---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_ORO_screen
  results: []
widget:
 - text: "The Paris Agreement target of limiting global surface warming to 1.5–2◦C compared to pre-industrial levels by 2100 will still heavily impact the ocean. While ambitious mitigation and adaptation are both needed, the ocean provides major opportunities for action to reduce climate change globally and its impacts on vital ecosystems and ecosystem services. A comprehensive and systematic assessment of 13 global- and local-scale, ocean-based measures was performed to help steer the development and implementation of technologies and actions toward a sustainable outcome. We show that (1) all measures have tradeoffs and multiple criteria must be used for a comprehensive assessment of their potential, (2) greatest benefit is derived by combining global and local solutions, some of which could be implemented or scaled-up immediately, (3) some measures are too uncertain to be recommended yet, (4) political consistency must be achieved through effective cross-scale governance mechanisms, (5) scientific effort must focus on effectiveness, co-benefits, disbenefits, and costs of poorly tested as well as new and emerging measures."
---


# distilbert_ORO_screen

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) to classify whether an article's text (title + abstract + keywords) is relevant to an Ocean-related option to mitigate or adapt to climate change.
Note that this model was fit on the full 'seen' dataset, therefore predictions will only be approximations of those reported in the article (in prep.).  
It achieves the following results on the evaluation set:
- Train Loss: 0.3204
- Train Binary Accuracy: 0.8819
- Validation Loss: 1.5870
- Validation Binary Accuracy: 0.3519
- Epoch: 3

## Model description

This model predicts a label that indicated 'relevance' for an ocean related option as a value between 0 and 1. Therefore a number > 0.5 indicates it is more likely to be relevant.

## Intended uses & limitations

This model is intended to be applied to article text (title + abstract + keywords) retrieved from citation indexed databases such as Web of Science or Scopus using a search query. This can be used to autonomously screen for relevant articles from a large volume of literature and can be used in analyses that provide a granular map of the distribution of relevant studies.
NB for more specific research questions, this should be complemented with manual literature review methodology (e.g. see the [Collaboration for Environmental Evidence Guidelines](https://environmentalevidence.org/information-for-authors/) )

## Training and evaluation data

For a description of the dataset, see the paper (in prep.)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamW', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay': 0.0, 'exclude_from_weight_decay': None}
- training_precision: float32

### Training results

| Train Loss | Train Binary Accuracy | Validation Loss | Validation Binary Accuracy | Epoch |
|:----------:|:---------------------:|:---------------:|:--------------------------:|:-----:|
| 0.5592     | 0.6278                | 1.2920          | 0.2050                     | 0     |
| 0.4488     | 0.7952                | 1.3261          | 0.2612                     | 1     |
| 0.3743     | 0.8281                | 1.7808          | 0.2439                     | 2     |
| 0.3204     | 0.8819                | 1.5870          | 0.3519                     | 3     |


### Framework versions

- Transformers 4.30.2
- TensorFlow 2.11.0
- Datasets 2.18.0
- Tokenizers 0.13.3