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climate_text_classification_mini_model
This model is a fine-tuned version of distilbert-base-uncased on the climate-tagging-labelled-datasets dataset. It achieves the following results on the evaluation set:
- Loss: 0.7516
- Precision: 0.7941
- Recall: 0.9643
- Accuracy: 0.8
- F1: {'f1': 0.8709677419354839}
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 10 | 0.5469 | 0.875 | 0.75 | 0.75 | {'f1': 0.8076923076923077} |
| No log | 2.0 | 20 | 0.7516 | 0.7941 | 0.9643 | 0.8 | {'f1': 0.8709677419354839} |
Framework versions
- Transformers 4.26.1
- Pytorch 1.7.0a0
- Datasets 2.9.0
- Tokenizers 0.13.2
