metadata

license: apache-2.0
base_model: alex-miller/ODABert
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: iati-climate-classifier
    results: []
datasets:
  - alex-miller/iati-policy-markers
language:
  - en
  - fr
  - es
  - de
pipeline_tag: text-classification
widget:
  - text: >-
      VCA WWF Bolivia The programme will focus on women, young people and
      indigenous population living in the transboundary Pantanal - Chaco
      ecoregions (PACHA - Paraguay and Bolivia). Its objective is to “amplify
      their voices”, to ensure that they are participating, heard and taken into
      account in designing solutions for climate transition and common agendas
      to reach climate justice.
    example_title: Positive
  - text: >-
      HIV/AIDS prevention by education and awareness raising with emphasis on
      gender issues/El Salvador
    example_title: Negative

iati-climate-classifier

This model is a fine-tuned version of alex-miller/ODABert on a subset of the alex-miller/iati-policy-markers dataset.

It achieves the following results on the evaluation set:

Loss: 0.2377
Accuracy: 0.9138
F1: 0.9165
Precision: 0.8889
Recall: 0.9458

Model description

This model has been trained to identify climate mitigation and climate adaptation project titles and/or descriptions. It returns "0" for projects with no climate component, and "1" for projects with adaptation or mitigation as principal objectives.

Training procedure

Code to subset the dataset and train the model is available here.

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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.4992	1.0	876	0.8921	0.8978	0.2831	0.8530	0.9475
0.2706	2.0	1752	0.9038	0.9057	0.2446	0.8881	0.9241
0.2494	3.0	2628	0.9095	0.9114	0.2370	0.8927	0.9309
0.2393	4.0	3504	0.9112	0.9140	0.2385	0.8863	0.9435
0.2306	5.0	4380	0.9124	0.9152	0.2380	0.8870	0.9452
0.229	6.0	5256	0.2405	0.9121	0.9152	0.8836	0.9492
0.2255	7.0	6132	0.2377	0.9138	0.9165	0.8889	0.9458

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2