embeddinggemma-graph-instructions-hard-negatives-final

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("0xFarzad/embeddinggemma-graph-instructions-hard-negatives-final")
# Run inference
queries = [
    "\u003cROUTE_START\u003e\n\u003cSEG\u003e ST_1 -\u003e ST_2 \u003cDIR:LEFT\u003e \u003cLIGHT\u003e \u003cPOI: Citi Bike / amenity: bicycle_rental\u003e \u003cPOI: Hampton Inn / tourism: hotel\u003e\n\u003cSEG\u003e ST_2 -\u003e ST_3 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Santander / amenity: bank\u003e \u003cPOI_LEFT: Santander / amenity: atm\u003e \u003cPOI_RIGHT: Hampton Inn / tourism: hotel\u003e \u003cPOI_RIGHT: T-Mobile / shop: mobile_phone\u003e\n\u003cSEG\u003e ST_3 -\u003e ST_4 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Chase / amenity: bank\u003e \u003cPOI_LEFT: Chase / amenity: atm\u003e \u003cPOI_LEFT: Pret A Manger / amenity: fast_food / cuisine: sandwich\u003e\n\u003cSEG\u003e ST_4 -\u003e ST_5 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Chase / amenity: bank\u003e \u003cPOI_LEFT: Chase / amenity: atm\u003e \u003cPOI_LEFT: Pret A Manger / amenity: fast_food / cuisine: sandwich\u003e\n\u003cSEG\u003e ST_5 -\u003e ST_6 \u003cDIR:STRAIGHT\u003e \u003cPOI_LEFT: Santander / amenity: bank\u003e \u003cPOI_LEFT: Santander / amenity: atm\u003e \u003cPOI_RIGHT: Hampton Inn / tourism: hotel\u003e \u003cPOI_RIGHT: T-Mobile / shop: mobile_phone\u003e\n\u003cSEG\u003e ST_6 -\u003e ST_7 \u003cDIR:STRAIGHT\u003e \u003cPOI_RIGHT: Sheraton New York Times Square Hotel / tourism: hotel\u003e\n\u003cSEG\u003e ST_7 -\u003e ST_8 \u003cDIR:STRAIGHT\u003e \u003cPOI_RIGHT: Sheraton New York Times Square Hotel / tourism: hotel\u003e\n\u003cROUTE_END\u003e",
]
documents = [
    "After this block take a left at the third light and stop halfway down the block. After the second intersection you'll go down a block with several theaters. A Hampton Inn will be on your right and a bank ahead on the corner. Start by going straight through two intersections.",
    "After this block take a left at the third light and stop halfway down the block. After the second intersection you'll go down a block with several theaters. A Hampton Inn will be on your right and a bank ahead on the corner. Start by going straight through two intersections.",
    'At the next light with a playground on the far left, turn left. Stop in the middle of the playground before a fire station on the left side. Pass a T-intersection with Chase on the far left corner. Walk by a small church to the end of the street and turn left.',
]
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([[ 0.3893,  0.3893, -0.0502]])

<!-- ### 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.* --> <!-- ## 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.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 7,786 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: 16 tokens</li><li>mean: 257.72 tokens</li><li>max: 1468 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 44.19 tokens</li><li>max: 150 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 40.97 tokens</li><li>max: 115 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code><ROUTE_START><br><SEG> ST_1 -> ST_2 DIR:STRAIGHT <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes> <POI_LEFT: Brooks Brothers / shop: clothes><br><SEG> ST_2 -> ST_3 DIR:STRAIGHT <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes> <POI_LEFT: Brooks Brothers / shop: clothes><br><SEG> ST_3 -> ST_4 DIR:STRAIGHT <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes><br><SEG> ST_4 -> ST_5 DIR:LEFT <LIGHT><br><SEG> ST_5 -> ST_6 DIR:STRAIGHT <POI_LEFT: Rockefeller Center / tourism: attraction / historic: yes> <POI_LEFT: Nine West / shop: clothes><br><ROUTE_END></code>	<code>Turn left at the 2nd light with Nine West on the left corner.</code>	<code>Walk to the light with the fountain on your left and turn right.</code>
  <code><ROUTE_START><br><SEG> ST_1 -> ST_2 DIR:STRAIGHT <LIGHT> <POI_LEFT: highway: bus_stop> <POI_LEFT: Houndstooth Pub / amenity: bar><br><SEG> ST_2 -> ST_3 DIR:STRAIGHT <POI_LEFT: highway: bus_stop> <POI_LEFT: Houndstooth Pub / amenity: bar><br><SEG> ST_3 -> ST_4 DIR:STRAIGHT <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international><br><SEG> ST_4 -> ST_5 DIR:STRAIGHT <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international> <POI_RIGHT: White Castle / amenity: fast_food / cuisine: burger><br><SEG> ST_5 -> ST_6 DIR:STRAIGHT <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international> <POI_RIGHT: White Castle / amenity: fast_food / cuisine: burger><br><SEG> ST_6 -> ST_7 DIR:STRAIGHT <POI_LEFT: The Houndstooth Pub / amenity: pub> <POI_LEFT: Roast Kitchen / amenity: restaurant / cuisine: international> <POI_RIGHT: White ...</code>	<code>Cooper's Tavern is on the right corner. Go about half way down the block. Go to the lights and turn right. Go through the following three sets of lights. Stop right after McDonald's on the left.</code>	<code>Go through the following three sets of lights. Go about half way down the block. Cooper's Tavern is on the right corner. Stop right after McDonald's on the left. Go to the lights and turn right.</code>
  <code><ROUTE_START><br><SEG> ST_1 -> ST_2 DIR:STRAIGHT<br><SEG> ST_2 -> ST_3 DIR:STRAIGHT<br><SEG> ST_3 -> ST_4 DIR:RIGHT <LIGHT><br><SEG> ST_4 -> ST_5 DIR:STRAIGHT<br><SEG> ST_5 -> ST_6 DIR:STRAIGHT<br><SEG> ST_6 -> ST_7 DIR:STRAIGHT <POI_LEFT: Exki / amenity: restaurant / cuisine: fast_food><br><ROUTE_END></code>	<code>Go straight and take a right at the intersection. Continue straight through 2 intersections. then your destination will be right before Exki on the left.</code>	<code>Go straight and take a right at the intersection. Head to the first light and make a left. then your destination will be right before Exki on the left.</code>

• Loss: <code>contrastive_loss.ContrastiveLossWithInBatchAndHardNegatives</code>

Evaluation Dataset

Unnamed Dataset

• Size: 866 evaluation samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 866 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 16 tokens</li><li>mean: 262.37 tokens</li><li>max: 1270 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 43.59 tokens</li><li>max: 164 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 40.63 tokens</li><li>max: 115 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code><ROUTE_START><br><SEG> ST_1 -> ST_2 DIR:STRAIGHT <POI_LEFT: Chelsea Square Restaurant / amenity: restaurant><br><SEG> ST_2 -> ST_3 DIR:STRAIGHT<br><ROUTE_END></code>	<code>stop in the middle of the intersection at the next light.</code>	<code>Go through another light past Rail Line Diner.</code>
  <code><ROUTE_START><br><SEG> ST_1 -> ST_2 DIR:STRAIGHT <POI_LEFT: Insomnia Cookies / shop: bakery><br><SEG> ST_2 -> ST_3 DIR:STRAIGHT<br><SEG> ST_3 -> ST_4 DIR:STRAIGHT <LIGHT><br><SEG> ST_4 -> ST_5 DIR:STRAIGHT <POI_LEFT: Atmi / shop: alcohol><br><SEG> ST_5 -> ST_6 DIR:STRAIGHT <POI_LEFT: Atmi / shop: alcohol> <POI_RIGHT: leisure: park> <POI_LEFT: Urban Outfitters / shop: clothes><br><SEG> ST_6 -> ST_7 DIR:STRAIGHT <POI_LEFT: highway: bus_stop><br><SEG> ST_7 -> ST_8 DIR:RIGHT <LIGHT><br><SEG> ST_8 -> ST_9 DIR:RIGHT <LIGHT><br><SEG> ST_9 -> ST_10 DIR:STRAIGHT <POI_RIGHT: Momofuku Ssäm Bar / amenity: restaurant / cuisine: asian><br><SEG> ST_10 -> ST_11 DIR:STRAIGHT<br><ROUTE_END></code>	<code>stop a little more than half way down the block where Abe Lebewohl Park on the right begins between Atmi and Urban Outfitters will be on the left. Go to the traffic light where there is a bus stop on the near left corner and turn right. Go through two lights.</code>	<code>Go to the traffic light where there is a bus stop on the near left corner and turn right. Go through two lights. stop a little more than half way down the block where Abe Lebewohl Park on the right begins between Atmi and Urban Outfitters will be on the left.</code>
  <code><ROUTE_START><br><SEG> ST_1 -> ST_2 DIR:STRAIGHT<br><SEG> ST_2 -> ST_3 DIR:STRAIGHT<br><SEG> ST_3 -> ST_4 DIR:RIGHT <LIGHT><br><ROUTE_END></code>	<code>Turn right and go through the light immediately after.</code>	<code>Go through the next intersection and pass the park on your right.</code>

• Loss: <code>contrastive_loss.ContrastiveLossWithInBatchAndHardNegatives</code>

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• weight_decay: 0.01
• num_train_epochs: 5
• warmup_ratio: 0.1
• bf16: True
• dataloader_num_workers: 2
• load_best_model_at_end: True
• prompts: task: sentence similarity | query:

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: 8
• 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: 2e-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: 5
• max_steps: -1
• lr_scheduler_type: linear
• 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
• bf16: True
• 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: True
• dataloader_num_workers: 2
• 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_fused
• 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: task: sentence similarity | query:
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.1.1
• Transformers: 4.57.1
• PyTorch: 2.9.0+cu128
• Accelerate: 1.10.1
• Datasets: 4.2.0
• Tokenizers: 0.22.1

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",
}

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