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

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
• Maximum Sequence Length: 2048 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

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")
# Run inference
queries = [
    "\u0442\u0440\u0430\u043f \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u044b\u0439 100 x 100 mm d50 mm \u0441 \u0433\u0438\u0434\u0440\u043e\u0437\u0430\u0442\u0432\u043e\u0440\u043e\u043c",
]
documents = [
    'трап ани пласт горизонтальный d 50 mm 100 x 100 mm не регулируемый гидрозатвор',
    'лоток водоотводный standartpark basic dn100 1000 x 156 x 120 mm пластик 8020 m',
    'саморез шуж swfs pn 4 x 12 1000 шт',
]
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([[ 0.7530,  0.1274, -0.0019]])

Evaluation

Metrics

Triplet

• Dataset: hard-neg-eval
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9975

Training Details

Training Dataset

Unnamed Dataset

• Size: 123,502 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 21.44 tokens max: 56 tokens	min: 7 tokens mean: 25.22 tokens max: 48 tokens	min: 9 tokens mean: 26.08 tokens max: 50 tokens

• Samples:

  anchor	positive	negative
  ремонтный состав быстротвердеющий gerkules 5 кг	ремонтный состав gerkules gs 22 быстротвердеющий 5 кг	клей для плитки и керамогранита gerkules granit pro gm 245 25 кг
  тяга подвеса 350 mm	тяга подвеса 350 mm	подвес прямой 200 mm knauf 72519
  подложка вспененный полиэтилен 4 mm	подложка из вспененного полиэтилена 4 mm 1 п.m.	подложка из вспененного полиэтилена 5 mm 50 m 2

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 13,723 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 21.29 tokens max: 57 tokens	min: 7 tokens mean: 25.17 tokens max: 50 tokens	min: 7 tokens mean: 26.08 tokens max: 50 tokens

• Samples:

  anchor	positive	negative
  краска для стен и потолков морозостойкая 6.5 кг	краска для стен и потолков pufas decoself морозостойкая 6.5 кг	краска моющаяся pufas decoself морозостойкая нв 23.1 кг
  розетка компьютерная rj45 1 гнездо скрытая	розетка компьютерная rj 45 с у atlasdesign atn001083 1 гнездо карбон	розетка компьютерная rj 45 с у atlasdesign atn001085 2 гнезда карбон
  саморез кровельный 4.8 x 28 коричневый оцинкованный	саморез кровельный swfs premium ral 8017 zp 4.8 x 28 300 шт круглый конт р	саморез кровельный swfs premium ral 5002 zp 4.8 x 28 250 шт

• Loss: MultipleNegativesRankingLoss 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

Click to expand

• 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: {'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
• 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: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	hard-neg-eval_cosine_accuracy
0.1036	50	0.1448	-	-
0.2073	100	0.0509	0.0448	0.9884
0.3109	150	0.0373	-	-
0.4145	200	0.0285	0.0288	0.9923
0.5181	250	0.0291	-	-
0.6218	300	0.0264	0.0228	0.9944
0.7254	350	0.0252	-	-
0.8290	400	0.0236	0.0212	0.9953
0.9326	450	0.0258	-	-
1.0352	500	0.0217	0.0201	0.9956
1.1389	550	0.02	-	-
1.2425	600	0.0161	0.0182	0.9953
1.3461	650	0.0189	-	-
1.4497	700	0.0162	0.0164	0.9959
1.5534	750	0.0174	-	-
1.6570	800	0.0173	0.0160	0.9956
1.7606	850	0.0168	-	-
1.8642	900	0.015	0.0138	0.9972
1.9679	950	0.0132	-	-
2.0705	1000	0.0117	0.0129	0.9967
2.1741	1050	0.0111	-	-
2.2777	1100	0.0092	0.0120	0.9970
2.3813	1150	0.0117	-	-
2.485	1200	0.0101	0.0113	0.9972
2.5886	1250	0.0112	-	-
2.6922	1300	0.0098	0.0116	0.9972
2.7959	1350	0.0121	-	-
2.8995	1400	0.0095	0.0114	0.9975

• 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}
}