SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the netsecgame-embedding-finetuning-pairs-topology dataset. It maps sentences & paragraphs to a 1024-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: Qwen/Qwen3-Embedding-0.6B
Maximum Sequence Length: 32768 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- netsecgame-embedding-finetuning-pairs-topology

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': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
  (2): 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("stratosphere/Qwen3-Embedding-0.6B-netsecgame-finetuned-pairs2")
# Run inference
queries = [
    "[NETS] 192.168.3.0/24, 192.168.2.0/24, 213.47.23.192/26, 192.168.1.0/24 [HOSTS] 192.168.2.3, 192.168.2.6, 192.168.2.2, 192.168.2.5, 192.168.2.4, 213.47.23.195, 192.168.1.4, 192.168.1.2, 192.168.2.1, 192.168.1.3 [CTRL] 213.47.23.195, 192.168.2.2 [SRVC] 192.168.2.2, 213.47.23.195, 192.168.2.4, 192.168.1.2",
]
documents = [
    '[NETS] 192.168.3.0/24, 192.168.2.0/24, 213.47.23.192/26, 192.168.1.0/24 [HOSTS] 192.168.2.3, 192.168.2.6, 192.168.2.2, 192.168.2.5, 192.168.2.4, 213.47.23.195, 192.168.1.4, 192.168.1.2, 192.168.2.1, 192.168.1.3 [CTRL] 213.47.23.195, 192.168.2.2 [SRVC] 192.168.2.2, 213.47.23.195, 192.168.2.5, 192.168.1.2',
    '[NETS] 10.0.46.0/24, 10.0.47.0/24, 10.0.45.0/24, 55.34.2.4/26 [HOSTS] 10.0.46.6, 10.0.47.1, 10.0.47.6, 10.0.46.1, 10.0.47.3, 10.0.47.4, 55.34.2.5, 10.0.47.5, 10.0.47.2, 10.0.46.2, 10.0.46.4, 10.0.46.3, 10.0.46.5 [CTRL] 10.0.47.3, 10.0.47.4, 55.34.2.5, 10.0.47.2, 10.0.46.3 [SRVC] 55.34.2.5, 10.0.46.2, 10.0.47.2, 10.0.47.4, 10.0.46.3, 10.0.47.3, 10.0.46.5, 10.0.46.4, 10.0.47.5 [DATA] 10.0.47.4, 10.0.47.2, 10.0.47.3',
    '[NETS] 172.19.0.0/24, 54.123.53.29/26, 172.19.1.0/24, 172.19.2.0/24 [HOSTS] 172.19.1.4, 172.19.1.1, 101.32.5.23, 172.19.1.5, 172.19.2.2, 172.19.2.3, 172.19.1.6, 172.19.1.3, 172.19.2.4, 172.19.1.2 [CTRL] 172.19.1.4, 172.19.1.5, 101.32.5.23, 172.19.2.3, 172.19.1.3, 172.19.2.4 [SRVC] 101.32.5.23, 172.19.1.5, 172.19.1.3, 172.19.2.4, 172.19.2.3, 172.19.1.4, 172.19.2.2',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.0644, 0.0168, 0.0084]])

Evaluation

Metrics

Binary Classification

Dataset: topology_val
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.976
cosine_accuracy_threshold	0.767
cosine_f1	0.9757
cosine_f1_threshold	0.767
cosine_precision	0.9761
cosine_recall	0.9754
cosine_ap	0.9797
cosine_mcc	0.952

Training Details

Training Dataset

netsecgame-embedding-finetuning-pairs-topology

Dataset: netsecgame-embedding-finetuning-pairs-topology at 8c78c97
Size: 21,011 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 121 tokens
mean: 289.98 tokens
max: 463 tokens

min: 121 tokens
mean: 291.36 tokens
max: 504 tokens

min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence1	sentence2	label
`[NETS] 172.20.7.0/24, 172.20.6.0/24, 172.20.8.0/24, 32.8.23.1/26 [HOSTS] 172.20.8.2, 172.20.7.1, 172.20.8.4, 172.20.7.6, 172.20.7.4, 172.20.7.5, 172.20.7.3, 172.20.8.3, 32.8.23.1, 172.20.7.2 [CTRL] 32.8.23.1, 172.20.7.2 [SRVC] 32.8.23.1, 172.20.7.3, 172.20.7.5`	`[NETS] 172.20.7.0/24, 172.20.6.0/24, 172.20.8.0/24, 32.8.23.1/26 [HOSTS] 172.20.8.2, 172.20.7.1, 172.20.8.4, 172.20.7.6, 172.20.7.4, 172.20.7.5, 172.20.7.3, 172.20.8.3, 32.8.23.1, 172.20.7.2 [CTRL] 172.20.7.5, 32.8.23.1 [SRVC] 32.8.23.1, 172.20.7.3, 172.20.7.4`	`1.0`
`[NETS] 192.168.3.0/24, 192.168.2.0/24, 213.47.23.192/26, 192.168.1.0/24 [HOSTS] 192.168.2.3, 192.168.2.6, 192.168.2.2, 192.168.2.5, 192.168.2.4, 213.47.23.195, 192.168.1.4, 192.168.1.2, 192.168.2.1, 192.168.1.3 [CTRL] 192.168.2.4, 213.47.23.195, 192.168.2.2, 192.168.2.3 [SRVC] 213.47.23.195, 192.168.2.5, 192.168.2.3, 192.168.2.4, 192.168.2.2`	`[NETS] 10.7.44.0/24, 10.7.45.0/24, 54.123.53.29/26, 10.7.43.0/24 [HOSTS] 10.7.44.6, 10.7.45.4, 10.7.44.4, 10.7.44.5, 10.7.44.1, 10.7.44.3, 10.7.45.2, 10.7.45.3, 54.123.53.21, 10.7.44.2 [CTRL] 10.7.45.4, 54.123.53.21, 10.7.44.6 [SRVC] 54.123.53.21, 10.7.45.4, 10.7.44.5, 10.7.44.3`	`0.0`
`[NETS] 172.20.7.0/24, 172.20.6.0/24, 172.20.8.0/24, 32.8.23.1/26 [HOSTS] 172.20.8.2, 172.20.7.1, 172.20.8.4, 172.20.7.6, 172.20.7.4, 172.20.7.5, 172.20.7.3, 172.20.8.3, 32.8.23.1, 172.20.7.2 [CTRL] 32.8.23.1, 172.20.7.2 [SRVC] 172.20.7.2, 172.20.7.3, 32.8.23.1`	`[NETS] 172.20.7.0/24, 172.20.6.0/24, 172.20.8.0/24, 32.8.23.1/26 [HOSTS] 172.20.8.2, 172.20.7.1, 172.20.8.4, 172.20.7.6, 172.20.7.4, 172.20.7.5, 172.20.7.3, 172.20.8.3, 32.8.23.1, 172.20.7.2 [CTRL] 32.8.23.1, 172.20.7.3 [SRVC] 172.20.7.3, 172.20.7.2, 32.8.23.1`	`1.0`

Loss: OnlineContrastiveLoss

Evaluation Dataset

netsecgame-embedding-finetuning-pairs-topology

Dataset: netsecgame-embedding-finetuning-pairs-topology at 8c78c97
Size: 2,626 evaluation samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	label
type	string	string	float
details	min: 140 tokens mean: 290.95 tokens max: 461 tokens	min: 140 tokens mean: 292.04 tokens max: 448 tokens	min: 0.0 mean: 0.51 max: 1.0

Samples:

sentence1	sentence2	label
`[NETS] 192.168.3.0/24, 192.168.2.0/24, 213.47.23.192/26, 192.168.1.0/24 [HOSTS] 192.168.2.3, 192.168.2.6, 192.168.2.2, 192.168.2.5, 192.168.2.4, 213.47.23.195, 192.168.1.4, 192.168.1.2, 192.168.2.1, 192.168.1.3 [CTRL] 192.168.2.2, 213.47.23.195, 192.168.2.3 [SRVC] 192.168.2.2, 213.47.23.195, 192.168.2.5, 192.168.2.4, 192.168.2.3`	`[NETS] 192.168.3.0/24, 192.168.2.0/24, 213.47.23.192/26, 192.168.1.0/24 [HOSTS] 192.168.2.3, 192.168.2.6, 192.168.2.2, 192.168.2.5, 192.168.2.4, 213.47.23.195, 192.168.1.4, 192.168.1.2, 192.168.2.1, 192.168.1.3 [CTRL] 192.168.2.4, 213.47.23.195, 192.168.2.3 [SRVC] 192.168.2.3, 213.47.23.195, 192.168.2.4, 192.168.2.5, 192.168.2.2`	`1.0`
`[NETS] 10.7.44.0/24, 10.7.45.0/24, 54.123.53.29/26, 10.7.43.0/24 [HOSTS] 10.7.44.6, 10.7.45.4, 10.7.44.4, 10.7.44.5, 10.7.44.1, 10.7.44.3, 10.7.45.2, 10.7.45.3, 54.123.53.21, 10.7.44.2 [CTRL] 54.123.53.21, 10.7.45.4, 10.7.44.4, 10.7.44.2 [SRVC] 54.123.53.21, 10.7.44.4, 10.7.44.2, 10.7.45.4`	`[NETS] 172.20.7.0/24, 172.20.6.0/24, 172.20.8.0/24, 32.8.23.1/26 [HOSTS] 172.20.8.2, 172.20.7.1, 172.20.8.4, 172.20.7.6, 172.20.7.4, 172.20.7.5, 172.20.7.3, 172.20.8.3, 32.8.23.1, 172.20.7.2 [CTRL] 172.20.7.4, 32.8.23.1, 172.20.7.3, 172.20.7.2 [SRVC] 32.8.23.1, 172.20.7.2, 172.20.7.3, 172.20.7.4`	`0.0`
`[NETS] 172.20.7.0/24, 172.20.6.0/24, 172.20.8.0/24, 32.8.23.1/26 [HOSTS] 172.20.8.2, 172.20.7.1, 172.20.8.4, 172.20.7.6, 172.20.7.4, 172.20.7.5, 172.20.7.3, 172.20.8.3, 32.8.23.1, 172.20.7.2 [CTRL] 172.20.7.4, 172.20.8.2, 32.8.23.1, 172.20.7.2 [SRVC] 172.20.7.2, 172.20.7.4, 172.20.7.3, 172.20.8.2, 32.8.23.1, 172.20.7.5, 172.20.8.4`	`[NETS] 172.19.0.0/24, 54.123.53.29/26, 172.19.1.0/24, 172.19.2.0/24 [HOSTS] 172.19.1.4, 172.19.1.1, 101.32.5.23, 172.19.1.5, 172.19.2.2, 172.19.2.3, 172.19.1.6, 172.19.1.3, 172.19.2.4, 172.19.1.2 [CTRL] 172.19.2.2, 172.19.2.4, 101.32.5.23, 172.19.1.2 [SRVC] 172.19.1.2, 101.32.5.23, 172.19.2.2, 172.19.1.4, 172.19.2.4`	`0.0`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 1e-06
weight_decay: 0.01
num_train_epochs: 1
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 2
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-06
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: 0.0
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: True
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_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: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	topology_val_cosine_ap
-1	-1	-	-	0.9463
0.0038	10	0.1292	-	-
0.0076	20	0.1221	-	-
0.0114	30	0.1199	-	-
0.0152	40	0.0346	-	-
0.0190	50	0.0498	-	-
0.0228	60	0.0443	-	-
0.0267	70	0.0529	-	-
0.0305	80	0.0571	-	-
0.0343	90	0.0624	-	-
0.0381	100	0.0877	-	-
0.0419	110	0.0396	-	-
0.0457	120	0.0323	-	-
0.0495	130	0.0454	-	-
0.0533	140	0.0493	-	-
0.0571	150	0.0284	-	-
0.0609	160	0.0494	-	-
0.0647	170	0.0424	-	-
0.0685	180	0.0314	-	-
0.0723	190	0.0488	-	-
0.0761	200	0.0236	-	-
0.0800	210	0.0253	-	-
0.0838	220	0.0368	-	-
0.0876	230	0.035	-	-
0.0914	240	0.047	-	-
0.0952	250	0.0083	-	-
0.0990	260	0.023	-	-
0.1028	270	0.0548	-	-
0.1066	280	0.0168	-	-
0.1104	290	0.0555	-	-
0.1142	300	0.0315	-	-
0.1180	310	0.0185	-	-
0.1218	320	0.042	-	-
0.1256	330	0.027	-	-
0.1294	340	0.028	-	-
0.1333	350	0.044	-	-
0.1371	360	0.0265	-	-
0.1409	370	0.0056	-	-
0.1447	380	0.0669	-	-
0.1485	390	0.0675	-	-
0.1523	400	0.0319	-	-
0.1561	410	0.0204	-	-
0.1599	420	0.0277	-	-
0.1637	430	0.0562	-	-
0.1675	440	0.0366	-	-
0.1713	450	0.0671	-	-
0.1751	460	0.0445	-	-
0.1789	470	0.037	-	-
0.1828	480	0.0185	-	-
0.1866	490	0.0198	-	-
0.1904	500	0.0692	0.1194	0.9778
0.1942	510	0.0376	-	-
0.1980	520	0.0158	-	-
0.2018	530	0.0065	-	-
0.2056	540	0.0387	-	-
0.2094	550	0.0611	-	-
0.2132	560	0.0574	-	-
0.2170	570	0.0139	-	-
0.2208	580	0.0046	-	-
0.2246	590	0.0265	-	-
0.2284	600	0.0101	-	-
0.2322	610	0.0428	-	-
0.2361	620	0.022	-	-
0.2399	630	0.049	-	-
0.2437	640	0.053	-	-
0.2475	650	0.0467	-	-
0.2513	660	0.018	-	-
0.2551	670	0.0172	-	-
0.2589	680	0.0286	-	-
0.2627	690	0.0301	-	-
0.2665	700	0.041	-	-
0.2703	710	0.0666	-	-
0.2741	720	0.0153	-	-
0.2779	730	0.0225	-	-
0.2817	740	0.0077	-	-
0.2856	750	0.0362	-	-
0.2894	760	0.0558	-	-
0.2932	770	0.0345	-	-
0.2970	780	0.049	-	-
0.3008	790	0.0147	-	-
0.3046	800	0.0402	-	-
0.3084	810	0.0292	-	-
0.3122	820	0.0217	-	-
0.3160	830	0.0201	-	-
0.3198	840	0.007	-	-
0.3236	850	0.0366	-	-
0.3274	860	0.0118	-	-
0.3312	870	0.0668	-	-
0.3350	880	0.014	-	-
0.3389	890	0.0133	-	-
0.3427	900	0.039	-	-
0.3465	910	0.0573	-	-
0.3503	920	0.023	-	-
0.3541	930	0.0019	-	-
0.3579	940	0.0327	-	-
0.3617	950	0.0347	-	-
0.3655	960	0.0229	-	-
0.3693	970	0.0064	-	-
0.3731	980	0.0298	-	-
0.3769	990	0.028	-	-
0.3807	1000	0.0261	0.1100	0.9797
0.3845	1010	0.0392	-	-
0.3883	1020	0.0497	-	-
0.3922	1030	0.0315	-	-
0.3960	1040	0.0117	-	-
0.3998	1050	0.0092	-	-
0.4036	1060	0.0299	-	-
0.4074	1070	0.0642	-	-
0.4112	1080	0.0279	-	-
0.4150	1090	0.0557	-	-
0.4188	1100	0.0057	-	-
0.4226	1110	0.0109	-	-
0.4264	1120	0.0223	-	-
0.4302	1130	0.0244	-	-
0.4340	1140	0.0043	-	-
0.4378	1150	0.013	-	-
0.4417	1160	0.0111	-	-
0.4455	1170	0.0087	-	-
0.4493	1180	0.052	-	-
0.4531	1190	0.0481	-	-
0.4569	1200	0.0418	-	-
0.4607	1210	0.078	-	-
0.4645	1220	0.024	-	-
0.4683	1230	0.002	-	-
0.4721	1240	0.0274	-	-
0.4759	1250	0.0223	-	-
0.4797	1260	0.0203	-	-
0.4835	1270	0.0412	-	-
0.4873	1280	0.0547	-	-
0.4911	1290	0.015	-	-
0.4950	1300	0.0275	-	-
0.4988	1310	0.0304	-	-
0.5026	1320	0.0181	-	-
0.5064	1330	0.015	-	-
0.5102	1340	0.0384	-	-
0.5140	1350	0.0388	-	-
0.5178	1360	0.0181	-	-
0.5216	1370	0.0089	-	-
0.5254	1380	0.0668	-	-
0.5292	1390	0.0042	-	-
0.5330	1400	0.0147	-	-
0.5368	1410	0.0125	-	-
0.5406	1420	0.0301	-	-
0.5445	1430	0.0523	-	-
0.5483	1440	0.0277	-	-
0.5521	1450	0.0295	-	-
0.5559	1460	0.076	-	-
0.5597	1470	0.0386	-	-
0.5635	1480	0.0231	-	-
0.5673	1490	0.0243	-	-
0.5711	1500	0.021	0.1171	0.9804
0.5749	1510	0.0041	-	-
0.5787	1520	0.0159	-	-
0.5825	1530	0.0159	-	-
0.5863	1540	0.002	-	-
0.5901	1550	0.0022	-	-
0.5939	1560	0.0044	-	-
0.5978	1570	0.034	-	-
0.6016	1580	0.0151	-	-
0.6054	1590	0.0123	-	-
0.6092	1600	0.0005	-	-
0.6130	1610	0.0342	-	-
0.6168	1620	0.0086	-	-
0.6206	1630	0.0053	-	-
0.6244	1640	0.0013	-	-
0.6282	1650	0.0051	-	-
0.6320	1660	0.0269	-	-
0.6358	1670	0.0025	-	-
0.6396	1680	0.0207	-	-
0.6434	1690	0.0295	-	-
0.6472	1700	0.0085	-	-
0.6511	1710	0.005	-	-
0.6549	1720	0.0193	-	-
0.6587	1730	0.0392	-	-
0.6625	1740	0.0159	-	-
0.6663	1750	0.0293	-	-
0.6701	1760	0.0017	-	-
0.6739	1770	0.0004	-	-
0.6777	1780	0.0054	-	-
0.6815	1790	0.0013	-	-
0.6853	1800	0.025	-	-
0.6891	1810	0.0115	-	-
0.6929	1820	0.0007	-	-
0.6967	1830	0.025	-	-
0.7006	1840	0.028	-	-
0.7044	1850	0.0101	-	-
0.7082	1860	0.0393	-	-
0.7120	1870	0.0372	-	-
0.7158	1880	0.0068	-	-
0.7196	1890	0.0473	-	-
0.7234	1900	0.0234	-	-
0.7272	1910	0.0142	-	-
0.7310	1920	0.0253	-	-
0.7348	1930	0.0014	-	-
0.7386	1940	0.0826	-	-
0.7424	1950	0.0252	-	-
0.7462	1960	0.0672	-	-
0.7500	1970	0.0018	-	-
0.7539	1980	0.0174	-	-
0.7577	1990	0.0643	-	-
0.7615	2000	0.0003	0.1032	0.9801
0.7653	2010	0.0483	-	-
0.7691	2020	0.0262	-	-
0.7729	2030	0.0283	-	-
0.7767	2040	0.0214	-	-
0.7805	2050	0.0107	-	-
0.7843	2060	0.0156	-	-
0.7881	2070	0.0006	-	-
0.7919	2080	0.0005	-	-
0.7957	2090	0.0313	-	-
0.7995	2100	0.0234	-	-
0.8034	2110	0.0195	-	-
0.8072	2120	0.0235	-	-
0.8110	2130	0.0066	-	-
0.8148	2140	0.0021	-	-
0.8186	2150	0.0021	-	-
0.8224	2160	0.0014	-	-
0.8262	2170	0.0106	-	-
0.8300	2180	0.0019	-	-
0.8338	2190	0.022	-	-
0.8376	2200	0.0072	-	-
0.8414	2210	0.0364	-	-
0.8452	2220	0.0103	-	-
0.8490	2230	0.0171	-	-
0.8528	2240	0.0153	-	-
0.8567	2250	0.0241	-	-
0.8605	2260	0.021	-	-
0.8643	2270	0.0007	-	-
0.8681	2280	0.0007	-	-
0.8719	2290	0.0224	-	-
0.8757	2300	0.034	-	-
0.8795	2310	0.0392	-	-
0.8833	2320	0.0375	-	-
0.8871	2330	0.0196	-	-
0.8909	2340	0.0253	-	-
0.8947	2350	0.0191	-	-
0.8985	2360	0.0379	-	-
0.9023	2370	0.0172	-	-
0.9061	2380	0.0407	-	-
0.9100	2390	0.0321	-	-
0.9138	2400	0.0375	-	-
0.9176	2410	0.0084	-	-
0.9214	2420	0.0243	-	-
0.9252	2430	0.0302	-	-
0.9290	2440	0.0245	-	-
0.9328	2450	0.0243	-	-
0.9366	2460	0.0214	-	-
0.9404	2470	0.0147	-	-
0.9442	2480	0.0051	-	-
0.9480	2490	0.0163	-	-
0.9518	2500	0.008	0.1013	0.9797
0.9556	2510	0.0218	-	-
0.9595	2520	0.0079	-	-
0.9633	2530	0.0071	-	-
0.9671	2540	0.0456	-	-
0.9709	2550	0.0016	-	-
0.9747	2560	0.06	-	-
0.9785	2570	0.0054	-	-
0.9823	2580	0.0384	-	-
0.9861	2590	0.0225	-	-
0.9899	2600	0.0354	-	-
0.9937	2610	0.0347	-	-
0.9975	2620	0.0026	-	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 5.2.0
Transformers: 4.57.6
PyTorch: 2.10.0+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",
}

Downloads last month: 2

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for stratosphere/Qwen3-Embedding-0.6B-netsecgame-finetuned-pairs2

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-Embedding-0.6B

Finetuned

(169)

this model

Dataset used to train stratosphere/Qwen3-Embedding-0.6B-netsecgame-finetuned-pairs2

Paper for stratosphere/Qwen3-Embedding-0.6B-netsecgame-finetuned-pairs2

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 12

Evaluation results

Cosine Accuracy on topology val
self-reported

0.976
Cosine Accuracy Threshold on topology val
self-reported

0.767
Cosine F1 on topology val
self-reported

0.976
Cosine F1 Threshold on topology val
self-reported

0.767
Cosine Precision on topology val
self-reported

0.976
Cosine Recall on topology val
self-reported

0.975
Cosine Ap on topology val
self-reported

0.980
Cosine Mcc on topology val
self-reported

0.952