---
license: apache-2.0
base_model: alex-miller/ODABert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: iati-drr-classifier
  results: []
datasets:
- alex-miller/iati-policy-markers
pipeline_tag: text-classification
widget:
  - text: "Core pandemic preparedness and response and integrated global health priorities"
    example_title: "Positive"
  - text: "Education programs for the disabled and access to learning opportunities for children"
    example_title: "Negative"
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# iati-drr-classifier

This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on a subset of the [alex-miller/iati-policy-markers](https://huggingface.co/datasets/alex-miller/iati-policy-markers) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3910
- Accuracy: 0.8207

## Model description

This model has been trained to identify disaster risk reduction (DRR) project titles and/or descriptions. It returns "0" for projects with no DRR component, and "1" for projects with DRR as a principal or significant objective.

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

Code to subset the dataset and train the model is available [here](https://github.com/akmiller01/iati-policy-marker-hf-dataset/blob/main/use_cases/drr_train.ipynb).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.607         | 1.0   | 508  | 0.5053          | 0.7507   |
| 0.4569        | 2.0   | 1016 | 0.4289          | 0.7980   |
| 0.4011        | 3.0   | 1524 | 0.4009          | 0.8143   |
| 0.3786        | 4.0   | 2032 | 0.3910          | 0.8207   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2