This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the measuring-embeddings-v5 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: intfloat/multilingual-e5-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- measuring-embeddings-v5

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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/measuring-embeddings-v5.1")
# Run inference
sentences = [
    'list of measurement systems',
    'What is a Calibration Point?\nA Calibration Point represents a specific data entry in a calibration process, comparing an expected reference value to an actual measured value. These points are fundamental in ensuring measurement accuracy and identifying deviations.\n\nKey Aspects of Calibration Points:\n- Calibration Report Association: Each calibration point belongs to a specific calibration report, linking it to a broader calibration procedure.\n- Reference Values: Theoretical or expected values used as a benchmark for measurement validation.\n- Measured Values: The actual recorded values during calibration, reflecting the instrument’s response.\n- Errors: The difference between reference and measured values, indicating possible measurement inaccuracies.\nCalibration points are essential for evaluating instrument performance, ensuring compliance with standards, and maintaining measurement reliability.',
    'What is a Calibration Record?\nA Calibration Record documents the calibration process of a specific equipment tag, ensuring that its measurements remain accurate and reliable. Calibration is a critical process in maintaining measurement precision and compliance with standards.\n\nKey Aspects of a Calibration Record:\n- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.\n- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.\n- Range Values: The minimum and maximum measurement values covered during the calibration process.\n- Calibration Status: Indicates whether the calibration was approved or saved for further review.\n- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).\n- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.\nCalibration records play a fundamental role in quality assurance, helping maintain measurement integrity and regulatory compliance.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

measuring-embeddings-v5

Dataset: measuring-embeddings-v5 at 90b5410
Size: 3,630 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 7.46 tokens
max: 17 tokens
min: 80 tokens
mean: 181.99 tokens
max: 406 tokens
min: 0.0
mean: 0.23
max: 0.95

Samples:

sentence1	sentence2	score
`measurement technology name`	What is uncertainty?[object Object]Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.[object Object][object Object]Types of uncertainty:[object Object]There are two main types of uncertainty:[object Object]1. Uncertainty of magnitudes (variables):[object Object] - Refers to the uncertainty of specific variables, such as temperature or pressure.[object Object] - It is calculated after calibrating a device or obtained from the [object Object] manufacturer's manual.[object Object] - This uncertainty serves as a starting point for further calculations related to the equipment.[object Object][object Object]2. Uncertainty of the measurement system:[object Object] - Refers to the uncertainty calculated for the overall flow measurement.[object Object] - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.[object Object][object Object]Key points:[object Object]- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...	`0.001`
`transmitter calibration record`	What is an Uncertainty Curve Point?[object Object]An Uncertainty Curve Point represents a data point used to construct the uncertainty curve of a measurement system. These curves help analyze how measurement uncertainty behaves under different flow rate conditions, ensuring accuracy and reliability in uncertainty assessments.[object Object][object Object]Key Aspects of an Uncertainty Curve Point:[object Object]- Uncertainty File ID: Links the point to the specific uncertainty dataset, ensuring traceability.[object Object]Equipment Tag ID: Identifies the equipment associated with the uncertainty measurement, crucial for system validation.[object Object]- Uncertainty Points: Represent uncertainty values recorded at specific conditions, forming part of the overall uncertainty curve.[object Object]- Flow Rate Points: Corresponding flow rate values at which the uncertainty was measured, essential for evaluating performance under varying operational conditions.[object Object]These points are fundamental for generating uncertainty curves, which are used in calibration, validation, and compliance assess...	`0.001`
`measurement magnitude uncertainty`	What is uncertainty?[object Object]Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.[object Object][object Object]Types of uncertainty:[object Object]There are two main types of uncertainty:[object Object]1. Uncertainty of magnitudes (variables):[object Object] - Refers to the uncertainty of specific variables, such as temperature or pressure.[object Object] - It is calculated after calibrating a device or obtained from the [object Object] manufacturer's manual.[object Object] - This uncertainty serves as a starting point for further calculations related to the equipment.[object Object][object Object]2. Uncertainty of the measurement system:[object Object] - Refers to the uncertainty calculated for the overall flow measurement.[object Object] - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.[object Object][object Object]Key points:[object Object]- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...	`0.95`

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

Evaluation Dataset

measuring-embeddings-v5

Dataset: measuring-embeddings-v5 at 90b5410
Size: 778 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 778 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 7.44 tokens
max: 17 tokens
min: 80 tokens
mean: 184.77 tokens
max: 406 tokens
min: 0.0
mean: 0.23
max: 0.95

Samples:

sentence1	sentence2	score
`measurement type`	What is an Equipment Class?[object Object]An Equipment Class categorizes different types of equipment based on their function or role within a measurement system. This classification helps in organizing and distinguishing equipment types for operational, maintenance, and analytical purposes.[object Object][object Object]Each Equipment Class groups related equipment under a common category. Examples include:[object Object][object Object]Primary → Main measurement device in a system.[object Object]Secondary → Supporting measurement device, often used for verification.[object Object]Tertiary → Additional measurement equipment.[object Object]Valves → Flow control devices used in the system.[object Object]By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets.	`0.001`
`latest uncertainty result`	What is an Uncertainty Composition?[object Object]An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty.[object Object][object Object]Key Aspects of an Uncertainty Component:[object Object]- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.[object Object]- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.[object Object]- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.[object Object]Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.	`0.75`
`uncertainty calculation ID 593`	What is an Uncertainty Composition?[object Object]An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty.[object Object][object Object]Key Aspects of an Uncertainty Component:[object Object]- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.[object Object]- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.[object Object]- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.[object Object]Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.	`0.95`

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
gradient_accumulation_steps: 8
learning_rate: 5e-06
weight_decay: 0.01
max_grad_norm: 0.5
num_train_epochs: 20
lr_scheduler_type: cosine
warmup_ratio: 0.1

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-06
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 0.5
num_train_epochs: 20
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: False
fp16: False
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
6.2467	350	5.4993	-
7.1233	400	5.1991	-
8.0	450	4.7573	0.6509
8.8811	500	4.8783	-
9.7577	550	4.4897	-
10.6344	600	3.9524	0.6758
11.5110	650	3.679	-
12.3877	700	3.4076	-
13.2643	750	3.8909	0.6588
14.1410	800	3.1191	-
15.0176	850	3.5478	-
15.8987	900	3.2201	0.6553
16.7753	950	3.3027	-
17.6520	1000	2.4874	-
18.5286	1050	2.9745	0.6615
19.4053	1100	2.8649	-

Framework Versions

Python: 3.11.0
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 1.4.0
Datasets: 3.3.2
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",
}

CoSENTLoss

bibtex

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 7.46 tokens max: 17 tokens	min: 80 tokens mean: 181.99 tokens max: 406 tokens	min: 0.0 mean: 0.23 max: 0.95