This is a sentence-transformers model finetuned from Shuu12121/CodeModernBERT-Owl-4.1. 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: Shuu12121/CodeModernBERT-Owl-4.1
Maximum Sequence Length: 1024 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': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, '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': False, 'include_prompt': True})
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Register the router instance.\n\n@return void',
    "protected function registerRouter()\n\t{\n\t\t$this->app['router'] = $this->app->share(function($app)\n\t\t{\n\t\t\treturn new Router($app['events'], $app);\n\t\t});\n\t}",
    'func (d *Driver) DiffSize(id string, idMappings *idtools.IDMappings, parent string, parentMappings *idtools.IDMappings, mountLabel string) (size int64, err error) {\n\tif d.useNaiveDiff() || !d.isParent(id, parent) {\n\t\treturn d.naiveDiff.DiffSize(id, idMappings, parent, parentMappings, mountLabel)\n\t}\n\treturn directory.Size(d.getDiffPath(id))\n}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 6,960,000 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 50.31 tokens
max: 1024 tokens
min: 28 tokens
mean: 164.73 tokens
max: 1024 tokens
min: 1.0
mean: 1.0
max: 1.0

Samples:

sentence_0	sentence_1	label
`// GetNodeID returns the NodeID field if it's non-nil, zero value otherwise.`	`func (a *App) GetNodeID() string {[object Object] if a == nil`
`// SignVote signs a canonical representation of the vote, along with the[object Object]// chainID. Implements PrivValidator.`	`func (pv FilePV) SignVote(chainID string, vote types.Vote) error {[object Object] if err := pv.signVote(chainID, vote); err != nil {[object Object] return fmt.Errorf("error signing vote: %v", err)[object Object] }[object Object] return nil[object Object]}`	`1.0`
`//GetQyAccessToken 获取access_token`	func (ctx *Context) GetQyAccessToken() (accessToken string, err error) {[object Object] ctx.accessTokenLock.Lock()[object Object] defer ctx.accessTokenLock.Unlock()[object Object][object Object] accessTokenCacheKey := fmt.Sprintf("qy_access_token_%s", ctx.AppID)[object Object] val := ctx.Cache.Get(accessTokenCacheKey)[object Object] if val != nil {[object Object] accessToken = val.(string)[object Object] return[object Object] }[object Object][object Object] //从微信服务器获取[object Object] var resQyAccessToken ResQyAccessToken[object Object] resQyAccessToken, err = ctx.GetQyAccessTokenFromServer()[object Object] if err != nil {[object Object] return[object Object] }[object Object][object Object] accessToken = resQyAccessToken.AccessToken[object Object] return[object Object]}	`1.0`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 250
per_device_eval_batch_size: 250
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 250
per_device_eval_batch_size: 250
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: 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: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
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
use_ipex: 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: False
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}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
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: False
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: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0180	500	0.9746
0.0359	1000	0.1636
0.0539	1500	0.1502
0.0718	2000	0.1374
0.0898	2500	0.1314
0.1078	3000	0.1241
0.1257	3500	0.1152
0.1437	4000	0.1146
0.1616	4500	0.1065
0.1796	5000	0.1014
0.1976	5500	0.0983
0.2155	6000	0.0987
0.2335	6500	0.0917
0.2514	7000	0.0912
0.2694	7500	0.0896
0.2874	8000	0.086
0.3053	8500	0.0811
0.3233	9000	0.0813
0.3412	9500	0.082
0.3592	10000	0.0759
0.3772	10500	0.0753
0.3951	11000	0.0722
0.4131	11500	0.0707
0.4310	12000	0.0699
0.4490	12500	0.0698
0.4670	13000	0.0679
0.4849	13500	0.0653
0.5029	14000	0.0641
0.5208	14500	0.063
0.5388	15000	0.0621
0.5568	15500	0.061
0.5747	16000	0.0581
0.5927	16500	0.0555
0.6106	17000	0.0552
0.6286	17500	0.0551
0.6466	18000	0.0533
0.6645	18500	0.0521
0.6825	19000	0.051
0.7004	19500	0.0509
0.7184	20000	0.0499
0.7364	20500	0.0468
0.7543	21000	0.0484
0.7723	21500	0.0466
0.7902	22000	0.0446
0.8082	22500	0.0453
0.8261	23000	0.0442
0.8441	23500	0.0424
0.8621	24000	0.0434
0.8800	24500	0.0416
0.8980	25000	0.0406
0.9159	25500	0.0404
0.9339	26000	0.0398
0.9519	26500	0.0406
0.9698	27000	0.0387
0.9878	27500	0.0386
1.0057	28000	0.0311
1.0237	28500	0.0193
1.0417	29000	0.0197
1.0596	29500	0.0186
1.0776	30000	0.0192
1.0955	30500	0.0194
1.1135	31000	0.0196
1.1315	31500	0.0198
1.1494	32000	0.0203
1.1674	32500	0.02
1.1853	33000	0.0184
1.2033	33500	0.0181
1.2213	34000	0.0195
1.2392	34500	0.0186
1.2572	35000	0.0184
1.2751	35500	0.0184
1.2931	36000	0.0194
1.3111	36500	0.0191
1.3290	37000	0.0183
1.3470	37500	0.0179
1.3649	38000	0.0179
1.3829	38500	0.0178
1.4009	39000	0.018
1.4188	39500	0.0182
1.4368	40000	0.0188
1.4547	40500	0.0172
1.4727	41000	0.0169
1.4907	41500	0.0173
1.5086	42000	0.0166
1.5266	42500	0.0157
1.5445	43000	0.0168
1.5625	43500	0.0158
1.5805	44000	0.016
1.5984	44500	0.0166
1.6164	45000	0.0168
1.6343	45500	0.0162
1.6523	46000	0.0153
1.6703	46500	0.0149
1.6882	47000	0.0158
1.7062	47500	0.0152
1.7241	48000	0.0147
1.7421	48500	0.0146
1.7601	49000	0.0145
1.7780	49500	0.0148
1.7960	50000	0.015
1.8139	50500	0.0145
1.8319	51000	0.0142
1.8499	51500	0.014
1.8678	52000	0.0139
1.8858	52500	0.0133
1.9037	53000	0.0135
1.9217	53500	0.0131
1.9397	54000	0.0134
1.9576	54500	0.013
1.9756	55000	0.0132
1.9935	55500	0.0122
2.0115	56000	0.0089
2.0295	56500	0.0061
2.0474	57000	0.0061
2.0654	57500	0.006
2.0833	58000	0.0062
2.1013	58500	0.0058
2.1193	59000	0.0059
2.1372	59500	0.0059
2.1552	60000	0.0059
2.1731	60500	0.0058
2.1911	61000	0.0059
2.2091	61500	0.0058
2.2270	62000	0.0059
2.2450	62500	0.0058
2.2629	63000	0.0057
2.2809	63500	0.0055
2.2989	64000	0.0056
2.3168	64500	0.0056
2.3348	65000	0.0056
2.3527	65500	0.0057
2.3707	66000	0.0055
2.3886	66500	0.0056
2.4066	67000	0.0054
2.4246	67500	0.0055
2.4425	68000	0.0052
2.4605	68500	0.0053
2.4784	69000	0.0052
2.4964	69500	0.0053
2.5144	70000	0.0052
2.5323	70500	0.0052
2.5503	71000	0.0051
2.5682	71500	0.0049
2.5862	72000	0.005
2.6042	72500	0.0047
2.6221	73000	0.0048
2.6401	73500	0.0047
2.6580	74000	0.0048
2.6760	74500	0.0048
2.6940	75000	0.0048
2.7119	75500	0.0047
2.7299	76000	0.0047
2.7478	76500	0.0046
2.7658	77000	0.0046
2.7838	77500	0.0044
2.8017	78000	0.0046
2.8197	78500	0.0047
2.8376	79000	0.0045
2.8556	79500	0.0043
2.8736	80000	0.0045
2.8915	80500	0.0044
2.9095	81000	0.0045
2.9274	81500	0.0045
2.9454	82000	0.0043
2.9634	82500	0.0042
2.9813	83000	0.0041
2.9993	83500	0.0044

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.53.0
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

bibtex

@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