|
--- |
|
tags: |
|
- geology |
|
- geophysics |
|
- geoscience |
|
license: apache-2.0 |
|
language: |
|
- en |
|
metrics: |
|
- accuracy |
|
base_model: |
|
- google/gemma-2-2b |
|
new_version: ShebMichel/geobot_teacher-v0 |
|
pipeline_tag: question-answering |
|
library_name: keras |
|
--- |
|
|
|
# Model Card for Model ID and Description |
|
This model have been fine-tuned using Gemma_2b_en. The data train on is a syntetic 253 QA pair generated from wide topics of geoscience in ChatGPT. |
|
The model perform well with a nbre of epoch =75. So the training is availaible on my kaggle repo: |
|
|
|
 or my github repo |
|
|
|
 |
|
|
|
The general idea is to have a bot which assess geoscience student assessment as fast as possible with the resulting of either a pass or fail. |
|
So, if an exam is submitted, the bot will report student and predicted answers as well as the evaluation/metric between the two. |
|
Then finally, use that metric to compile whether it is a pass or fail (coming soon) |
|
|
|
|
|
- **Developed by:** Dr. Michel M. Nzikou |
|
- **Funded by [optional]:** KaggleX- Fellow cohort 4 and Google with GCP credit |
|
- **Model type:** Text generation models: Chatbot |
|
- **Language(s) (NLP):** Python 3.10, keras==3.6.0, keras_nlp==0.15.1 |
|
- **License:** Apache 2.0 |
|
- **Finetuned from model [optional]:** Gemma_2b_en |
|
|
|
# Sample Data |
|
|
|
Please download the file geology-exam-test_for_gemma_model_2b_en_253_75.json to test the UI. |
|
However, use the two questions if you are using kaggle notebook. Otherwise, create a json file similar to the downloaded file with the same structure. |
|
|
|
"Question": "How do sedimentary rocks form?", "Response": "Sedimentary rocks form from the accumulation of sediments." |
|
"Question": "What is igneous rock formation?", "Response": "Igneous rocks form when molten rock cools and solidifies." |
|
|
|
To test or evaluate the model, try tweaking the question and see how it respond? Please do not hesitate to contact me for further development of collaboration. |
|
|
|
## Bias, Risks, and Limitations |
|
|
|
- The smaller dataset fine-tuned is a great limitation, however, we have the pipeline ready and if you have a small set, you could |
|
use the github repo (to be filled soon) to train your model. |
|
- Bias from data generation using existing llm model. However, the sample were pre-processed before being used for fine-tuned. |
|
|
|
|
|
### How to Get Started with the Model |
|
|
|
Test the app with your own questions, if not download it and fine-tune on top of this one. If you do so, share your variant model card. |
|
|
|
|
|
### Environmental Impact |
|
|
|
As we know the more we use paper assessment, we have to cut more tree, so this model is a green model. |
|
- **Hardware Type:** [GPU T4 *2] |
|
- **Hours used:** [5hours] |
|
- **Cloud Provider:** [Kaggle] |
|
- **Compute Region:** [AU] |
|
- **Carbon Emitted:** [CO2 emission to fill in the gap here :)] |
|
|
|
|
|
|
|
#### Model Card Authors [optional] |
|
Dr. Michel M. Nzikou, Research Fellow, Center of Exploration Targeting, UWA, Perth, Australia |
|
|
|
#### Model Card Contact |
|
michel.nzikou@alumni.uleth.ca/michel.nzikoumamboukou@uwa.edu.au |
