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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: w266_model3_BERT_CNN |
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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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# w266_model3_BERT_CNN |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./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: 0.7935 |
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- Accuracy: {'accuracy': 0.67} |
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- F1: {'f1': 0.6539863523155215} |
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- Precision: {'precision': 0.6655888523241464} |
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- Recall: {'recall': 0.67} |
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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: 8 |
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- eval_batch_size: 8 |
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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: 5.0 |
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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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| 0.7881 | 1.0 | 1923 | 0.8177 | {'accuracy': 0.638} | {'f1': 0.6219209356584174} | {'precision': 0.6325213408748697} | {'recall': 0.638} | |
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| 0.649 | 2.0 | 3846 | 0.8257 | {'accuracy': 0.669} | {'f1': 0.6701535233107099} | {'precision': 0.672307962349643} | {'recall': 0.669} | |
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| 0.4771 | 3.0 | 5769 | 0.8922 | {'accuracy': 0.676} | {'f1': 0.6778795418743319} | {'precision': 0.6805694646691987} | {'recall': 0.676} | |
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| 0.3403 | 4.0 | 7692 | 1.4285 | {'accuracy': 0.669} | {'f1': 0.666176554548987} | {'precision': 0.6653390405441227} | {'recall': 0.669} | |
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| 0.2088 | 5.0 | 9615 | 1.7417 | {'accuracy': 0.67} | {'f1': 0.6716636513157895} | {'precision': 0.6752339933799478} | {'recall': 0.67} | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.3 |
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- Tokenizers 0.13.3 |
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