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bert-multilabel-sector-classifier
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0563
- Precision Micro: 0.9091
- Precision Weighted: 0.9080
- Precision Samples: 0.9149
- Recall Micro: 0.8553
- Recall Weighted: 0.8553
- Recall Samples: 0.8996
- Accuracy: 0.8026
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: 5e-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: cosine
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0601 | 1.0 | 464 | 0.0563 | 0.9091 | 0.9080 | 0.9149 | 0.8553 | 0.8553 | 0.8996 | 0.8026 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
