---
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-seed5
  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-seed5

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: 0.5059
- Accuracy: 0.9004
- Accuracy Sensor 0: 0.9014
- Auroc Sensor 0: 0.9556
- Accuracy Sensor 1: 0.8987
- Auroc Sensor 1: 0.9560
- Accuracy Sensor 2: 0.9201
- Auroc Sensor 2: 0.9711
- Accuracy Aggregated: 0.8813
- Auroc Aggregated: 0.9554

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2902        | 0.9997 | 781  | 0.3671          | 0.8431   | 0.8573            | 0.9025         | 0.8378            | 0.8994         | 0.8717            | 0.9333         | 0.8057              | 0.8989           |
| 0.1961        | 1.9994 | 1562 | 0.2889          | 0.8868   | 0.8871            | 0.9334         | 0.8813            | 0.9320         | 0.9069            | 0.9565         | 0.8718              | 0.9332           |
| 0.1311        | 2.9990 | 2343 | 0.3433          | 0.8875   | 0.8886            | 0.9531         | 0.8797            | 0.9519         | 0.9099            | 0.9714         | 0.8718              | 0.9536           |
| 0.0788        | 4.0    | 3125 | 0.3613          | 0.9028   | 0.9005            | 0.9566         | 0.9027            | 0.9579         | 0.9199            | 0.9716         | 0.8882              | 0.9555           |
| 0.043         | 4.9984 | 3905 | 0.5059          | 0.9004   | 0.9014            | 0.9556         | 0.8987            | 0.9560         | 0.9201            | 0.9711         | 0.8813              | 0.9554           |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1