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bert-base-uncased-finetuned-sdg
This model is a fine-tuned version of bert-base-uncased on the OSDG dataset. It achieves the following results on the evaluation set:
- Loss: 0.3094
- Acc: 0.9195
Model description
Classifies text to the first 16 SDGs!
Intended uses & limitations
Assess policy documents, classify text to SDGs, etc.
Training and evaluation data
OSDG data. Updated version from October.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|---|---|---|---|---|
| 0.3768 | 1.0 | 269 | 0.3758 | 0.8933 |
| 0.2261 | 2.0 | 538 | 0.3088 | 0.9095 |
| 0.1038 | 3.0 | 807 | 0.3094 | 0.9195 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.0a0+8a1a93a
- Datasets 2.5.2
- Tokenizers 0.13.1
