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, 'architecture': '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 = [
    '// reBuild partially rebuilds a site given the filesystem events.\n// It returns whetever the content source was changed.\n// TODO(bep) clean up/rewrite this method.',
    'func (s *Site) processPartial(events []fsnotify.Event) (whatChanged, error) {\n\n\tevents = s.filterFileEvents(events)\n\tevents = s.translateFileEvents(events)\n\n\ts.Log.DEBUG.Printf("Rebuild for events %q", events)\n\n\th := s.h\n\n\t// First we need to determine what changed\n\n\tvar (\n\t\tsourceChanged       = []fsnotify.Event{}\n\t\tsourceReallyChanged = []fsnotify.Event{}\n\t\tcontentFilesChanged []string\n\t\ttmplChanged         = []fsnotify.Event{}\n\t\tdataChanged         = []fsnotify.Event{}\n\t\ti18nChanged         = []fsnotify.Event{}\n\t\tshortcodesChanged   = make(map[string]bool)\n\t\tsourceFilesChanged  = make(map[string]bool)\n\n\t\t// prevent spamming the log on changes\n\t\tlogger = helpers.NewDistinctFeedbackLogger()\n\t)\n\n\tcachePartitions := make([]string, len(events))\n\n\tfor i, ev := range events {\n\t\tcachePartitions[i] = resources.ResourceKeyPartition(ev.Name)\n\n\t\tif s.isContentDirEvent(ev) {\n\t\t\tlogger.Println("Source changed", ev)\n\t\t\tsourceChanged = append(sourceChanged, ev)\n\t\t}\n\t\tif s.isLayoutDirEvent(ev) {\n\t\t\tlogger.Println("Template changed", ev)\n\t\t\ttmplChanged = append(tmplChanged, ev)\n\n\t\t\tif strings.Contains(ev.Name, "shortcodes") {\n\t\t\t\tshortcode := filepath.Base(ev.Name)\n\t\t\t\tshortcode = strings.TrimSuffix(shortcode, filepath.Ext(shortcode))\n\t\t\t\tshortcodesChanged[shortcode] = true\n\t\t\t}\n\t\t}\n\t\tif s.isDataDirEvent(ev) {\n\t\t\tlogger.Println("Data changed", ev)\n\t\t\tdataChanged = append(dataChanged, ev)\n\t\t}\n\t\tif s.isI18nEvent(ev) {\n\t\t\tlogger.Println("i18n changed", ev)\n\t\t\ti18nChanged = append(dataChanged, ev)\n\t\t}\n\t}\n\n\t// These in memory resource caches will be rebuilt on demand.\n\tfor _, s := range s.h.Sites {\n\t\ts.ResourceSpec.ResourceCache.DeletePartitions(cachePartitions...)\n\t}\n\n\tif len(tmplChanged) > 0 || len(i18nChanged) > 0 {\n\t\tsites := s.h.Sites\n\t\tfirst := sites[0]\n\n\t\ts.h.init.Reset()\n\n\t\t// TOD(bep) globals clean\n\t\tif err := first.Deps.LoadResources(); err != nil {\n\t\t\treturn whatChanged{}, err\n\t\t}\n\n\t\tfor i := 1; i < len(sites); i++ {\n\t\t\tsite := sites[i]\n\t\t\tvar err error\n\t\t\tdepsCfg := deps.DepsCfg{\n\t\t\t\tLanguage:      site.language,\n\t\t\t\tMediaTypes:    site.mediaTypesConfig,\n\t\t\t\tOutputFormats: site.outputFormatsConfig,\n\t\t\t}\n\t\t\tsite.Deps, err = first.Deps.ForLanguage(depsCfg, func(d *deps.Deps) error {\n\t\t\t\td.Site = &site.Info\n\t\t\t\treturn nil\n\t\t\t})\n\t\t\tif err != nil {\n\t\t\t\treturn whatChanged{}, err\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(dataChanged) > 0 {\n\t\ts.h.init.data.Reset()\n\t}\n\n\tfor _, ev := range sourceChanged {\n\t\tremoved := false\n\n\t\tif ev.Op&fsnotify.Remove == fsnotify.Remove {\n\t\t\tremoved = true\n\t\t}\n\n\t\t// Some editors (Vim) sometimes issue only a Rename operation when writing an existing file\n\t\t// Sometimes a rename operation means that file has been renamed other times it means\n\t\t// it\'s been updated\n\t\tif ev.Op&fsnotify.Rename == fsnotify.Rename {\n\t\t\t// If the file is still on disk, it\'s only been updated, if it\'s not, it\'s been moved\n\t\t\tif ex, err := afero.Exists(s.Fs.Source, ev.Name); !ex || err != nil {\n\t\t\t\tremoved = true\n\t\t\t}\n\t\t}\n\t\tif removed && IsContentFile(ev.Name) {\n\t\t\th.removePageByFilename(ev.Name)\n\t\t}\n\n\t\tsourceReallyChanged = append(sourceReallyChanged, ev)\n\t\tsourceFilesChanged[ev.Name] = true\n\t}\n\n\tfor shortcode := range shortcodesChanged {\n\t\t// There are certain scenarios that, when a shortcode changes,\n\t\t// it isn\'t sufficient to just rerender the already parsed shortcode.\n\t\t// One example is if the user adds a new shortcode to the content file first,\n\t\t// and then creates the shortcode on the file system.\n\t\t// To handle these scenarios, we must do a full reprocessing of the\n\t\t// pages that keeps a reference to the changed shortcode.\n\t\tpagesWithShortcode := h.findPagesByShortcode(shortcode)\n\t\tfor _, p := range pagesWithShortcode {\n\t\t\tcontentFilesChanged = append(contentFilesChanged, p.File().Filename())\n\t\t}\n\t}\n\n\tif len(sourceReallyChanged) > 0 || len(contentFilesChanged) > 0 {\n\t\tvar filenamesChanged []string\n\t\tfor _, e := range sourceReallyChanged {\n\t\t\tfilenamesChanged = append(filenamesChanged, e.Name)\n\t\t}\n\t\tif len(contentFilesChanged) > 0 {\n\t\t\tfilenamesChanged = append(filenamesChanged, contentFilesChanged...)\n\t\t}\n\n\t\tfilenamesChanged = helpers.UniqueStrings(filenamesChanged)\n\n\t\tif err := s.readAndProcessContent(filenamesChanged...); err != nil {\n\t\t\treturn whatChanged{}, err\n\t\t}\n\n\t}\n\n\tchanged := whatChanged{\n\t\tsource: len(sourceChanged) > 0 || len(shortcodesChanged) > 0,\n\t\tother:  len(tmplChanged) > 0 || len(i18nChanged) > 0 || len(dataChanged) > 0,\n\t\tfiles:  sourceFilesChanged,\n\t}\n\n\treturn changed, nil\n\n}',
    'func WebPageImageResolver(doc *goquery.Document) ([]candidate, int) {\n\timgs := doc.Find("img")\n\n\tvar candidates []candidate\n\tsignificantSurface := 320 * 200\n\tsignificantSurfaceCount := 0\n\tsrc := ""\n\timgs.Each(func(i int, tag *goquery.Selection) {\n\t\tvar surface int\n\t\tsrc = getImageSrc(tag)\n\t\tif src == "" {\n\t\t\treturn\n\t\t}\n\n\t\twidth, _ := tag.Attr("width")\n\t\theight, _ := tag.Attr("height")\n\t\tif width != "" {\n\t\t\tw, _ := strconv.Atoi(width)\n\t\t\tif height != "" {\n\t\t\t\th, _ := strconv.Atoi(height)\n\t\t\t\tsurface = w * h\n\t\t\t} else {\n\t\t\t\tsurface = w\n\t\t\t}\n\t\t} else {\n\t\t\tif height != "" {\n\t\t\t\tsurface, _ = strconv.Atoi(height)\n\t\t\t} else {\n\t\t\t\tsurface = 0\n\t\t\t}\n\t\t}\n\n\t\tif surface > significantSurface {\n\t\t\tsignificantSurfaceCount++\n\t\t}\n\n\t\ttagscore := score(tag)\n\t\tif tagscore >= 0 {\n\t\t\tc := candidate{\n\t\t\t\turl:     src,\n\t\t\t\tsurface: surface,\n\t\t\t\tscore:   score(tag),\n\t\t\t}\n\t\t\tcandidates = append(candidates, c)\n\t\t}\n\t})\n\n\tif len(candidates) == 0 {\n\t\treturn nil, 0\n\t}\n\n\treturn candidates, significantSurfaceCount\n\n}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6432, 0.3254],
#         [0.6432, 1.0000, 0.3839],
#         [0.3254, 0.3839, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 58,800 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: 54.95 tokens
max: 1001 tokens
min: 28 tokens
mean: 177.54 tokens
max: 1024 tokens
min: 1.0
mean: 1.0
max: 1.0

Samples:

sentence_0	sentence_1	label
`// CASNext is a non-callback, loop-based version of CAS method.[object Object]//[object Object]// Usage is like this:[object Object]//[object Object]// var state memcached.CASState[object Object]// for client.CASNext(vb, key, exp, &state) {[object Object]// state.Value = some_mutation(state.Value)[object Object]// }[object Object]// if state.Err != nil { ... }`	func (c Client) CASNext(vb uint16, k string, exp int, state CASState) bool {[object Object] if state.initialized {[object Object] if !state.Exists {[object Object] // Adding a new key:[object Object] if state.Value == nil {[object Object] state.Cas = 0[object Object] return false // no-op (delete of non-existent value)[object Object] }[object Object] state.resp, state.Err = c.Add(vb, k, 0, exp, state.Value)[object Object] } else {[object Object] // Updating / deleting a key:[object Object] req := &gomemcached.MCRequest{[object Object] Opcode: gomemcached.DELETE,[object Object] VBucket: vb,[object Object] Key: []byte(k),[object Object] Cas: state.Cas}[object Object] if state.Value != nil {[object Object] req.Opcode = gomemcached.SET[object Object] req.Opaque = 0[object Object] req.Extras = []byte{0, 0, 0, 0, 0, 0, 0, 0}[object Object] req.Body = state.Value[object Object][object Object] flags := 0[object Object] exp := 0 // ??? Should we use initialexp here instead?[object Object] binary.BigEndian.PutUint64(req.Extras, uint64(flags)<<32	uint64(exp)) } state.resp, state.Err = c.Send(req) } // If the response status is KEY_EEXISTS or NOT_STORED there's a conflict and we'll need to // get the new value (below). Otherwise, we're done (either ...
`// RestoreResourcePools restores a bulk of resource pools, usually from a backup.`	func (f *Facade) RestoreResourcePools(ctx datastore.Context, pools []pool.ResourcePool) error {[object Object] defer ctx.Metrics().Stop(ctx.Metrics().Start("Facade.RestoreResourcePools"))[object Object] // Do not DFSLock here, ControlPlaneDao does that[object Object] var alog audit.Logger[object Object] for _, pool := range pools {[object Object] alog = f.auditLogger.Message(ctx, "Adding ResourcePool").Action(audit.Add).Entity(&pool)[object Object] pool.DatabaseVersion = 0[object Object] if err := f.addResourcePool(ctx, &pool); err != nil {[object Object] if err == ErrPoolExists {[object Object] if err := f.updateResourcePool(ctx, &pool); err != nil {[object Object] glog.Errorf("Could not restore resource pool %s via update: %s", pool.ID, err)[object Object] return alog.Error(err)[object Object] }[object Object] } else {[object Object] glog.Errorf("Could not restore resource pool %s via add: %s", pool.ID, err)[object Object] return alog.Error(err)[object Object] }[object Object] }[object Object] alog.Succeeded()[object Object] }[object Object] return nil[object Object]}	`1.0`
`// run starts a goroutine to handle client connects and broadcast events.`	func (s *Streamer) run() {[object Object] go func() {[object Object] for {[object Object] select {[object Object] case cl := <-s.connecting:[object Object] s.clients[cl] = true[object Object][object Object] case cl := <-s.disconnecting:[object Object] delete(s.clients, cl)[object Object][object Object] case event := <-s.event:[object Object] for cl := range s.clients {[object Object] // TODO: non-blocking broadcast[object Object] //select {[object Object] //case cl <- event: // Try to send event to client[object Object] //default:[object Object] // fmt.Println("Channel full. Discarding value")[object Object] //}[object Object] cl <- event[object Object] }[object Object] }[object Object] }[object Object] }()[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: 40
per_device_eval_batch_size: 40
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: 40
per_device_eval_batch_size: 40
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
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.3401	500	0.3327
0.6803	1000	0.1644
1.0204	1500	0.127
1.3605	2000	0.0227
1.7007	2500	0.0221
2.0408	3000	0.0195
2.3810	3500	0.0056
2.7211	4000	0.0046

Framework Versions

Python: 3.10.12
Sentence Transformers: 5.0.0
Transformers: 4.53.1
PyTorch: 2.7.0+cu128
Accelerate: 1.7.0
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