---
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-seed4
  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-seed4

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: 0.3733
- Accuracy: 0.9086
- Accuracy Sensor 0: 0.9144
- Auroc Sensor 0: 0.9506
- Accuracy Sensor 1: 0.9050
- Auroc Sensor 1: 0.9584
- Accuracy Sensor 2: 0.9332
- Auroc Sensor 2: 0.9753
- Accuracy Aggregated: 0.8820
- Auroc Aggregated: 0.9557

## 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  | 0.3411          | 0.8441   | 0.8553            | 0.9103         | 0.8390            | 0.9066         | 0.8633            | 0.9334         | 0.8188              | 0.8975           |
| 0.2003        | 1.9994 | 1562 | 0.2859          | 0.8852   | 0.8929            | 0.9380         | 0.8778            | 0.9380         | 0.9319            | 0.9638         | 0.8384              | 0.9361           |
| 0.1366        | 2.9990 | 2343 | 0.2701          | 0.8945   | 0.9041            | 0.9549         | 0.8902            | 0.9570         | 0.9245            | 0.9755         | 0.8591              | 0.9539           |
| 0.0812        | 4.0    | 3125 | 0.2992          | 0.9046   | 0.9166            | 0.9542         | 0.8947            | 0.9585         | 0.9339            | 0.9765         | 0.8730              | 0.9567           |
| 0.0381        | 4.9984 | 3905 | 0.3733          | 0.9086   | 0.9144            | 0.9506         | 0.9050            | 0.9584         | 0.9332            | 0.9753         | 0.8820              | 0.9557           |


### Framework versions

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