README.md

---
base_model: BAAI/bge-m3
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4532
- loss:CoSENTLoss
widget:
- source_sentence: гантели грифы штанги гири
  sentences:
  - гири
  - коммутатор poe web настраиваемый utp3526ts-psb
  - игровой монитор lg xg2705
- source_sentence: vt vt9602
  sentences:
  - подгрифок для скрипки 1 4 wittner ultra 918141
  - электросамокат white siberia nerpa pro 3600w 2023 elka зеленый
  - компьютер pc itmultra 2 v 2
- source_sentence: фен dyson supersonic hd08 replika
  sentences:
  - стабилизатор smooth-x combo белый
  - dyson supersonic hd08 replika
  - ip-dal30ir0280p ver2
- source_sentence: aresa ar-4205
  sentences:
  - холодильник olto rf-140 c  черный
  - aresa ar-3905
  - champion g200vk-1
- source_sentence: букеты шаров сеты для детей
  sentences:
  - букеты шаров сеты для него
  - дрипка geekvape loop rda
  - труба гладкая жесткая 16 мм 3 м
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9092748477762634
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8959000349666695
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9103703525656046
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8944672696951159
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9102936678180418
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8945285994969848
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8951660474126123
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8872903553527511
      name: Spearman Dot
    - type: pearson_max
      value: 0.9103703525656046
      name: Pearson Max
    - type: spearman_max
      value: 0.8959000349666695
      name: Spearman Max
---

# SentenceTransformer based on BAAI/bge-m3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision babcf60cae0a1f438d7ade582983d4ba462303c2 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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}) 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})
)
```

## 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("seregadgl101/test_bge_10ep")
# Run inference
sentences = [
    'букеты шаров сеты для детей',
    'букеты шаров сеты для него',
    'дрипка geekvape loop rda',
]
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]
```

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9093     |
| **spearman_cosine** | **0.8959** |
| pearson_manhattan   | 0.9104     |
| spearman_manhattan  | 0.8945     |
| pearson_euclidean   | 0.9103     |
| spearman_euclidean  | 0.8945     |
| pearson_dot         | 0.8952     |
| spearman_dot        | 0.8873     |
| pearson_max         | 0.9104     |
| spearman_max        | 0.8959     |

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### Recommendations

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 4,532 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                         |
  | details | <ul><li>min: 4 tokens</li><li>mean: 14.45 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.09 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.6</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                    | sentence2                                                    | score            |
  |:-------------------------------------------------------------|:-------------------------------------------------------------|:-----------------|
  | <code>батут evo jump internal 12ft</code>                    | <code>батут evo jump internal 12ft</code>                    | <code>1.0</code> |
  | <code>наручные часы orient casual</code>                     | <code>наручные часы orient</code>                            | <code>1.0</code> |
  | <code>электрический духовой шкаф weissgauff eov 19 mw</code> | <code>электрический духовой шкаф weissgauff eov 19 mx</code> | <code>0.4</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 504 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | float                                                          |
  | details | <ul><li>min: 4 tokens</li><li>mean: 14.93 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.1 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                     | sentence2                                               | score            |
  |:------------------------------------------------------------------------------|:--------------------------------------------------------|:-----------------|
  | <code>потолочный светильник yeelight smart led ceiling light c2001s500</code> | <code>yeelight smart led ceiling light c2001s500</code> | <code>1.0</code> |
  | <code>канцелярские принадлежности</code>                                      | <code>канцелярские принадлежности разные</code>         | <code>0.4</code> |
  | <code>usb-магнитола acv avs-1718g</code>                                      | <code>автомагнитола acv avs-1718g</code>                | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `save_only_model`: True
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates

#### 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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_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`: 10
- `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`: True
- `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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | loss   | sts-dev_spearman_cosine |
|:------:|:----:|:------:|:-----------------------:|
| 1.4109 | 50   | 2.1693 | 0.7897                  |
| 2.8219 | 100  | 2.3041 | 0.8553                  |
| 4.2328 | 150  | 2.4628 | 0.8737                  |
| 5.6437 | 200  | 2.5485 | 0.8877                  |
| 7.0547 | 250  | 2.4879 | 0.8945                  |
| 8.4656 | 300  | 2.5508 | 0.8955                  |
| 9.8765 | 350  | 2.5626 | 0.8959                  |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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