hreyulog/embedinggemma_arkts

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: 512 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': 512, '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("XX/embedinggemma_arkts")
# Run inference
queries = [
    "Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts",
]
documents = [
    "public pointValuesToPixel(pts: number[]) {\n    this.mMatrixValueToPx.mapPoints(pts);\n    this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n    this.mMatrixOffset.mapPoints(pts);\n  }",
    'makeNode(uiContext: UIContext): FrameNode {\n    this.rootNode = new FrameNode(uiContext);\n    if (this.rootNode !== null) {\n      this.rootRenderNode = this.rootNode.getRenderNode();\n    }\n    return this.rootNode;\n  }',
    'export interface OnlineLunarYear {\n  year: number;\n  zodiac: string;\n  ganzhi: string;\n  leapMonth: number;\n  isLeapYear: boolean;\n  leapMonthDays?: number;\n  solarTerms: SolarTermInfo[];\n  festivals: LunarFestival[];\n}',
]
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([[ 0.8923,  0.0264, -0.0212]])

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Evaluation Results

On arkts-code-docstring dataset split test

Training Details

Training Dataset

Dataset: XX

• —Size: 39,122 training samples
• —Columns: <code>sentence0</code> and <code>sentence1</code>
• —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 97.17 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 94.4 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | sentence0 | sentence1 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>移除登录状态监听</code> | <code>public removeLoginStateListener(listener: (isLoggedIn: boolean) => void) {\n const index = this.loginStateListeners.indexOf(listener);\n if (index !== -1) {\n this.loginStateListeners.splice(index, 1);\n }\n }</code> | | <code>PUT请求</code> | <code>static put<T = Object>(url: string, data?: Object, config: RequestConfig = {}): Promise<HttpResponse<T>> {<br> const putConfig: RequestConfig = {<br> method: http.RequestMethod.PUT,<br> headers: config.headers,<br> timeout: config.timeout,<br> data: data<br> };<br> return HttpUtil.request<T>(url, putConfig);<br> }</code> | | <code>Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts</code> | <code>public pointValuesToPixel(pts: number[]) {\n this.mMatrixValueToPx.mapPoints(pts);\n this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n this.mMatrixOffset.mapPoints(pts);\n }</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —num_train_epochs: 2
• —multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

• —do_predict: False
• —eval_strategy: no
• —prediction_loss_only: True
• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —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
• —num_train_epochs: 2
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_ratio: None
• —warmup_steps: 0
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —enable_jit_checkpoint: False
• —save_on_each_node: False
• —save_only_model: False
• —restore_callback_states_from_checkpoint: False
• —use_cpu: False
• —seed: 42
• —data_seed: None
• —bf16: False
• —fp16: False
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: -1
• —ddp_backend: None
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_prefetch_factor: None
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —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: adamwtorchfused
• —optim_args: None
• —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
• —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_for_metrics: []
• —eval_do_concat_batches: True
• —auto_find_batch_size: False
• —full_determinism: False
• —ddp_timeout: 1800
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —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
• —use_cache: False
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: round_robin
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• —Python: 3.10.19
• —Sentence Transformers: 5.2.2
• —Transformers: 5.0.0
• —PyTorch: 2.9.1
• —Accelerate: 1.12.0
• —Datasets: 4.5.0
• —Tokenizers: 0.22.2

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