bert-finetuned-unpunctual-text-segmentation
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0007
- Precision: 0.9996
- Recall: 0.9984
- F1: 0.9990
- Accuracy: 0.9998
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0023 | 1.0 | 11252 | 0.0017 | 0.9983 | 0.9953 | 0.9968 | 0.9995 |
0.0006 | 2.0 | 22504 | 0.0010 | 0.9994 | 0.9980 | 0.9987 | 0.9998 |
0.0001 | 3.0 | 33756 | 0.0007 | 0.9996 | 0.9984 | 0.9990 | 0.9998 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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