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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-seed3 |
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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-seed3 |
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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: 0.4321 |
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- Accuracy: 0.9160 |
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- Accuracy Sensor 0: 0.9279 |
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- Auroc Sensor 0: 0.9601 |
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- Accuracy Sensor 1: 0.9017 |
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- Auroc Sensor 1: 0.9575 |
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- Accuracy Sensor 2: 0.9458 |
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- Auroc Sensor 2: 0.9593 |
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- Accuracy Aggregated: 0.8887 |
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- Auroc Aggregated: 0.9488 |
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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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| 0.281 | 0.9997 | 781 | 0.4126 | 0.8254 | 0.8474 | 0.9025 | 0.8423 | 0.9144 | 0.8339 | 0.9166 | 0.7778 | 0.8949 | |
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| 0.1966 | 1.9994 | 1562 | 0.2290 | 0.9123 | 0.9079 | 0.9312 | 0.9138 | 0.9490 | 0.9288 | 0.9424 | 0.8990 | 0.9313 | |
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| 0.1412 | 2.9990 | 2343 | 0.2619 | 0.9043 | 0.9059 | 0.9537 | 0.8838 | 0.9581 | 0.9410 | 0.9551 | 0.8863 | 0.9464 | |
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| 0.0757 | 4.0 | 3125 | 0.2862 | 0.9224 | 0.9307 | 0.9622 | 0.9153 | 0.9626 | 0.9464 | 0.9601 | 0.8971 | 0.9516 | |
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| 0.0356 | 4.9984 | 3905 | 0.4321 | 0.9160 | 0.9279 | 0.9601 | 0.9017 | 0.9575 | 0.9458 | 0.9593 | 0.8887 | 0.9488 | |
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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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