--- language: en tags: - snomed-ct - text-generation --- # My Model Name ## 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")