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---
license: bsd-3-clause
base_model: Salesforce/codegen-350M-mono
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: codegen-350M-mono-measurement_pred-diamonds-seed4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codegen-350M-mono-measurement_pred-diamonds-seed4
This model is a fine-tuned version of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3745
- Accuracy: 0.9126
- Accuracy Sensor 0: 0.9165
- Auroc Sensor 0: 0.9601
- Accuracy Sensor 1: 0.9099
- Auroc Sensor 1: 0.9647
- Accuracy Sensor 2: 0.9342
- Auroc Sensor 2: 0.9771
- Accuracy Aggregated: 0.8898
- Auroc Aggregated: 0.9613
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 64
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2756 | 0.9997 | 781 | 0.3221 | 0.8643 | 0.8659 | 0.9177 | 0.8499 | 0.9112 | 0.9025 | 0.9476 | 0.8388 | 0.9090 |
| 0.1793 | 1.9994 | 1562 | 0.2547 | 0.8960 | 0.9032 | 0.9461 | 0.8847 | 0.9450 | 0.9345 | 0.9710 | 0.8617 | 0.9433 |
| 0.1281 | 2.9990 | 2343 | 0.2960 | 0.8797 | 0.8882 | 0.9563 | 0.8726 | 0.9584 | 0.9133 | 0.9719 | 0.8447 | 0.9553 |
| 0.0685 | 4.0 | 3125 | 0.3088 | 0.9049 | 0.9163 | 0.9597 | 0.9014 | 0.9638 | 0.9259 | 0.9765 | 0.8761 | 0.9609 |
| 0.0342 | 4.9984 | 3905 | 0.3745 | 0.9126 | 0.9165 | 0.9601 | 0.9099 | 0.9647 | 0.9342 | 0.9771 | 0.8898 | 0.9613 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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