---
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-seed3
  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-seed3

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: 0.3757
- Accuracy: 0.9134
- Accuracy Sensor 0: 0.9235
- Auroc Sensor 0: 0.9559
- Accuracy Sensor 1: 0.8989
- Auroc Sensor 1: 0.9539
- Accuracy Sensor 2: 0.9486
- Auroc Sensor 2: 0.9653
- Accuracy Aggregated: 0.8826
- Auroc Aggregated: 0.9553

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.287         | 0.9997 | 781  | 0.4392          | 0.8094   | 0.8151            | 0.8977         | 0.8235            | 0.9036         | 0.8395            | 0.9106         | 0.7594              | 0.8793           |
| 0.2108        | 1.9994 | 1562 | 0.2409          | 0.9058   | 0.9011            | 0.9242         | 0.9062            | 0.9344         | 0.9238            | 0.9424         | 0.8920              | 0.9178           |
| 0.1549        | 2.9990 | 2343 | 0.2347          | 0.9119   | 0.9185            | 0.9519         | 0.8929            | 0.9546         | 0.9481            | 0.9605         | 0.8883              | 0.9476           |
| 0.0887        | 4.0    | 3125 | 0.2867          | 0.9139   | 0.9243            | 0.9558         | 0.9057            | 0.9547         | 0.9473            | 0.9653         | 0.8785              | 0.9543           |
| 0.0444        | 4.9984 | 3905 | 0.3757          | 0.9134   | 0.9235            | 0.9559         | 0.8989            | 0.9539         | 0.9486            | 0.9653         | 0.8826              | 0.9553           |


### Framework versions

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