d4-embeddings-v3.0

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

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the d4-embeddings-triplet_loss 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 <!-- at revision 274baa43b0e13e37fafa6428dbc7938e62e5c439 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • d4-embeddings-triplet_loss

<!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/d4-embeddings-v3.0")
# Run inference
sentences = [
    'PTE BRAGANÇA PAULISTA C',
    'What is an Uncertainty Composition?\nAn 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.\n\nKey Aspects of an Uncertainty Component:\n- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.\n- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.\n- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.\nUnderstanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.',
    'What is a Measurement Unit?\nA Measurement Unit defines the standard for quantifying a physical magnitude (e.g., temperature, pressure, volume). It establishes a consistent reference for interpreting values recorded in a measurement system.\n\nEach measurement unit is associated with a specific magnitude, ensuring that values are correctly interpreted within their context. For example:\n\n- °C (Celsius) → Used for temperature\n- psi (pounds per square inch) → Used for pressure\n- m³ (cubic meters) → Used for volume\nMeasurement units are essential for maintaining consistency across recorded data, ensuring comparability, and enabling accurate calculations within measurement systems.',
]
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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

d4-embeddings-triplet_loss

• Dataset: d4-embeddings-triplet_loss at 4d11c52
• Size: 4,151 training samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 3 tokens</li><li>mean: 9.03 tokens</li><li>max: 19 tokens</li></ul>	<ul><li>min: 80 tokens</li><li>mean: 213.47 tokens</li><li>max: 406 tokens</li></ul>	<ul><li>min: 27 tokens</li><li>mean: 168.24 tokens</li><li>max: 406 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>Orifice Diameter (mm)</code>	<code>What is uncertainty?<br>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.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code>	<code>What is an Equipment Class?<br>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.<br><br>Each Equipment Class groups related equipment under a common category. Examples include:<br><br>Primary → Main measurement device in a system.<br>Secondary → Supporting measurement device, often used for verification.<br>Tertiary → Additional measurement equipment.<br>Valves → Flow control devices used in the system.<br>By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets.</code>
  <code>prueba_gonzalo</code>	<code>What is a measurement system?<br>Measurement systems are essential components in industrial measurement and processing. They are identified by a unique Tag and are associated with a specific installation and fluid type. These systems utilize different measurement technologies, including differential (DIF) and linear (LIN), depending on the application. Measurement systems can be classified based on their application type, such as fiscal or custody transfer. </code>	<code>What is a Measured Magnitude Value?<br>A Measured Magnitude Value represents a DAILY 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.<br><br>Key Aspects of a Measured Magnitude Value:<br>- Measurement Date: The timestamp indicating when the measurement was recorded.<br>- Measured Value: The daily numeric result of the recorded physical magnitude.<br>- Measurement System Association: Links the measured value to a specific measurement system responsible for capturing the data.<br>- Variable Association: Identifies the specific variable (e.g., temperature, pressure, flow rate) corresponding to the recorded value.<br>Measured magnitude values are crucial for real-time monitoring, historical analysis, and calibration processes within measurement systems.<br><br>Database advices:<br>This values also are in historics of a flow computer report. Although, to go directl...</code>
  <code>Vazao Instantanea</code>	<code>What is uncertainty?<br>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.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code>	<code>What is a report index or historic index?<br>Indexes represent the recorded reports generated by flow computers, classified into two types: <br>- Hourly reports Index: Store data for hourly events.<br>- Daily reports Index: Strore data for daily events.<br><br>These reports, also referred to as historical data or flow computer historical records, contain raw, first-hand measurements directly collected from the flow computer. The data has not been processed or used in any calculations, preserving its original state for analysis or validation.<br><br>The index is essential for locating specific values within the report.</code>

• Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 0.3
  }
  

Evaluation Dataset

d4-embeddings-triplet_loss

• Dataset: d4-embeddings-triplet_loss at 4d11c52
• Size: 1,038 evaluation samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 3 tokens</li><li>mean: 9.02 tokens</li><li>max: 19 tokens</li></ul>	<ul><li>min: 27 tokens</li><li>mean: 209.21 tokens</li><li>max: 406 tokens</li></ul>	<ul><li>min: 27 tokens</li><li>mean: 172.36 tokens</li><li>max: 406 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>FQI-4301.4522B</code>	<code>What is a measurement system?<br>Measurement systems are essential components in industrial measurement and processing. They are identified by a unique Tag and are associated with a specific installation and fluid type. These systems utilize different measurement technologies, including differential (DIF) and linear (LIN), depending on the application. Measurement systems can be classified based on their application type, such as fiscal or custody transfer. </code>	<code>What is uncertainty?<br>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.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code>
  <code>PTE GUARATINGUETA B</code>	<code>What are historical report values?<br>These represent the recorded data points within flow computer reports. Unlike the report index, which serves as a reference to locate reports, these values contain the actual measurements and calculated data stored in the historical records.<br><br>Flow computer reports store two types of data values:<br><br>- Hourly data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis.<br>- Daily data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis.<br>Each value is directly linked to its respective report index, ensuring traceability to the original flow computer record. These values maintain their raw integrity, providing a reliable source for analysis and validation.</code>	<code>What is Equipment?<br>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.<br><br>Key Aspects of Equipment:<br>- Serial Number: A unique identifier assigned to each equipment unit for tracking and reference.<br>- Current State: Indicates whether the equipment is currently in use (ACT) or inactive (INA).<br>- Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, thermometer), allowing classification and management.<br>Equipment plays a critical role in measurement systems, ensuring accuracy and reliability in data collection and processing.</code>
  <code>PTE BRAGANÇA PAULISTA B</code>	<code>What is an Uncertainty Composition?<br>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.<br><br>Key Aspects of an Uncertainty Component:<br>- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.<br>- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.<br>- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.<br>Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.</code>	<code>What is uncertainty?<br>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.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code>

• Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 0.3
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 80
• per_device_eval_batch_size: 80
• weight_decay: 0.01
• max_grad_norm: 0.5
• num_train_epochs: 15
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• fp16: True
• dataloader_num_workers: 4

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 80
• per_device_eval_batch_size: 80
• 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: 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: 15
• 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: 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: 4
• 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

</details>

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

@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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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