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- ---
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- library_name: transformers
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- tags: []
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- ---
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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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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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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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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
 
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- #### Hardware
 
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
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- **BibTeX:**
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- **APA:**
 
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- ## Glossary [optional]
 
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
 
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+ # LawGPT: Indian Penal Code Assistant
 
 
 
 
 
 
 
 
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+ This model is fine-tuned to assist with questions related to the Indian Penal Code (IPC). It can process legal case details and provide relevant IPC sections.
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  ## Model Details
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+ - **Base Model**: Mistral-7B-Instruct-v0.2
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+ - **Fine-tuning**: This model has been fine-tuned on a dataset of Indian legal cases and IPC sections.
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+ - **Task**: Legal case analysis and IPC section identification
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## How to Use
 
 
 
 
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+ You can use this model directly on the Hugging Face platform or via the Hugging Face API.
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+ ### Using the Model on Hugging Face
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+ 1. Go to the "Inference API" tab on this model's Hugging Face page.
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+ 2. In the input box, enter your legal case details or question, following this format:
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+ ```
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+ [INST] Process the following legal case details and provide the relevant sections of the Indian Penal Code:
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+ {Your case details or legal question here}
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+ [/INST]
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+ ```
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+ 3. Click "Compute" to get the model's response.
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+ ### Example Input
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+ ```
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+ [INST] Process the following legal case details and provide the relevant sections of the Indian Penal Code:
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+ B.K. Bhattacharya, G.S. Chatterjee for P.K. Bose, for the respondent. The Judgment of the Court was delivered by Bachawat, J. The complainant Sarajit Kumar Bose was a forest ranger having his headquarters at Bara Bazar range in the district of Purulia. Bibhuti Bhusan Dasgupta was the editor and Ram Chandra Adhikari was the printer and publisher of "Mukti" a local Bengali weekly journal with its registered office at Purulia town. The letter which bore the caption "Wild law in the land of the Nags (barbarians)", contained several defamatory statements concerning Sarajit Bose. On September 27, 1961 the Magistrate examined the complainant and issued summons to the three accused. On the application of Bibhuti Dasgupta the Magistrate passed an order on December 12, 1961 dispensing with his personal appearance and permitting him to appear by his pleader. P.K. Chatterjee, for the appellants. On his complaint, Sripati Gope and Bibhuti Dasgupta were charged with an offence punishable under sec.500 of the Indian Penal Code and Ram Adhikari was charged with an offence punishable under sec.501 I.P.C.
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+ [/INST]
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+ ```
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+ ### Example Output
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+ ```
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+ Based on the legal case details provided, the relevant sections of the Indian Penal Code (IPC) are:
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+ 1. Section 500 IPC: Defamation
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+ This section is relevant because Sripati Gope and Bibhuti Dasgupta were charged with an offence punishable under this section. It deals with defamation, which aligns with the mention of "defamatory statements" in the case details.
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+ 2. Section 501 IPC: Printing or engraving matter known to be defamatory
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+ Ram Adhikari, being the printer and publisher, was charged under this section. It specifically deals with printing or engraving defamatory content.
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+ These sections are directly mentioned in the case details and are the primary charges against the accused individuals. The case revolves around defamatory statements published in a local Bengali weekly journal, which is why these sections of the IPC are applicable.
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+ ```
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+ ## Limitations
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+ - This model is specifically trained for Indian legal contexts and may not perform well on legal systems from other countries.
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+ - The model's responses should be treated as suggestions and not as definitive legal advice. Always consult with a qualified legal professional for actual legal matters.
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+ - The model's knowledge cutoff date is April 2024, and it may not be aware of legal changes or precedents after this date.
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+ ## Feedback and Issues
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+ If you encounter any issues or have suggestions for improvement, please open an issue in the GitHub repository or contact the maintainers through Hugging Face.
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+ ## Citation
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+ If you use this model in your research or application, please cite it as:
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+ ```
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+ @misc{LawGPT2024,
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+ author = {Shreyas-Dev},
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+ title = {LawGPT: Indian Penal Code Assistant},
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+ year = {2024},
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+ publisher = {GitHub},
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+ journal = {GitHub repository},
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+ howpublished = {\url{https://huggingface.co/shreyas-dev/lawipc-ft}}
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+ }
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+ ```
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+ Thank you for using LawGPT!