---
license: apache-2.0
language:
- en
library_name: tensorflow, keras
pipeline_tag: text-generation
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is an RNN model for text generation tasks.
This model is having more contextual understanding than traditional RNN

## Model Details
The model uses bigrams as tokens, thus providing more contextual relevence
It also uses a different ouput layer consisting of sigmoid activated neurons to handle larger vocabulary sizes

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** ArchBase
- **Model type:** Reccurrent Neural Network
- **Language(s) (NLP):** Probably english (it depends heavily on dataset)
- **License:** Apache license 2.0


## 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. -->
This can be used for text generation tasks where running large computationally intensive architectures are not applicable

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
For simpler text generation tasks where long range contextual understanding is not must


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Not applicable for production/commercial use
May generate illegal/bad/meaningless responses thay maybe harmful

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
May generate illegal/bad/meaningless responses thay maybe harmful.
The model can't handle longer sequences larger than 50 words with contextual relevence

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
May generate illegal/bad/meaningless responses thay maybe harmful

## How to Get Started with the Model

Just run the main.py file

almost basic documentation will be in program itself detailed manual will be in manual.txt file

## Training Details

### Training Data

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[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. -->
Final training loss: 0.0322
Final validation loss: 5.6888

#### 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

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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

<!-- 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:** Trained using Nvidia rtx 2050, using cudnn and cuda dependencies
- **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

Nvidia Geforce rtx 2050

#### Software

cudnn, cuda, tensorflow

## 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:**

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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. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

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## Model Card Contact

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