

![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.
## Model Details
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.

### Model Description
The model has been fine-tuned on a GPT-2 architecture and trained with a task-specific parameter for text generation. The do_sample parameter is set to true, which means that the model can generate text on its own rather than simply copying from the input. The max_length parameter is set to 50, which means that the maximum length of the generated text will be 50 tokens.

- **Developed by:** [OpenAI]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Language Model]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [GPT-2]

### 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


### Direct Use

This model can be used for text generation in the medical research domain. It can be used to generate text for a variety of purposes, such as research papers, reports, and summaries.


### Downstream Use [optional]

The model can be fine-tuned for downstream tasks such as summarization, question answering, and text classification.


### Out-of-Scope Use

This model may not perform as well on text outside the medical research domain. It is important to carefully evaluate the generated text to ensure that it is appropriate for the intended use.

## Bias, Risks, and Limitations
This modelcard acknowledges that all language models have limitations and potential biases. The model may produce biased or inaccurate outputs if the input data contains bias or if the training data is not diverse enough. The risks of using the model include the possibility of generating misleading or harmful information.

### Recommendations
            To mitigate potential risks and limitations, users of the model should carefully evaluate the generated text and consider the following recommendations:
1)Evaluate the input data for potential bias and ensure that it is diverse and representative.
2)Consider fine-tuning the model on additional data to improve its accuracy and reduce the risk of bias.
3)Review and edit the generated text before use to ensure that it is appropriate for the intended purpose.
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.

{"language": ["en"], "library_name": "adapter-transformers", "tags": ["biology", "medical", "chemistry", "text-generation-inference"], "metrics": ["accuracy"], "pipeline_tag": "text-generation"}
adapter-transformers pytorch gpt2 biology medical chemistry text-generation-inference text-generation en region:us
TAGS
#adapter-transformers #pytorch #gpt2 #biology #medical #chemistry #text-generation-inference #text-generation #en #region-us 



!Screenshot 2023-05-05 URL
!Screenshot 2023-05-05 URL
!Screenshot 2023-05-05 URL
!Screenshot 2023-05-05 URL
!Screenshot 2023-05-05 URL

# 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.
## Model Details
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.

### Model Description
The model has been fine-tuned on a GPT-2 architecture and trained with a task-specific parameter for text generation. The do_sample parameter is set to true, which means that the model can generate text on its own rather than simply copying from the input. The max_length parameter is set to 50, which means that the maximum length of the generated text will be 50 tokens.

- Developed by: [OpenAI]
- Shared by [optional]: 
- Model type: [Language Model]
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: [GPT-2]

### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: 

## Uses


### Direct Use

This model can be used for text generation in the medical research domain. It can be used to generate text for a variety of purposes, such as research papers, reports, and summaries.


### Downstream Use [optional]

The model can be fine-tuned for downstream tasks such as summarization, question answering, and text classification.


### Out-of-Scope Use

This model may not perform as well on text outside the medical research domain. It is important to carefully evaluate the generated text to ensure that it is appropriate for the intended use.

## Bias, Risks, and Limitations
This modelcard acknowledges that all language models have limitations and potential biases. The model may produce biased or inaccurate outputs if the input data contains bias or if the training data is not diverse enough. The risks of using the model include the possibility of generating misleading or harmful information.

### Recommendations
            To mitigate potential risks and limitations, users of the model should carefully evaluate the generated text and consider the following recommendations:
1)Evaluate the input data for potential bias and ensure that it is diverse and representative.
2)Consider fine-tuning the model on additional data to improve its accuracy and reduce the risk of bias.
3)Review and edit the generated text before use to ensure that it is appropriate for the intended purpose.
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.

# 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. ## Model Details
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. ### Model Description
The model has been fine-tuned on a GPT-2 architecture and trained with a task-specific parameter for text generation. The do_sample parameter is set to true, which means that the model can generate text on its own rather than simply copying from the input. The max_length parameter is set to 50, which means that the maximum length of the generated text will be 50 tokens.

- Developed by: [OpenAI]
- Shared by [optional]: 
- Model type: [Language Model]
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: [GPT-2] ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use

This model can be used for text generation in the medical research domain. It can be used to generate text for a variety of purposes, such as research papers, reports, and summaries. ### Downstream Use [optional]

The model can be fine-tuned for downstream tasks such as summarization, question answering, and text classification. ### Out-of-Scope Use

This model may not perform as well on text outside the medical research domain. It is important to carefully evaluate the generated text to ensure that it is appropriate for the intended use. ## Bias, Risks, and Limitations
This modelcard acknowledges that all language models have limitations and potential biases. The model may produce biased or inaccurate outputs if the input data contains bias or if the training data is not diverse enough. The risks of using the model include the possibility of generating misleading or harmful information. ### Recommendations
            To mitigate potential risks and limitations, users of the model should carefully evaluate the generated text and consider the following recommendations:
1)Evaluate the input data for potential bias and ensure that it is diverse and representative.
2)Consider fine-tuning the model on additional data to improve its accuracy and reduce the risk of bias.
3)Review and edit the generated text before use to ensure that it is appropriate for the intended purpose.
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.
TAGS
#adapter-transformers #pytorch #gpt2 #biology #medical #chemistry #text-generation-inference #text-generation #en #region-us 
 # 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. ## Model Details
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. ### Model Description
The model has been fine-tuned on a GPT-2 architecture and trained with a task-specific parameter for text generation. The do_sample parameter is set to true, which means that the model can generate text on its own rather than simply copying from the input. The max_length parameter is set to 50, which means that the maximum length of the generated text will be 50 tokens.

- Developed by: [OpenAI]
- Shared by [optional]: 
- Model type: [Language Model]
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: [GPT-2] ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use

This model can be used for text generation in the medical research domain. It can be used to generate text for a variety of purposes, such as research papers, reports, and summaries. ### Downstream Use [optional]

The model can be fine-tuned for downstream tasks such as summarization, question answering, and text classification. ### Out-of-Scope Use

This model may not perform as well on text outside the medical research domain. It is important to carefully evaluate the generated text to ensure that it is appropriate for the intended use. ## Bias, Risks, and Limitations
This modelcard acknowledges that all language models have limitations and potential biases. The model may produce biased or inaccurate outputs if the input data contains bias or if the training data is not diverse enough. The risks of using the model include the possibility of generating misleading or harmful information. ### Recommendations
            To mitigate potential risks and limitations, users of the model should carefully evaluate the generated text and consider the following recommendations:
1)Evaluate the input data for potential bias and ensure that it is diverse and representative.
2)Consider fine-tuning the model on additional data to improve its accuracy and reduce the risk of bias.
3)Review and edit the generated text before use to ensure that it is appropriate for the intended purpose.
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.