buelfhood/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

• 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}) 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 <username@cs.rmit.edu.> 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(246060*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}
}