SentenceTransformer based on huggingface/CodeBERTa-small-v1
This is a sentence-transformers model finetuned from huggingface/CodeBERTa-small-v1 on the soco_train_java dataset. 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: huggingface/CodeBERTa-small-v1
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': '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
model = SentenceTransformer("buelfhood/SOCO-Java-CodeBERTa-MNRL-Triplets-BATL-FullEVAL-BCE")
sentences = [
'import java.net.*;\nimport java.io.*;\n\npublic class BruteForce {\n private String strUserName;\n private String strURL;\n private int iAttempts;\n \n public BruteForce(String strURL,String strUserName) {\n this.strURL = strURL;\n this.strUserName = strUserName;\n this.iAttempts = 0 ;\n\n }\n \n public String getPassword(){\n URL u;\n String result ="";\n PassGenBrute PG = new PassGenBrute(3);\n URLConnection uc;\n String strPassword = new String();\n String strEncode;\n try{\n while (result.compareTo("HTTP/1.1 200 OK")!=0){\n \n strEncode = PG.getNewPassword();\n u = new URL(strURL);\n uc = u.openConnection();\n uc.setDoInput(true);\n uc.setDoOutput(true);\n strPassword = strEncode;\n strEncode = strUserName + ":" + strEncode;\n \n strEncode = new String(Base64.encode(strEncode.getBytes()));\n uc.setRequestProperty("Authorization"," " + strEncode);\n \n result = uc.getHeaderField(0);\n uc = null;\n u = null;\n iAttempts++;\n }\n\n }\n catch (Exception me) {\n System.out.println("MalformedURLException: "+me);\n }\n return(strPassword);\n }\n \n public int getAttempts(){\n return (iAttempts);\n };\n \n public static void main (String arg[]){\n timeStart = 0;\n timeEnd = 0;\n \n if (arg.length == 2) {\n BruteForce BF = new BruteForce(arg[0],arg[1]);\n System.out.println("Processing ... ");\n timeStart = System.currentTimeMillis();\n \n System.out.println("Password = " + BF.getPassword());\n timeEnd = System.currentTimeMillis();\n System.out.println("Total Time Taken = " + (timeEnd - timeStart) + " (msec)");\n System.out.println("Total Attempts = " + BF.getAttempts());\n }\n else {\n System.out.println("[Usage] java BruteForce <URL> <USERNAME>");\n\n }\n\n }\n}\n\nclass PassGenBrute {\n private char[] password;\n public PassGenBrute(int lenght) {\n password = new char[lenght];\n for (int i = 0; i < lenght; i++){\n password[i] = 65;\n }\n password[0]--;\n }\n \n public String getNewPassword()\n throws PasswordFailureException{\n password[0]++;\n\n try {\n for (int i=0; i<password.length ; i++){\n if (password[i] == 90) {\n password[i] = 97;\n }\n if (password[i] > 122) {\n password[i] = 65;\n password[i+1]++;\n }\n }\n }\n catch (RuntimeException re){\n throw new PasswordFailureException ();\n }\n return new String(password);\n }\n}\n\nclass PasswordFailureException extends RuntimeException {\n\n public PasswordFailureException() {\n }\n}',
'import java.net.*;\nimport java.io.*;\n\n\npublic class Dictionary {\n private String strUserName;\n private String strURL;\n private String strDictPath;\n private int iAttempts;\n\n \n public Dictionary(String strURL,String strUserName,String strDictPath) {\n this.strURL = strURL;\n this.strUserName = strUserName;\n this.iAttempts = 0 ;\n this.strDictPath = strDictPath;\n }\n \n\n public String getPassword(){\n URL u;\n String result ="";\n PassGenDict PG = new PassGenDict(3,strDictPath);\n URLConnection uc;\n String strPassword = new String();\n String strEncode;\n try{\n while (result.compareTo("HTTP/1.1 200 OK")!=0){\n \n strEncode = PG.getNewPassword();\n u = new URL(strURL);\n uc = u.openConnection();\n uc.setDoInput(true);\n uc.setDoOutput(true);\n strPassword = strEncode;\n strEncode = strUserName + ":" + strEncode;\n \n strEncode = new String(Base64.encode(strEncode.getBytes()));\n uc.setRequestProperty("Authorization"," " + strEncode);\n \n result = uc.getHeaderField(0);\n uc = null;\n u = null;\n iAttempts++;\n }\n\n }\n catch (Exception me) {\n System.out.println("MalformedURLException: "+me);\n }\n return(strPassword);\n }\n \n public int getAttempts(){\n return (iAttempts);\n };\n \n public static void main(String arg[]){\n timeStart = 0;\n timeEnd = 0;\n \n if (arg.length == 3) {\n Dictionary BF = new Dictionary(arg[0],arg[1],arg[2]);\n\n System.out.println("Processing ... ");\n timeStart = System.currentTimeMillis();\n System.out.println("Password = " + BF.getPassword());\n timeEnd = System.currentTimeMillis();\n System.out.println("Total Time Taken = " + (timeEnd - timeStart) + " (msec)");\n System.out.println("Total Attempts = " + BF.getAttempts());\n }\n else {\n System.out.println("[Usage] java BruteForce <URL> <USERNAME> <Dictionary path>");\n\n }\n\n }\n}\n\n\nclass PassGenDict {\n\n private char[] password;\n private String line;\n int iPassLenght;\n private BufferedReader inputFile;\n public PassGenDict(int lenght, String strDictPath) {\n try{\n inputFile = new BufferedReader(new FileReader(strDictPath));\n }\n catch (Exception e){\n }\n iPassLenght = lenght;\n }\n \n public String getNewPassword()\n throws PasswordFailureException{\n try {\n {\n line = inputFile.readLine();\n }while (line.length() != iPassLenght);\n\n }\n catch (Exception e){\n throw new PasswordFailureException ();\n }\n return (line);\n }\n}\n\nclass PasswordFailureException extends RuntimeException {\n\n public PasswordFailureException() {\n }\n}',
'import java.util.*;\nimport java.io.*;\nimport javax.swing.text.html.*;\n\n\npublic class WatchDog {\n\n public WatchDog() {\n\n }\n public static void main (String args[]) {\n DataInputStream newin;\n\n try{\n System.out.println("ishti");\n\n System.out.println("Downloading first copy");\n Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O oldfile.html");\n String[] cmdDiff = {"//sh", "-c", "diff oldfile.html newfile.html > Diff.txt"};\n String[] cmdMail = {"//sh", "-c", "mailx -s \\"Diffrence\\" \\"@cs.rmit.edu.\\" < Diff.txt"};\n while(true){\n Thread.sleep(24*60*60*1000);\n System.out.println("Downloading new copy");\n Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O newfile.html");\n Thread.sleep(2000);\n Runtime.getRuntime().exec(cmdDiff);\n Thread.sleep(2000);\n newin = new DataInputStream( new FileInputStream( "Diff.txt"));\n if (newin.readLine() != null){\n System.out.println("Sending Mail");\n Runtime.getRuntime().exec(cmdMail);\n Runtime.getRuntime().exec("cp newfile.html oldfile.html");\n\n }\n }\n\n }\n catch(Exception e){\n e.printStackTrace();\n }\n\n }\n\n}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities)
Evaluation
Metrics
Binary Classification
| Metric |
Value |
| cosine_accuracy |
0.9982 |
| cosine_accuracy_threshold |
0.9613 |
| cosine_f1 |
0.5303 |
| cosine_f1_threshold |
0.9568 |
| cosine_precision |
0.7292 |
| cosine_recall |
0.4167 |
| cosine_ap |
0.4484 |
| cosine_mcc |
0.5504 |
Training Details
Training Dataset
soco_train_java
Evaluation Dataset
soco_train_java
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates
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: 8
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: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}
Training Logs
| Epoch |
Step |
Training Loss |
binary-class-evaluator_cosine_ap |
| -1 |
-1 |
- |
0.4484 |
| 0.0414 |
100 |
0.9709 |
- |
| 0.0827 |
200 |
0.3769 |
- |
| 0.1241 |
300 |
0.3881 |
- |
| 0.1655 |
400 |
0.3426 |
- |
| 0.2069 |
500 |
0.3565 |
- |
| 0.2482 |
600 |
0.3384 |
- |
| 0.2896 |
700 |
0.3339 |
- |
| 0.3310 |
800 |
0.3299 |
- |
| 0.3724 |
900 |
0.3332 |
- |
| 0.4137 |
1000 |
0.3459 |
- |
| 0.4551 |
1100 |
0.3161 |
- |
| 0.4965 |
1200 |
0.3092 |
- |
| 0.5379 |
1300 |
0.328 |
- |
| 0.5792 |
1400 |
0.3313 |
- |
| 0.6206 |
1500 |
0.3224 |
- |
| 0.6620 |
1600 |
0.3427 |
- |
| 0.7034 |
1700 |
0.2988 |
- |
| 0.7447 |
1800 |
0.3094 |
- |
| 0.7861 |
1900 |
0.3158 |
- |
| 0.8275 |
2000 |
0.2845 |
- |
| 0.8688 |
2100 |
0.2949 |
- |
| 0.9102 |
2200 |
0.2986 |
- |
| 0.9516 |
2300 |
0.3049 |
- |
| 0.9930 |
2400 |
0.3662 |
- |
Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
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",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}