metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2130621
  - loss:ContrastiveLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: Kim Chol-sam
    sentences:
      - Stankevich Sergey Nikolayevich
      - Kim Chin-So’k
      - Julen Lopetegui Agote
  - source_sentence: دينا بنت عبد الحميد
    sentences:
      - Alexia van Amsberg
      - Anthony Nicholas Colin Maitland Biddulph, 5th Baron Biddulph
      - Dina bint Abdul-Hamíd
  - source_sentence: Մուհամեդ բեն Նաիֆ Ալ Սաուդ
    sentences:
      - Karpov Anatoly Evgenyevich
      - GNPower Mariveles Coal Plant [former]
      - Muhammed bin Nayef bin Abdul Aziz Al Saud
  - source_sentence: Edward Gnehm
    sentences:
      - Шауэрте, Хартмут
      - Ханзада Филипп, Эдинбург герцогі
      - AFX
  - source_sentence: Schori i Lidingö
    sentences:
      - Yordan Canev
      - ကားပေါ့ အန်နာတိုလီ
      - BYSTROV, Mikhail Ivanovich
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - cosine_mcc
model-index:
  - name: Graphlet-AI/eridu
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: sentence transformers paraphrase multilingual MiniLM L12 v2
          type: sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2
        metrics:
          - type: cosine_accuracy
            value: 0.9885216725241056
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7183246612548828
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9824706124974221
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7085607051849365
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9782229269572558
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9867553479166427
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9971022799526896
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9739458779668466
            name: Cosine Mcc

Graphlet-AI/eridu

Deep fuzzy matching people and company names for multilingual entity resolution using representation learning... that incorporates a deep understanding of people and company names and can work much better than string distance methods!

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for person and company name matching using the Open Sanctions matcher training data. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used as part of a deep, fuzzy entity resolution process.

NOTE: this model is a work in progress. It is not yet ready for production use!

Model Details

TLDR: 5 Lines of Code

from sentence_transformers import SentenceTransformer


# Download from the 🤗 Hub
model = SentenceTransformer("Graphlet-AI/eridu")

names = [
    "Russell Jurney",
    "Russ Jurney",
    "Русс Джерни",
]

embeddings = model.encode(names)
print(embeddings.shape)
# [3, 384]

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

print(similarities.numpy())
# [[0.9999999  0.99406826 0.99406105]
#  [0.9940683  1.         0.9969202 ]
#  [0.99406105 0.9969202  1.        ]]

Project Eridu Overview

This project is a deep fuzzy matching system for person and company names for entity resolution using representation learning. It is designed to match people and company names across languages and character sets, using a pre-trained text embedding model from HuggingFace that we fine-tune using contrastive learning on 2 million labeled pairs of person and company names from the Open Sanctions Matcher training data. The project includes a command-line interface (CLI) utility for training the model and comparing pairs of names using cosine similarity.

Matching people and company names is an intractable problem using traditional parsing based methods: there is too much variation across cultures and jurisdictions to solve the problem by humans programming. This results in complex, cost prohibitive enterprise solutions for name matching like IBM InfoSphere Global Name Management. Machine learning is used on problems like this one of cultural relevance, where the time to manually programming a solution appproaches infinity, to automatically write a program. Since 2008 there has been an explosion of deep learning methods that automate feature engineering via representation learning methods including such as text embeddings.

This project loads the pre-trained paraphrase-multilingual-MiniLM-L12-v2 paraphrase model from HuggingFace and fine-tunes it for the name matching task using contrastive learning on more than 2 million labeled pairs of matching and non-matching (just as important) person and company names from the Open Sanctions Matcher training data to create a deep fuzzy matching system for entity resolution.

This model is available on HuggingFace Hub as Graphlet-AI/eridu and can be used in any Python project using the Sentence Transformers library in five lines of code. The model is designed to be used for entity resolution tasks, such as matching people and company names across different languages and character sets when matching records.

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

Model Sources

Documentation: Graphlet-AI/eridu Documentation
Repository: Graphlet-AI/eridu on GitHub
Hugging Face: Graphlet-AI/eridu on Hugging Face
PyPi Package: Graphlet-AI/eridu on PyPi

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
)

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("Graphlet-AI/eridu")

# Run inference
sentences = [
    'Schori i Lidingö',
    'Yordan Canev',
    'ကားပေါ့ အန်နာတိုလီ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Binary Classification

Dataset: sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9885
cosine_accuracy_threshold	0.7183
cosine_f1	0.9825
cosine_f1_threshold	0.7086
cosine_precision	0.9782
cosine_recall	0.9868
cosine_ap	0.9971
cosine_mcc	0.9739

Training Details

Training Dataset

Unnamed Dataset

Size: 2,130,621 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 3 tokens
mean: 9.32 tokens
max: 57 tokens

min: 3 tokens
mean: 9.16 tokens
max: 54 tokens

min: 0.0
mean: 0.34
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 3 tokens mean: 9.32 tokens max: 57 tokens	min: 3 tokens mean: 9.16 tokens max: 54 tokens	min: 0.0 mean: 0.34 max: 1.0

Samples:

sentence1	sentence2	label
`캐스린 설리번`	`Kathryn D. Sullivanová`	`1.0`
`ଶିବରାଜ ଅଧାଲରାଓ ପାଟିଲ`	`Aleksander Lubocki`	`0.0`
`Пырванов, Георги`	`アナトーリー・セルジュコフ`	`0.0`

Loss: ContrastiveLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 2,663,276 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 3 tokens
mean: 9.34 tokens
max: 102 tokens

min: 4 tokens
mean: 9.11 tokens
max: 100 tokens

min: 0.0
mean: 0.33
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 3 tokens mean: 9.34 tokens max: 102 tokens	min: 4 tokens mean: 9.11 tokens max: 100 tokens	min: 0.0 mean: 0.33 max: 1.0

Samples:

sentence1	sentence2	label
`Ева Херман`	`I Xuan Karlos`	`0.0`
`Кличков Андрій Євгенович`	`Андрэй Яўгенавіч Клычкоў`	`1.0`
`Кинах А.`	`Senator John Hickenlooper`	`0.0`

Loss: ContrastiveLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 768
per_device_eval_batch_size: 768
gradient_accumulation_steps: 4
learning_rate: 3e-05
weight_decay: 0.01
num_train_epochs: 4
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
optim: adafactor

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 768
per_device_eval_batch_size: 768
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: 3e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
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: 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: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
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}
tp_size: 0
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: adafactor
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
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	sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2_cosine_ap
-1	-1	-	-	0.7140
0.7207	500	0.038	-	-
0.9989	693	-	0.0028	0.9911
1.4425	1000	0.0128	-	-
1.9989	1386	-	0.0021	0.9956
2.1643	1500	0.0084	-	-
2.8850	2000	0.0065	-	-
2.9989	2079	-	0.0015	0.9968
3.6068	2500	0.0056	-	-
3.9989	2772	-	0.0014	0.9971

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.9
Sentence Transformers: 3.4.1
Transformers: 4.51.3
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

ContrastiveLoss

@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}
}