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--- |
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license: bsd-3-clause |
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base_model: Salesforce/codegen-350M-mono |
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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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model-index: |
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- name: codegen-350M-mono-measurement_pred-diamonds-seed7 |
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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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# codegen-350M-mono-measurement_pred-diamonds-seed7 |
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This model is a fine-tuned version of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4759 |
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- Accuracy: 0.9018 |
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- Accuracy Sensor 0: 0.9093 |
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- Auroc Sensor 0: 0.9563 |
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- Accuracy Sensor 1: 0.9046 |
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- Auroc Sensor 1: 0.9558 |
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- Accuracy Sensor 2: 0.9110 |
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- Auroc Sensor 2: 0.9461 |
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- Accuracy Aggregated: 0.8822 |
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- Auroc Aggregated: 0.9403 |
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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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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 64 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy Sensor 0 | Auroc Sensor 0 | Accuracy Sensor 1 | Auroc Sensor 1 | Accuracy Sensor 2 | Auroc Sensor 2 | Accuracy Aggregated | Auroc Aggregated | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:| |
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| 0.3029 | 0.9997 | 781 | 0.5009 | 0.7947 | 0.7920 | 0.8988 | 0.7962 | 0.9030 | 0.8191 | 0.8947 | 0.7717 | 0.8803 | |
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| 0.2099 | 1.9994 | 1562 | 0.4386 | 0.8330 | 0.8430 | 0.9267 | 0.8214 | 0.9266 | 0.8523 | 0.9287 | 0.8154 | 0.9148 | |
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| 0.1366 | 2.9990 | 2343 | 0.3970 | 0.8638 | 0.8850 | 0.9499 | 0.8800 | 0.9485 | 0.8568 | 0.9428 | 0.8336 | 0.9330 | |
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| 0.0719 | 4.0 | 3125 | 0.3534 | 0.9090 | 0.9121 | 0.9578 | 0.9090 | 0.9575 | 0.9209 | 0.9470 | 0.8940 | 0.9424 | |
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| 0.0379 | 4.9984 | 3905 | 0.4759 | 0.9018 | 0.9093 | 0.9563 | 0.9046 | 0.9558 | 0.9110 | 0.9461 | 0.8822 | 0.9403 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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