tensorflow, keras text-generation en arxiv:1910.09700 license:apache-2.0 region:us
---
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

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


## Uses

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

### Direct Use

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For simpler text generation tasks where long range contextual understanding is not must


### Out-of-Scope Use

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

## Bias, Risks, and Limitations

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May generate illegal/bad/meaningless responses thay maybe harmful.
The model can't handle longer sequences larger than 50 words with contextual relevence

### Recommendations

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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

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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]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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[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:** 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]

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**BibTeX:**

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**APA:**

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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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## Model Card Contact

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