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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-seed1 |
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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-seed1 |
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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: 0.4083 |
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- Accuracy: 0.9134 |
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- Accuracy Sensor 0: 0.9153 |
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- Auroc Sensor 0: 0.9651 |
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- Accuracy Sensor 1: 0.9094 |
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- Auroc Sensor 1: 0.9502 |
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- Accuracy Sensor 2: 0.9317 |
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- Auroc Sensor 2: 0.9780 |
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- Accuracy Aggregated: 0.8974 |
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- Auroc Aggregated: 0.9672 |
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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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| 0.2812 | 0.9997 | 781 | 0.2931 | 0.8747 | 0.8785 | 0.9058 | 0.8806 | 0.9047 | 0.8897 | 0.9331 | 0.8499 | 0.9009 | |
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| 0.1938 | 1.9994 | 1562 | 0.2940 | 0.8844 | 0.8760 | 0.9330 | 0.9017 | 0.9300 | 0.9160 | 0.9574 | 0.8438 | 0.9252 | |
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| 0.1202 | 2.9990 | 2343 | 0.2551 | 0.9080 | 0.9055 | 0.9601 | 0.9119 | 0.9504 | 0.9235 | 0.9757 | 0.8910 | 0.9615 | |
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| 0.0779 | 4.0 | 3125 | 0.2902 | 0.9178 | 0.9194 | 0.9667 | 0.9164 | 0.9516 | 0.9309 | 0.9799 | 0.9044 | 0.9680 | |
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| 0.035 | 4.9984 | 3905 | 0.4083 | 0.9134 | 0.9153 | 0.9651 | 0.9094 | 0.9502 | 0.9317 | 0.9780 | 0.8974 | 0.9672 | |
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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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