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Lauther/
measuring-embeddings-v1

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SentenceTransformer based on intfloat/multilingual-e5-large-instruct

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the embeddings-train-semantic 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:
- embeddings-train-semantic

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/emb-multilingual-e5-large-instruct-3e")
# Run inference
sentences = [
    'What columns store the uncertainty values?',
    'How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.',
    'What is uncertainty?\nUncertainty 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.\n\nTypes of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of magnitudes (variables):\n    - Refers to the uncertainty of specific variables, such as temperature or pressure.\n    - It is calculated after calibrating a device or obtained from the equipment manufacturer\'s manual.\n    - This uncertainty serves as a starting point for further calculations related to the equipment.\n\n2. Uncertainty of the measurement system:\n    - Refers to the uncertainty calculated for the overall flow measurement.\n    - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of the measurement system. Think of them as the "building blocks."\n- Do not confuse the two types of uncertainty:\n    - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).\n    - **Uncertainty of the measurement system**: Specific to the overall flow measurement.\n\nDatabase storage for uncertainties:\nIn the database, uncertainty calculations are stored in two separate tables:\n1. Uncertainty of magnitudes (variables):\n    - Stores the uncertainty values for specific variables (e.g., temperature, pressure).\n\n2. Uncertainty of the measurement system:\n    - Stores the uncertainty values for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n- To find the uncertainty of the measurement system, join the measurement systems table with the uncertainty of the measurement system table.\n- To find the uncertainty of a specific variable (magnitude), join the measurement systems table with the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not confuse the two types of uncertainty:\n- If the user requests the uncertainty of the measurement system, use the first join (measurement systems table + uncertainty of the measurement system table).\n- If the user requests the uncertainty of a specific variable (magnitude) in a report, use the second join (measurement systems table + uncertainty of magnitudes table).',
]
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

embeddings-train-semantic

Dataset: embeddings-train-semantic at ce90f53
Size: 5,220 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 18.3 tokens
max: 102 tokens
min: 120 tokens
mean: 257.3 tokens
max: 512 tokens
min: 0.0
mean: 0.23
max: 1.0

Samples:

sentence1	sentence2	score
`What is the data type of differential pressure in the measurement system?`	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 equipment 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 of ...	`0.15000000000000002`
`What is the structure of the &&&equipment_data&&& table?`	How are flow computers and measurement systems related?[object Object]Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.[object Object][object Object]Database terminology:[object Object]In the database, this relationship is referred to as:[object Object]- Meter streams[object Object]- Meter runs[object Object]- Sections[object Object][object Object]Storage of the relationship:[object Object]The relationship between a flow computer and its assigned measurement system is stored in a special table.[object Object][object Object]User context:[object Object]When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.	`0.35000000000000003`
`Find the columns in the flow computer table that identify the flow computer.`	What kind of data store an equipment?[object Object]Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.[object Object][object Object]Data storage:[object Object]- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.[object Object]- These values are [object Object] from the fluid (e.g., raw pressure, temperature, or volume readings). [object Object], such as uncertainty.[object Object]- The values stored in the variable values table are [object Object] from variable uncertainty values, which are calculated separately and represent the margin of error.[object Object][object Object]Accessing the data:[object Object]- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.[object Object]- The readings are stored in a "variable values" table within the database.[object Object][object Object]Linking variable names:[object Object]If the user needs to kno...	`0.1`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Evaluation Dataset

embeddings-train-semantic

Dataset: embeddings-train-semantic at ce90f53
Size: 652 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 652 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 17.8 tokens
max: 102 tokens
min: 120 tokens
mean: 253.84 tokens
max: 512 tokens
min: 0.0
mean: 0.24
max: 0.9

Samples:

sentence1	sentence2	score
`How can I filter uncertainty reports by equipment tag?`	How does a flow computer generate and store reports?[object Object]A flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format.[object Object][object Object]Report structure:[object Object]- Each report includes:[object Object]- Date and time of the data recording.[object Object]- Data recorded from flow computers.[object Object][object Object]Data storage in tables:[object Object]The reports are saved in two tables:[object Object]1. Main table (Index):[object Object] - Stores the date, time, and flow computer identifier.[object Object]2. Detail table:[object Object] - Stores the measured values associated with the report.[object Object][object Object]Connection to the Modbus table:[object Object]The flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.	`0.09999999999999999`
`What is the purpose of the flow_data table?`	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 equipment 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 of ...	`0.15000000000000002`
`What is the column name for the report date in the Reports table?`	What is equipment calibration?[object Object]Calibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body.[object Object][object Object]Purpose of calibration:[object Object]The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.[object Object][object Object]Calibration cycles:[object Object]There are two main calibration cycles:[object Object]1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.[object Object]2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.[object Object][object Object]Calibration uncertainty:[object Object]- Uncertainty is included in the results of a calibration.[object Object]- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...	`0.1`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 2e-05
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: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
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: 3
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: 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
0.0307	10	1.5374	-
0.0613	20	1.0251	-
0.0920	30	0.361	-
0.1226	40	0.1819	-
0.1533	50	0.186	-
0.1839	60	0.1697	-
0.2146	70	0.1437	-
0.2452	80	0.172	-
0.2759	90	0.1199	-
0.3065	100	0.1278	-
0.3372	110	0.1037	-
0.3678	120	0.1156	-
0.3985	130	0.0971	-
0.4291	140	0.0911	-
0.4598	150	0.1158	0.0249
0.4904	160	0.0906	-
0.5211	170	0.106	-
0.5517	180	0.0921	-
0.5824	190	0.0748	-
0.6130	200	0.0741	-
0.6437	210	0.0894	-
0.6743	220	0.0815	-
0.7050	230	0.0771	-
0.7356	240	0.1156	-
0.7663	250	0.0857	-
0.7969	260	0.0566	-
0.8276	270	0.0716	-
0.8582	280	0.0662	-
0.8889	290	0.0963	-
0.9195	300	0.0678	0.0212
0.9502	310	0.077	-
0.9808	320	0.0642	-
1.0092	330	0.0725	-
1.0398	340	0.0701	-
1.0705	350	0.0549	-
1.1011	360	0.0699	-
1.1318	370	0.0714	-
1.1625	380	0.0745	-
1.1931	390	0.0754	-
1.2238	400	0.0486	-
1.2544	410	0.047	-
1.2851	420	0.076	-
1.3157	430	0.0689	-
1.3464	440	0.0629	-
1.3770	450	0.0657	0.0178
1.4077	460	0.0622	-
1.4383	470	0.0657	-
1.4690	480	0.0498	-
1.4996	490	0.0653	-
1.5303	500	0.0715	-
1.5609	510	0.0615	-
1.5916	520	0.0441	-
1.6222	530	0.0566	-
1.6529	540	0.0524	-
1.6835	550	0.0423	-
1.7142	560	0.0441	-
1.7448	570	0.0553	-
1.7755	580	0.0572	-
1.8061	590	0.0686	-
1.8368	600	0.06	0.0146
1.8674	610	0.0562	-
1.8981	620	0.0517	-
1.9287	630	0.0498	-
1.9594	640	0.0424	-
1.9900	650	0.0729	-
2.0184	660	0.0347	-
2.0490	670	0.06	-
2.0797	680	0.0441	-
2.1103	690	0.0409	-
2.1410	700	0.0416	-
2.1716	710	0.0345	-
2.2023	720	0.024	-
2.2330	730	0.0458	-
2.2636	740	0.0465	-
2.2943	750	0.0494	0.0132
2.3249	760	0.0388	-
2.3556	770	0.0363	-
2.3862	780	0.0441	-
2.4169	790	0.0378	-
2.4475	800	0.0484	-
2.4782	810	0.051	-
2.5088	820	0.0464	-
2.5395	830	0.036	-
2.5701	840	0.0423	-
2.6008	850	0.0278	-
2.6314	860	0.0474	-
2.6621	870	0.0357	-
2.6927	880	0.0386	-
2.7234	890	0.0334	-
2.7540	900	0.0199	0.0127
2.7847	910	0.0381	-
2.8153	920	0.0415	-
2.8460	930	0.0274	-
2.8766	940	0.0353	-
2.9073	950	0.0423	-
2.9379	960	0.0267	-
2.9686	970	0.042	-

Framework Versions

Python: 3.11.0
Sentence Transformers: 3.4.0
Transformers: 4.48.1
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",
}

	sentence1	sentence2	score
type	string	string	float
details	min: 8 tokens mean: 18.3 tokens max: 102 tokens	min: 120 tokens mean: 257.3 tokens max: 512 tokens	min: 0.0 mean: 0.23 max: 1.0

Lauther/measuring-embeddings-v1

SentenceTransformer based on intfloat/multilingual-e5-large-instruct

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

embeddings-train-semantic

Evaluation Dataset

embeddings-train-semantic

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

Lauther/
measuring-embeddings-v1