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--- |
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library_name: transformers |
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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: BERT_ST_DA_1800 |
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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_ST_DA_1800 |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1918 |
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- Precision: 0.9710 |
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- Recall: 0.9712 |
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- F1: 0.9711 |
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- Accuracy: 0.9675 |
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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: 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 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1075 | 1.0 | 1050 | 0.1338 | 0.9633 | 0.9650 | 0.9641 | 0.9616 | |
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| 0.0565 | 2.0 | 2100 | 0.1253 | 0.9661 | 0.9687 | 0.9674 | 0.9647 | |
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| 0.0358 | 3.0 | 3150 | 0.1386 | 0.9691 | 0.9703 | 0.9697 | 0.9666 | |
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| 0.0211 | 4.0 | 4200 | 0.1516 | 0.9701 | 0.9707 | 0.9704 | 0.9670 | |
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| 0.0118 | 5.0 | 5250 | 0.1586 | 0.9697 | 0.9726 | 0.9711 | 0.9676 | |
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| 0.0084 | 6.0 | 6300 | 0.1791 | 0.9685 | 0.9698 | 0.9691 | 0.9654 | |
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| 0.0054 | 7.0 | 7350 | 0.1849 | 0.9692 | 0.9692 | 0.9692 | 0.9657 | |
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| 0.0031 | 8.0 | 8400 | 0.1887 | 0.9690 | 0.9708 | 0.9699 | 0.9660 | |
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| 0.0023 | 9.0 | 9450 | 0.1931 | 0.9705 | 0.9703 | 0.9704 | 0.9669 | |
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| 0.0017 | 10.0 | 10500 | 0.1918 | 0.9710 | 0.9712 | 0.9711 | 0.9675 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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