metadata
base_model: BAAI/bge-large-en-v1.5
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1024
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
After rescue, survivors may require hospital treatment. This must be
provided as quickly as possible. The SMC should consider having ambulance
and hospital facilities ready.
sentences:
- What should the SMC consider having ready after a rescue?
- What is critical for mass rescue operations?
- >-
What can computer programs do to relieve the search planner of
computational burden?
- source_sentence: >-
SMCs conduct communication searches when facts are needed to supplement
initially reported information. Efforts are continued to contact the
craft, to find out more about a possible distress situation, and to
prepare for or to avoid a search effort. Section 3.5 has more information
on communication searches.MEDICO Communications
sentences:
- >-
What is generally produced by dead-reckoning navigation alone for search
aircraft?
- >-
What should be the widths of rectangular areas to be covered with a PS
pattern and the lengths of rectangular areas to be covered with a CS
pattern?
- What is the purpose of SMCs conducting communication searches?
- source_sentence: >-
SAR facilities include designated SRUs and other resources which can be
used to conduct or support SAR operations. An SRU is a unit composed of
trained personnel and provided with equipment suitable for the expeditious
and efficient conduct of search and rescue. An SRU can be an air,
maritime, or land-based facility. Facilities selected as SRUs should be
able to reach the scene of distress quickly and, in particular, be
suitable for one or more of the following operations:– providing
assistance to prevent or reduce the severity of accidents and the hardship
of survivors, e.g., escorting an aircraft, standing by a sinking vessel;–
conducting a search;– delivering supplies and survival equipment to the
scene;– rescuing survivors;– providing food, medical or other initial
needs of survivors; and– delivering the survivors to a place of safety.
sentences:
- >-
What are the types of SAR facilities that can be used to conduct or
support SAR operations?
- >-
What is the scenario in which a simulated communication search is
carried out and an air search is planned?
- What is discussed in detail in various other places in this Manual?
- source_sentence: >-
Support facilities enable the operational response resources (e.g., the
RCC and SRUs) to provide the SAR services. Without the supporting
resources, the operational resources cannot sustain effective operations.
There is a wide range of support facilities and services, which include
the following:Training facilities Facility maintenanceCommunications
facilities Management functionsNavigation systems Research and
developmentSAR data providers (SDPs) PlanningMedical facilities
ExercisesAircraft landing fields Refuelling servicesVoluntary services
(e.g., Red Cross) Critical incident stress counsellors Computer resources
sentences:
- How many ways are there to train SAR specialists and teams?
- What types of support facilities are mentioned in the context?
- What is the duration of a prolonged blast?
- source_sentence: >-
Sound funding decisions arise out of accurate assessments made of the SAR
system. To measure the performance or effectiveness of a SAR system
usually requires collecting information or statistics and establishing
agreed-upon goals. All pertinent information should be collected,
including where the system failed to perform as it should have; failures
and successes provide valuable information in assessing effectiveness and
determining means to improve.
sentences:
- >-
What is required to measure the performance or effectiveness of a SAR
system?
- What is the purpose of having an SRR?
- >-
What is the effect of decreasing track spacing on the area that can be
searched?
model-index:
- name: SentenceTransformer based on BAAI/bge-large-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7719298245614035
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9298245614035088
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.956140350877193
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7719298245614035
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3099415204678363
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1912280701754386
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7719298245614035
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9298245614035088
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.956140350877193
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8884520476480379
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8524470899470901
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.85244708994709
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7543859649122807
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9122807017543859
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.956140350877193
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9912280701754386
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7543859649122807
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.304093567251462
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1912280701754386
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09912280701754386
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7543859649122807
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9122807017543859
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.956140350877193
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9912280701754386
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8791120820747885
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8425438596491228
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8431704260651629
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7456140350877193
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8947368421052632
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9385964912280702
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9649122807017544
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7456140350877193
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2982456140350877
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18771929824561406
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09649122807017543
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7456140350877193
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8947368421052632
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9385964912280702
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9649122807017544
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8623224236283672
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8287628794207742
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8310819942011893
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7017543859649122
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8245614035087719
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8771929824561403
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9385964912280702
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7017543859649122
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27485380116959063
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17543859649122803
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09385964912280703
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7017543859649122
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8245614035087719
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8771929824561403
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9385964912280702
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8146917044508328
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7757031467557786
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7788889950899075
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6228070175438597
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7543859649122807
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7894736842105263
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8596491228070176
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6228070175438597
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25146198830409355
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15789473684210523
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08596491228070174
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6228070175438597
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7543859649122807
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7894736842105263
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8596491228070176
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7406737402395112
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.703104984683932
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.71092932980045
name: Cosine Map@100
SentenceTransformer based on BAAI/bge-large-en-v1.5
This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5 on the json dataset. It maps sentences & paragraphs to a 1024-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: BAAI/bge-large-en-v1.5
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
(2): Normalize()
)
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
model = SentenceTransformer("tessimago/bge-large-repmus-cross_entropy")
sentences = [
'Sound funding decisions arise out of accurate assessments made of the SAR system. To measure the performance or effectiveness of a SAR system usually requires collecting information or statistics and establishing agreed-upon goals. All pertinent information should be collected, including where the system failed to perform as it should have; failures and successes provide valuable information in assessing effectiveness and determining means to improve. ',
'What is required to measure the performance or effectiveness of a SAR system?',
'What is the effect of decreasing track spacing on the area that can be searched?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7719 |
cosine_accuracy@3 |
0.9298 |
cosine_accuracy@5 |
0.9561 |
cosine_accuracy@10 |
1.0 |
cosine_precision@1 |
0.7719 |
cosine_precision@3 |
0.3099 |
cosine_precision@5 |
0.1912 |
cosine_precision@10 |
0.1 |
cosine_recall@1 |
0.7719 |
cosine_recall@3 |
0.9298 |
cosine_recall@5 |
0.9561 |
cosine_recall@10 |
1.0 |
cosine_ndcg@10 |
0.8885 |
cosine_mrr@10 |
0.8524 |
cosine_map@100 |
0.8524 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7544 |
cosine_accuracy@3 |
0.9123 |
cosine_accuracy@5 |
0.9561 |
cosine_accuracy@10 |
0.9912 |
cosine_precision@1 |
0.7544 |
cosine_precision@3 |
0.3041 |
cosine_precision@5 |
0.1912 |
cosine_precision@10 |
0.0991 |
cosine_recall@1 |
0.7544 |
cosine_recall@3 |
0.9123 |
cosine_recall@5 |
0.9561 |
cosine_recall@10 |
0.9912 |
cosine_ndcg@10 |
0.8791 |
cosine_mrr@10 |
0.8425 |
cosine_map@100 |
0.8432 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7456 |
cosine_accuracy@3 |
0.8947 |
cosine_accuracy@5 |
0.9386 |
cosine_accuracy@10 |
0.9649 |
cosine_precision@1 |
0.7456 |
cosine_precision@3 |
0.2982 |
cosine_precision@5 |
0.1877 |
cosine_precision@10 |
0.0965 |
cosine_recall@1 |
0.7456 |
cosine_recall@3 |
0.8947 |
cosine_recall@5 |
0.9386 |
cosine_recall@10 |
0.9649 |
cosine_ndcg@10 |
0.8623 |
cosine_mrr@10 |
0.8288 |
cosine_map@100 |
0.8311 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.7018 |
cosine_accuracy@3 |
0.8246 |
cosine_accuracy@5 |
0.8772 |
cosine_accuracy@10 |
0.9386 |
cosine_precision@1 |
0.7018 |
cosine_precision@3 |
0.2749 |
cosine_precision@5 |
0.1754 |
cosine_precision@10 |
0.0939 |
cosine_recall@1 |
0.7018 |
cosine_recall@3 |
0.8246 |
cosine_recall@5 |
0.8772 |
cosine_recall@10 |
0.9386 |
cosine_ndcg@10 |
0.8147 |
cosine_mrr@10 |
0.7757 |
cosine_map@100 |
0.7789 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.6228 |
cosine_accuracy@3 |
0.7544 |
cosine_accuracy@5 |
0.7895 |
cosine_accuracy@10 |
0.8596 |
cosine_precision@1 |
0.6228 |
cosine_precision@3 |
0.2515 |
cosine_precision@5 |
0.1579 |
cosine_precision@10 |
0.086 |
cosine_recall@1 |
0.6228 |
cosine_recall@3 |
0.7544 |
cosine_recall@5 |
0.7895 |
cosine_recall@10 |
0.8596 |
cosine_ndcg@10 |
0.7407 |
cosine_mrr@10 |
0.7031 |
cosine_map@100 |
0.7109 |
Training Details
Training Dataset
json
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epoch
per_device_train_batch_size
: 32
per_device_eval_batch_size
: 16
gradient_accumulation_steps
: 16
learning_rate
: 2e-05
num_train_epochs
: 4
lr_scheduler_type
: cosine
warmup_ratio
: 0.1
bf16
: True
tf32
: True
load_best_model_at_end
: True
optim
: adamw_torch_fused
All Hyperparameters
Click to expand
overwrite_output_dir
: False
do_predict
: False
eval_strategy
: epoch
prediction_loss_only
: True
per_device_train_batch_size
: 32
per_device_eval_batch_size
: 16
per_gpu_train_batch_size
: None
per_gpu_eval_batch_size
: None
gradient_accumulation_steps
: 16
eval_accumulation_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
: 4
max_steps
: -1
lr_scheduler_type
: cosine
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
: True
fp16
: False
fp16_opt_level
: O1
half_precision_backend
: auto
bf16_full_eval
: False
fp16_full_eval
: False
tf32
: True
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
: True
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_fused
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
: False
hub_always_push
: False
gradient_checkpointing
: False
gradient_checkpointing_kwargs
: None
include_inputs_for_metrics
: False
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
batch_sampler
: batch_sampler
multi_dataset_batch_sampler
: proportional
Training Logs
Epoch |
Step |
dim_128_cosine_map@100 |
dim_256_cosine_map@100 |
dim_512_cosine_map@100 |
dim_64_cosine_map@100 |
dim_768_cosine_map@100 |
1.0 |
2 |
0.7770 |
0.8173 |
0.8316 |
0.6838 |
0.8448 |
2.0 |
4 |
0.7858 |
0.8221 |
0.8326 |
0.6993 |
0.8478 |
3.0 |
6 |
0.7801 |
0.8297 |
0.8412 |
0.7101 |
0.8517 |
4.0 |
8 |
0.7789 |
0.8311 |
0.8432 |
0.7109 |
0.8524 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
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}
}