BGE-mapping-tool / README.md

splendor1811

Add new SentenceTransformer model

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metadata

language:
  - vi
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:6066
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: splendor1811/BGE-base-banking-ONE-v0106
widget:
  - source_sentence: HUYNHDEANHKHOA COMPANY LIMITED
    sentences:
      - SHYH JIUH INDUSTRIAL CO.,LTD
      - KENG HIN ENGINEERING CO
      - CTTNHHTHIETKE MYTHUAT VA TINHOC HUYNHDEANHKHOA
  - source_sentence: YUEQING RONGSHENG ELECTRICAL APPLIANCES LTD.
    sentences:
      - RAUCH FRUCHTSÄFTE GMBH & CO OG
      - YUEQING RONGSHENG INTRODUCED ELECTRICAL APPLIANCES CO., LTD
      - BACH MY TRADING, SERVICES AND CONSTRUCTION COMPANY LIMITED
  - source_sentence: ZHENFA TEXTILE COMPANY, LIMITED
    sentences:
      - DATANI LOGISTICS COMPANY LIMITED
      - Quan Pham Electrical Equipment Co., Ltd.
      - ZHENFA TEXTILE CO., LIMITED
  - source_sentence: CONG TY TNHH KY THUAT VSI
    sentences:
      - CTCAD SERVICES TRADING COMPANY LIMITED
      - CHAKTOMUK RESOURCES SUPPLY IMPORT EXPORT CO.,LTD
      - VSI ENGINEERING COMPANY LIMITED
  - source_sentence: VIET ANH DUONG CO., LTD
    sentences:
      - ROCK GRANITES
      - CTY TNHH VIET ANH DUONG
      - SOVAN SEUNGDEN CO.,LTD
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: BGE-mapping-tool
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 1024
          type: dim_1024
        metrics:
          - type: cosine_accuracy@1
            value: 0.844213649851632
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9169139465875371
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9540059347181009
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.983679525222552
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.844213649851632
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.8209693372898119
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.8172106824925816
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.7956973293768547
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.04068450507619647
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.11841496782149599
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.1962584728163363
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.38186014447142047
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8055555734311861
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8893975790118221
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8042214818897099
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.8501483679525222
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9124629080118695
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9525222551928784
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.983679525222552
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8501483679525222
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.8199802176063304
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.8160237388724035
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.7958456973293768
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.040903524434681704
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.1182842627204645
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.1959109963561002
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.38199438214275006
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8060214331494838
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8915365503273512
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8045717264189338
            name: Cosine Map@100

BGE-mapping-tool

This is a sentence-transformers model finetuned from splendor1811/BGE-base-banking-ONE-v0106 on the json 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: splendor1811/BGE-base-banking-ONE-v0106
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: vi
License: apache-2.0

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': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("splendor1811/BGE-mapping-tool")
# Run inference
sentences = [
    'VIET ANH DUONG CO., LTD',
    'CTY TNHH VIET ANH DUONG',
    'ROCK GRANITES',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.9738, -0.0072],
#         [ 0.9738,  1.0000,  0.0011],
#         [-0.0072,  0.0011,  1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_1024
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 1024
}
```

Metric	Value
cosine_accuracy@1	0.8442
cosine_accuracy@3	0.9169
cosine_accuracy@5	0.954
cosine_accuracy@10	0.9837
cosine_precision@1	0.8442
cosine_precision@3	0.821
cosine_precision@5	0.8172
cosine_precision@10	0.7957
cosine_recall@1	0.0407
cosine_recall@3	0.1184
cosine_recall@5	0.1963
cosine_recall@10	0.3819
cosine_ndcg@10	0.8056
cosine_mrr@10	0.8894
cosine_map@100	0.8042

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.8501
cosine_accuracy@3	0.9125
cosine_accuracy@5	0.9525
cosine_accuracy@10	0.9837
cosine_precision@1	0.8501
cosine_precision@3	0.82
cosine_precision@5	0.816
cosine_precision@10	0.7958
cosine_recall@1	0.0409
cosine_recall@3	0.1183
cosine_recall@5	0.1959
cosine_recall@10	0.382
cosine_ndcg@10	0.806
cosine_mrr@10	0.8915
cosine_map@100	0.8046

Training Details

Training Dataset

json

Dataset: json
Size: 6,066 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 4 tokens
mean: 15.22 tokens
max: 35 tokens

min: 5 tokens
mean: 15.46 tokens
max: 48 tokens

	anchor	positive
type	string	string
details	min: 4 tokens mean: 15.22 tokens max: 35 tokens	min: 5 tokens mean: 15.46 tokens max: 48 tokens

Samples:

anchor	positive
`ZHONGSHAN CHINHAO MOLD HARDWARE COMPANY LIMITED`	`ZHONGSHAN CHINHAO MOLD HARDWARE CO., LTD.`
`Changshu Longte International Trade Co., Ltd`	`CHANGSHU LONGTE INTERNATIONAL TRADE CO., LTD`
`ACT CHEMICAL AND THERAPEUTIC LABORATORIES SARL`	`MEDICAL SUPPLIES PHARMACEUTICALS & EQUIPMENT`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768
    ],
    "matryoshka_weights": [
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
gradient_accumulation_steps: 8
learning_rate: 2e-05
num_train_epochs: 5
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
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: 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: 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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
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}
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	dim_1024_cosine_ndcg@10	dim_768_cosine_ndcg@10
0.1054	10	0.4225	-	-
0.2108	20	0.2415	-	-
0.3162	30	0.1252	-	-
0.4216	40	0.0765	-	-
0.5270	50	0.0573	-	-
0.6324	60	0.0345	-	-
0.7378	70	0.0448	-	-
0.8432	80	0.0355	-	-
0.9486	90	0.0996	-	-
1.0	95	-	0.7994	0.7994
1.0527	100	0.0194	-	-
1.1581	110	0.0418	-	-
1.2635	120	0.0339	-	-
1.3689	130	0.0427	-	-
1.4743	140	0.0339	-	-
1.5797	150	0.0333	-	-
1.6851	160	0.0396	-	-
1.7905	170	0.0877	-	-
1.8959	180	0.0608	-	-
2.0	190	0.0352	0.8037	0.8031
2.1054	200	0.023	-	-
2.2108	210	0.0638	-	-
2.3162	220	0.0401	-	-
2.4216	230	0.0274	-	-
2.5270	240	0.0405	-	-
2.6324	250	0.0305	-	-
2.7378	260	0.0414	-	-
2.8432	270	0.0178	-	-
2.9486	280	0.0535	-	-
3.0	285	-	0.8008	0.8012
3.0527	290	0.0629	-	-
3.1581	300	0.0283	-	-
3.2635	310	0.0567	-	-
3.3689	320	0.0167	-	-
3.4743	330	0.0349	-	-
3.5797	340	0.053	-	-
3.6851	350	0.0517	-	-
3.7905	360	0.0603	-	-
3.8959	370	0.0323	-	-
4.0	380	0.0229	0.8042	0.8055
4.1054	390	0.0476	-	-
4.2108	400	0.06	-	-
4.3162	410	0.0412	-	-
4.4216	420	0.0553	-	-
4.5270	430	0.0446	-	-
4.6324	440	0.016	-	-
4.7378	450	0.0302	-	-
4.8432	460	0.0223	-	-
4.9486	470	0.0649	-	-
5.0	475	-	0.8056	0.806

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.0
Transformers: 4.56.1
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.22.0

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}