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KAILAS

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 <!-- Provide a quick summary of what the model is/does. -->
-This domain-adapted (RoBERTa)[https://huggingface.co/roberta-base] based Encoder model is finetuned using select scientific journals and articles. It's intended purpose is to aid in NLP efforts within NASA. e.g.: Information retrieval, Intelligent search and discovery,
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** NASA IMPACT and IBM Research
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## 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. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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.
-[More Information Needed]
 ## Training Details
 ### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[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. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### 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
-<!-- This should link to a Dataset Card if possible. -->
-[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
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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
-[More Information Needed]
-#### 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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 <!-- Provide a quick summary of what the model is/does. -->
+This domain-adapted,(RoBERTa)[https://huggingface.co/roberta-base] based, Encoder-only transformer model is finetuned using select scientific journals and articles related to NASA Science Mission Directorate(SMD). It's intended purpose is to aid in NLP efforts within NASA. e.g.: Information retrieval, Intelligent search and discovery.
 ## Model Details
+- RoBERTa as base model
+- Custom tokenizer
+- 125M parameters
+- Masked Language Modeling (MLM) pretraining strategy
 ### Model Description
+<!-- - **Developed by:** NASA IMPACT and IBM Research
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed] -->
 ## Uses
+- Named Entity Recognition (NER), Information revreival, sentence-transformers.
 ## Training Details
 ### Training Data
+The model was trained on the following datasets:
+1.  Wikipedia English dump of February 1, 2020
+2.  NASA own data
+3.  NASA papers
+4.  NASA Earth Science papers
+5.  NASA Astrophysics Data System
+6.  PubMed abstract
+7.  PMC : subset with commercial license
+ The sizes of the dataset is shown in the following chart.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61099e5d86580d4580767226/CTNkn0WHS268hvidFmoqj.png)
+<!-- Provide the basic links for the model.
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+-->
+### Training Procedure
+The model was trained on fairseq 0.12.1 with PyTorch 1.9.1 on transformer version 4.2.0. Masked Language Modeling (MLM) is the pretraining stragegy used.
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 ## Evaluation
+### BLURB Benchmark
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61099e5d86580d4580767226/K0IpQnTQmrfQJ1JXxn1B6.png)
+### Pruned SQuAD2.0 (SQ2) Benchmark
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61099e5d86580d4580767226/R4oMJquUz4puah3lvd5Ve.png)
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### NASA SMD Experts Benchmark
+WIP!
+## Citation
+Please use the DOI provided by Huggingface to cite the model.
 ## Model Card Authors [optional]
+Bishwaranjan Bhattacharjee, IBM Research
+Muthukumaran Ramasubramanian, NASA-IMPACT ([email protected])
 ## Model Card Contact
+Muthukumaran Ramasubramanian ([email protected])