license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
model-index:
- name: ODABert
results: []
datasets:
- alex-miller/oecd-dac-crs
widget:
- text: Official Development [MASK].
example_title: ODA
- text: Climate adaptation and climate [MASK].
example_title: Climate
ODABert
This model is a fine-tuned version of bert-base-multilingual-uncased on the OECD DAC CRS project titles and descriptions dataset. It achieves the following results on the evaluation set:
- Loss: 0.9961
Model description
A 3 epoch fine-tune of BERT base multilingual uncased on development and humanitarian finance project titles and descriptions from the OECD DAC CRS. Vocabulary of the base model was expanded by 1,059 tokens (1% increase) based on the most prevalent tokens in the CRS that were not present in the base model vocabulary.
Intended uses & limitations
Developed as an experiment to see whether fine-tuning on the CRS would help improve classifier models built on top of this MLM. Although it's built on a multilingual model, an the finetuning texts do include other languages, English will be the most prevalent.
Training and evaluation data
See the OECD DAC CRS project titles and descriptions dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2133 | 1.0 | 58119 | 1.1296 |
| 1.098 | 2.0 | 116238 | 1.0336 |
| 1.0441 | 3.0 | 174357 | 0.9958 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2