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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- recall |
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model-index: |
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- name: bert-base-cased |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-cased |
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This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6750 |
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- F1 Macro: 0.9031 |
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- F1: 0.9370 |
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- F1 Neg: 0.8692 |
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- Acc: 0.915 |
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- Prec: 0.9336 |
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- Recall: 0.9405 |
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- Mcc: 0.8063 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:| |
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| 0.1886 | 1.0 | 2125 | 0.3952 | 0.8938 | 0.9283 | 0.8593 | 0.905 | 0.9425 | 0.9145 | 0.7884 | |
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| 0.0578 | 2.0 | 4250 | 0.6750 | 0.9031 | 0.9370 | 0.8692 | 0.915 | 0.9336 | 0.9405 | 0.8063 | |
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| 0.0243 | 3.0 | 6375 | 0.7559 | 0.8922 | 0.9294 | 0.8550 | 0.905 | 0.9294 | 0.9294 | 0.7843 | |
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| 0.0084 | 4.0 | 8500 | 0.8553 | 0.9001 | 0.9353 | 0.8649 | 0.9125 | 0.9301 | 0.9405 | 0.8003 | |
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| 0.0131 | 5.0 | 10625 | 0.8916 | 0.8974 | 0.9333 | 0.8615 | 0.91 | 0.9299 | 0.9368 | 0.7949 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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