls or any other expenses of a similar nature that
is made from your room, you will be
deemed to be the contracting party.
- >-
Pets are not allowed in the restaurant during breakfast time
(7:00 – 10:30) for hygienic reasons due to the breakfast’s buffet style.
An
exception is the case when the hotel terrace is open, as pets can be
taken to
the terrace through the hotel's main entrance and they can stay there
during
breakfast.
- >2-
In the event of a disturbance, one polite request (warning) will
be given to reduce the noise. If our request is not followed, the guest
will be asked to leave
the hotel without refund and may be charged Guest Compensation
Disturbance Fee.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- dot_mcc
model-index:
- name: >-
SentenceTransformer based on
sentence-transformers/multi-qa-mpnet-base-dot-v1
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Unknown
type: unknown
metrics:
- type: dot_accuracy
value: 0.6745562130177515
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 49.0201301574707
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.4932735426008969
name: Dot F1
- type: dot_f1_threshold
value: 35.02415466308594
name: Dot F1 Threshold
- type: dot_precision
value: 0.32934131736526945
name: Dot Precision
- type: dot_recall
value: 0.9821428571428571
name: Dot Recall
- type: dot_ap
value: 0.3294144882113245
name: Dot Ap
- type: dot_mcc
value: -0.03920743101752848
name: Dot Mcc
SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1
This is a sentence-transformers model finetuned from sentence-transformers/multi-qa-mpnet-base-dot-v1. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: sentence-transformers/multi-qa-mpnet-base-dot-v1
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Dot Product
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Marco127/Argu_T3")
# Run inference
sentences = [
' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.',
' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.',
'\nWithout limiting the generality of the aforementioned, it applies to pay-to-view TV programmes or videos, as\nwell as telephone calls or any other expenses of a similar nature that is made from your room, you will be\ndeemed to be the contracting party.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Binary Classification
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
dot_accuracy | 0.6746 |
dot_accuracy_threshold | 49.0201 |
dot_f1 | 0.4933 |
dot_f1_threshold | 35.0242 |
dot_precision | 0.3293 |
dot_recall | 0.9821 |
dot_ap | 0.3294 |
dot_mcc | -0.0392 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 672 training samples
- Columns:
sentence1
,sentence2
, andlabel
- Approximate statistics based on the first 672 samples:
sentence1 sentence2 label type string string int details - min: 11 tokens
- mean: 48.63 tokens
- max: 156 tokens
- min: 11 tokens
- mean: 48.63 tokens
- max: 156 tokens
- 0: ~66.67%
- 1: ~33.33%
- Samples:
sentence1 sentence2 label
The pets can not be left without supervision if there is a risk of causing any
damage or might disturb other guests.
The pets can not be left without supervision if there is a risk of causing any
damage or might disturb other guests.0
Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these
rules are available at the Front Desk upon request.
Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these
rules are available at the Front Desk upon request.0
Consuming the products from the minibar involves additional costs. You can find the
prices in the kitchen area.
Consuming the products from the minibar involves additional costs. You can find the
prices in the kitchen area.0
- Loss:
ContrastiveLoss
with these parameters:{ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true }
Evaluation Dataset
Unnamed Dataset
- Size: 169 evaluation samples
- Columns:
sentence1
,sentence2
, andlabel
- Approximate statistics based on the first 169 samples:
sentence1 sentence2 label type string string int details - min: 13 tokens
- mean: 46.01 tokens
- max: 156 tokens
- min: 13 tokens
- mean: 46.01 tokens
- max: 156 tokens
- 0: ~66.86%
- 1: ~33.14%
- Samples:
sentence1 sentence2 label
I understand and accept that the BON Hotels Group collects the personal information ("personal
information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of
all in my party, expressly consent and grant permission to the BON Hotels Group to: -
collect, collate, process, study and use the personal information; and
communicate directly with me/us from time to time, unless I have stated to the contrary below.
I understand and accept that the BON Hotels Group collects the personal information ("personal
information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of
all in my party, expressly consent and grant permission to the BON Hotels Group to: -
collect, collate, process, study and use the personal information; and
communicate directly with me/us from time to time, unless I have stated to the contrary below.0
However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which
period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse
against us.However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which
period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse
against us.0
In cases where the hotel
suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it
may charge a compensation fee in proportion to the damage. Moral damage may be for
example disturbing other guests, thus ruining the reputation of the hotel.In cases where the hotel
suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it
may charge a compensation fee in proportion to the damage. Moral damage may be for
example disturbing other guests, thus ruining the reputation of the hotel.0
- Loss:
ContrastiveLoss
with these parameters:{ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16learning_rate
: 1e-05num_train_epochs
: 2warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | dot_ap |
---|---|---|
-1 | -1 | 0.3294 |
Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
Citation
BibTeX
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
ContrastiveLoss
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}