Modelpublic

buelfhood/SOCO-Java-CodeT5Small-ST

sourceHugging Faceupdated 1y agoView on Hugging Face

0likes26downloads

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}) with Transformer model: 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-Java-CodeT5Small-ST")
# Run inference
sentences = [
    '\n\n\n\n\n\nimport java.io.*;\nimport java.net.*;\n\n\n\npublic class Dictionary\n{\n   public static void main (String args[]) throws IOException,\n   MalformedURLException\n   {\n      final String username = "";\n      final String fullurl = "http://sec-crack.cs.rmit.edu./SEC/2/";\n      final String dictfile = "/usr/share/lib/dict/words";\n      String temppass;\n      String password = "";\n      URL url = new URL(fullurl);\n      boolean cracked = false;\n\n       startTime = System.currentTimeMillis();\n\n      \n      BufferedReader r = new BufferedReader(new FileReader(dictfile));\n\n      while((temppass = r.readLine()) != null && !cracked)\n      {  \n         \n         if(temppass.length() <= 3)\n         {\n            \n            if(isAlpha(temppass))\n            {\n               \n               Authenticator.setDefault(new MyAuthenticator(username,temppass));\n               try{\n                  BufferedReader x = new BufferedReader(new InputStreamReader(\n                     url.openStream()));\n                  cracked = true;\n                  password = temppass;\n               } catch(Exception e){}\n            }\n         }\n      }\n\n       stopTime = System.currentTimeMillis();\n      \n      if(!cracked)\n         System.out.println("Sorry, couldnt find the password");\n      else\n         System.out.println("Password found: "+password);\n      System.out.println("Time taken: "+(stopTime-startTime));\n   }\n\n   public static boolean isAlpha(String s)\n   {\n      boolean v = true;\n      for(int i=0; i<s.length(); i++)\n      {\n         if(!Character.isLetter(s.charAt(i)))\n            v = false;\n      }\n      return ;\n   }\n}\n\n',
    '\n\nimport java.net.*;\nimport java.text.*;  \nimport java.util.*;  \nimport java.io.*;\n\npublic class WatchDog {\n\n  public WatchDog() {\n\n    StringBuffer stringBuffer1 = new StringBuffer();\n    StringBuffer stringBuffer2 = new StringBuffer();\n    int i,j = 0;\n\n    try{\n\n      URL yahoo = new URL("http://www.cs.rmit.edu./students/"); \n      BufferedReader in = new BufferedReader(new InputStreamReader(yahoo.openStream()));\n\n      String inputLine = "";\n      String inputLine1 = "";\n      String changedtext= "";\n      String changedflag= "";\n\n\n      Thread.sleep(180);\n\n      BufferedReader in1 = new BufferedReader(new InputStreamReader(yahoo.openStream()));\n\n\n      while ((inputLine = in.readLine()) != null) {\n           inputLine1 = in1.readLine();\n           if (inputLine.equals(inputLine1)) {\n              System.out.println("equal");\n           }\n           else {\n              System.out.println("Detected a Change");\n              System.out.println("Line Before the change:" + inputLine);\n              System.out.println("Line After the change:" + inputLine1);\n              changedtext = changedtext + inputLine + inputLine1;\n              changedflag = "Y";\n           }\n           \n      }\n\n      if (in1.readLine() != null ) {\n         System.out.println("Detected a Change");\n         System.out.println("New Lines Added  ");\n         changedtext = changedtext + "New Lines added";\n         changedflag = "Y";\n      }\n\n      in.print();\n      in1.print();\n\n      if (changedflag.equals("Y")) {\n         String smtphost ="smtp.mail.rmit.edu." ; \n         String from = "@rmit.edu."; \n         String  = "janaka1@optusnet.." ; \n      }\n\n\n    }\n    catch(Exception e){ System.out.println("exception:" + e);}\n\t \n}\n\t\t\n    public static void main (String[] args) throws Exception {\n\t\tWatchDog u = new WatchDog();\n    }\n}\n',
    '\n\n\n\nimport java.util.*;\nimport java.net.*;\nimport java.io.*;\nimport javax.swing.*;\n\npublic class PasswordCombination\n{\n    private int      pwdCounter = 0;\n    private   int   startTime;\n    private String   str1,str2,str3;\n    private String   url = "http://sec-crack.cs.rmit.edu./SEC/2/";\n    private String   loginPwd;\n    private String[] password;\n    private HoldSharedData data;\n    private char[] chars = {\'A\',\'B\',\'C\',\'D\',\'E\',\'F\',\'G\',\'H\',\'I\',\'J\',\'K\',\'L\',\'M\',\n                            \'N\',\'O\',\'P\',\'Q\',\'R\',\'S\',\'T\',\'U\',\'V\',\'W\',\'X\',\'Y\',\'Z\',\n                            \'a\',\'b\',\'c\',\'d\',\'e\',\'f\',\'g\',\'h\',\'i\',\'j\',\'k\',\'l\',\'m\',\n                            \'n\',\'o\',\'p\',\'q\',\'r\',\'s\',\'t\',\'u\',\'v\',\'w\',\'x\',\'y\',\'z\'};\n\n    public PasswordCombination()\n    {\n        System.out.println("Programmed by   for INTE1070 Assignment 2");\n\n        String input = JOptionPane.showInputDialog( "Enter number of threads" );\n        if(  input == null  )\n           System.exit(0);\n\n        int numOfConnections = Integer.parseInt( input );\n        startTime = System.currentTimeMillis();\n        int pwdCounter = 52*52*52 + 52*52 + 52;\n        password = new String[pwdCounter];\n\n\n        loadPasswords();\n        System.out.println( "Total Number of Passwords: " + pwdCounter );\n        createConnectionThread( numOfConnections );\n    }\n\n    private void doPwdCombination()\n    {\n        for( int i = 0; i < 52; i ++ )\n        {\n            str1 = "" + chars[i];\n            password[pwdCounter++] = "" + chars[i];\n            System.err.print( str1 + " | " );\n\n            for( int j = 0; j < 52; j ++ )\n            {\n                str2 = str1 + chars[j];\n                password[pwdCounter++] = str1 + chars[j];\n\n                for( int k = 0; k < 52; k ++ )\n                {\n                    str3 = str2 + chars[k];\n                    password[pwdCounter++] = str2 + chars[k];\n                }\n            }\n        }\n    }\n\n    private void loadPasswords( )\n    {\n        FileReader     fRead;\n        BufferedReader buf;\n        String         line = null;\n        String         fileName = "words";\n\n        try\n        {\n            fRead = new FileReader( fileName );\n            buf = new BufferedReader(fRead);\n\n            while((line = buf.readLine( )) != null)\n            {\n                password[pwdCounter++] = line;\n            }\n        }\n        catch(FileNotFoundException e)\n        {\n            System.err.println("File not found: " + fileName);\n        }\n        catch(IOException ioe)\n        {\n            System.err.println("IO Error " + ioe);\n        }\n    }\n\n    private void createConnectionThread( int input )\n    {\n        data = new HoldSharedData( startTime, password, pwdCounter );\n\n        int numOfThreads = input;\n        int batch = pwdCounter/numOfThreads + 1;\n        numOfThreads = pwdCounter/batch + 1;\n        System.out.println("Number of Connection Threads Used=" + numOfThreads);\n        ConnectionThread[] connThread = new ConnectionThread[numOfThreads];\n\n        for( int index = 0; index < numOfThreads; index ++ )\n        {\n            connThread[index] = new ConnectionThread( url, index, batch, data );\n            connThread[index].conn();\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.shape)
# [3, 3]

<!--

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: 33,411 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: 52 tokens</li><li>mean: 444.58 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 52 tokens</li><li>mean: 470.35 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~99.80%</li><li>1: ~0.20%</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code> import java.util.; import java.io.; public class MyTimer { public static void main(String args[]) { Watchdog watch = new Watchdog(); Timer time = new Timer(); time.schedule(watch,864000000,864000000); } } </code> | <code> import java.io.; import java.; import java.net.; import java.util.; public class Dictionary { public static void main (String[] args) throws IOException { BufferedReader stdin = new BufferedReader (new InputStreamReader(System.in)); d = new Date().getTime(); FileReader fr = new FileReader("/usr/share/lib/dict/words"); BufferedReader bufr = new BufferedReader(fr); String word = bufr.readLine(); int total = 960; String[] pws = new String[total]; int count = 0; while (word!=null){ if (word.length()<=3) { pws[count] = word; count++;} word = bufr.readLine(); } int i=0; int response = 0; for (i=0;i<count;i++){ String uname = ""; String userinfo = uname + ":" + pws[i]; try{ String encoding = new bf.misc.BASE64Encoder().encode (userinfo.getBytes()); URL url = new URL("http://sec-crack.cs.rmit.edu./SEC/2/"); HttpURLConn...</code> | <code>0</code> | | <code> import java.io.; import java.util.; class BruteForce{ public static void main(String args[]){ String pass,s; char a,b,c; int z=0; int attempt=0; Process p; char password[]={'A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q', 'R','S','T','U','V','W','X','Y','Z','a','b','c','d','e','f','g','h', 'i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'}; z = System.currentTimeMillis(); int at=0; for(int i=0;i<password.length;i++){ for(int j=0;j<password.length;j++){ for(int k=0;k<password.length;k++){ pass=String.valueOf(password[i])+String.valueOf(password[j])+String.valueOf(password[k]); try { System.out.println("Trying crack using: "+pass); at++; p = Runtime.getRuntime().exec("wget --http-user= --http-passwd="+pass+" http://sec-crack.cs.rmit.edu./SEC/2/index.php"); try{ p.waitFor(); } catch(Exception q){} z = p.exitValue(); ...</code> | <code> import java.io.; import java.util.Vector; import java.util.Date; interface UnaryPredicate { boolean execute(Object obj); } public class DiffPrint { static String outFile=""; public static abstract class Base { protected Base(Object[] a,Object[] b) { try { outfile = new PrintWriter(new FileWriter(outFile)); } catch (Exception e) { e.printStackTrace(); } file0 = a; file1 = b; } protected UnaryPredicate ignore = null; protected Object[] file0, file1; public void print_script(Diff.change script) { Diff.change next = script; while (next != null) { Diff.change t, end; t = next; end = hunkfun(next); next = end; end = null; print_hunk(t); end = next; } outfile.flush(); } protected Diff.change hunkfun(Diff.change hunk) { ...</code> | <code>0</code> | | <code>package java.httputils; import java.io.BufferedInputStream; import java.io.BufferedOutputStream; import java.io.BufferedReader; import java.io.FileInputStream; import java.io.FileNotFoundException; import java.io.FileOutputStream; import java.io.FileReader; import java.io.IOException; import java.io.OutputStream; public class WatchDog { protected final int MILLIS_IN_HOUR = (60 60 1000); protected int interval = 24; protected String URL = "http://www.cs.rmit.edu./students/"; protected String fileName = "WatchDogContent.html"; protected String command = "./alert_mail.sh"; protected String savedContent; protected String retrievedContent; public WatchDog() { super(); } public void run() throws Exception { HttpRequestClient client = null; System.out.println(getClass().getName() + "Retrieving baseline copy of: " + getURL()); client = new HttpRequestClie...</code> | <code> import java.; import java.io.; import java.util.; public class Dictionary { public String[] passwds; public int passwdNum; public static void main(String[] args) throws IOException { Dictionary dic=new Dictionary(); dic.doDictionary(); System.exit(1); } void doDictionary() throws IOException { Runtime rt=Runtime.getRuntime(); passwds=new String[32768]; passwdNum=0; time1=new Date().getTime(); try { File f = new File ("words"); FileReader fin = new FileReader (f); BufferedReader buf = new BufferedReader(fin); passwds[0]="00"; System.out.println(" loading words...."); { passwds[passwdNum]=buf.readLine(); passwdNum++; }while(passwds[passwdNum-1]!=null); System.out.println("Finish loading words."); } catch (FileNotFoundException exc) { System.out.println ("File Not Found"); } catch (IOException exc) { System.out.println ("IOException 1"); } catch (NullPointerException exc) { System.out.println ("NullPointerEx...</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
—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: 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: {'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

</details>

Training Logs

Epoch	Step	Training Loss
0.2393	500	0.2122
0.4787	1000	0.1686
0.7180	1500	0.2193
0.9574	2000	0.2084

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

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}
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->