0xFarzad/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
• 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("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]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 7,786 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 257.72 tokens max: 1468 tokens	min: 4 tokens mean: 44.19 tokens max: 150 tokens	min: 5 tokens mean: 40.97 tokens max: 115 tokens

• Samples:

  anchor	positive	negative
  ST_1 -> ST_2 DIR:STRAIGHT ST_2 -> ST_3 DIR:STRAIGHT ST_3 -> ST_4 DIR:STRAIGHT ST_4 -> ST_5 DIR:LEFT ST_5 -> ST_6 DIR:STRAIGHT	Turn left at the 2nd light with Nine West on the left corner.	Walk to the light with the fountain on your left and turn right.
  ST_1 -> ST_2 DIR:STRAIGHT ST_2 -> ST_3 DIR:STRAIGHT ST_3 -> ST_4 DIR:STRAIGHT ST_4 -> ST_5 DIR:STRAIGHT ST_5 -> ST_6 DIR:STRAIGHT ST_6 -> ST_7 DIR:STRAIGHT	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.	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.
  ST_1 -> ST_2 DIR:STRAIGHT ST_2 -> ST_3 DIR:STRAIGHT ST_3 -> ST_4 DIR:RIGHT ST_4 -> ST_5 DIR:STRAIGHT ST_5 -> ST_6 DIR:STRAIGHT ST_6 -> ST_7 DIR:STRAIGHT	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.	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.

• Loss: contrastive_loss.ContrastiveLossWithInBatchAndHardNegatives

Evaluation Dataset

Unnamed Dataset

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

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 262.37 tokens max: 1270 tokens	min: 5 tokens mean: 43.59 tokens max: 164 tokens	min: 5 tokens mean: 40.63 tokens max: 115 tokens

• Samples:

  anchor	positive	negative
  ST_1 -> ST_2 DIR:STRAIGHT ST_2 -> ST_3 DIR:STRAIGHT	stop in the middle of the intersection at the next light.	Go through another light past Rail Line Diner.
  ST_1 -> ST_2 DIR:STRAIGHT ST_2 -> ST_3 DIR:STRAIGHT ST_3 -> ST_4 DIR:STRAIGHT ST_4 -> ST_5 DIR:STRAIGHT ST_5 -> ST_6 DIR:STRAIGHT ST_6 -> ST_7 DIR:STRAIGHT ST_7 -> ST_8 DIR:RIGHT ST_8 -> ST_9 DIR:RIGHT ST_9 -> ST_10 DIR:STRAIGHT ST_10 -> ST_11 DIR:STRAIGHT	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.	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.
  ST_1 -> ST_2 DIR:STRAIGHT ST_2 -> ST_3 DIR:STRAIGHT ST_3 -> ST_4 DIR:RIGHT	Turn right and go through the light immediately after.	Go through the next intersection and pass the park on your right.

• Loss: contrastive_loss.ContrastiveLossWithInBatchAndHardNegatives

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

Click to expand

• 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: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.1028	100	1.168	-
0.2055	200	0.856	-
0.3083	300	0.7349	-
0.4111	400	0.7102	-
0.5139	500	0.7844	1.0139
0.6166	600	0.8398	-
0.7194	700	0.6467	-
0.8222	800	0.7531	-
0.9250	900	0.7469	-
1.0277	1000	0.7163	0.8791
1.1305	1100	0.6351	-
1.2333	1200	0.5194	-
1.3361	1300	0.5055	-
1.4388	1400	0.5584	-
1.5416	1500	0.5229	0.7156
1.6444	1600	0.4807	-
1.7472	1700	0.5424	-
1.8499	1800	0.5885	-
1.9527	1900	0.5101	-
2.0555	2000	0.4153	0.6557
2.1583	2100	0.3408	-
2.2610	2200	0.356	-
2.3638	2300	0.3845	-
2.4666	2400	0.3322	-
2.5694	2500	0.3347	0.6803
2.6721	2600	0.3297	-
2.7749	2700	0.3092	-
2.8777	2800	0.3704	-
2.9805	2900	0.3689	-
3.0832	3000	0.2179	0.7215
3.1860	3100	0.1906	-
3.2888	3200	0.2338	-
3.3916	3300	0.1779	-
3.4943	3400	0.2546	-
3.5971	3500	0.2066	0.6497
3.6999	3600	0.237	-
3.8027	3700	0.239	-
3.9054	3800	0.2162	-
4.0082	3900	0.2095	-
4.1110	4000	0.1101	0.6722
4.2138	4100	0.1108	-
4.3165	4200	0.1059	-
4.4193	4300	0.1235	-
4.5221	4400	0.1061	-
4.6249	4500	0.1231	0.6879
4.7276	4600	0.1017	-
4.8304	4700	0.1236	-
4.9332	4800	0.0849	-

• 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",
}