karan842
/

IPC-gemma

@@ -1,199 +1,54 @@
 ---
 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]

 ---
 library_name: transformers
+tags:
+- legal
+license: apache-2.0
+datasets:
+- karan842/ipc-sections
+metrics:
+- accuracy
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
 ### Model Description
+**IPC-Gemma: AI Model for Indian Legal Code Analysis**
+IPC-Gemma is an advanced AI model designed to assist with tasks related to the Indian Penal Code (IPC). Trained on a comprehensive dataset of IPC sections and legal case information, this model is capable of analyzing textual descriptions of illegal activities and providing relevant insights.
+Key features of IPC-Gemma:
+1. **IPC Section Lookup**: Given a description of an illegal act, the model can quickly identify the corresponding IPC section that covers that offense.
+2. **Offense and Punishment Prediction**: Based on the input description, IPC-Gemma can predict the specific offense committed and the associated punishment or penalty.
+3. **Interpretable Outputs**: Along with the predicted offense and punishment, the model provides the relevant IPC section details to offer transparency and context around its analysis.
+4. **Legal Domain Expertise**: Leveraging the Gemma language model architecture, IPC-Gemma has been fine-tuned to excel in understanding and reasoning about legal terminology and concepts.
+This model is particularly useful for legal professionals, law enforcement agencies, and individuals seeking to better understand the Indian legal system. By automating the process of identifying offenses and their corresponding punishments, IPC-Gemma can save time, improve accuracy, and enhance decision-making in a variety of legal and criminal justice applications.
+Whether you need to quickly look up the relevant IPC section for a given scenario or analyze the potential consequences of an illegal act, IPC-Gemma is a powerful AI tool that can provide valuable insights and support.
+- **Developed by:** Karan Shingde
+- **Language(s) (NLP):** English
+- **Finetuned from model:** google/gemma-2b-it
 ### 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. -->
+Training Data Link: https://huggingface.co/datasets/karan842/ipc-sections
 #### Hardware
+  Kaggle GPU-P100
 ## Model Card Authors [optional]
+Karan Shingde: [email protected]