SentenceTransformer based on microsoft/unixcoder-base-unimodal
This is a
sentence-transformers
model finetuned from
microsoft/unixcoder-base-unimodal
. 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/unixcoder-base-unimodal
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-UniXcoder-ST" )
# Run inference
sentences = [
'\npublic class ImageFile\n{\n\tprivate String imageUrl;\n\tprivate int imageSize;\n\n\tpublic ImageFile(String url, int size)\n\t{\n\t\timageUrl=url;\n\t\timageSize=size;\n\t}\n\n\tpublic String getImageUrl()\n\t{\n\t\treturn imageUrl;\n\t}\n\n\tpublic int getImageSize()\n\t{\n\t\treturn imageSize;\n\t}\n}\n' ,
'import java.io.*;\nimport java.net.*;\n\npublic class BruteForce {\n public static void main(String[] args) {\n BruteForce brute=new BruteForce();\n brute.start();\n\n\n }\n\n\npublic void start() {\nchar passwd[]= new char[3];\nString password;\nString username="";\nString auth_data;\nString server_res_code;\nString required_server_res_code="200";\nint cntr=0;\n\ntry {\n\nURL url = new URL("http://sec-crack.cs.rmit.edu./SEC/2/");\nURLConnection conn=null;\n\n\n for (int i=65;i<=122;i++) {\n if(i==91) { i=i+6; }\n passwd[0]= (char) i;\n\n for (int j=65;j<=122;j++) {\n if(j==91) { j=j+6; }\n passwd[1]=(char) j;\n\n for (int k=65;k<=122;k++) {\n if(k==91) { k=k+6; }\n passwd[2]=(char) k;\n password=new String(passwd);\n password=password.trim();\n auth_data=null;\n auth_data=username + ":" + password;\n auth_data=auth_data.trim();\n auth_data=getBasicAuthData(auth_data);\n auth_data=auth_data.trim();\n conn=url.openConnection();\n conn.setDoInput (true);\n conn.setDoOutput(true);\n conn.setRequestProperty("GET", "/SEC/2/ HTTP/1.1");\n conn.setRequestProperty ("Authorization", auth_data);\n server_res_code=conn.getHeaderField(0);\n server_res_code=server_res_code.substring(9,12);\n server_res_code.trim();\n cntr++;\n System.out.println(cntr + " . " + "PASSWORD SEND : " + password + " SERVER RESPONSE : " + server_res_code);\n if( server_res_code.compareTo(required_server_res_code)==0 )\n {System.out.println("PASSWORD IS : " + password + " SERVER RESPONSE : " + server_res_code );\n i=j=k=123;}\n }\n\n }\n\n }\n }\n catch (Exception e) {\n System.err.print(e);\n }\n }\n\npublic String getBasicAuthData (String getauthdata) {\n\nchar base64Array [] = {\n \'A\', \'B\', \'C\', \'D\', \'E\', \'F\', \'G\', \'H\',\n \'I\', \'J\', \'K\', \'L\', \'M\', \'N\', \'O\', \'P\',\n \'Q\', \'R\', \'S\', \'T\', \'U\', \'V\', \'W\', \'X\',\n \'Y\', \'Z\', \'a\', \'b\', \'c\', \'d\', \'e\', \'f\',\n \'g\', \'h\', \'i\', \'j\', \'k\', \'l\', \'m\', \'n\',\n \'o\', \'p\', \'q\', \'r\', \'s\', \'t\', \'u\', \'v\',\n \'w\', \'x\', \'y\', \'z\', \'0\', \'1\', \'2\', \'3\',\n \'4\', \'5\', \'6\', \'7\', \'8\', \'9\', \'+\', \'/\' } ;\n\n String encodedString = "";\n byte bytes [] = getauthdata.getBytes ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length) {\n b3 = 0;\n pad = 1;\n }\n else\n b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n encodedString += base64Array [c2];\n switch (pad) {\n case 0:\n encodedString += base64Array [c3];\n encodedString += base64Array [c4];\n break;\n case 1:\n encodedString += base64Array [c3];\n encodedString += "=";\n break;\n case 2:\n encodedString += "==";\n break;\n }\n }\n return " " + encodedString;\n }\n}' ,
'package java.httputils;\n\nimport java.io.IOException;\nimport java.net.MalformedURLException;\nimport java.sql.Timestamp;\n\n\npublic class RunnableBruteForce extends BruteForce implements Runnable\n{\n protected int rangeStart, rangeEnd;\n protected boolean stop = false;\n \n public RunnableBruteForce()\n {\n super();\n }\n\n \n public void run()\n {\n process();\n }\n\n public static void main(String[] args)\n {\n }\n \n public int getRangeEnd()\n {\n return rangeEnd;\n }\n\n \n public int getRangeStart()\n {\n return rangeStart;\n }\n\n \n public void setRangeEnd(int i)\n {\n rangeEnd = i;\n }\n\n \n public void setRangeStart(int i)\n {\n rangeStart = i;\n }\n\n \n public boolean isStop()\n {\n return stop;\n }\n\n \n public void setStop(boolean b)\n {\n stop = b;\n }\n\n public void process()\n {\n String password = "";\n \n System.out.println(Thread.currentThread().getName() +\n "-> workload: " +\n this.letters[getRangeStart()] + " " +\n this.letters[getRangeEnd() - 1]);\n setStart(new Timestamp(System.currentTimeMillis()));\n\n for (int i = getRangeStart();\n i < getRangeEnd();\n i++)\n {\n System.out.println(Thread.currentThread().getName() +\n "-> Trying words beginning with: " +\n letters[i]);\n for (int i2 = 0;\n i2 < letters.length;\n i2++)\n {\n for (int i3 = 0;\n i3 < letters.length;\n i3++)\n {\n if (isStop())\n {\n return;\n }\n try\n {\n char [] arr = new char [] {letters[i], letters[i2], letters[i3]};\n String pwd = new String(arr);\n \n if (Thread.currentThread().getName().equals("Thread-1") && pwd.equals("bad"))\n {\n System.out.println(Thread.currentThread().getName() +\n "-> Trying password: " +\n pwd);\n }\n attempts++;\n\n BasicAuthHttpRequest req =\n new BasicAuthHttpRequest(\n getURL(),\n getUserName(),\n pwd);\n System.out.println("Got the password");\n setPassword(pwd);\n setEnd(new Timestamp(System.currentTimeMillis()));\n setContent(req.getContent().toString());\n\n \n this.setChanged();\n this.notifyObservers(this.getContent());\n return;\n }\n catch (MalformedURLException e)\n {\n e.printStackTrace();\n return;\n }\n catch (IOException e)\n {\n\n }\n }\n }\n }\n\n \n setEnd(new Timestamp(System.currentTimeMillis()));\n }\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: 51 tokens
mean: 449.02 tokens
max: 512 tokens
min: 51 tokens
mean: 464.04 tokens
max: 512 tokens
Samples:
sentence_0
sentence_1
label
import java.io.
;
import java.net.
;
public class BruteForce
{
public static void main(String args[]) throws IOException,
MalformedURLException
{
final String username = "";
final String fullurl = "
http://sec-crack.cs.rmit.edu./SEC/2/
";
String temppass;
String password = "";
URL url = new URL(fullurl);
boolean cracked = false;
String c[] = {"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"};
startTime = System.currentTimeMillis();
for(int i = 0; i < 52 && !cracked; i++) {
temppass = c[i];
Authenticator.setDefault(new MyAuthenticator(username, temppass));
try{
BufferedReader r = ...
import java.net.
;
import java.io.
;
public class SendEMail {
public void SendEMail(){}
public void sendMail(String recipient,String c, String subject){
try {
Socket s = new Socket("yallara.cs.rmit.edu.", 25);
BufferedReader in = new BufferedReader
(new InputStreamReader(s.getInputStream(), "8859_1"));
BufferedWriter out = new BufferedWriter
(new OutputStreamWriter(s.getOutputStream(), "8859_1"));
send(in, out, "HELO theWorld");
send(in, out, "MAIL FROM: ");
send(in, out, "RCPT : "+recipient);
send(in, out, "DATA");
send(out, "Subject: "+ subject);
send(out, "From: WatchDog.java");
send (out, "\n");
BufferedReader reader;
String line;
reader = new BufferedReader(new InputStreamReader(new FileInputStream()));
line = reader.readLine();
while (line != null){
send(out, line);
line = reader.readLine();
}
send...
0
import java.util.
;
import java.net.
;
import java.io.*;
public class Dictionary
{
boolean connected = false;
int counter;
Vector words = new Vector();
Dictionary()
{
counter = 0;
this.readWords();
this.startAttack();
}
public void startAttack()
{
while(counter {
connected = sendRequest();
if(connected == true)
{
System.out.print("The password is: ");
System.out.println((String)words.elementAt(counter-1));
counter = words.size();
}
}
}
public void readWords()
{
String line;
try
{
BufferedReader buffer = new BufferedReader(
new FileReader("/usr/share/lib/dict/words"));
line = buffer.readLine();
while(line != null)
{
if(line.length() <= 3)
...
import java.io.
;
import java.net.
;
import java.net.URL;
import java.net.URLConnection;
import java.util.*;
public class BruteForce {
public static void main(String[] args) throws IOException {
int start , end, total;
start = System.currentTimeMillis();
String username = "";
String password = null;
String host = "
http://sec-crack.cs.rmit.edu./SEC/2/
";
String letters = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";
int lettersLen = letters.length();
int passwordLen=3;
int passwords=0;
int twoChar=0;
url.misc.BASE64Encoder base = new url.misc.BASE64Encoder();
String authenticate = "";
String realm = null, domain = null, hostname = null;
header = null;
int responseCode;
String responseMsg;
int temp1=0;
int temp2=0;
int temp3=0;
for (int a=...
0
public class SMTPException extends Exception {
private String msg;
public SMTPException(String message) {
msg = message;
}
public String getMessage() {
return msg;
}
}
import java.net.
;
import java.io.
;
import java.
;
import java.util.
;
public class Dictionary {
private static String commandLine = "curl
http://sec-crack.cs.rmit.edu./SEC/2/index.php
-I -u :";
private String password;
private String previous;
private String url;
private int startTime;
private int endTime;
private int totalTime;
private float averageTime;
private boolean finish;
private Process curl;
private BufferedReader bf, responseLine;
public Dictionary() {
first();
finish = true;
previous = "";
Runtime run = Runtime.getRuntime();
startTime =new Date().getTime();
int i=0;
try {
try {
bf = new BufferedReader(new FileReader("words"));
}
catch(FileNotFoundException notFound) {
bf = new BufferedReader(new FileReader("/usr/share/lib/dict/words"));
}
while((password = bf.readLine()) != null) {
if(password....
0
Loss:
BatchAllTripletLoss
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
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
: 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
0.2393
500
0.2443
0.4787
1000
0.2228
0.7180
1500
0.2148
0.9574
2000
0.1666
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}
}