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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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metrics: |
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- recall |
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- precision |
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- accuracy |
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model-index: |
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- name: distilbert-sql-timeout-classifier-with-trained-tokenizer |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: generator |
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type: generator |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Recall |
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type: recall |
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value: 0.7370441458733206 |
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- name: Precision |
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type: precision |
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value: 0.15262321144674085 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8761327655857626 |
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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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# distilbert-sql-timeout-classifier-with-trained-tokenizer |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4898 |
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- Recall: 0.7370 |
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- Precision: 0.1526 |
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- Affect Rate: 0.1164 |
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- Accuracy: 0.8761 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | Affect Rate | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:-----------:|:--------:| |
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| 0.5018 | 1.0 | 1946 | 0.3744 | 0.6929 | 0.1758 | 0.0924 | 0.8988 | |
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| 0.3196 | 2.0 | 3892 | 0.4938 | 0.7390 | 0.1294 | 0.1414 | 0.8512 | |
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| 0.2219 | 3.0 | 5838 | 0.4898 | 0.7370 | 0.1526 | 0.1164 | 0.8761 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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