This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the d4-embeddings 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:
- d4-embeddings

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/d4-embeddings-v2.0")
# Run inference
sentences = [
    'PTE SUZANO',
    '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.',
    'What is a flow computer?\nA flow computer is a device used in measurement engineering. It collects analog and digital data from flow meters and other sensors.\n\nKey features of a flow computer:\n- It has a unique name, firmware version, and manufacturer information.\n- It is designed to record and process data such as temperature, pressure, and fluid volume (for gases or oils).',
]
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

d4-embeddings

Dataset: d4-embeddings at 09fb8a5
Size: 11,165 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 3 tokens
mean: 8.23 tokens
max: 19 tokens
min: 27 tokens
mean: 187.19 tokens
max: 406 tokens
0: ~66.20%
1: ~33.80%

Samples:

sentence1	sentence2	label
`Ramal ESVOL - TEVOL (GASVOL 14")`	What is Equipment?[object Object]An Equipment represents a physical device that may be used within a measurement system. Equipment can be active or inactive and is classified by type, such as transmitters, thermometers, or other measurement-related devices.[object Object][object Object]Key Aspects of Equipment:[object Object]- Serial Number: A unique identifier assigned to each equipment unit for tracking and reference.[object Object]- Current State: Indicates whether the equipment is currently in use (ACT) or inactive (INA).[object Object]- Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, thermometer), allowing classification and management.[object Object]Equipment plays a critical role in measurement systems, ensuring accuracy and reliability in data collection and processing.	`0`
`Mol (%) CO`	What is an Equipment Tag?[object Object]An Equipment Tag is a unique label string identifier assigned to equipment that is actively installed and in use within a measurement system. It differentiates between equipment in general (which may be in storage or inactive) and equipment that is currently operational in a system.[object Object][object Object]Key Aspects of Equipment Tags:[object Object]- Equipment-Tag: A distinct label or identifier that uniquely marks the equipment in operation.[object Object]- Equipment ID: Links the tag to the corresponding equipment unit.[object Object]- Belonging Measurement System: Specifies which measurement system the tagged equipment is part of.[object Object]- Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer), aiding in organization and system integration.[object Object]The Equipment Tag is essential for tracking and managing operational equipment within a measurement system, ensuring proper identification, monitoring, and maintenance.	`0`
`FQI-4715-1411`	`What is a flow computer?[object Object]A flow computer is a device used in measurement engineering. It collects analog and digital data from flow meters and other sensors.[object Object][object Object]Key features of a flow computer:[object Object]- It has a unique name, firmware version, and manufacturer information.[object Object]- It is designed to record and process data such as temperature, pressure, and fluid volume (for gases or oils).`	`0`

Loss: ContrastiveLoss with these parameters:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 0.5, 4 "size_average": true 5}

Evaluation Dataset

d4-embeddings

Dataset: d4-embeddings at 09fb8a5
Size: 2,392 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 3 tokens
mean: 8.22 tokens
max: 19 tokens
min: 27 tokens
mean: 183.06 tokens
max: 406 tokens
0: ~66.30%
1: ~33.70%

Samples:

sentence1	sentence2	label
`PTE UTE JUIZ DE FORA (IGREJINHA) B`	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 ...	`1`
`measure type`	What is a Calibration Record?[object Object]A 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.[object Object][object Object]Key Aspects of a Calibration Record:[object Object]- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.[object Object]- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.[object Object]- Range Values: The minimum and maximum measurement values covered during the calibration process.[object Object]- Calibration Status: Indicates whether the calibration was approved or saved for further review.[object Object]- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).[object Object]- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.[object Object]Calibration r...	`0`
`daily flow rate`	What is a Measured Magnitude Value?[object Object]A Measured Magnitude Value represents a [object Object] recorded physical measurement of a variable within a monitored fluid. These values are essential for tracking system performance, analyzing trends, and ensuring accurate monitoring of fluid properties.[object Object][object Object]Key Aspects of a Measured Magnitude Value:[object Object]- Measurement Date: The timestamp indicating when the measurement was recorded.[object Object]- Measured Value: The daily numeric result of the recorded physical magnitude.[object Object]- Measurement System Association: Links the measured value to a specific measurement system responsible for capturing the data.[object Object]- Variable Association: Identifies the specific variable (e.g., temperature, pressure, flow rate) corresponding to the recorded value.[object Object]Measured magnitude values are crucial for real-time monitoring, historical analysis, and calibration processes within measurement systems.[object Object][object Object]Database advices:[object Object]This values also are in [object Object]. Although, to go directl...	`1`

Loss: ContrastiveLoss with these parameters:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 0.5, 4 "size_average": true 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 12
per_device_eval_batch_size: 12
gradient_accumulation_steps: 8
weight_decay: 0.01
max_grad_norm: 0.5
num_train_epochs: 5
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: 12
per_device_eval_batch_size: 12
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-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 0.5
num_train_epochs: 5
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
0.4296	50	0.1345	-
0.8593	100	0.0512	-
1.2836	150	0.041	0.0051
1.7132	200	0.0344	-
2.1375	250	0.0324	-
2.5671	300	0.0284	0.0038
2.9968	350	0.0296	-
3.4211	400	0.0261	-
3.8507	450	0.0268	0.0035
4.2750	500	0.0244	-
4.7046	550	0.0249	-

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",
}

ContrastiveLoss

bibtex

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

	sentence1	sentence2	label
type	string	string	int
details	min: 3 tokens mean: 8.23 tokens max: 19 tokens	min: 27 tokens mean: 187.19 tokens max: 406 tokens	0: ~66.20% 1: ~33.80%