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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: results |
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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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# results |
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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: 0.2919 |
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- Precision: 0.8957 |
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- Recall: 0.8226 |
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- F1: 0.8576 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
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| 0.3611 | 0.2 | 500 | 0.3194 | 0.8640 | 0.8324 | 0.8479 | |
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| 0.3106 | 0.4 | 1000 | 0.3039 | 0.8905 | 0.8013 | 0.8435 | |
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| 0.3027 | 0.6 | 1500 | 0.2954 | 0.9022 | 0.7927 | 0.8439 | |
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| 0.2952 | 0.81 | 2000 | 0.2864 | 0.8966 | 0.8185 | 0.8558 | |
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| 0.2905 | 1.01 | 2500 | 0.2875 | 0.8973 | 0.8150 | 0.8542 | |
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| 0.2605 | 1.21 | 3000 | 0.2841 | 0.8924 | 0.8369 | 0.8637 | |
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| 0.2591 | 1.41 | 3500 | 0.2820 | 0.8926 | 0.8444 | 0.8678 | |
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| 0.2574 | 1.61 | 4000 | 0.2826 | 0.8916 | 0.8359 | 0.8629 | |
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| 0.2602 | 1.81 | 4500 | 0.2764 | 0.8989 | 0.8291 | 0.8626 | |
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| 0.2561 | 2.01 | 5000 | 0.2813 | 0.8891 | 0.8454 | 0.8667 | |
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| 0.2195 | 2.22 | 5500 | 0.2869 | 0.9072 | 0.8110 | 0.8564 | |
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| 0.2209 | 2.42 | 6000 | 0.2845 | 0.9002 | 0.8216 | 0.8591 | |
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| 0.2178 | 2.62 | 6500 | 0.2827 | 0.8991 | 0.8285 | 0.8624 | |
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| 0.22 | 2.82 | 7000 | 0.2919 | 0.8957 | 0.8226 | 0.8576 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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