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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-seed2 |
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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-seed2 |
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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: 0.4189 |
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- Accuracy: 0.9210 |
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- Accuracy Sensor 0: 0.9298 |
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- Auroc Sensor 0: 0.9628 |
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- Accuracy Sensor 1: 0.9259 |
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- Auroc Sensor 1: 0.9711 |
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- Accuracy Sensor 2: 0.9266 |
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- Auroc Sensor 2: 0.9619 |
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- Accuracy Aggregated: 0.9019 |
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- Auroc Aggregated: 0.9592 |
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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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| 0.2961 | 0.9997 | 781 | 0.4800 | 0.7906 | 0.8122 | 0.9078 | 0.7952 | 0.9255 | 0.8160 | 0.9280 | 0.7391 | 0.8990 | |
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| 0.1901 | 1.9994 | 1562 | 0.3107 | 0.8847 | 0.9115 | 0.9491 | 0.8649 | 0.9604 | 0.8951 | 0.9532 | 0.8674 | 0.9397 | |
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| 0.1154 | 2.9990 | 2343 | 0.3076 | 0.9009 | 0.9154 | 0.9575 | 0.8946 | 0.9656 | 0.9255 | 0.9576 | 0.8682 | 0.9492 | |
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| 0.0708 | 4.0 | 3125 | 0.3162 | 0.9207 | 0.9297 | 0.9621 | 0.9245 | 0.9710 | 0.9285 | 0.9619 | 0.9001 | 0.9587 | |
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| 0.0314 | 4.9984 | 3905 | 0.4189 | 0.9210 | 0.9298 | 0.9628 | 0.9259 | 0.9711 | 0.9266 | 0.9619 | 0.9019 | 0.9592 | |
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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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