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--- |
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license: apache-2.0 |
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base_model: bert-large-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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: results_bert-large-uncased |
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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_bert-large-uncased |
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2128 |
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- Accuracy: 0.9141 |
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- Precision: 0.9182 |
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- Recall: 0.9421 |
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- F1: 0.9300 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.6415 | 0.09 | 50 | 0.5315 | 0.7175 | 0.6981 | 0.9394 | 0.8010 | |
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| 0.4007 | 0.18 | 100 | 0.7702 | 0.7243 | 0.9892 | 0.5505 | 0.7074 | |
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| 0.5158 | 0.28 | 150 | 0.4075 | 0.8591 | 0.8904 | 0.8748 | 0.8825 | |
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| 0.3934 | 0.37 | 200 | 0.2809 | 0.8763 | 0.9354 | 0.8546 | 0.8932 | |
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| 0.2691 | 0.46 | 250 | 0.3406 | 0.8832 | 0.8837 | 0.9294 | 0.9060 | |
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| 0.2814 | 0.55 | 300 | 0.2582 | 0.8768 | 0.8512 | 0.9651 | 0.9046 | |
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| 0.2735 | 0.64 | 350 | 0.2715 | 0.8953 | 0.8708 | 0.9711 | 0.9182 | |
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| 0.2411 | 0.74 | 400 | 0.2389 | 0.9103 | 0.9242 | 0.9279 | 0.9260 | |
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| 0.2371 | 0.83 | 450 | 0.2081 | 0.9104 | 0.9212 | 0.9316 | 0.9264 | |
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| 0.1974 | 0.92 | 500 | 0.2128 | 0.9141 | 0.9182 | 0.9421 | 0.9300 | |
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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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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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