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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-seed7
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-seed7
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: 0.4759
- Accuracy: 0.9018
- Accuracy Sensor 0: 0.9093
- Auroc Sensor 0: 0.9563
- Accuracy Sensor 1: 0.9046
- Auroc Sensor 1: 0.9558
- Accuracy Sensor 2: 0.9110
- Auroc Sensor 2: 0.9461
- Accuracy Aggregated: 0.8822
- Auroc Aggregated: 0.9403
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.3029 | 0.9997 | 781 | 0.5009 | 0.7947 | 0.7920 | 0.8988 | 0.7962 | 0.9030 | 0.8191 | 0.8947 | 0.7717 | 0.8803 |
| 0.2099 | 1.9994 | 1562 | 0.4386 | 0.8330 | 0.8430 | 0.9267 | 0.8214 | 0.9266 | 0.8523 | 0.9287 | 0.8154 | 0.9148 |
| 0.1366 | 2.9990 | 2343 | 0.3970 | 0.8638 | 0.8850 | 0.9499 | 0.8800 | 0.9485 | 0.8568 | 0.9428 | 0.8336 | 0.9330 |
| 0.0719 | 4.0 | 3125 | 0.3534 | 0.9090 | 0.9121 | 0.9578 | 0.9090 | 0.9575 | 0.9209 | 0.9470 | 0.8940 | 0.9424 |
| 0.0379 | 4.9984 | 3905 | 0.4759 | 0.9018 | 0.9093 | 0.9563 | 0.9046 | 0.9558 | 0.9110 | 0.9461 | 0.8822 | 0.9403 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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