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@@ -27,52 +27,41 @@ It also uses a different ouput layer consisting of sigmoid activated neurons to
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  - **Language(s) (NLP):** Probably english (it depends heavily on dataset)
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  - **License:** Apache license 2.0
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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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  ## 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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@@ -85,11 +74,8 @@ Use the code below to get started with the model.
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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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  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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  #### Hardware
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- [More Information Needed]
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  #### Software
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- [More Information Needed]
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  ## Citation [optional]
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  - **Language(s) (NLP):** Probably english (it depends heavily on dataset)
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  - **License:** Apache license 2.0
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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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+ This can be used for text generation tasks where running large computationally intensive architectures are not applicable
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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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+ For simpler text generation tasks where long range contextual understanding is not must
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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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+ Not applicable for production/commercial use
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+ May generate illegal/bad/meaningless responses thay maybe harmful
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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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+ May generate illegal/bad/meaningless responses thay maybe harmful.
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+ The model can't handle longer sequences larger than 50 words with contextual relevence
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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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+ May generate illegal/bad/meaningless responses thay maybe harmful
 
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  ## How to Get Started with the Model
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+ Just run the main.py file
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+ almost basic documentation will be in program itself detailed manual will be in manual.txt file
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  ## Training Details
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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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+ Final training loss: 0.0322
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+ Final validation loss: 5.6888
 
 
 
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  #### Training Hyperparameters
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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:** Trained using Nvidia rtx 2050, using cudnn and cuda dependencies
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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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  #### Hardware
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+ Nvidia Geforce rtx 2050
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  #### Software
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+ cudnn, cuda, tensorflow
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  ## Citation [optional]
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