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MattStammers/
chatty_mapper

Name: chatty_mapper
Rating: 2.5 (10 reviews)

text-generation-inference

endpoints_compatible

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A First Attempt at Getting LLMs to map SNOMED codes

This model is really poor and represents a first stab at finetuning Mixtral to map SNOMED codes. It doesn't really work and I would recommend using a newer model as this one is way out of date now.

Model description

This is a text generation model for SNOMED-CT. As it is text-generation, it is prone to hallucination and should not be used for any kind of production purpose but it was fun to build. It is based on Mixtral7b and was fine-tuned on a part of the SNOMED-CT corpus then tested against a gold-standard.

How to use

Provide code snippets on how to use your model.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "MattStammers/chatty_mapper"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Your example here
Model Performance
Accuracy: 0.0
Precision: 0.0
Recall: 0.0
Example DataFrame head:                   ParameterName  SNOMEDCode  \
0                   *Heart rate   364075005   
1  Peripheral oxygen saturation   431314004   
2        Mean arterial pressure  1285244000   
3     *Diastolic blood pressure   271650006   
4      *Systolic blood pressure   271649006   

                              ExtractedSNOMEDNumbers  CorrectPrediction  
0                                            3222222              False  
1  4222222000000000000000000000000000000000000000...              False  
2                                                NaN              False  
3                                                NaN              False  
4                                                NaN              False  

Limitations and bias
It is prone to wandering and certainly not medical-grade.

Acknowledgments
Thanks to the Mixtral AI team for creating the base model.

Save the model card in the model directory with open(f"models/chatty_mapper/README.md", "w") as f: f.write(model_card_content)

Use Hugging Face's Repository class for Git operations repo = Repository(local_dir=model_save_path, clone_from=repo_url) repo.git_add() repo.git_commit("Initial model upload with model card and metrics") repo.git_push()

print(f"Model, model card, and metrics successfully pushed to: https://huggingface.co/MattStammers/chatty_mapper")