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zuster_fietje

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metadata
library_name: transformers
tags:
  - medical
license: mit
datasets:
  - ekrombouts/Gardenia_instruct_dataset
  - ekrombouts/Olympia_SAMPC_dataset
language:
  - nl
base_model:
  - BramVanroy/fietje-2-instruct

Model Card for Model ID

This model is a fine-tuned version of bramvanrooy/fietje-2, designed to generate responses based on nursing home reports.

Model Details

  • Developed by: Eva Rombouts
  • Model type: Causal Language Model
  • Language(s) (NLP): Dutch
  • License: MIT
  • Finetuned from model [optional]: BramVanroy/fietje-2-instruct

Model Sources

Uses

Direct Use

Generating summaries and responses based on nursing home reports.

Out-of-Scope Use

Not suitable for generating medical advice or any other critical decision-making processes.

Bias, Risks, and Limitations

The model may generate biased or inaccurate responses. Users should verify the generated content.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ekrombouts/zuster_fietje"

model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = """Rapportages:
Mw was vanmorgen incontinent van urine, bed was ook nat. Mw is volledig verzorgd, bed is verschoond,
Mw. haar kledingkast is opgeruimd.
Mw. zei:"oooh kind, ik heb zo'n pijn. Mijn benen. Dat gaat nooit meer weg." Mw. zat in haar rolstoel en haar gezicht trok weg van de pijn en kreeg traanogen. Mw. werkte goed mee tijdens adl. en was vriendelijk aanwezig. Pijn. Mw. kreeg haar medicatie in de ochtend, waaronder pijnstillers. 1 uur later adl. gegeven.
Mevr. in de ochtend ondersteund met wassen en aankleden. Mevr was rustig aanwezig.
Mw is volledig geholpen met ochtendzorg, mw haar haren zijn gewassen. Mw haar nagels zijn kort geknipt.
Mevr heeft het ontbijt op bed genuttigd. Daarna mocht ik na de tweede poging Mevr ondersteunen met wassen en aankleden.

Instructie:
Beschrijf de lichamelijke klachten

Antwoord:
"""

input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(input_ids, max_new_tokens=1024)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training Details

Training Data

  • ekrombouts/Gardenia_instruct_dataset
  • ekrombouts/Olympia_SAMPC_dataset

Training Procedure

Training Hyperparameters

  • Training regime: fp16 mixed precision

Evaluation

Evaluated on a subset of nursing home reports.

Metrics

Qualitative assessment of generated responses.

Results

[More Information Needed]

Environmental Impact

  • Hardware Type: GPU (NVIDIA A100)
  • Hours used: 8 hours
  • Cloud Provider: Google
  • Compute Region: europe-west4
  • Carbon Emitted: 54 kg CO2 eq.

BibTeX:

@misc{zuster_fietje,
  author = {Eva Rombouts},
  title = {Zuster Fietje: A Fine-Tuned Model for Nursing Home Reports},
  year = {2024},
  url = {https://huggingface.co/ekrombouts/zuster_fietje},
}```