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sumups-batch3-model
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2370
- Precision: 0.0282
- Recall: 0.1030
- F1: 0.0442
- Accuracy: 0.5356
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 20 | 1.3966 | 0.0062 | 0.0257 | 0.0100 | 0.4634 |
No log | 2.0 | 40 | 1.3064 | 0.0170 | 0.0495 | 0.0253 | 0.4869 |
No log | 3.0 | 60 | 1.2607 | 0.0213 | 0.0752 | 0.0332 | 0.5157 |
No log | 4.0 | 80 | 1.2307 | 0.0242 | 0.0871 | 0.0379 | 0.5352 |
No log | 5.0 | 100 | 1.2370 | 0.0282 | 0.1030 | 0.0442 | 0.5356 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2