rifatul123
/

Primary_doctor_v1

@@ -11,6 +11,14 @@ tags:
 - chemistry
 - text-generation-inference
 ---
 # Model Card for Model ID
 This modelcard describes a fine-tuned GPT-2 language model for medical research using a personally collected dataset.
 The model is intended for text generation in the medical research domain.
@@ -63,137 +71,3 @@ This modelcard acknowledges that all language models have limitations and potent
 4)Provide clear and transparent documentation of the model's limitations and potential biases to users and stakeholders.
 ## How to Get Started with the Model
 To use the model, load it in your preferred programming language using the transformers library, and pass in the input text. The model will generate text based on the input, using the task-specific parameters that have been set.
-## Training Details
-### Training Data
-<!-- This should link to a Data 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 Data 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
-![Screenshot 2023-05-05 092541.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/9gMBxc270uN8agP8n6-5m.png)
-![Screenshot 2023-05-05 094102.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/kqOUgU2wyxLDP1gKnCKPC.png)
-![Screenshot 2023-05-05 094303.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/WpNXVBwbLCNNvWJ65dJI8.png)
-![Screenshot 2023-05-05 094409.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/HZ1YdlwfZAi8CPlvrcqDr.png)
-![Screenshot 2023-05-05 094542.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/h9EJw9fRNMBpOwJVVw6zI.png)
-#### 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
-[More Information Needed]
-#### Software
-[More Information Needed]
-## 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:**
-[More Information Needed]
-## 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]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 - chemistry
 - text-generation-inference
 ---
+![Screenshot 2023-05-05 092541.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/9gMBxc270uN8agP8n6-5m.png)
+![Screenshot 2023-05-05 094102.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/kqOUgU2wyxLDP1gKnCKPC.png)
+![Screenshot 2023-05-05 094303.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/WpNXVBwbLCNNvWJ65dJI8.png)
+![Screenshot 2023-05-05 094409.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/HZ1YdlwfZAi8CPlvrcqDr.png)
+![Screenshot 2023-05-05 094542.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/h9EJw9fRNMBpOwJVVw6zI.png)
 # Model Card for Model ID
 This modelcard describes a fine-tuned GPT-2 language model for medical research using a personally collected dataset.
 The model is intended for text generation in the medical research domain.
 4)Provide clear and transparent documentation of the model's limitations and potential biases to users and stakeholders.
 ## How to Get Started with the Model
 To use the model, load it in your preferred programming language using the transformers library, and pass in the input text. The model will generate text based on the input, using the task-specific parameters that have been set.