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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: FPB_finetuned_v1 |
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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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# FPB_finetuned_v1 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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: 0.4649 |
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- Accuracy: 0.9303 |
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- F1: 0.9303 |
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- Precision: 0.9303 |
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- Recall: 0.9303 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.7905 | 1.0 | 97 | 0.6913 | 0.7504 | 0.7471 | 0.7458 | 0.7504 | |
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| 0.3516 | 2.0 | 194 | 0.3914 | 0.8476 | 0.8480 | 0.8517 | 0.8476 | |
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| 0.2545 | 3.0 | 291 | 0.3302 | 0.8882 | 0.8870 | 0.8911 | 0.8882 | |
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| 0.1225 | 4.0 | 388 | 0.3488 | 0.8723 | 0.8730 | 0.8801 | 0.8723 | |
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| 0.0674 | 5.0 | 485 | 0.3910 | 0.8970 | 0.8961 | 0.8963 | 0.8970 | |
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| 0.0458 | 6.0 | 582 | 0.4545 | 0.9028 | 0.9022 | 0.9036 | 0.9028 | |
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| 0.0963 | 7.0 | 679 | 0.3467 | 0.9100 | 0.9100 | 0.9104 | 0.9100 | |
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| 0.0781 | 8.0 | 776 | 0.4528 | 0.8999 | 0.8991 | 0.8996 | 0.8999 | |
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| 0.0961 | 9.0 | 873 | 0.3966 | 0.9042 | 0.9049 | 0.9091 | 0.9042 | |
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| 0.0643 | 10.0 | 970 | 0.3486 | 0.9158 | 0.9159 | 0.9160 | 0.9158 | |
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| 0.0521 | 11.0 | 1067 | 0.5745 | 0.8955 | 0.8931 | 0.9030 | 0.8955 | |
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| 0.0162 | 12.0 | 1164 | 0.4968 | 0.9042 | 0.9047 | 0.9070 | 0.9042 | |
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| 0.0106 | 13.0 | 1261 | 0.4925 | 0.9158 | 0.9161 | 0.9171 | 0.9158 | |
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| 0.0056 | 14.0 | 1358 | 0.5128 | 0.9129 | 0.9126 | 0.9149 | 0.9129 | |
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| 0.0116 | 15.0 | 1455 | 0.4791 | 0.9202 | 0.9199 | 0.9197 | 0.9202 | |
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| 0.0004 | 16.0 | 1552 | 0.4417 | 0.9216 | 0.9214 | 0.9218 | 0.9216 | |
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| 0.0121 | 17.0 | 1649 | 0.4378 | 0.9202 | 0.9199 | 0.9205 | 0.9202 | |
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| 0.0003 | 18.0 | 1746 | 0.4624 | 0.9245 | 0.9245 | 0.9247 | 0.9245 | |
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| 0.0001 | 19.0 | 1843 | 0.4697 | 0.9274 | 0.9275 | 0.9277 | 0.9274 | |
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| 0.0001 | 20.0 | 1940 | 0.4649 | 0.9303 | 0.9303 | 0.9303 | 0.9303 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Tokenizers 0.15.2 |
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