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Upload finetuned embeddinggemma (multiple negatives)

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:112
  - loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
  - source_sentence: 슈파인
    sentences:
      - park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹
      - >-
        tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인,
        abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
      - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
  - source_sentence: 그만 정지 멈추지 그만
    sentences:
      - stopAction | 어느 위치에서든 장비 즉각 멈춤 | 멈춰
      - park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹
      - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
  - source_sentence: 이렉트
    sentences:
      - >-
        tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인,
        abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
      - >-
        tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인,
        abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
      - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
  - source_sentence: 아니
    sentences:
      - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
      - responseNo | 부정의 응답 | 아니요, 노
      - park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹
  - source_sentence: 멈춰
    sentences:
      - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
      - >-
        tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인,
        abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
      - stopAction | 어느 위치에서든 장비 즉각 멈춤 | 멈춰
pipeline_tag: sentence-similarity
library_name: sentence-transformers

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("sentence_transformers_model_id")
# Run inference
queries = [
    "\uba48\ucdb0",
]
documents = [
    'stopAction | 어느 위치에서든 장비 즉각 멈춤 | 멈춰',
    'tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로',
    'tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로',
]
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([[0.6204, 0.0847, 0.1969]])

Training Details

Training Dataset

Unnamed Dataset

Size: 112 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 112 samples:
sentence_0 sentence_1
type string string
details
min: 3 tokens
mean: 7.67 tokens
max: 20 tokens

min: 13 tokens
mean: 37.14 tokens
max: 62 tokens

	sentence_0	sentence_1
type	string	string
details	min: 3 tokens mean: 7.67 tokens max: 20 tokens	min: 13 tokens mean: 37.14 tokens max: 62 tokens

Samples:

sentence_0	sentence_1
`체스트 PA`	`tubeToStandCenter \| 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine \| 튜브 스탠드 센터로`
`튜브 스탠드 백색 센치로 센터 맞춰줘`	`tubeToStandCenter \| 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine \| 튜브 스탠드 센터로`
`튜브`	`tubeToTableCenter \| 튜브를 테이블 센터를 향하도록 이동 \| 튜브 테이블 센터로`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 1
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: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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
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: {}

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.2.2
Transformers: 5.0.0
PyTorch: 2.7.1+cpu
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}
}