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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Marco127/Base_Test1_")
# Run inference
sentences = [
    '\nA hotel guest may not leave the room to another person, even if the time for which he or she has paid has\nnot expired.',
    '\nA hotel guest may not leave the room to another person, even if the time for which he or she has paid has\nnot expired.',
    'Orders for accommodation services made in writing or by other means, which have been\nconfirmed by the hotel and have not been cancelled by the customer in a timely manner, are\nmutually binding. The front office manager keeps a record of all received and confirmed\norders.',
]
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	0.6671
dot_accuracy_threshold	48.9305
dot_f1	0.4987
dot_f1_threshold	33.9523
dot_precision	0.3325
dot_recall	0.9964
dot_ap	0.3126
dot_mcc	0.0

Training Details

Training Dataset

Unnamed Dataset

Size: 3,362 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 11 tokens
mean: 48.75 tokens
max: 156 tokens
min: 11 tokens
mean: 48.75 tokens
max: 156 tokens
0: ~69.20%
1: ~30.80%

Samples:

sentence1	sentence2	label
`Hotel guests may receive visits in their hotel rooms from guests not staying in the hotel.[object Object]Visitors must present a personal document at the hotel reception and register in the visitors'[object Object]book. These visits can last for only a maximum of 2 hours and must finish until 10:00 pm.`	`Hotel guests may receive visits in their hotel rooms from guests not staying in the hotel.[object Object]Visitors must present a personal document at the hotel reception and register in the visitors'[object Object]book. These visits can last for only a maximum of 2 hours and must finish until 10:00 pm.`	`0`
`[object Object]We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as[object Object]well as using our best endeavours to advise you if that is not the case.`	`[object Object]We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as[object Object]well as using our best endeavours to advise you if that is not the case.`	`0`
`[object Object]Pool and Fitness Room hours and guidelines are provided at check in. All rules and times will be enforced to[object Object]allow efficient operation of the hotel and for the comfort and safety of all guests.`	`[object Object]Pool and Fitness Room hours and guidelines are provided at check in. All rules and times will be enforced to[object Object]allow efficient operation of the hotel and for the comfort and safety of all guests.`	`1`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 841 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 841 samples:
sentence1 sentence2 label
type string string int
details
min: 11 tokens
mean: 48.1 tokens
max: 156 tokens
min: 11 tokens
mean: 48.1 tokens
max: 156 tokens
0: ~66.71%
1: ~33.29%

Samples:

sentence1	sentence2	label
`In the case of fire, guests are obliged to notify the reception without hesitation, either[object Object]directly, or on the phone (0) and may use a portable fire extinguisher located at the corridors[object Object]of each floor to extinguish the flames. The use of the elevator in case of fire is prohibited!`	`In the case of fire, guests are obliged to notify the reception without hesitation, either[object Object]directly, or on the phone (0) and may use a portable fire extinguisher located at the corridors[object Object]of each floor to extinguish the flames. The use of the elevator in case of fire is prohibited!`	`0`
`[object Object]Children should be accompanied in locations such as stairways etc.[object Object] The rooms are for accommodation service. Each individual staying in a room[object Object]must be registered at the reception.`	`[object Object]Children should be accompanied in locations such as stairways etc.[object Object] The rooms are for accommodation service. Each individual staying in a room[object Object]must be registered at the reception.`	`0`
`[object Object]Towels for the Fitness Room and Pool are located in those areas. Towels from guest rooms are not to be[object Object]taken to the Pool or Fitness Room.`	`[object Object]Towels for the Fitness Room and Pool are located in those areas. Towels from guest rooms are not to be[object Object]taken to the Pool or Fitness Room.`	`0`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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	-	-	0.3126
0.4739	100	0.0011	0.0001	-
0.9479	200	0.0002	0.0000	-
1.4218	300	0.0	0.0000	-
1.8957	400	0.0001	0.0000	-
2.3697	500	0.0	0.0000	-
2.8436	600	0.0	0.0000	-
3.3175	700	0.0	0.0000	-
3.7915	800	0.0	0.0000	-
4.2654	900	0.0	0.0000	-
4.7393	1000	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

bibtex

@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

bibtex

@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}
}

	sentence1	sentence2	label
type	string	string	int
details	min: 11 tokens mean: 48.75 tokens max: 156 tokens	min: 11 tokens mean: 48.75 tokens max: 156 tokens	0: ~69.20% 1: ~30.80%