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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: 20240320102435_big_hinton |
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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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# 20240320102435_big_hinton |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0351 |
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- Precision: 0.9436 |
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- Recall: 0.9308 |
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- F1: 0.9372 |
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- Accuracy: 0.9859 |
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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: 0.001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 69 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 256 |
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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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- lr_scheduler_warmup_steps: 350 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0805 | 0.09 | 300 | 0.0626 | 0.9020 | 0.8843 | 0.8931 | 0.9758 | |
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| 0.0969 | 0.18 | 600 | 0.0770 | 0.8912 | 0.8486 | 0.8694 | 0.9704 | |
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| 0.0879 | 0.27 | 900 | 0.0682 | 0.8943 | 0.8733 | 0.8837 | 0.9735 | |
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| 0.0778 | 0.36 | 1200 | 0.0612 | 0.9013 | 0.8891 | 0.8952 | 0.9762 | |
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| 0.0703 | 0.44 | 1500 | 0.0564 | 0.9137 | 0.8909 | 0.9021 | 0.9779 | |
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| 0.0638 | 0.53 | 1800 | 0.0521 | 0.9244 | 0.8975 | 0.9107 | 0.9799 | |
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| 0.0579 | 0.62 | 2100 | 0.0480 | 0.9309 | 0.9029 | 0.9167 | 0.9812 | |
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| 0.0534 | 0.71 | 2400 | 0.0447 | 0.9323 | 0.9095 | 0.9208 | 0.9825 | |
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| 0.049 | 0.8 | 2700 | 0.0399 | 0.9329 | 0.9236 | 0.9282 | 0.9841 | |
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| 0.0451 | 0.89 | 3000 | 0.0373 | 0.9411 | 0.9226 | 0.9318 | 0.9849 | |
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| 0.0424 | 0.98 | 3300 | 0.0351 | 0.9436 | 0.9308 | 0.9372 | 0.9859 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.0a0+6a974be |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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