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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-seed0 |
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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-seed0 |
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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: 0.3882 |
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- Accuracy: 0.9138 |
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- Accuracy Sensor 0: 0.9051 |
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- Auroc Sensor 0: 0.9644 |
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- Accuracy Sensor 1: 0.9165 |
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- Auroc Sensor 1: 0.9454 |
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- Accuracy Sensor 2: 0.9324 |
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- Auroc Sensor 2: 0.9773 |
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- Accuracy Aggregated: 0.9010 |
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- Auroc Aggregated: 0.9670 |
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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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| 0.2891 | 0.9997 | 781 | 0.3732 | 0.8385 | 0.8367 | 0.9076 | 0.8089 | 0.9026 | 0.8669 | 0.9409 | 0.8414 | 0.9123 | |
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| 0.179 | 1.9994 | 1562 | 0.3287 | 0.8639 | 0.8532 | 0.9392 | 0.8835 | 0.9339 | 0.8852 | 0.9648 | 0.8338 | 0.9430 | |
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| 0.1181 | 2.9990 | 2343 | 0.2500 | 0.9084 | 0.8967 | 0.9587 | 0.9138 | 0.9382 | 0.9327 | 0.9744 | 0.8906 | 0.9623 | |
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| 0.0614 | 4.0 | 3125 | 0.3212 | 0.9095 | 0.8961 | 0.9636 | 0.9145 | 0.9457 | 0.9238 | 0.9774 | 0.9036 | 0.9662 | |
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| 0.0267 | 4.9984 | 3905 | 0.3882 | 0.9138 | 0.9051 | 0.9644 | 0.9165 | 0.9454 | 0.9324 | 0.9773 | 0.9010 | 0.9670 | |
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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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