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SOCO-Java-PLBART-ST

buelfhood / SOCO-Java-PLBART-ST

Run on huggingface.co
huggingface.co
Total runs: 28
24-hour runs: 1
7-day runs: 3
30-day runs: 7
Model's Last Updated: June 12 2025
sentence-similarity

Introduction of SOCO-Java-PLBART-ST

Model Details of SOCO-Java-PLBART-ST

SentenceTransformer based on uclanlp/plbart-java-cs

This is a sentence-transformers model finetuned from uclanlp/plbart-java-cs . 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: uclanlp/plbart-java-cs
  • 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: PLBartModel 
  (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-PLBART-ST")
# Run inference
sentences = [
    '\nimport java.util.*;\n\npublic class WatchDog\n{\n   private Timer t;\n\n   public WatchDog()\n   {\n     t  = new Timer();\n      TimerTask task = new TimerTask()\n      {\n         public void run()\n\t {\n\t    Dog doggy = new Dog();\n\t }\n      };\n      \n      t.schedule(task, 0, 86400000);\n   }\n   public static void main( String[] args)\n   {\n      WatchDog wd = new WatchDog();\n   }\n}\n',
    'import\tjava.io.*;\n\nclass WatchDog {\n    public static void main(String args[]) {\n    \n\t    if (args.length<1)\n\t {\n       System.out.println("Correct Format Filename  email address <[email protected].> of the recordkeeper"); \n        System.exit(1);\t\n\t }\n\n\twhile (true)\n\t\t{\n\t\t\n\t\t\n  FileInputStream stream=null;\n  DataInputStream word=null;\n  String input="   "; \n\n\n\ttry {\n\n\n       String ls_str;\n    \n    \t   \n\t  Process ls_proc = Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students");\n   \t\ttry {\n\t\tThread.sleep(2000);\n\t\t}catch (Exception e) {\n    System.err.println("Caught ThreadException: " +e.getMessage());\n\t  }\n\n\t\tString[] cmd = {"//sh","-c", "diff Index2.html index.html >report.txt "};\n\n\t   ls_proc = Runtime.getRuntime().exec(cmd);\n\t\t          \n\t\t\t\n\t\t\ttry {\n\t\tThread.sleep(2000);\n\t\t}catch (Exception e) {\n    System.err.println("Caught ThreadException: " +e.getMessage());\n\t  }\n\t\t\n\t\t\n\t\t\n\t\tif (ls_proc.exitValue()==2) \n\t\t{\n\t\t  \t   System.out.println("The file was checked for first time,  email sent");\n\n           Process move = Runtime.getRuntime().exec("mv index.html Index2.html");\n\t\t   \n\n\t\t}\n\t\telse\n\t\t{\n\n\t\t\t\tstream = new FileInputStream ("report.txt"); \n\t\t\t\tword =new DataInputStream(stream);\n\n\n\t\t\t\tif (word.available() !=0)\n\t\t\t\t{\n\n\t\t\t\t\ttry\n\t\t\t\t\t{\n\n\t\t\t\t\tString[] cmd1 = {"//sh","-c","diff Index2.html index.html | mail "+args[0]};\n\t\t\t\t\t Process proc = Runtime.getRuntime().exec(cmd1);\n\t\t\t\t\t Thread.sleep(2000);\n\t\t\t\t\tProcess move = Runtime.getRuntime().exec("mv index.html Index2.html");\n\t\t\t\t\tThread.sleep(2000);\n\t\t\t\t\tSystem.out.println("Difference Found  , Email Sent");\n\n\t\t\t\t\t}\n\t\t\t\t\tcatch (Exception e1) {\n\t\t\t\t\t\t\tSystem.err.println(e1);\n\t\t\t\t\t\t\tSystem.exit(1);\n\t\t\t\t\t\t\n\t\t\t\t\t  \n\t\t\t\t\t\t}\n\t\t\t\t\t \n\t \n\t \n\t\t\t\t }\n\t\t\t\t else\n\t\t\t\t\t{\n\t\t\t\t\t\t   System.out.println(" Differnce Detected");\n\n\n\t\t\t\t\t}\n\t\t}\n\t}\n\t\n\n\t catch (IOException e1) {\n\t    System.err.println(e1);\n\t    System.exit(1);\n\t\n  \n\t}\ntry\n{\nword.close();\nstream.close(); \n\t\n}\n \ncatch (IOException e)\n{ \nSystem.out.println("Error in closing input file:\\n" + e.toString()); \n} \n       \t\ntry {\nThread.sleep(20000);  \n    }\ncatch (Exception e) \n\t{\nSystem.err.println("Caught ThreadException: " +e.getMessage());\n\t}\n\t\t\n\n        }   \n\n\t} \n\t\n   }',
    '\n\n\n\nimport java.io.InputStream;\nimport java.util.Properties;\n\nimport javax.naming.Context;\nimport javax.naming.InitialContext;\nimport javax.rmi.PortableRemoteObject;\nimport javax.sql.DataSource;\n\n\n\n\npublic class BruteForcePropertyHelper {\n\n\tprivate static Properties bruteForceProps;\n\n\n\n\tpublic BruteForcePropertyHelper() {\n\t}\n\n\n\t\n\n\tpublic static String getProperty(String pKey){\n\t\ttry{\n\t\t\tinitProps();\n\t\t}\n\t\tcatch(Exception e){\n\t\t\tSystem.err.println("Error init\'ing the burteforce Props");\n\t\t\te.printStackTrace();\n\t\t}\n\t\treturn bruteForceProps.getProperty(pKey);\n\t}\n\n\n\tprivate static void initProps() throws Exception{\n\t\tif(bruteForceProps == null){\n\t\t\tbruteForceProps = new Properties();\n\n\t\t\tInputStream fis =\n\t\t\t\tBruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");\n\t\t\tbruteForceProps.load(fis);\n\t\t}\n\t}\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: 35 tokens
    • mean: 413.67 tokens
    • max: 512 tokens
    • min: 35 tokens
    • mean: 432.24 tokens
    • max: 512 tokens
    • 0: ~99.70%
    • 1: ~0.30%
  • Samples:
    sentence_0 sentence_1 label


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


    public class Dictionary {

    public static void main(String[] args) {

    String attackURL = " http://sec-crack.cs.rmit.edu./SEC/2/index.php ";
    String userID = "";
    String Password="";
    String userPassword="";

    File inputFile = new File("/usr/share/lib/dict/words");
    FileReader fin = null;
    BufferedReader bf = null;

    try {
    startmillisecond = System.currentTimeMillis();
    URL url = new URL(attackURL);
    fin = new FileReader(inputFile);
    bf = new BufferedReader(fin);
    int count = 0;
    while ((Password = bf.readLine()) !=null) {
    if (Password.length() < 4) {
    count++;
    try {
    userPassword = userID + ":" + Password;
    System.out.println("User & Password :" + userPassword);
    String encoding = Base64Converter.encode (userPassword.getBytes());

    URLConnection uc = url.openConnection();
    uc.setRequestProperty ("Authorization", " " + enc...

    public class Base64 {

    final static String baseTable = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";


    public static String encode(byte[] bytes) {

    String tmp = "";
    int i = 0;
    byte pos;

    for(i=0; i < (bytes.length - bytes.length%3); i+=3) {

    pos = (byte) ((bytes[i] >> 2) & 63);
    tmp = tmp + baseTable.charAt(pos);

    pos = (byte) (((bytes[i] & 3) << 4) + ((bytes[i+1] >> 4) & 15));
    tmp = tmp + baseTable.charAt( pos );

    pos = (byte) (((bytes[i+1] & 15) << 2) + ((bytes[i+2] >> 6) & 3));
    tmp = tmp + baseTable.charAt(pos);

    pos = (byte) (((bytes[i+2]) & 63));
    tmp = tmp + baseTable.charAt(pos);



    if(((i+2)%56) == 0) {
    tmp = tmp + "\r\n";
    }
    }

    if(bytes.length % 3 != 0) {

    if(bytes.length % 3 == 2) {

    pos = (byte) ((bytes[i] >> 2) & 63);
    tmp = tmp + baseTable.charAt(pos);

    pos = (byte) (((bytes[i] & 3) << 4) + ((bytes[i+1] >> 4) & 15));
    tmp = tmp + baseTable.charAt( pos );

    ...     		0

    import java.io.*;

    public class Dictionary
    {

    public static void main(String args[])throws Exception
    {
    String s = null;
    String pass="";
    int at=0;
    String strLine="";
    int i=0;

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

    start =System.currentTimeMillis();
    try
    {
    while((pass=strLine = in.readLine()) != null)
    {

    if(pass.length()==3)
    {

    System.out.println(pass);
    at++;

    Process p = Runtime.getRuntime().exec("wget --http-user= --http-passwd="+pass+" http://sec-crack.cs.rmit.edu./SEC/2/index.php ");
    p.waitFor();
    i = p.exitValue();

    if(i==0)
    {
    finish=System.currentTimeMillis();
    ...     		import java.util. ;
    import java.io.     ;



    public class WatchDog {

    public WatchDog() {

    }
    public static void main(String args[]) {
    DataInputStream newin;

    try{


    System.out.println("Downloading first copy");
    Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O oldfile.html");
    String[] cmdDiff = {"//sh", "-c", "diff oldfile.html newfile.html > Diff.txt"};
    String[] cmdMail = {"//sh", "-c", "mailx -s "Diffrence" "@cs.rmit.edu." < Diff.txt"};
    while(true){
    Thread.sleep(24 60 60*1000);
    System.out.println("Downloading new copy");
    Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O newfile.html");
    Thread.sleep(2000);
    Runtime.getRuntime().exec(cmdDiff);
    Thread.sleep(2000);
    newin = new DataInputStream( new FileInputStream( "Diff.txt"));
    if (newin.readLine() != null){
    System.out.println("Sending Mail");
    ...     		0


    import java.Thread;
    import java.io. ;
    import java.net.     ;

    public class BruteForce extends Thread {
    final char[] CHARACTERS = {'A','a','E','e','I','i','O','o','U','u','R','r','N','n','S','s','T','t','L','l','B','b','C','c','D','d','F','f','G','g','H','h','J','j','K','k','M','m','P','p','V','v','W','w','X','x','Z','z','Q','q','Y','y'};
    final static int SUCCESS=1,
    FAILED=0,
    UNKNOWN=-1;
    private static String host,
    path,
    user;
    private Socket target;
    private InputStream input;
    private OutputStream output;
    private byte[] data;
    private int threads,
    threadno,
    response;
    public static boolean solved = false;
    BruteForce parent;


    public BruteForce(String host, String path, String user, int threads, int threadno, BruteForce parent)
    {
    super();
    this.parent = parent;
    this.host = host;
    this.path = path;
    this.user ...







    import java.io. ;
    import java.net.     ;
    import javax.swing.Timer;
    import java.awt.event.*;
    import javax.swing.JOptionPane;

    public class WatchDog
    {
    private static Process pro = null;
    private static Runtime run = Runtime.getRuntime();

    public static void main(String[] args)
    {
    String cmd = null;
    try
    {
    cmd = new String("wget -O original.txt http://www.cs.rmit.edu./students/ ");

    pro = run.exec(cmd);
    System.out.println(cmd);
    }
    catch (IOException e)
    {
    }

    class Watch implements ActionListener
    {
    BufferedReader in = null;
    String str = null;
    Socket socket;
    public void actionPerformed (ActionEvent event)
    {

    try
    {
    System.out.println("in Watch!");
    String cmd = new String();
    int ERROR = 1;
    cmd = new String("wget -O new.txt http://www.cs.rmit.edu./students/ ");


    System.out.println(cmd);
    cmd = new String("diff original.txt new.txt");
    pro = run.exec(cmd);
    System.out.println(cmd);
    in = new Buf...     		0
  • Loss: BatchAllTripletLoss
Training Hyperparameters
Non-Default Hyperparameters
  • per_device_train_batch_size : 32
  • per_device_eval_batch_size : 32
  • 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 : 32
  • per_device_eval_batch_size : 32
  • 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
Training Logs
Epoch Step Training Loss
0.4785 500 0.3437
0.9569 1000 0.3653
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}
}

Runs of buelfhood SOCO-Java-PLBART-ST on huggingface.co

28
Total runs
1
24-hour runs
2
3-day runs
3
7-day runs
7
30-day runs

More Information About SOCO-Java-PLBART-ST huggingface.co Model

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SOCO-Java-PLBART-ST huggingface.co is an AI model on huggingface.co that provides SOCO-Java-PLBART-ST's model effect (), which can be used instantly with this buelfhood SOCO-Java-PLBART-ST model. huggingface.co supports a free trial of the SOCO-Java-PLBART-ST model, and also provides paid use of the SOCO-Java-PLBART-ST. Support call SOCO-Java-PLBART-ST model through api, including Node.js, Python, http.

SOCO-Java-PLBART-ST huggingface.co Url

https://huggingface.co/buelfhood/SOCO-Java-PLBART-ST

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buelfhood SOCO-Java-PLBART-ST online free url in huggingface.co:

https://huggingface.co/buelfhood/SOCO-Java-PLBART-ST

SOCO-Java-PLBART-ST install

SOCO-Java-PLBART-ST is an open source model from GitHub that offers a free installation service, and any user can find SOCO-Java-PLBART-ST on GitHub to install. At the same time, huggingface.co provides the effect of SOCO-Java-PLBART-ST install, users can directly use SOCO-Java-PLBART-ST installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

SOCO-Java-PLBART-ST install url in huggingface.co:

https://huggingface.co/buelfhood/SOCO-Java-PLBART-ST

Url of SOCO-Java-PLBART-ST

SOCO-Java-PLBART-ST huggingface.co Url

Provider of SOCO-Java-PLBART-ST huggingface.co

buelfhood
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