metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:684
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1
widget:
  - source_sentence: >-

      We request that guests report any complaints and defects to the hotel
      reception or hotel

      management in person. Your complaints shall be attended to immediately.
    sentences:
      - >-

        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.
      - >-

        We request that guests report any complaints and defects to the hotel
        reception or hotel

        management in person. Your complaints shall be attended to immediately.
      - >-

        Guests who take accommodation after midnight, shall still pay the price
        for

        accommodation for the whole of the preceding night. The hotel’s official
        Check-in time is

        from 02:00 pm. For a possible early check-in, please consult with the
        reservation team, or

        the reception in advance.
  - source_sentence: >-

      Hotel guests may receive visits in their hotel rooms from guests not
      staying in the hotel.

      Visitors must present a personal document at the hotel reception and
      register in the visitors'

      book. These visits can last for only a maximum of 2 hours and must finish
      until 10:00 pm.
    sentences:
      - >-

        Hotel guests may receive visits in their hotel rooms from guests not
        staying in the hotel.

        Visitors must present a personal document at the hotel reception and
        register in the visitors'

        book. These visits can last for only a maximum of 2 hours and must
        finish until 10:00 pm.
      - >2-
          If you do not want someone to enter
        your room, please hang the "do not disturb” card on your room’s outside
        door handle. It can

        be found in the entrance area of your room.
      - >-

        Hotel guests may receive visits in their hotel rooms from guests not
        staying in the hotel.

        Visitors must present a personal document at the hotel reception and
        register in the visitors'

        book. These visits can last for only a maximum of 2 hours and must
        finish until 10:00 pm.
  - source_sentence: >-

      Guests may not use their own electrical appliances in the hotel building
      except for those

      serving for personal hygiene (electrical shavers or massaging machines,
      hairdryers etc.), or

      personal computers and telephone chargers. The rooms own electrical
      devices shall only be

      used according to their main purpose.
    sentences:
      - >-

        Pets are allowed in the hotel restaurant only from 12:00, provided the

        animal's behavior and cleanliness are adequate and they do not disturb
        other

        guests. 
      - >-

        Guests may not use their own electrical appliances in the hotel building
        except for those

        serving for personal hygiene (electrical shavers or massaging machines,
        hairdryers etc.), or

        personal computers and telephone chargers. The rooms own electrical
        devices shall only be

        used according to their main purpose.
      - >2-
         For a possible late check-out please consult with the reception
        in time, and upon availability we may grant a later check-out for a
        supplemental fee.
  - source_sentence: >-

      The hotel may provide accommodation only for guests who register in the
      regular

      manner. For this purpose, the guest must present a personal document
      (citizen's

      identification card), or a valid passport to the receptionist. Accepting
      these Rules of the

      House is also obligatory for the registration.
    sentences:
      - >-

        Hotel guests are obliged to abide by the provisions of these hotel
        regulations. In the case of

        serious violation, the reception or hotel management may withdraw from
        the contract on

        accommodation services before the elapse of the agreed period.
      - >-

        Hotel guests are responsible for given room keys during their whole
        stay. In case of loss, the

        guests are asked to inform reception staff immediately in order to
        prevent abusing the key.

        Losing the room key will result in a penalty of 20 Eur, which is to be
        paid on the spot, at the

        reception.
      - >-

        The hotel may provide accommodation only for guests who register in the
        regular

        manner. For this purpose, the guest must present a personal document
        (citizen's

        identification card), or a valid passport to the receptionist. Accepting
        these Rules of the

        House is also obligatory for the registration.
  - source_sentence: >-

      Guests are responsible for damages caused to hotel property according to
      the valid legal

      prescriptions of Hungary.
    sentences:
      - >-

        We shall be happy to listen to any suggestions for improvement of the
        accommodation

        and catering services in the hotel. In case of any complaints we shall
        purposefully arrange

        the rectification of any insufficiencies.
      - >-

        Guests are responsible for damages caused to hotel property according to
        the valid legal

        prescriptions of Hungary.
      - >-

        Guests are responsible for damages caused to hotel property according to
        the valid legal

        prescriptions of Hungary.
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: 0.6549707602339181
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 48.36168670654297
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.5142857142857143
            name: Dot F1
          - type: dot_f1_threshold
            value: 40.011634826660156
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.36
            name: Dot Precision
          - type: dot_recall
            value: 0.9
            name: Dot Recall
          - type: dot_ap
            value: 0.3570718807651215
            name: Dot Ap
          - type: dot_mcc
            value: 0.03879793956580217
            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/Base_T")
# Run inference
sentences = [
    '\nGuests are responsible for damages caused to hotel property according to the valid legal\nprescriptions of Hungary.',
    '\nGuests are responsible for damages caused to hotel property according to the valid legal\nprescriptions of Hungary.',
    '\nWe shall be happy to listen to any suggestions for improvement of the accommodation\nand catering services in the hotel. In case of any complaints we shall purposefully arrange\nthe rectification of any insufficiencies.',
]
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	0.655
dot_accuracy_threshold	48.3617
dot_f1	0.5143
dot_f1_threshold	40.0116
dot_precision	0.36
dot_recall	0.9
dot_ap	0.3571
dot_mcc	0.0388

Training Details

Training Dataset

Unnamed Dataset

Size: 684 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 684 samples:
sentence1 sentence2 label
type string string int
details
min: 17 tokens
mean: 42.77 tokens
max: 71 tokens

min: 17 tokens
mean: 42.77 tokens
max: 71 tokens

0: ~67.11%
1: ~32.89%

	sentence1	sentence2	label
type	string	string	int
details	min: 17 tokens mean: 42.77 tokens max: 71 tokens	min: 17 tokens mean: 42.77 tokens max: 71 tokens	0: ~67.11% 1: ~32.89%

Samples:

sentence1	sentence2	label
`If a guest fails to vacate the room within the designated time, reception shall charge this guest for the following night's accommodation fee.`	`If a guest fails to vacate the room within the designated time, reception shall charge this guest for the following night's accommodation fee.`	`0`
`If you do not want someone to enter your room, please hang the "do not disturb” card on your room’s outside door handle. It can be found in the entrance area of your room.`	`If you do not want someone to enter your room, please hang the "do not disturb” card on your room’s outside door handle. It can be found in the entrance area of your room.`	`0`
`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.`	`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.`	`0`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 171 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 171 samples:
sentence1 sentence2 label
type string string int
details
min: 17 tokens
mean: 42.01 tokens
max: 71 tokens

min: 17 tokens
mean: 42.01 tokens
max: 71 tokens

0: ~64.91%
1: ~35.09%

	sentence1	sentence2	label
type	string	string	int
details	min: 17 tokens mean: 42.01 tokens max: 71 tokens	min: 17 tokens mean: 42.01 tokens max: 71 tokens	0: ~64.91% 1: ~35.09%

Samples:

sentence1	sentence2	label
`We shall be happy to listen to any suggestions for improvement of the accommodation and catering services in the hotel. In case of any complaints we shall purposefully arrange the rectification of any insufficiencies.`	`We shall be happy to listen to any suggestions for improvement of the accommodation and catering services in the hotel. In case of any complaints we shall purposefully arrange the rectification of any insufficiencies.`	`0`
`Between the hours of 10:00 pm and 06:00 am guests are obliged to maintain low noise levels.`	`Between the hours of 10:00 pm and 06:00 am guests are obliged to maintain low noise levels.`	`0`
`The hotel’s inner courtyard parking facility may be used only upon availability of parking slots. Slots marked as ’Private’ are to be left free for their owners. For parking fees please consult the reception or see the website of the hotel.`	`The hotel’s inner courtyard parking facility may be used only upon availability of parking slots. Slots marked as ’Private’ are to be left free for their owners. For parking fees please consult the reception or see the website of the hotel.`	`1`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 5
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	dot_ap
-1	-1	-	-	0.3571
2.2791	100	0.0011	0.0000	-
4.5581	200	0.0	0.0000	-

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}