---
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