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- library_name: transformers
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- tags: []
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
 
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- ## Model Details
 
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- ### Model Description
 
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- <!-- Provide a longer summary of what this model is. -->
 
 
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- 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. -->
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ## Citation [optional]
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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+ # Gophos - Sophos Log Interpreter - Gemma 2B-IT Fine-tuned Model
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+ ## Overview
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+ This repository contains a fine-tuned version of the Gemma 2B-IT model, tailored specifically for interpreting Sophos logs exported from Splunk. The model is hosted on Hugging Face for easy integration and usage in various applications requiring interpretation and analysis of Sophos logs.
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+ ## Model Description
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+ The Gemma 2B-IT model, has been fine-tuned using a dataset of Sophos logs extracted from Splunk. Through this fine-tuning process, the model has been optimized to effectively interpret and extract meaningful information from Sophos logs, facilitating tasks such as threat detection, security analysis, and incident response.
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+ ## Usage
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+ To utilize the model, simply install the Hugging Face `transformers` library and load the model using its unique identifier or name:
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+ ```python
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+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
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+ # Load the fine-tuned Gemma 2B-IT model
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+ model = AutoModelForSequenceClassification.from_pretrained("SadokBarbouche/gophos)
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+ # Load the tokenizer
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+ tokenizer = AutoTokenizer.from_pretrained("SadokBarbouche/gophos")
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+ ```
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+ ## Data Preparation
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+ The fine-tuning of the Gemma 2B-IT model was conducted using a dataset of Sophos logs exported from Splunk. The dataset was preprocessed to ensure compatibility with the model architecture and to optimize training performance.
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+ ## Acknowledgements
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+ We would like to acknowledge the creators of the Gemma 2B-IT model for their pioneering work in natural language understanding. Additionally, we extend our gratitude to the contributors of the Hugging Face `transformers` library for their valuable tools and resources.