sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:63802 loss:CoSENTLoss custom_code arxiv:1908.10084 base_model:jinaai/jina-embeddings-v3 base_model:finetune:jinaai/jina-embeddings-v3 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: jinaai/jina-embeddings-v3
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:63802
- loss:CoSENTLoss
widget:
- source_sentence: машинка детская самоходная бибикар желтый
  sentences:
  - 'машинка детская красная бибикар '
  - моторное масло alpine dx1 5w 30 5л 0101662
  - 'спинбайк schwinn ic7 '
- source_sentence: 'велосипед stels saber 20 фиолетовый '
  sentences:
  - 'детские спортивные комплексы '
  - 'велосипед bmx stels saber 20 v010 2020 '
  - 50218 кабель ugreen hd132 hdmi zinc alloy optical fiber cable черный 40m
- source_sentence: гидравличесские прессы
  sentences:
  - пресс гидравлический ручной механизмом
  - ракетка для настольного тенниса fora 7
  - 'объектив panasonic 20mm f1 7 asph ii h h020ae k '
- source_sentence: 'бокс пластиковый монтажной платой щмп п 300х200х130 мм ip65 proxima
    ящики щитки шкафы '
  sentences:
  - батарейный отсек для 4xаа открытый проволочные выводы разъем dcx2 1 battery holder
    4xaa 6v dc
  - 'bugera bc15 '
  - 'бокс пластиковый монтажной платой щмп п 500х350х190 мм ip65 proxima ящики щитки
    шкафы '
- source_sentence: 'honor watch gs pro black '
  sentences:
  - 'honor watch gs pro white '
  - трансформер pituso carlo hb gy 06 lemon
  - 'электровелосипед колхозник volten greenline 500w '
model-index:
- name: SentenceTransformer based on jinaai/jina-embeddings-v3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: example dev
      type: example-dev
    metrics:
    - type: pearson_cosine
      value: 0.47736782328677585
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.49693031448879005
      name: Spearman Cosine
---

# SentenceTransformer based on jinaai/jina-embeddings-v3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3). 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:** [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) <!-- at revision 30996fea06f69ecd8382ee4f11e29acaf6b5405e -->
- **Maximum Sequence Length:** 8194 tokens
- **Output Dimensionality:** 1024 dimensions
- **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(
  (transformer): Transformer(
    (auto_model): XLMRobertaLoRA(
      (roberta): XLMRobertaModel(
        (embeddings): XLMRobertaEmbeddings(
          (word_embeddings): ParametrizedEmbedding(
            250002, 1024, padding_idx=1
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (token_type_embeddings): ParametrizedEmbedding(
            1, 1024
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
        )
        (emb_drop): Dropout(p=0.1, inplace=False)
        (emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder): XLMRobertaEncoder(
          (layers): ModuleList(
            (0-23): 24 x Block(
              (mixer): MHA(
                (rotary_emb): RotaryEmbedding()
                (Wqkv): ParametrizedLinearResidual(
                  in_features=1024, out_features=3072, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (inner_attn): SelfAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (inner_cross_attn): CrossAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (out_proj): ParametrizedLinear(
                  in_features=1024, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout1): Dropout(p=0.1, inplace=False)
              (drop_path1): StochasticDepth(p=0.0, mode=row)
              (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (mlp): Mlp(
                (fc1): ParametrizedLinear(
                  in_features=1024, out_features=4096, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (fc2): ParametrizedLinear(
                  in_features=4096, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout2): Dropout(p=0.1, inplace=False)
              (drop_path2): StochasticDepth(p=0.0, mode=row)
              (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            )
          )
        )
        (pooler): XLMRobertaPooler(
          (dense): ParametrizedLinear(
            in_features=1024, out_features=1024, bias=True
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (activation): Tanh()
        )
      )
    )
  )
  (pooler): 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})
  (normalizer): 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("seregadgl/t2")
# Run inference
sentences = [
    'honor watch gs pro black ',
    'honor watch gs pro white ',
    'трансформер pituso carlo hb gy 06 lemon',
]
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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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.4774     |
| **spearman_cosine** | **0.4969** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 63,802 training samples
* Columns: <code>doc</code>, <code>candidate</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | doc                                                                               | candidate                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 14.82 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.58 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>0: ~85.20%</li><li>1: ~14.80%</li></ul> |
* Samples:
  | doc                                                    | candidate                                                              | label          |
  |:-------------------------------------------------------|:-----------------------------------------------------------------------|:---------------|
  | <code>массажер xiaomi massage gun eu bhr5608eu </code> | <code>перкуссионный массажер xiaomi massage gun mini bhr6083gl </code> | <code>0</code> |
  | <code>безударная дрель ingco ed50028 </code>           | <code>ударная дрель ingco id211002 </code>                             | <code>0</code> |
  | <code>жидкость old smuggler 30мл 20мг </code>          | <code>жидкость old smuggler salt 30ml marlboro 20mg</code>             | <code>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"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 7,090 evaluation samples
* Columns: <code>doc</code>, <code>candidate</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | doc                                                                               | candidate                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 14.91 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.56 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>0: ~84.20%</li><li>1: ~15.80%</li></ul> |
* Samples:
  | doc                                                                                                           | candidate                                                                                                    | label          |
  |:--------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>круглое пляжное парео селфи коврик пляжная подстилка пляжное покрывало пляжный коврик пироженко </code> | <code>круглое пляжное парео селфи коврик пляжная подстилка пляжное покрывало пляжный коврик клубника </code> | <code>0</code> |
  | <code>аккумулятор батарея для ноутбука asus g751 </code>                                                      | <code>аккумулятор батарея для ноутбука asus g75 series</code>                                                | <code>0</code> |
  | <code>миксер bosch mfq3520 mfq 3520 </code>                                                                   | <code>миксер bosch mfq 4020 </code>                                                                          | <code>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
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-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`: 2
- `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`: 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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | example-dev_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|
| 0      | 0    | -             | -               | 0.1562                      |
| 0.1254 | 500  | 4.2363        | 3.5101          | 0.3313                      |
| 0.2508 | 1000 | 3.0049        | 2.8592          | 0.4536                      |
| 0.3761 | 1500 | 2.6306        | 2.8977          | 0.4704                      |
| 0.5015 | 2000 | 2.6472        | 2.6703          | 0.4827                      |
| 0.6269 | 2500 | 2.6626        | 2.6757          | 0.4837                      |
| 0.7523 | 3000 | 2.6137        | 2.6397          | 0.4883                      |
| 0.8776 | 3500 | 2.676         | 2.5394          | 0.4936                      |
| 1.0030 | 4000 | 2.4997        | 2.5984          | 0.4931                      |
| 1.1284 | 4500 | 2.4901        | 2.6219          | 0.4946                      |
| 1.2538 | 5000 | 2.4293        | 2.6319          | 0.4943                      |
| 1.3791 | 5500 | 2.3914        | 2.7122          | 0.4936                      |
| 1.5045 | 6000 | 2.465         | 2.6573          | 0.4970                      |
| 1.6299 | 6500 | 2.5711        | 2.6388          | 0.4965                      |
| 1.7553 | 7000 | 2.5012        | 2.6323          | 0.4967                      |
| 1.8806 | 7500 | 2.5775        | 2.6231          | 0.4969                      |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.3.1
- Transformers: 4.46.3
- PyTorch: 2.4.0
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## 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",
}
```

#### 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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