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tanvikapoor07/
offline-ai-command-model

text-generation-inference

endpoints_compatible

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Model Details

Model Description

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: Tanvi Kapoor
Funded by [optional]: Self / Academic Project
Shared by [optional]: Tanvi Kapoor
Model type: Fine-tuned T5-small (Text-to-Text Transformer)
Language(s) (NLP): English
License:
Finetuned from model [optional]: t5-small

Model Sources [optional]

Repository: https://huggingface.co/tanvikapoor07/offline-ai-command-model
Paper [optional]: https://arxiv.org/abs/1910.10683 (T5 paper)
Demo [optional]: Integrated in offline chatbot system (Java + Python)

Uses

Direct Use

This model is designed to convert natural language queries into structured task-based commands.

Downstream Use [optional]

This model is integrated into a larger offline AI chatbot system.

System Architecture:

Java (OOP-based UI) handles user interaction
Python backend processes requests
Fine-tuned T5 model converts queries into structured commands

The generated commands are then executed to perform tasks such as:

Unit conversion
Translation
Summarization
Mathematical calculations
Code assistance

Out-of-Scope Use

This model is not designed for:

Open-domain conversational AI (like ChatGPT)
Generating long-form or creative text
Handling highly complex or ambiguous queries
Real-time decision-making systems
Sensitive domains like medical, legal, or financial advice

Bias, Risks, and Limitations

The model performance depends on the training dataset size and diversity
Limited generalization for unseen or complex queries
May produce incorrect structured commands for ambiguous inputs
Does not verify correctness of tasks (e.g., calculations)
Trained on synthetic data, so real-world variations may not always be handled accurately

Recommendations

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:

python

from transformers import pipeline

pipe = pipeline("text2text-generation", model="tanvikapoor07/offline-ai-command-model")

result = pipe("Convert 5 km to meters")
print(result)

## Model Card Contact

[More Information Needed

## Training Details

### Training Data

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The model was trained on a custom-generated dataset consisting of 200+ examples.
The dataset includes:
Unit conversion queries
Translation tasks
Summarization queries
Explanation and definition prompts
Mathematical calculations
Chat intent classification
The dataset was programmatically generated to ensure diversity and scalability.

### 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]

Preprocessing
Input queries were prefixed with a task identifier ("convert command:")
Text was tokenized using T5 tokenizer
Padding and truncation applied to maintain uniform input size

#### 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. -->

Training regime: fp32
Epochs: 20
Batch size: 4
Learning rate: 5e-4

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

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[More Information Needed]

### Results

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#### 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

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#### Hardware

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#### Software

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## 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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**APA:**

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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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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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