Modelpublic

Lauther/d4-embeddings-v2.0

sourceHugging Faceupdated 1y agoView on Hugging Face

0likes70downloads

Model Card

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 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:

bash

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]

<!--

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

—Dataset: d4-embeddings at 09fb8a5
—Size: 11,165 training samples
—Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 8.23 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 187.19 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>0: ~66.20%</li><li>1: ~33.80%</li></ul> |
—Samples: | sentence1 | sentence2 | label | |:----------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>Ramal ESVOL - TEVOL (GASVOL 14")</code> | <code>What is Equipment? 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. Key Aspects of Equipment: - Serial Number: A unique identifier assigned to each equipment unit for tracking and reference. - Current State: Indicates whether the equipment is currently in use (ACT) or inactive (INA). - Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, thermometer), allowing classification and management. Equipment plays a critical role in measurement systems, ensuring accuracy and reliability in data collection and processing.</code> | <code>0</code> | | <code>Mol (%) CO</code> | <code>What is an Equipment Tag? 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. Key Aspects of Equipment Tags: - Equipment-Tag: A distinct label or identifier that uniquely marks the equipment in operation. - Equipment ID: Links the tag to the corresponding equipment unit. - Belonging Measurement System: Specifies which measurement system the tagged equipment is part of. - Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer), aiding in organization and system integration. The Equipment Tag is essential for tracking and managing operational equipment within a measurement system, ensuring proper identification, monitoring, and maintenance.</code> | <code>0</code> | | <code>FQI-4715-1411</code> | <code>What is a flow computer? A flow computer is a device used in measurement engineering. It collects analog and digital data from flow meters and other sensors. Key features of a flow computer: - It has a unique name, firmware version, and manufacturer information. - It is designed to record and process data such as temperature, pressure, and fluid volume (for gases or oils).</code> | <code>0</code> |
—Loss: <code>ContrastiveLoss</code> with these parameters:

json

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

Evaluation Dataset

d4-embeddings

—Dataset: d4-embeddings at 09fb8a5
—Size: 2,392 evaluation samples
—Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 8.22 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 183.06 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>0: ~66.30%</li><li>1: ~33.70%</li></ul> |
—Samples: | sentence1 | sentence2 | label | |:------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>PTE UTE JUIZ DE FORA (IGREJINHA) B</code> | <code>What is uncertainty? 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. Types of uncertainty: There are two main types of uncertainty: 1. Uncertainty of magnitudes (variables): - Refers to the uncertainty of specific variables, such as temperature or pressure. - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual. - This uncertainty serves as a starting point for further calculations related to the equipment. 2. Uncertainty of the measurement system: - Refers to the uncertainty calculated for the overall flow measurement. - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system. Key points: - The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>1</code> | | <code>measure type</code> | <code>What is a Calibration Record? 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. Key Aspects of a Calibration Record: - Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules. - Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance. - Range Values: The minimum and maximum measurement values covered during the calibration process. - Calibration Status: Indicates whether the calibration was approved or saved for further review. - Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi). - Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments. Calibration r...</code> | <code>0</code> | | <code>daily flow rate</code> | <code>What is a Measured Magnitude Value? 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. Key Aspects of a Measured Magnitude Value: - Measurement Date: The timestamp indicating when the measurement was recorded. - Measured Value: The daily numeric result of the recorded physical magnitude. - Measurement System Association: Links the measured value to a specific measurement system responsible for capturing the data. - Variable Association: Identifies the specific variable (e.g., temperature, pressure, flow rate) corresponding to the recorded value. Measured magnitude values are crucial for real-time monitoring, historical analysis, and calibration processes within measurement systems. Database advices: This values also are in historics of a flow computer report. Although, to go directl...</code> | <code>1</code> |
—Loss: <code>ContrastiveLoss</code> with these parameters:

json

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

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

<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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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>

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}
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->