---
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: 0.4321
- Accuracy: 0.9160
- Accuracy Sensor 0: 0.9279
- Auroc Sensor 0: 0.9601
- Accuracy Sensor 1: 0.9017
- Auroc Sensor 1: 0.9575
- Accuracy Sensor 2: 0.9458
- Auroc Sensor 2: 0.9593
- Accuracy Aggregated: 0.8887
- Auroc Aggregated: 0.9488

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.281         | 0.9997 | 781  | 0.4126          | 0.8254   | 0.8474            | 0.9025         | 0.8423            | 0.9144         | 0.8339            | 0.9166         | 0.7778              | 0.8949           |
| 0.1966        | 1.9994 | 1562 | 0.2290          | 0.9123   | 0.9079            | 0.9312         | 0.9138            | 0.9490         | 0.9288            | 0.9424         | 0.8990              | 0.9313           |
| 0.1412        | 2.9990 | 2343 | 0.2619          | 0.9043   | 0.9059            | 0.9537         | 0.8838            | 0.9581         | 0.9410            | 0.9551         | 0.8863              | 0.9464           |
| 0.0757        | 4.0    | 3125 | 0.2862          | 0.9224   | 0.9307            | 0.9622         | 0.9153            | 0.9626         | 0.9464            | 0.9601         | 0.8971              | 0.9516           |
| 0.0356        | 4.9984 | 3905 | 0.4321          | 0.9160   | 0.9279            | 0.9601         | 0.9017            | 0.9575         | 0.9458            | 0.9593         | 0.8887              | 0.9488           |


### Framework versions

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