---
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-seed1
  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-seed1

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: 0.4208
- Accuracy: 0.9039
- Accuracy Sensor 0: 0.8951
- Auroc Sensor 0: 0.9544
- Accuracy Sensor 1: 0.9114
- Auroc Sensor 1: 0.9468
- Accuracy Sensor 2: 0.9304
- Auroc Sensor 2: 0.9752
- Accuracy Aggregated: 0.8787
- Auroc Aggregated: 0.9601

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2957        | 0.9997 | 781  | 0.3062          | 0.8755   | 0.8833            | 0.8927         | 0.8724            | 0.8925         | 0.8966            | 0.9193         | 0.8494              | 0.8893           |
| 0.1972        | 1.9994 | 1562 | 0.2602          | 0.8922   | 0.8898            | 0.9341         | 0.9076            | 0.9355         | 0.9133            | 0.9617         | 0.8582              | 0.9350           |
| 0.1195        | 2.9990 | 2343 | 0.2889          | 0.8943   | 0.8747            | 0.9475         | 0.9022            | 0.9347         | 0.9168            | 0.9700         | 0.8835              | 0.9516           |
| 0.0784        | 4.0    | 3125 | 0.3078          | 0.9104   | 0.9084            | 0.9574         | 0.9125            | 0.9486         | 0.9380            | 0.9760         | 0.8828              | 0.9611           |
| 0.0347        | 4.9984 | 3905 | 0.4208          | 0.9039   | 0.8951            | 0.9544         | 0.9114            | 0.9468         | 0.9304            | 0.9752         | 0.8787              | 0.9601           |


### Framework versions

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