Model Card for Model ID
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
- Developed by: ArchBase
- Model type: Reccurrent Neural Network
- Language(s) (NLP): Probably english (it depends heavily on dataset)
- License: Apache license 2.0
Uses
This can be used for text generation tasks where running large computationally intensive architectures are not applicable
Direct Use
For simpler text generation tasks where long range contextual understanding is not must
Out-of-Scope Use
Not applicable for production/commercial use
May generate illegal/bad/meaningless responses thay maybe harmful
Bias, Risks, and 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
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
[More Information Needed]
Training Procedure
Final training loss: 0.0322
Final validation loss: 5.6888
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- 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
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Compute Infrastructure
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Hardware
Nvidia Geforce rtx 2050
Software
cudnn, cuda, tensorflow
Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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