|
--- |
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base_model: jinaai/jina-embeddings-v3 |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:63802 |
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- loss:CoSENTLoss |
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widget: |
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- source_sentence: машинка детская самоходная бибикар желтый |
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sentences: |
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- 'машинка детская красная бибикар ' |
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- моторное масло alpine dx1 5w 30 5л 0101662 |
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- 'спинбайк schwinn ic7 ' |
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- source_sentence: 'велосипед stels saber 20 фиолетовый ' |
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sentences: |
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- 'детские спортивные комплексы ' |
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- 'велосипед bmx stels saber 20 v010 2020 ' |
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- 50218 кабель ugreen hd132 hdmi zinc alloy optical fiber cable черный 40m |
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- source_sentence: гидравличесские прессы |
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sentences: |
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- пресс гидравлический ручной механизмом |
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- ракетка для настольного тенниса fora 7 |
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- 'объектив panasonic 20mm f1 7 asph ii h h020ae k ' |
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- source_sentence: 'бокс пластиковый монтажной платой щмп п 300х200х130 мм ip65 proxima |
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ящики щитки шкафы ' |
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sentences: |
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- батарейный отсек для 4xаа открытый проволочные выводы разъем dcx2 1 battery holder |
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4xaa 6v dc |
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- 'bugera bc15 ' |
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- 'бокс пластиковый монтажной платой щмп п 500х350х190 мм ip65 proxima ящики щитки |
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шкафы ' |
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- source_sentence: 'honor watch gs pro black ' |
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sentences: |
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- 'honor watch gs pro white ' |
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- трансформер pituso carlo hb gy 06 lemon |
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- 'электровелосипед колхозник volten greenline 500w ' |
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model-index: |
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- name: SentenceTransformer based on jinaai/jina-embeddings-v3 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: example dev |
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type: example-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.47736782328677585 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.49693031448879005 |
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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 |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) <!-- at revision 30996fea06f69ecd8382ee4f11e29acaf6b5405e --> |
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- **Maximum Sequence Length:** 8194 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
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(transformer): Transformer( |
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(auto_model): XLMRobertaLoRA( |
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(roberta): XLMRobertaModel( |
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(embeddings): XLMRobertaEmbeddings( |
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(word_embeddings): ParametrizedEmbedding( |
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250002, 1024, padding_idx=1 |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(token_type_embeddings): ParametrizedEmbedding( |
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1, 1024 |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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) |
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(emb_drop): Dropout(p=0.1, inplace=False) |
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(emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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(encoder): XLMRobertaEncoder( |
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(layers): ModuleList( |
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(0-23): 24 x Block( |
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(mixer): MHA( |
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(rotary_emb): RotaryEmbedding() |
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(Wqkv): ParametrizedLinearResidual( |
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in_features=1024, out_features=3072, bias=True |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(inner_attn): SelfAttention( |
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(drop): Dropout(p=0.1, inplace=False) |
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) |
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(inner_cross_attn): CrossAttention( |
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(drop): Dropout(p=0.1, inplace=False) |
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) |
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(out_proj): ParametrizedLinear( |
|
in_features=1024, out_features=1024, bias=True |
|
(parametrizations): ModuleDict( |
|
(weight): ParametrizationList( |
|
(0): LoRAParametrization() |
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) |
|
) |
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) |
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) |
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(dropout1): Dropout(p=0.1, inplace=False) |
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(drop_path1): StochasticDepth(p=0.0, mode=row) |
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(norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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(mlp): Mlp( |
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(fc1): ParametrizedLinear( |
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in_features=1024, out_features=4096, bias=True |
|
(parametrizations): ModuleDict( |
|
(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(fc2): ParametrizedLinear( |
|
in_features=4096, out_features=1024, bias=True |
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(parametrizations): ModuleDict( |
|
(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
|
) |
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) |
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) |
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(dropout2): Dropout(p=0.1, inplace=False) |
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(drop_path2): StochasticDepth(p=0.0, mode=row) |
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(norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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) |
|
) |
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) |
|
(pooler): XLMRobertaPooler( |
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(dense): ParametrizedLinear( |
|
in_features=1024, out_features=1024, bias=True |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(activation): Tanh() |
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) |
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) |
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) |
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) |
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(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}) |
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(normalizer): Normalize() |
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) |
|
``` |
|
|
|
## 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) |
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print(embeddings.shape) |
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# [3, 1024] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
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|
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#### Semantic Similarity |
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|
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* Dataset: `example-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.4774 | |
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| **spearman_cosine** | **0.4969** | |
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|
|
<!-- |
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## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
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|
|
<!-- |
|
### Recommendations |
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|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
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|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
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|
|
|
|
* Size: 63,802 training samples |
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* Columns: <code>doc</code>, <code>candidate</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | doc | candidate | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
|
| type | string | string | int | |
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| 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: |
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| doc | candidate | label | |
|
|:-------------------------------------------------------|:-----------------------------------------------------------------------|:---------------| |
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| <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> | |
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| <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 |
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|
|
|
|
* 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: |
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| doc | candidate | label | |
|
|:--------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:---------------| |
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| <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 |
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- `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", |
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} |
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``` |
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#### CoSENTLoss |
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```bibtex |
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@online{kexuefm-8847, |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
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author={Su Jianlin}, |
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year={2022}, |
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month={Jan}, |
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url={https://kexue.fm/archives/8847}, |
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} |
|
``` |
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