Maksim-KOS/embeddinggemma-300m-saturn-planet-v2

SentenceTransformer based on google/embeddinggemma-300m

This is a sentence-transformers model finetuned from google/embeddinggemma-300m. It maps sentences & paragraphs to a 768-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: google/embeddinggemma-300m <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
• —Maximum Sequence Length: 2048 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (4): 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("Maksim-KOS/embeddinggemma-300m-saturn-planet-v2")
# Run inference
queries = [
    "\u0428\u043a\u0443\u0440\u043a\u0430 \u0432\u043e\u0434\u043e\u0441\u0442\u043e\u0439\u043a\u0430\u044f \u043d\u0430 \u0442\u043a\u0430\u043d\u0435\u0432\u043e\u0439 \u043e\u0441\u043d\u043e\u0432\u0435 P80 80\u043c\u043c \u0440\u0443\u043b\u043e\u043d 20\u043c",
]
documents = [
    'Шкурка шлифовальная водостойкая на тканевой основе 805 мм, 20Н (P80), рулон 20 м',
    'Шкурка шлифовальная водостойкая на тканевой основе 805 мм 25Н (P60) 1 п.м.',
    'Перчатки КЩС тип 2 тонкие',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.8399,  0.2156, -0.0400]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Triplet

• —Dataset: hard-neg-eval
• —Evaluated with <code>TripletEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 124,228 training samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 21.2 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 26.83 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 27.71 tokens</li><li>max: 51 tokens</li></ul> |
• —Samples: | anchor | positive | negative | |:--------------------------------------------------------------|:------------------------------------------------------------------------|:-----------------------------------------------------------------------------| | <code>Кабель Камкабель КГтп 2х2,5 220 В (100 м), шт</code> | <code>Кабель КГтп-ХЛ 2х2,5мм2, гибкий, черный (бухта-100п.м.)</code> | <code>Кабель КГтп-ХЛ 2х1,5мм2 TDM гибкий, черный (бухта-100п.м.)</code> | | <code>Клей для плитки Основит Стройбриг С0T АС12 25 кг</code> | <code>Клей для плитки Стройбриг Гранификс АС12 усиленный (25 кг)</code> | <code>Клей для плитки Основит Мастпликс AC121 Т выравнивающий (25 кг)</code> | | <code>линолеум juteks аванта 4м</code> | <code>Линолеум бытовой Juteks Avanta NXT Kortes 4 (4 м)</code> | <code>Линолеум бытовой Juteks Avanta Fort 5 (4 м)</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 13,804 evaluation samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 21.33 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.59 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.72 tokens</li><li>max: 55 tokens</li></ul> |
• —Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | <code>Свая винтовая d76х2000 мм, шт</code> | <code>Свая винтовая d=76 мм, L=2000 мм, d винтовой части = 220 мм</code> | <code>Столб заборный винт., L=3000 мм (60х60 мм L=2000 мм, d=51 мм L=1000 мм)</code> | | <code>Нивелир лазерный Condtrol Neo Х200 (1-2-115) с магнитной шайбой, шт</code> | <code>Лазерный нивелир Condtrol NEO X200</code> | <code>Нивелир лазерный Condtrol NEO X2-360</code> | | <code>керамогранит светло-коричневый 594х147 мм</code> | <code>Керамогранит Березакерамика Шеми GP, светло-коричневый, 594х147х9 мм</code> | <code>Керамогранит Керамин Портланд 3, мат., светло-бежевый, 600х600х10 мм</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —gradient_accumulation_steps: 8
• —learning_rate: 1e-05
• —weight_decay: 0.01
• —num_train_epochs: 10
• —lr_scheduler_type: cosine
• —warmup_ratio: 0.1
• —load_best_model_at_end: True
• —optim: adamw_torch
• —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: 32
• —per_device_eval_batch_size: 32
• —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: 1e-05
• —weight_decay: 0.01
• —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: None
• —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
• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamw_torch
• —optim_args: None
• —adafactor: False
• —group_by_length: False
• —length_column_name: length
• —project: huggingface
• —trackio_space_id: trackio
• —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: no
• —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: True
• —prompts: None
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Framework Versions

• —Python: 3.12.3
• —Sentence Transformers: 5.2.0
• —Transformers: 4.57.6
• —PyTorch: 2.9.1+cu128
• —Accelerate: 1.12.0
• —Datasets: 4.5.0
• —Tokenizers: 0.22.2

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",
}

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}
}

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