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splendor1811
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BGE-mapping-tool_epoch3

Sentence Similarity
sentence-transformers
Safetensors
Vietnamese
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:6066
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
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 ---
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](https://www.SBERT.net) model finetuned from [splendor1811/BGE-base-banking-ONE-v0106](https://huggingface.co/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](https://huggingface.co/splendor1811/BGE-base-banking-ONE-v0106) <!-- at revision 9c233176fc7eb592572824ebb465bbe59997a308 -->
- **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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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("splendor1811/BGE-mapping-tool_epoch3")
# 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]])
```
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"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 [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"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 |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 6,066 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 15.22 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.46 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------|:----------------------------------------------------------|
| <code>ZHONGSHAN CHINHAO MOLD HARDWARE COMPANY LIMITED</code> | <code>ZHONGSHAN CHINHAO MOLD HARDWARE CO., LTD.</code> |
| <code>Changshu Longte International Trade Co., Ltd</code> | <code>CHANGSHU LONGTE INTERNATIONAL TRADE CO., LTD</code> |
| <code>ACT CHEMICAL AND THERAPEUTIC LABORATORIES SARL</code> | <code>MEDICAL SUPPLIES PHARMACEUTICALS & EQUIPMENT</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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`: {}
</details>
### 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
```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",
}
```
#### MatryoshkaLoss
```bibtex
@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
```bibtex
@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}
}
```
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